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OSA Publishing > Biomedical Optics Express > Volume 11 > Issue 11 > Page 6799
Flexible A-scan rate MHz-OCT: efficient computational downscaling by coherent averaging
Tom Pfeiffer, Madita Göb, Wolfgang Draxinger, Sebastian Karpf, Jan Philip Kolb, and Robert Huber
Tom Pfeiffer,1,2 Madita Göb,1 Wolfgang Draxinger,1 Sebastian Karpf,1 Jan Philip Kolb,1,3 and Robert Huber1,*
1Institut für Biomedizinische Optik, Universität zu Lübeck, Peter-Monnik-Weg 4, 23562 Lübeck, Germany
2Optores GmbH, Gollierstr. 70, 80339 Munich, Germany
3Medizinisches Laserzentrum Lübeck GmbH, Peter-Monnik-Weg 4, 23562 Lübeck, Germany
*Corresponding author: robert.huber@uni-luebeck.de
Tom Pfeiffer https://orcid.org/0000-0001-7791-1126
Sebastian Karpf https://orcid.org/0000-0002-7665-5914
Robert Huber https://orcid.org/0000-0001-9785-6574
T Pfeiffer
M Göb
W Draxinger
S Karpf
J Kolb
R Huber
•https://doi.org/10.1364/BOE.402477
Tom Pfeiffer, Madita Göb, Wolfgang Draxinger, Sebastian Karpf, Jan Philip Kolb, and Robert Huber, "Flexible A-scan rate MHz-OCT: efficient computational downscaling by coherent averaging," Biomed. Opt. Express 11, 6799-6811 (2020)
Multi-Megahertz OCT: High quality 3D imaging at 20 million A-scans and 4.5 GVoxels per second (OE)
Multi-MHz retinal OCT (BOE)
Extended coherence length megahertz FDML and its application for anterior segment imaging (BOE)
High speed imaging
Swept lasers
Swept sources
Revised Manuscript: September 18, 2020
Manuscript Accepted: September 28, 2020
In order to realize adjustable A-scan rates of fast optical coherence tomography (OCT) systems, we investigate averaging of OCT image data acquired with a MHz-OCT system based on a Fourier Domain Mode Locked (FDML) laser. Increased system sensitivity and image quality can be achieved with the same system at the cost of lower imaging speed. Effectively, the A-scan rate can be reduced in software by a freely selectable factor. We demonstrate a detailed technical layout of the strategies necessary to achieve efficient coherent averaging. Since there are many new challenges specific to coherent averaging in swept source MHz-OCT, we analyze them point by point and describe the appropriate solutions. We prove that coherent averaging is possible at MHz OCT-speed without special interferometer designs or digital phase stabilization. We find, that in our system up to ∼100x coherent averaging is possible while achieving a sensitivity increase close to the ideal values. This corresponds to a speed reduction from 3.3 MHz to 33 kHz and a sensitivity gain of 20 dB. We show an imaging comparison between coherent and magnitude averaging of a human finger knuckle joint in vivo with 121 dB sensitivity for the coherent case. Further, the benefits of computational downscaling in low sensitivity MHz-OCT systems are analyzed.
© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
Modern FDML lasers have paved the way to high fidelity swept source OCT systems with A-scan rates up to several Megahertz [1] and coherence lengths superior to usual spectrometer based systems [2–4]. Today also other technologies can achieve MHz A-scan rates [5–10]. While MHz-OCT enables many new applications such as wide field ophthalmic [11–14], video-rate delay-free live 4D-OCT [15,16] and high resolution intravascular OCT without motion artifacts [17,18], the short acquisition times always come at the cost of system sensitivity. At a given laser power incident on the sample, faster systems exhibit lower ultimate shot noise limited sensitivity [19,20]. But in some situations image quality is more important than acquisition speed.
Ideally the same OCT system could perform high speed imaging with reduced sensitivity and high sensitivity imaging at lower speed. Still, the hardware of most high speed swept source OCT systems cannot run at arbitrary A-scan rates, even though there are light sources with adjustable wavelength sweep repetition rate [9,21–23]. This is due to the particular optoelectrical periphery of modern OCT engines that have to be specifically tailored to a certain A-scan rate. In most real OCT systems, analog electronic filters, the bandwidth of photo-receivers, the mechanical response of resonant scanners and several other components must be designed for a certain A-scan rate to achieve shot noise limited sensitivity.
An alternative approach to realize variable A-scan rate OCT is to retain fast image acquisition times, but virtually reduce the A-scan rate afterwards in software by averaging, which also improves sensitivity. The ability of coherent averaging to reduce white Gaussian noise more effectively than incoherent averaging has already been demonstrated on OCT data [24,25]. Coherent averaging has been applied to fast MHz-OCT systems by Siddiqui et al. [10], but not for FDML-lasers.
Only coherent averaging, which comprises averaging of fringes or the complex output of the fast Fourier transform (FFT), can improve sensitivity in the same way as lower imaging speed does. Now on one hand, coherent averaging should be highly compatible with FDML based MHz-OCT since FDML lasers exhibit very high phase stability and the high speed effectively suppresses negative effects by sample motion [4]. On the other hand, the low transimpedance gain of MHz-OCT photodetectors increases the negative effect of non-Gaussian, correlated noises such as excess laser noise and analog to digital converter noise on the averaging result. These non-Gaussian noise contributions are more resistant to complex averaging. Hence, white noises such as shot noise or Johnson noise are not necessarily the limiting factors for effective averaging in FDML based MHz-OCT systems. A special feature of FDML based MHz-OCT is the use of optical buffering [26]. Several successive frequency sweeps, after buffering, are optical copies of the same sweep that leaves the FDML laser. Therefore, these copies hold almost the same laser noise, so that averaging of corresponding A-scans cannot effectively reduce the overall noise if the laser noise is the dominant noise source. We developed a new ultra-low noise FDML laser design with vastly reduced amplitude and phase noise. In this work we investigate the feasibility and effect of coherent averaging in MHz-OCT using this new laser.
All data shown has been acquired using a home-built OCT system including a 1300 nm FDML laser running at 411,386 Hz with optical buffer stages resulting in an eightfold effective A-scan rate of 3.3 MHz. The laser operates in the "ultra low noise" / high coherence mode and is similar to the setup described in this paper [4]. The stability of the interferometer was not specially optimized. The measured system sensitivity was 106 dB with 40 mW optical power on the sample. In one particular experimental setup the sensitivity was artificially reduced to 70 dB by inserting a neutral density filter into the sample arm. All data was acquired using a 4 GS/s, 8-bit digitizer (GaGe, CobraMax).
2.1 Coherent averaging
In complex averaging, the complex FFT output of the OCT signal is averaged. In swept source OCT, averaging the output of the FFT is equivalent to averaging the recorded transients from the photoreceiver. Direct averaging of these transients is computationally more efficient because only one FFT per A-scan is required. In the following, we will denote both averaging methods as coherent averaging and we will denote averaging of the squared magnitude as magnitude averaging.
In order to merge several A-scans into one by averaging, these should ideally be recorded at the exact same position. However, for fast scanned imaging it is usually more practical to slowly and continuously scan the sample and to use only A-scans that have the same structural information, i.e. A-scans whose spots almost entirely overlap on the sample. For the imaging experiments the translation between successive A-scans was always kept below one hundredth of the lateral resolution and can thus be assumed stationary in all images presented in this paper. The scanning step size of about one hundredth of the lateral resolution was chosen according to the results presented in section 3, which indicate less efficiency when averaging more than 100 A-scans. In practice, the number of sampled A-scans may be adjusted to the applied number of averaged A-scans to increase the imaging speed. Thus, when averaging less than 100 A-scans, the scanning step size may be increased.
2.2 Problem of timing errors in high speed swept source OCT
Timing errors are very critical in coherent averaging as they cause phase errors in the frequency domain. This specifically applies to MHz-OCT systems, since they usually operate with analog to digital converters at sampling rates of 4 Gsamples/s or more. This means, that if a timing accuracy of 1/10th of a cycle at maximum imaging depth is required, a maximum timing jitter of roughly 25 picoseconds only can be tolerated. This makes all coherent processing in fast MHz-OCT more challenging than in slower systems.
2.3 Problem of trigger jitter using high speed ADCs
Triggering the data acquisition for each A-scan is hardly possible with today's ADC-technology since electronic noise in the rising edges of the control signal for the trigger event would generate too much timing jitter. Also, the trigger rearming times of several 100 ns would cause significant data loss. To avoid trigger jitter between the recorded A-scans, the interference signals of all A-scans of a frame were recorded continuously. However, this shifts the problem of timing mismatches to the post-processing step, where the single A-scans have to be cut out from the dataset with high (sub sample interval) temporal precision. This is particularly critical at high frequencies. Close to the Nyquist limit, a shift of the interference signal by one sample interval causes a phase jump of pi, so that coherent averaging of two A-scans with such a high error would almost cause signal cancelation.
2.4 Problem of determining the laser sweep rate/sample clock with 10−8 accuracy
Since we use one trigger event to acquire the data of an entire frame and then extract the individual A-scan data in the computer in post processing, we need to exactly know the duration of each A-scan to avoid errors in the data extraction. Assuming 1200 samples per A-scan, one B-scan consisting of 10,000 lines and a desired timing accuracy of 1/10 of a sample we need to know the ratio between the sample clock frequency of the ADC and laser sweep repetition rate with an accuracy of 1/(1200·10000·10) = 10−8.
So to precisely extract the A-scans of the continuously acquired data of one frame, it is necessary to know the exact ratio of the sample rate of the data acquisition and the FDML frequency of the laser, as in our case the latter is frequently adjusted by an automated control loop [27]. One way to achieve this is to synchronize the data acquisition and the FDML laser.
For example, a phase-locked loop (PLL) can be used to generate the sample clock as a multiple of the FDML frequency, given that the data acquisition card supports PLLs. This has the advantage that successive sweeps are always sampled at the same relative time positions, so that the recorded transients can be directly added for averaging. Since the data acquisition card used in this work does not allow direct clocking of the ADC and for reasons explained in the next two sections, the system was not synchronized in the way described above. Instead, the FDML frequency and the digitizing clock were generated from the same high-precision 10 MHz reference clock with known frequency ratio.
2.5 Problem of timing variations in buffered lasers
Song et al. described the problem of increased phase variation between buffered sweeps [28,29]. The time intervals between successive sweeps that are identical copies do not depend on the sample rate and FDML laser frequency, but only on the lengths of the fiber coils in the buffer stage. We accurately measured these time intervals by calculating an autocorrelation function between a laser noise dominated intensity signal (sweep of detuned FDML laser) and the signal of each of the seven copies generated in the buffer stage. Based on the maxima positions of these autocorrelation functions, it was possible to determine the different delays generated in the buffer stage with an accuracy better than 25 ps, which is ten times shorter than the resolution of the digitizer card. Since the delays are independent of the FDML frequency and the sample rate of the data acquisition, they only have to be measured once for each buffer stage. Furthermore, we expect great advantage of the precise measurements of fiber lengths or time delays for other phase sensitive OCT applications as well, as the relative phases between successive sweeps don't suffer from phase variation of the laser source. When the exact ratio of the FDML laser's filter frequency and digitizer sample rate as well as the lengths of the buffer stage coils are known, extracting the A-scans is straightforward. The original data was upsampled in the frequency domain by zero padding. After the inverse FFT, the A-scans were extracted using a linear interpolation. By this strategy it is possible to ensure correct sample timing down to the order of 25ps, effectively shifting the signal by fractional samples.
It should be noted that the buffered copies of a sweep, as they pass different fiber lengths in the buffer stages, have different time to wavelength mappings due to fiber dispersion. In this work, fiber with low dispersion around 1300 nm (Corning SMF28e) is used in the buffer stage, which makes a numerical dispersion correction before averaging unnecessary. However, this should be considered when this technique is employed using other wavelengths and fibers, e.g. FDML lasers at 1050nm for retinal imaging.
2.6 Problem of multiplexed acquisition and interleaving spurs
In modern data acquisition devices, high sampling rates are often achieved by sampling an analog input from several analog-to-digital converters (ADCs) by time division multiplexing (TDM). In the data acquisition card used in this work, four ADCs with a sampling rate of 1 GS/s are installed in order to achieve a sampling rate of 4 GS/s employing TDM. Since the different ADC channels have slightly different gain factors and offsets, even with the most accurate calibration, the A/D conversion causes distortions, so-called interleaving spurs in frequency domain. Similar effects (timing spurs) can also be caused by timing errors between the sample clocks for each ADC. In our configuration, after some magnitude averaging, the different interleaving tracks become clearly visible as noise in the vertical center of the A-scans with a maximum at half the Nyquist frequency. This results in an uneven background in the OCT images which represents the frequency-dependent average noise energy. When the digitizer card and the A-scan rate are synchronized in a way that the samples per A-scan are an integer multiple of the number of digitizers in the card, these interleaving spurs will become a phase stable fixed pattern noise that cannot be reduced by coherent averaging. However, if this situation is avoided coherent averaging can effectively reduce interleaving noise which will be illustrated in the following sections. This means in our case, that we must choose a sampling rate such that the number of samples per A-scan is not a multiple of 4.
2.7 Measurement of the averaging efficiency
In order to characterize and quantify the described problems and to investigate the effectiveness of the strategies for remedy, we performed a series of measurements without scanning on a defined mirror reflection and applied a number of different approaches to analyze the data. To estimate an upper limit for the efficiency of coherent averaging in our setup, we measured the decrease in image noise. First, an OCT dataset consisting of 1024 A-scans of a mirror was recorded with an OD 4 filter placed in the sample arm to reduce the signal intensity. The noise level was measured as a function of N averaged A-scans. Figure 1(a) shows the amplitude squared of the FFT (OCT depth signal) of a single A-scan (no averaging). The interleaving spurs are clearly visible as a narrow spectral region of increased noise around the center frequency.
Further, the mean of the amplitude squared of ten consecutive A-scans (magnitude averaged) and the amplitude squared of ten coherently averaged A-scans were calculated, displayed in Fig. 1(b) and Fig. 1(c). Comparing those signals reveals that in coherent averaging the mean noise power is significantly decreased, while the OCT signal of the mirror is similar in both averaging methods. Please note that the increase in peak amplitude of the averaged signals in Fig. 1(b) and Fig. 1(c) compared to the unaveraged A-scan in Fig. 1(a) is not a result of the averaging process. The unaveraged A-scan shown in Fig. 1(a) was randomly selected and has a lower amplitude due to intensity fluctuations caused by external influences on the imaging setup.
2.8 SNR improvement by magnitude averaging
The often-quoted statement, that magnitude averaging does not increase the signal to noise (SNR) level is not always correct. It is only true if the signal exhibits a large fluctuating background. In our case of a very stable background it can be determined by measuring a highly averaged signal prior to the actual measurement. Then this "smooth" background is subtracted from the OCT data, i.e. from the individual scans. So only the fluctuations and deviations from this highly averaged background effectively contribute to loss in SNR and noise.
As the mean noise power is frequency dependent, it causes a depth dependent background that is – in our case - almost constant from A-scan to A-scan. This background will increase the noise signal standard deviation measured within an averaged A-scan. When the mean noise power levels are very constant, as it is at least in our system, this background can be measured and subtracted and will not decrease the systems sensitivity. We use this to create a background corrected version of the 10x magnitude averaged A-scan for fair comparison of the SNR.
Therefore, this background was determined by averaging 1000 A-scans from another dataset acquired in the same way [Fig. 1(d)]. It should be noted that this is not a real background, since it still contains the peak of the mirror reflection. The 1000x averaged "background" is subtracted from the tenfold magnitude averaged A-scan resulting in the background corrected version of the 10x magnitude averaged A-scan shown in Fig. 1(e).
To analyze the effect but also the limits of averaging, we calculated the standard deviation of the coherently averaged A-Scan [Fig. 1(c)] and the magnitude averaged A-scan with subtracted background [Fig. 1(e)]. Of course, for the calculation of the standard deviation only the depth signal without the reflection peak is considered. The different values of standard deviation are then plotted against the number of averaged scans, to analyze the effect of averaging on SNR improvement in both cases: coherent and magnitude averaging.
Fig. 1. OCT depth scan of a mirror attenuated by an OD 4 filter (corresponding to 80 dB attenuation due to double passing) (a) without averaging, (b) with tenfold magnitude averaging, (c) with tenfold coherent averaging and (d) with thousand fold magnitude averaging. In (e) the difference of the amplitude squared of the FFT of b and d is plotted. All OCT data was acquired at the exact same position on the sample using a stationary beam.
Both modalities, magnitude and coherent averaging lead to a reduction in noise. However, as depicted in Fig. 1(c) and Fig. 1(e), in coherent averaging the signal amplitude is preserved while the variance of the signal is drastically reduced whereas in magnitude averaging the mean noise power is constant and only after subtracting the background "bias" the noise signal will be reduced. Theoretically, coherent averaging can achieve a noise reduction scaling with 1/N, where N is the number of averaged traces. Magnitude averaging, as known from Gaussian errors of statistically uncorrelated events, should yield a 1/sqrt(N) improvement. In the following we will discuss, how many A-scans can be averaged with our system and still achieve a noise reduction performance close to theory. We also quantify the effect of signal loss due to fringe washout by phase drift – an effect that only affects coherent averaging.
Fig. 2. Noise and signal reduction dependent on the number of averaged A-scans. (a) Decrease in noise (standard deviation of the amplitude squared) by magnitude (mag) averaging (red) or by coherent (complex – cpx) averaging (blue) depending on the number of averaged A-scans. The red and blue lines are the average of 80 records. The color-shaded bands correspond to a standard error around the mean value. (b) Decrease of the signal maximum due to magnitude or coherent averaging depending on the number of averaged A-scans. The results are based on measurements shown in Fig. 1, which were acquired at the exact same position on the sample using a stationary beam.
3.1 Noise reduction of coherent and magnitude averaging: 1/N versus 1/sqrt(N)
The difference in noise reduction between the two versions of averaging as a function of the number (N) of averaged A-scans is illustrated in Fig. 2(a). The coherent averaging curve (blue) and the magnitude averaging curve (red) represent the mean of 80 independent measurements and the shaded area falls within one standard error of all measurements around the mean. Further, the theoretical limits are plotted, 1/N for coherent averaging (black line) and 1/sqrt(N) for magnitude averaging (dashed black line). With averaging of up to about 100 A-scans, the measured noise suppression closely follows the theoretical limit but clearly deviates when more A-scans are averaged. This shows, that averaging up to 100 A-scans is reasonable to improve the noise performance as it is almost as good as theoretically possible, whereas the gain in sensitivity when averaging more than 100 A-scans will decrease. Interestingly, for our system, this value is roughly the same for both averaging strategies.
3.2 Signal degradation due to fringe washout
In coherent signal averaging we must also avoid that the sample signal (here the peak from the reflection) is not degraded due to A-scan to A-scan phase variations. Such phase variations may arise from phase noise of the laser, revisiting errors of the scanning optics or sample movement. However, the latter also affects slower systems – an effect known as "fringe wash out" [29]. Due to the different depth-dependent contributions to the phase error described in section 2 it is very complex to quantify the total phase error value of our swept source OCT system. The problem is that some contributions to the total phase error are depth-dependent, but some are not. For example, a timing jitter or limited coherence length of the FDML laser would lead to an increased phase error value at larger imaging depth. In contrast, sample motion or related movements of the scanning optics cause a fixed phase error for all different depths. An in-depth analysis giving absolute numbers for the phase error like in [30] is beyond the scope of this paper. However, it can be easily tested whether the phase stability of the laser and interferometer is sufficient by inspecting the signal reduction caused by coherent averaging of a static sample without scanning. In our case, when using coherent averaging the maximum of the mirror signal is only decreased by −0.75 dB over 1000 consecutive A-Scans [Fig. 2(b)], which indicates that the FDML laser as well as the electronics are phase stable enough. For magnitude averaging the peak intensity is also almost constant, the signal level only decreases by −0,5 dB [Fig. 2(b)].
3.3 Speed reduction potential
As evident from the graph, we should be able to downscale the A-scan rate of our system by a factor of 100 yielding a sensitivity comparable to that of a shot noise limited 33 kHz OCT system. When more than 100 A-scans are averaged an increasing deviation from the theoretical limit can be observed. This indicates that further averaging becomes less effective which may be due to dominant excess or ADC related noise. It might be advisable, though, to apply incoherent averaging on coherently averaged data to decrease speckle noise, as coherent averaging has no impact on speckle reduction. All in all this means, that our FDML based 3 MHz-OCT can be effectively operated at any A-scan rate between ∼30 kHz and 3 MHz with nearly shot noise limited sensitivity.
3.4 Coherent averaging in real in vivo imaging application
To verify the benefit of coherent averaging a human finger knuckle joint was used as imaging sample. The OCT data was acquired while the finger was pressed against a cover glass and index matching gel was used to suppress reflections at the skin surface. Figure 3 illustrates the effects of coherent averaging compared to magnitude averaging. Compromising imaging speed and sensitivity, 30 consecutive A-scans were averaged in both modalities, downscaling the system speed to an effective A-scan rate of 109 kHz. Taking into account the results displayed in Fig. 2, we should theoretically be able to apply 100x coherent averaging on the OCT signal, which would correspond to an effective A-scan rate of ∼32 kHz. However, to compare the achievable image quality to ∼100 kHz OCT systems, a typical acquisition speed value of today's commercial systems (Carl Zeiss Meditec AG, ZEISS PLEX Elite 9000; Topcon, DRI OCT Triton), we only applied 30x averaging. Subsequently, 15 slightly displaced B-scans were averaged incoherently to eliminate the speckle pattern. The cut levels where set differently for the two resulting images, which is necessary for a fair comparison of the two averaging modalities since coherent averaging reduces the background instead of just better defining it as incoherent averaging does. We preferred this strategy over histogram equalization since the visual impression of the OCT signal is better comparable this way.
Besides the finger there are three faint horizontal lines visible in Fig. 3. The slightly tilted (falling) bright horizontal line at the top is the bottom surface of the cover glass. The faint slightly tilted (rising) line below is the flipped image of the top surface of the cover glass. Both lines will not disappear by coherent averaging, because they represent a "real signal". The extremely faint perfectly straight horizontal line may be caused by numerical processing and hence not be suppressed by coherent averaging.
Assessing the image quality of the finger, both averaging techniques vastly improve the image quality, and we clearly see, that coherent averaging is superior. However, the difference between the two images is not as dramatic as may be expected. While, in the case of coherent averaging, improved contrast due to the reduced uneven background can be seen, especially at large imaging depths, a visible effect of the improved sensitivity cannot clearly be claimed. Theoretically, for 30x averaging we would expect a sensitivity improvement of $10\cdot\log ({30} )= 14.8\; \textrm{dB}$ by coherent averaging and 1$0\cdot\textrm{log}\left( {\sqrt{30}} \right) = 5\cdot\textrm{log}({30} )= 7.4\; \textrm{dB}$, by incoherent averaging, a difference of 7.4 dB. The reason, why this difference does not cause a dramatic difference in image quality, may be that the sensitivity of the MHz-OCT system at 3.3 MHz A-scan rate is already very high without averaging at 106 dB.
The additional ∼15 dB of sensitivity, yielding a total of ∼121 dB for the image in Fig. 3(b) may not change the appearance of the image very dramatically, since at such high sensitivities the image quality may be limited by other parameters. Effects like multiple scattering, limited dynamic range and actually the problem to display an image with more than 70 dB dynamic on a screen while maintaining fine and detailed contrast graduation may become the limiting factors. So the usefulness of coherent averaging in OCT systems with very high sensitivity of more than 100 dB may be limited.
Fig. 3. OCT images of a human finger knuckle joint. The data was acquired with a cover slide pressed to the finger and using ultrasound transmission gel at 3.28 MHz A-scan rate and 40 mW sample arm power. The dataset consists of 15 frames with 60000 A-scans each (∼0.3 s acquisition time). (a) An image without any averaging was constructed by stitching every 30th A-scan. (b) 30x magnitude (mag) and (c) 30x coherent (complex – cpx) A-scan averaging was applied on the dataset to form frames consisting of 2000 averaged A-scans. After averaging, for (b) and (c) all 15 frames were averaged incoherently resulting in the displayed OCT images. The white bars correspond to a distance of 1 mm in air.
However, in the field of retinal OCT, MHz systems usually exhibit only around 90 dB sensitivity due to lower laser power exposure limits for the eye. In such cases, an increased sensitivity may cause a more pronounced change in image quality. To examine the effect of coherent averaging on low sensitivity systems, we reduced the sensitivity of our MHz-OCT system to 70 dB by attenuating the sample arm with an OD 1.8 neutral density filter. In Fig. 4 an unaveraged OCT image of a piece of orange as a stationary sample is compared to its 30-fold magnitude and 30-fold coherently averaged images. In this case the difference between the two averaging modalities is much more obvious. While both techniques improve the image quality, the coherently averaged image reveals more details and the uneven background, that is most distinct in the middle third of the image, is vastly reduced. Again, the cut levels were adjusted for the higher dynamic range in the coherently averaged modality. The clearly visible advantages of coherent averaging in this situation indicate that downscaling the A-scan rate might be especially useful for sensitivity limited systems. Suitable candidates might be retinal MHz-OCT systems, where the limited applicable sample arm power of 1.4 mW leads to only 90-95 dB sensitivity.
Fig. 4. OCT image of a piece of orange, acquired with −36 dB attenuated sample arm power. The dataset consists of one frame with 60000 A-scans. (a) An image without any averaging was constructed by stitching every 30th A-scan. 30-fold (b) incoherent (magnitude – mag) and (c) coherent (complex – cpx) A-scan averaging was applied on the dataset to create the averaged OCT images. The scale bars correspond to a distance of 1 mm in air.
We investigated the feasibility of coherent averaging of A-scans using an FDML based MHz-OCT system. Due to the high electronic fringe frequencies in MHz-OCT, there are a lot of challenges to the electronics layout and the synchronization setup of the OCT-engine. We discuss step by step all the potential pitfalls and present individual solutions to them. A detailed design of our electronics- and synchronization-layout to achieve phase-stability is presented.
We show that we can average up to 100 A-scans while fully benefiting from the increased sensitivity. For averaging more than 100 A-scans this improvement deviates from the ideal curve. We expect that systems starting from lower initial sensitivity values allow for more coherent averaging since electronic noise, ADC multiplexing noise and similar, which can put a limit to the maximum achievable sensitivity, are relatively less dominant in these cases.
For our system this means that coherent computational downscaling of the 3.2 MHz-OCT to ∼30 kHz by averaging 100 A-scans is possible while fully harnessing the sensitivity improvements of slower imaging speed. We demonstrate imaging examples comparing 30x magnitude averaging with 30x coherent averaging. We observed that the improvement in image quality is rather small when using high sensitivity systems, but quite striking for low sensitivity systems.
The presented results may have a big impact on the system design of future OCT-engines. It has been realized that for many applications, a high MHz speed would be very beneficial. However, at the same time, it can be necessary to have very high sensitivity to detect faint signals - slower imaging speed might be acceptable. For example in ophthalmic OCT, MHz speed for densely sampled, ultra wide field imaging of the retina is required. Considering OCT angiography, MHz A-scan rates can improve the contrast of the human choriocapillaris and choroid as recently demonstrated [13,31]. At the same time, high sensitivity is desired to properly image the vitreous body, which usually requires lower speed due to shot noise limitations. Also, for angiographic OCT of slow flow, slower systems with higher sensitivity can be advantageous. For this reason, swept source OCT engines with adjustable sweep speed are desired. Even though most swept laser designs would in principle allow for adaptive sweep speed - even FDML lasers by adapting the buffering factor – but in reality most wavelength swept OCT lasers operate at a given frequency. In this work we prove that an OCT system with a fixed repetition rate MHz laser can perform high speed imaging with reduced sensitivity and also high sensitivity imaging at lower speed. Thus, for high sensitivity imaging slow OCT-engines are no longer needed, when the A-scan rate of high-speed systems is simply downscaled to improve sensitivity. So our results indicate that computational downscaling in software might be particularly compatible with retinal MHz-OCT systems. This will be investigated further in the future.
It should be noted, that the presented results might be different for other MHz OCT-laser sources like rapidly tunable VCSELs or stretched pulse sweeps, since they may have different noise and phase stability performance.
European Research Council (646669); Bundesministerium für Bildung und Forschung (13GW0227B); Deutsche Forschungsgemeinschaft (EXC 2167-390884018, HU 1006/6 270871130); state of Schleswig-Holstein, Germany Excellence Chair Program by the universities of Kiel and Luebeck.
TP: University of Lübeck (P), Optores GmbH (E), WD: Optores GmbH (I), RH: University of Lübeck (P), Optores GmbH (I,P,R), Optovue Inc. (I,R), Abott (I,R).
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8. S. Tozburun, M. Siddiqui, and B. J. Vakoc, "A rapid, dispersion-based wavelength-stepped and wavelength-swept laser for optical coherence tomography," Opt. Express 22(3), 3414 (2014). [CrossRef]
9. B. Potsaid, V. Jayaraman, J. Fujimoto, J. Jiang, P. J. Heim, and A. Cable, MEMS tunable VCSEL light source for ultrahigh speed 60kHz - 1 MHz axial scan rate and long range centimeter class OCT imaging (SPIE, 2012).
10. M. Siddiqui, S. Tozburun, E. Z. Zhang, and B. J. Vakoc, "Compensation of spectral and RF errors in swept-source OCT for high extinction complex demodulation," Opt. Express 23(5), 5508–5520 (2015). [CrossRef]
11. J. P. Kolb, T. Klein, C. L. Kufner, W. Wieser, A. S. Neubauer, and R. Huber, "Ultra-widefield retinal MHz-OCT imaging with up to 100 degrees viewing angle," Biomed. Opt. Express 6(5), 1534–1552 (2015). [CrossRef]
12. T. Klein, W. Wieser, C. M. Eigenwillig, B. R. Biedermann, and R. Huber, "Megahertz OCT for ultrawide-field retinal imaging with a 1050 nm Fourier domain mode-locked laser," Opt. Express 19(4), 3044–3062 (2011). [CrossRef]
13. C. Blatter, T. Klein, B. Grajciar, T. Schmoll, W. Wieser, R. Andre, R. Huber, and R. A. Leitgeb, "Ultrahigh-speed non-invasive widefield angiography," J. Biomed. Opt. 17(7), 0705051 (2012). [CrossRef]
14. T. Klein, W. Wieser, L. Reznicek, A. Neubauer, A. Kampik, and R. Huber, "Multi-MHz retinal OCT," Biomed. Opt. Express 4(10), 1890–1908 (2013). [CrossRef]
15. W. Wieser, W. Draxinger, T. Klein, S. Karpf, T. Pfeiffer, and R. Huber, "High definition live 3D-OCT in vivo: design and evaluation of a 4D OCT engine with 1 GVoxel/s," Biomed. Opt. Express 5(9), 2963–2977 (2014). [CrossRef]
16. J. P. Kolb, W. Draxinger, J. Klee, T. Pfeiffer, M. Eibl, T. Klein, W. Wieser, and R. Huber, "Live video rate volumetric OCT imaging of the retina with multi-MHz A-scan rates," PLoS One 14(3), e0213144 (2019). [CrossRef]
17. T. Wang, T. Pfeiffer, E. Regar, W. Wieser, H. van Beusekom, C. T. Lancee, G. Springeling, I. Krabbendam, A. F. W. van der Steen, R. Huber, and G. van Soest, "Heartbeat OCT: in vivo intravascular megahertz-optical coherence tomography," Biomed. Opt. Express 6(12), 5021–5032 (2015). [CrossRef]
18. T. Wang, W. Wieser, G. Springeling, R. Beurskens, C. T. Lancee, T. Pfeiffer, A. F. W. van der Steen, R. Huber, and G. van Soest, "Intravascular optical coherence tomography imaging at 3200 frames per second," Opt. Lett. 38(10), 1715 (2013). [CrossRef]
19. M. Choma, M. Sarunic, C. Yang, and J. Izatt, "Sensitivity advantage of swept source and Fourier domain optical coherence tomography," Opt. Express 11(18), 2183 (2003). [CrossRef]
20. R. Leitgeb, C. Hitzenberger, and A. Fercher, "Performance of fourier domain vs time domain optical coherence tomography," Opt. Express 11(8), 889 (2003). [CrossRef]
21. S. H. Yun, C. Boudoux, G. J. Tearney, and B. E. Bouma, "High-speed wavelength-swept semiconductor laser with a polygon-scanner-based wavelength filter," Opt. Lett. 28(20), 1981 (2003). [CrossRef]
22. R. Huber, M. Wojtkowski, K. Taira, J. G. Fujimoto, and K. Hsu, "Amplified, frequency swept lasers for frequency domain reflectometry and OCT imaging: design and scaling principles," Opt. Express 13(9), 3513–3528 (2005). [CrossRef]
23. M. Bonesi, M. P. Minneman, J. Ensher, B. Zabihian, H. Sattmann, P. Boschert, E. Hoover, R. A. Leitgeb, M. Crawford, and W. Drexler, "Akinetic all-semiconductor programmable swept-source at 1550 nm and 1310 nm with centimeters coherence length," Opt. Express 22(3), 2632–2655 (2014). [CrossRef]
24. P. H. Tomlins and R. K. Wang, "Digital phase stabilization to improve detection sensitivity for optical coherence tomography," Meas. Sci. Technol. 18(11), 3365–3372 (2007). [CrossRef]
25. M. Szkulmowski and M. Wojtkowski, "Averaging techniques for OCT imaging," Opt. Express 21(8), 9757 (2013). [CrossRef]
26. R. Huber, D. C. Adler, and J. G. Fujimoto, "Buffered Fourier domain mode locking: unidirectional swept laser sources for optical coherence tomography imaging at 370,000 lines/s," Opt. Lett. 31(20), 2975–2977 (2006). [CrossRef]
27. R. Huber and T. Pfeiffer, "Verfahren zur Erhaltung der Synchronität eines Fourier Domain Mode Locked (FDML) Lasers," Patent DE 10 2017 209 739 B3.
28. S. Song, W. Wei, B.-Y. Hsieh, I. Pelivanov, T. T. Shen, M. O'Donnell, and R. K. Wang, "Strategies to improve phase-stability of ultrafast swept source optical coherence tomography for single shot imaging of transient mechanical waves at 16 kHz frame rate," Appl. Phys. Lett. 108(19), 191104 (2016). [CrossRef]
29. S. H. Yun, G. J. Tearney, J. F. de Boer, and B. E. Bouma, "Motion artifacts in optical coherence tomography with frequency-domain ranging," Opt. Express 12(13), 2977 (2004). [CrossRef]
30. Z. Chen, M. Liu, M. Minneman, L. Ginner, E. Hoover, H. Sattmann, M. Bonesi, W. Drexler, and R. A. Leitgeb, "Phase-stable swept source OCT angiography in human skin using an akinetic source," Biomed. Opt. Express 7(8), 3032–3048 (2016). [CrossRef]
31. J. V. Migacz, I. Gorczynska, M. Azimipour, R. Jonnal, R. J. Zawadzki, and J. S. Werner, "Megahertz-rate optical coherence tomography angiography improves the contrast of the choriocapillaris and choroid in human retinal imaging," Biomed. Opt. Express 10(1), 50–65 (2019). [CrossRef]
W. Wieser, B. Biedermann, T. Klein, C. Eigenwillig, and R. Huber, "Multi-Megahertz OCT: High quality 3D imaging at 20 million A-scans and 4.5 GVoxels per second," Opt. Express 18(14), 14685–14704 (2010).
D. Adler, W. Wieser, F. Trépanier, J. Schmitt, and R. Huber, "Extended coherence length Fourier domain mode locked lasers at 1310 nm," Opt. Express 19(21), 20930–20939 (2011).
W. Wieser, T. Klein, D. C. Adler, F. Trépanier, C. M. Eigenwillig, S. Karpf, J. M. Schmitt, and R. Huber, "Extended coherence length megahertz FDML and its application for anterior segment imaging," Biomed. Opt. Express 3(10), 2647 (2012).
T. Pfeiffer, M. Petermann, W. Draxinger, C. Jirauschek, and R. Huber, "Ultra low noise Fourier domain mode locked laser for high quality megahertz optical coherence tomography," Biomed. Opt. Express 9(9), 4130–4148 (2018).
D. J. Fechtig, B. Grajciar, T. Schmoll, C. Blatter, R. M. Werkmeister, W. Drexler, and R. A. Leitgeb, "Line-field parallel swept source MHz OCT for structural and functional retinal imaging," Biomed. Opt. Express 6(3), 716 (2015).
D.-h. Choi, H. Hiro-Oka, K. Shimizu, and K. Ohbayashi, "Spectral domain optical coherence tomography of multi-MHz A-scan rates at 1310 nm range and real-time 4D-display up to 41 volumes/second," Biomed. Opt. Express 3(12), 3067 (2012).
T.-H. Tsai, B. Potsaid, Y. K. Tao, V. Jayaraman, J. Jiang, P. J. S. Heim, M. F. Kraus, C. Zhou, J. Hornegger, H. Mashimo, A. E. Cable, and J. G. Fujimoto, "Ultrahigh speed endoscopic optical coherence tomography using micromotor imaging catheter and VCSEL technology," Biomed. Opt. Express 4(7), 1119 (2013).
S. Tozburun, M. Siddiqui, and B. J. Vakoc, "A rapid, dispersion-based wavelength-stepped and wavelength-swept laser for optical coherence tomography," Opt. Express 22(3), 3414 (2014).
B. Potsaid, V. Jayaraman, J. Fujimoto, J. Jiang, P. J. Heim, and A. Cable, MEMS tunable VCSEL light source for ultrahigh speed 60kHz - 1 MHz axial scan rate and long range centimeter class OCT imaging (SPIE, 2012).
M. Siddiqui, S. Tozburun, E. Z. Zhang, and B. J. Vakoc, "Compensation of spectral and RF errors in swept-source OCT for high extinction complex demodulation," Opt. Express 23(5), 5508–5520 (2015).
J. P. Kolb, T. Klein, C. L. Kufner, W. Wieser, A. S. Neubauer, and R. Huber, "Ultra-widefield retinal MHz-OCT imaging with up to 100 degrees viewing angle," Biomed. Opt. Express 6(5), 1534–1552 (2015).
T. Klein, W. Wieser, C. M. Eigenwillig, B. R. Biedermann, and R. Huber, "Megahertz OCT for ultrawide-field retinal imaging with a 1050 nm Fourier domain mode-locked laser," Opt. Express 19(4), 3044–3062 (2011).
C. Blatter, T. Klein, B. Grajciar, T. Schmoll, W. Wieser, R. Andre, R. Huber, and R. A. Leitgeb, "Ultrahigh-speed non-invasive widefield angiography," J. Biomed. Opt. 17(7), 0705051 (2012).
T. Klein, W. Wieser, L. Reznicek, A. Neubauer, A. Kampik, and R. Huber, "Multi-MHz retinal OCT," Biomed. Opt. Express 4(10), 1890–1908 (2013).
W. Wieser, W. Draxinger, T. Klein, S. Karpf, T. Pfeiffer, and R. Huber, "High definition live 3D-OCT in vivo: design and evaluation of a 4D OCT engine with 1 GVoxel/s," Biomed. Opt. Express 5(9), 2963–2977 (2014).
J. P. Kolb, W. Draxinger, J. Klee, T. Pfeiffer, M. Eibl, T. Klein, W. Wieser, and R. Huber, "Live video rate volumetric OCT imaging of the retina with multi-MHz A-scan rates," PLoS One 14(3), e0213144 (2019).
T. Wang, T. Pfeiffer, E. Regar, W. Wieser, H. van Beusekom, C. T. Lancee, G. Springeling, I. Krabbendam, A. F. W. van der Steen, R. Huber, and G. van Soest, "Heartbeat OCT: in vivo intravascular megahertz-optical coherence tomography," Biomed. Opt. Express 6(12), 5021–5032 (2015).
T. Wang, W. Wieser, G. Springeling, R. Beurskens, C. T. Lancee, T. Pfeiffer, A. F. W. van der Steen, R. Huber, and G. van Soest, "Intravascular optical coherence tomography imaging at 3200 frames per second," Opt. Lett. 38(10), 1715 (2013).
M. Choma, M. Sarunic, C. Yang, and J. Izatt, "Sensitivity advantage of swept source and Fourier domain optical coherence tomography," Opt. Express 11(18), 2183 (2003).
R. Leitgeb, C. Hitzenberger, and A. Fercher, "Performance of fourier domain vs time domain optical coherence tomography," Opt. Express 11(8), 889 (2003).
S. H. Yun, C. Boudoux, G. J. Tearney, and B. E. Bouma, "High-speed wavelength-swept semiconductor laser with a polygon-scanner-based wavelength filter," Opt. Lett. 28(20), 1981 (2003).
R. Huber, M. Wojtkowski, K. Taira, J. G. Fujimoto, and K. Hsu, "Amplified, frequency swept lasers for frequency domain reflectometry and OCT imaging: design and scaling principles," Opt. Express 13(9), 3513–3528 (2005).
M. Bonesi, M. P. Minneman, J. Ensher, B. Zabihian, H. Sattmann, P. Boschert, E. Hoover, R. A. Leitgeb, M. Crawford, and W. Drexler, "Akinetic all-semiconductor programmable swept-source at 1550 nm and 1310 nm with centimeters coherence length," Opt. Express 22(3), 2632–2655 (2014).
P. H. Tomlins and R. K. Wang, "Digital phase stabilization to improve detection sensitivity for optical coherence tomography," Meas. Sci. Technol. 18(11), 3365–3372 (2007).
M. Szkulmowski and M. Wojtkowski, "Averaging techniques for OCT imaging," Opt. Express 21(8), 9757 (2013).
R. Huber, D. C. Adler, and J. G. Fujimoto, "Buffered Fourier domain mode locking: unidirectional swept laser sources for optical coherence tomography imaging at 370,000 lines/s," Opt. Lett. 31(20), 2975–2977 (2006).
R. Huber and T. Pfeiffer, "Verfahren zur Erhaltung der Synchronität eines Fourier Domain Mode Locked (FDML) Lasers," Patent DE 10 2017 209 739 B3.
S. Song, W. Wei, B.-Y. Hsieh, I. Pelivanov, T. T. Shen, M. O'Donnell, and R. K. Wang, "Strategies to improve phase-stability of ultrafast swept source optical coherence tomography for single shot imaging of transient mechanical waves at 16 kHz frame rate," Appl. Phys. Lett. 108(19), 191104 (2016).
S. H. Yun, G. J. Tearney, J. F. de Boer, and B. E. Bouma, "Motion artifacts in optical coherence tomography with frequency-domain ranging," Opt. Express 12(13), 2977 (2004).
Z. Chen, M. Liu, M. Minneman, L. Ginner, E. Hoover, H. Sattmann, M. Bonesi, W. Drexler, and R. A. Leitgeb, "Phase-stable swept source OCT angiography in human skin using an akinetic source," Biomed. Opt. Express 7(8), 3032–3048 (2016).
J. V. Migacz, I. Gorczynska, M. Azimipour, R. Jonnal, R. J. Zawadzki, and J. S. Werner, "Megahertz-rate optical coherence tomography angiography improves the contrast of the choriocapillaris and choroid in human retinal imaging," Biomed. Opt. Express 10(1), 50–65 (2019).
Adler, D.
Adler, D. C.
Andre, R.
Azimipour, M.
Beurskens, R.
Biedermann, B.
Biedermann, B. R.
Blatter, C.
Bonesi, M.
Boschert, P.
Boudoux, C.
Bouma, B. E.
Cable, A.
Cable, A. E.
Chen, Z.
Choi, D.-h.
Choma, M.
Crawford, M.
de Boer, J. F.
Draxinger, W.
Drexler, W.
Eibl, M.
Eigenwillig, C.
Eigenwillig, C. M.
Ensher, J.
Fechtig, D. J.
Fercher, A.
Fujimoto, J.
Fujimoto, J. G.
Ginner, L.
Gorczynska, I.
Grajciar, B.
Heim, P. J.
Heim, P. J. S.
Hiro-Oka, H.
Hitzenberger, C.
Hoover, E.
Hornegger, J.
Hsieh, B.-Y.
Hsu, K.
Huber, R.
Izatt, J.
Jayaraman, V.
Jiang, J.
Jirauschek, C.
Jonnal, R.
Kampik, A.
Karpf, S.
Klee, J.
Klein, T.
Kolb, J. P.
Krabbendam, I.
Kraus, M. F.
Kufner, C. L.
Lancee, C. T.
Leitgeb, R.
Leitgeb, R. A.
Liu, M.
Mashimo, H.
Migacz, J. V.
Minneman, M.
Minneman, M. P.
Neubauer, A.
Neubauer, A. S.
O'Donnell, M.
Ohbayashi, K.
Pelivanov, I.
Petermann, M.
Pfeiffer, T.
Potsaid, B.
Regar, E.
Reznicek, L.
Sarunic, M.
Sattmann, H.
Schmitt, J.
Schmitt, J. M.
Schmoll, T.
Shen, T. T.
Shimizu, K.
Siddiqui, M.
Song, S.
Springeling, G.
Szkulmowski, M.
Taira, K.
Tao, Y. K.
Tearney, G. J.
Tomlins, P. H.
Tozburun, S.
Trépanier, F.
Tsai, T.-H.
Vakoc, B. J.
van Beusekom, H.
van der Steen, A. F. W.
van Soest, G.
Wang, R. K.
Wang, T.
Wei, W.
Werkmeister, R. M.
Werner, J. S.
Wieser, W.
Wojtkowski, M.
Yang, C.
Yun, S. H.
Zabihian, B.
Zawadzki, R. J.
Zhang, E. Z.
Zhou, C.
Biomed. Opt. Express (11)
Meas. Sci. Technol. (1)


The video of Me is AwesoME ! 😻 It is the one and the only MV that Olivia and Meredith are included 😍My instinct is that TS7 era will be filled with the word "Awesome". What if there is a song called "Awesome"? . . . . #taylorswift #taylornation #taylurking #reputaylurking #reputation #taytay #13 #taylurking #meredithswift #fearless #oliviaswift #ts6 #1989 #itsbecky #1989 #red #speaknow #kaylor #taylena #l4l #swiftie #instagood #ts7 #me #me !


Gaby Di Zitti is a make-up and visual artist from Los Angeles and was chosen by her boyfriend Ryan Pressman to experience a Beef Session first hand. Punished Steve and DRE discuss with Gabs about Beefin it, her passion for drag, and her increased work on film and video projects.
Thierry Grose Ultimate Adversary stops in early in the show to discuss the Beefer's attendace of the recent Tears for Fears concert at the Santa Barbara Bowl earlier this September.
This show includes our first beef session which is available at parteon.com/lostbeat6 subscribe and pledge please.


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text:
E-Commerce Competition Enforcement Guide
United States – E-commerce Economics: Market Power and Enforcement in Vertical Markets
Rebecca Kirk Fair, Nikita Piankov and Emmanuel Frot
Contract terms up and down the supply chain have included practices such as resale price maintenance; most-favoured customer terms; exclusive contracts and conditional discounting; and tying and bundling, all of which have caught the attention of antitrust regulators in the US and the EU for many years. While these vertical restraints have been of concern to antitrust regulators because of the potential to inhibit competition, that scrutiny has been tempered particularly in the US because these restraints may also result in measurable benefits for customers, which can significantly complicate and sometimes outweigh potential anticompetitive effects. However, the use of these tactics in e-commerce settings, and the increasing importance and dynamism of e-commerce in the economy, have attracted renewed interest from regulators.
In this chapter, we first define 'e-commerce' and how it may differ from traditional commercial transactions. We then review vertical relationships and restraints in e-commerce and the challenges in evaluating them by traditional competition models. Explicitly defining markets, evaluating marketing power, and identifying the pro- and anticompetitive effects embedded in these vertical relationships all require bespoke tools and approaches. These issues will be specifically explored in the context of two recent cases involving e-commerce: Apple e-Books and In re: Online Travel Company Hotel Booking Antitrust Litigation.
What is E-commerce?
E-commerce applies technologies including computing power and the internet to reshape how firms interact with their suppliers and customers. These tools are nearly universal in many parts of the world (the US, EU and much of Asia) and widespread almost everywhere. Consumers and businesses alike have unprecedented access to product and service information online, and can conduct transactions remotely. E-commerce business models fall into three categories, each of which has different market economics.
Tangible good models include products that require some form of physical delivery or presence for consumption. Many tangible products offered online are identical to those found on the shelves of brick-and-mortar stores – the only difference is that the product can be researched and purchased online. Amazon is the most prominent firm using this model, delivering tangible products, most of which are also available through traditional retailers across the globe (initially a business-to-consumer model or B2C). Notably, Amazon began as an online distributor of books but quickly expanded its business to offer a nearly infinite array of other tangible goods sold to customers through the internet. Subsequently, Amazon has also become a business-to-business (B2B) service provider offering cloud computing and redistribution services to businesses, as well as a traditional brick-and-mortar retailer with Whole Foods grocery stores and product showrooms.
Intermediary models provide services to customers (often facilitating the purchase of other goods) through an online interface that changes the role of the intermediary with respect to understanding and acquiring the good or service. The online interface in an intermediary model often replaces a human agent, with resulting benefits and challenges for the user experience. For example, travel and hospitality services can be procured by paying a travel agent, or by going online and using Orbitz. Ride-sharing services like Uber or Lyft organise the vehicles for hire differently and offer consumers a different way to call for a ride than a traditional taxi service. In both cases, the consumer's goal in engaging with either service (travel agent vs online booking service, cab company vs Lyft app) is the same, but the service itself is fundamentally different.
Online products are wholly purchased and consumed online. For example, many forms of media content – video, music, books – can be accessed and consumed directly online. Because the cost of maintaining and distributing this content is often low, and because content creation may occur through separate third parties upstream, the value offered by these firms is often in aggregating content customers want in one location, pressuring providers to offer 'must-have' content and often generating revenue through advertising rather than subscription or pay per view, read or listen. For example, music streaming service Spotify connects consumers with content providers but also connects advertisers with consumers. It generates revenue from subscriptions for its ad-free premium services but it also generates revenue from advertisers on its free service. It then pays royalties to the content providers.
Altogether, the different forms of e-commerce make up a growing proportion of the US economy for both B2C and B2B markets. In B2C markets, some reports show that approximately 95 per cent of all Americans shop online.[2] In the US, online sales comprise about 9 per cent of total retail sales, which lags behind many other countries; for example, online sales account for nearly a quarter of total retail value in China.[3]
E-commerce also accounts for a significant portion of B2B markets. At the end of 2017, Forrester estimated that B2B e-commerce amounted to US$889 billion in the US, representing 11 per cent of total US B2B sales. Worldwide, the value of B2B e-commerce (US$7.6 trillion in 2017) is estimated to be more than three times that of B2C (US$2.4 trillion).[4]
Economic characteristics of e-commerce
E-commerce takes many forms and is difficult to systematically distinguish from traditional commerce. Nonetheless, it has several key economic characteristics relevant to the antitrust analysis of vertical relationships.
First, e-commerce allows firms and customers to interact across long distances and borders. While this discussion focuses on matters in the US, the antitrust analysis may vary for different transactions depending on the locations of the parties to the vertical relationship (both seller and customer). For example, resale-price maintenance (RPM) is evaluated under the rule of reason in the US, but not in Europe.[5] Thus, contractual arrangements that are acceptable in one jurisdiction may not be in another.
Second, because the cost of marketing and distributing products online requires lower up front investment, certain barriers to entry may be lower, particularly at the stage of distribution closest to the end customer. As any traditional brick-and-mortar retailer can attest, e-commerce has increased competition, facilitating and diversifying the number of competitors in a number of segments.[6] At the same time, certain barriers to entry may be higher in e-commerce. Entry may be particularly challenging when significant investments in brand or infrastructure are required, or when customers have a strong preference for particular products, services or content and will not use a distributor that cannot offer them. Furthermore, some have argued that data about consumers and their preferences may form such a barrier, as long-standing firms with deeper data may utilise that data to better target consumer needs.[7]
Third, the expansion of competing options for distribution, paired with the lower costs of reaching customers with both products and information, has also changed the way consumers collect information about products. The general expectation is that e-commerce reduces search costs for customers,[8] making it more difficult to sustain supracompetitive pricing. However, overwhelming options or platform loyalty may reduce the benefits of lower search costs. E-commerce provides customers with direct access to products and services. These services may be accessed through the service originator's digital channels, such as an airline's or a hotel chain's own web pages, or through third-party digital platforms that aggregate pricing and other information, provide payment mechanisms and allow for comparison shopping. Such platforms may co-exist with or substitute for the service originator's own digital channels.
Fourth, e-commerce facilitates aggregation – competing firms may offer a wider and cheaper array of products and services and generate substantial consumer welfare gains.[9] The tendency for e-commerce towards aggregation may also lead e-commerce firms to transform into more diversified platforms that bring together buyers and sellers, or content creators and content consumers. The two-sided or multisided nature of those platforms may create economic value through network effects across the platform. Specifically, the more buyers a platform recruits, the more attractive it may become to sellers (and vice versa). In some scenarios, the platform is truly multisided. For example, Spotify receives revenue from some users in the form of subscription fees and these users are attracted by its library of musical content. Spotify also sells time to advertisers, who are attracted by its community of subscribers. Spotify uses the revenue from both sources to compensate the providers of content.[10] Balancing these dimensions is an aspect of some, though not all, e-commerce models.
Vertical restraints and rule of reason analysis in e-commerce
Contractual constraints between buyers and suppliers are common in a variety of industries and sectors, including those in which e-commerce has made significant inroads. Those vertical restraints can and do have effects on competition. However, unlike horizontal agreements among competitors, they are not per se violations of antitrust law in the US. Such distinctions reflect both the limited risk to competition from vertical relationships absent market power and the potential procompetitive benefits.
Under such circumstances, regulators and economists in the US generally apply a three-step rule of reason analysis in which plaintiffs first must show that the vertical restraint causes harm to competition. Defendants may then demonstrate an offsetting benefit to competition, which, if successful, then shifts the burden back to the plaintiffs, who must demonstrate that such benefits could be achieved using less restrictive tactics. As part of the analysis, regulators and courts examine market definition and market power so that consumers at risk can be identified and potential benefits and effects evaluated. Specifically, vertical agreements require an analysis of competition both upstream (in which manufacturers or suppliers compete with similar firms offering competing brands or solutions) and downstream (in which retailers selling similar products compete for end customers).
Types of vertical agreements
Vertical agreements may directly affect the prices charged or may limit or influence the nature and scope of product distribution. For example, such agreements may restrict the price charged or advertised to consumers or require that customers make a certain amount of their purchases from the seller. These agreements have the potential to exclude rivals, but may also protect legitimate incentives to investment.
The need to consider explicitly these offsetting effects was specifically articulated in the 2007 Leegin decision by the US Supreme Court. This decision specifically ruled that vertical restraints should be assessed under the rule of reason and precluded holding such restrictions as per se illegal under §1 of the Sherman Act. The court noted that 'respected economic analysts . . . conclude that vertical price restraints can have procompetitive effects.'[11] To understand the potential pro- and anticompetitive effects of such vertical agreements requires a careful review of the contractual restraints in the context of e-commerce.
For example, consider tying and bundling, which are arrangements whereby a supplier makes the purchase of one good or service (the tying good) conditional to the purchase of a second good (the tied good), or literally combines the goods into a single package (a bundle). When a new entrant seeks to introduce a new complement, bundling or tying arrangements may be procompetitive and even necessary to introduce a competitive alternative to existing solutions. However, such arrangements may be more likely to be anticompetitive when aggregation creates opportunities for firms to privilege their own options in the downstream market, and potentially exclude rivals and inhibit entry. Similarly, a platform provider that also offers its own products through the platform may leverage such arrangements and exclude rivals.[12] A more subtle form of vertical restraint in the context of e-commerce that raises similar issues is preferential placement. For example, a search engine may preferentially feature search results associated with its own brand or a cable or wireless provider may prioritise bandwidth for its own content.
Exclusive distribution is another form of vertical restraint found among e-commerce firms. While e-commerce firms may aggregate upstream firms' offerings to consumers (downstream), upstream firms may prefer to limit distribution by rival providers to prevent competitors from developing a foothold. Given such incentives, upstream firms may seek exclusive distribution arrangements or volume commitments. Again, these restraints may be explicit, in the form of exclusive distribution agreements, or more subtle, such as offering discounts or preferential pricing to firms that commit to quantities or share of sales of a certain brand. In some circumstances, inter-brand competition (between competing supplier or manufacturer brands) may be sufficient to limit effects on customers. However, when consumer preferences or limited competitive options give upstream suppliers some market power, the weakening of intra-brand competition (i.e., competition between distributors offering the same brands) may lead to price increases or other anticompetitive effects.
Finally, vertical restraints may constrain prices offered, advertised or charged to consumers. Given the diversity and complexity of e-commerce competition, firms are often concerned with making investments in quality and marketing efforts, if those investments can be exploited by others who do not incur the investments and sell at lower prices. To preclude free-riding of this type, firms may use resale price maintenance (in which the upstream firm specifies the price at which a retailer can resell the product) and most favoured customer clauses (in which a firm specifies that it will receive a price no worse than that of any rival).
Again, if inter-brand competition is sufficient, such arrangements may on balance be procompetitive. However, they may weaken intra-brand price competition, resulting in offsetting effects that may be of concern to regulators.
Defining a market
Applying the rule of reason to evaluate vertical arrangements in e-commerce in the US requires an understanding of the relevant market. Whether products are available through traditional physical channels and online retailers or only available online, the competitive dynamics, evolving business strategies and solutions, and shifting consumer substitution patterns are all relevant to antitrust analysis. However, defining the relevant market is complicated in e-commerce, not only because substitution is less constrained by physical location or physical products, but also because supply-side substitution may come from unexpected places.
Online and offline distribution channels may serve the same market, even if the offerings are quite different (think Lyft and cabs). At the same time, for some consumers, online and offline options are not substitutable even when the end product is identical (a boutique on Rodeo Drive may not compete with a website that sells the same pair of shoes because the customers and their experience of the purchasing process are significantly different). In addition, rapid technological innovation may mean that product substitution options will be expanded through cross-platform competition facilitated by the low capital costs associated with incremental geographic or product expansions.
Finally, the agreements at issue may affect upstream inter-brand competition or downstream intra-brand competition. Assessing the competitive effects from foreclosure, collusion, or weakened price or quality competition requires some sense of their likelihood. Such questions are particularly challenging when firms are continually evolving; for example, Amazon's shift from being merely an aggregator in a vertical chain of B2C commerce to also becoming a horizontal competitor through its private label products.
Assessing market power
Just as market definition methodology is challenged by the rapid evolution of e-commerce strategies and shifting consumer substitution patterns, the evaluation of market power in such a dynamic context is more complicated than in traditional industries. While traditional assessments of market power such as HHI,[13] UPPI,[14] and direct evidence of firms exercising pricing power can be helpful when the underlying market is properly defined, near-term shifts in strategy and preferences may significantly affect conclusions.
The twin issues of demand and supply side substitution, as well as the relevant horizon to evaluate competitive effects, are central to evaluating transactions affected by or within e-commerce. For example, a federal judge blocked the 2016 merger between Staples and Office Depot in part because he rejected the argument that Amazon's recent foray into B2B office supplies and its expected expansion was sufficient to discipline competition post-merger. Some have asked whether such a position is reasonable, given the speed with which Netflix took over Blockbuster and Hollywood Video after their merger was blocked in 2005.[15] While the judge ultimately blocked the transaction, lingering questions include whether a merger of these traditional retailers provides sufficient market power to affect e-commerce related competition in the near-term and, if so, for how long? Might they have provided a more viable long-term competitive option if the merger had been approved?
On the other hand, the evaluation of an acquisition of competing online retail platforms is often focused on demand side substitution. For example, traditional competition analysis in conjunction with a regulatory review of Amazon's proposed acquisition of Zappos.com in 2009 may have focused on whether consumers buying shoes at Zappos were also shopping or gathering pricing information about shoes on Amazon. It may also have examined whether Zappos could move into the distribution of other products using its existing user interface, distribution experience and assets, and customer base (the acquisition was completed without a regulatory challenge in November 2009).
These questions are also relevant when examining market power in the context of vertical restraints. An exclusive distribution agreement or preferential product placement may be of little concern when a new e-commerce site is introduced. However, if that site becomes a leading aggregator or secures a large loyal or locked-in customer base, it may have sufficient market power upstream or downstream to stifle competition. Given the rapid pace of innovation and shifting strategies of many e-commerce companies, the evaluation of closeness of competition in the near-term, and the question of the appropriate horizon for competitive discipline from the supply side, are more complicated than in other industries. Thus, while traditional economic analyses may be sufficient to examine these industries, their application may be more complex when e-commerce is involved.
Analysing pro- and anticompetitive effects
As clearly articulated by the Supreme Court in Leegin (2007) and demonstrated in subsequent cases,[16] alternative theories of competitive harm are relevant when evaluating market power in a clearly defined market, as well as the efficiency motives and potential welfare enhancements that arise from vertical agreements. When firms depend on up- and downstream parties to provide their services or reach their customers, coordination and information sharing become necessary. To correctly coordinate decision making; to account for competition and consumer preferences; and to ensure profitable investments, vertical agreements and restraints may be necessary. For example, when suppliers and retailers are making relationship-specific investments, hold-up risk (in which the bargaining position of the party making the investment decreases once the investment is made) may lead to exploitation and sub-optimal outcomes. Long-term contracts including volume commitments or exclusivity provisions may provide the necessary assurances to both parties. Such benefits must be then compared to the potential foreclosure of competing upstream suppliers and whether the contractual arrangement is overly restrictive given the procompetitive justifications.
Similar issues arise when competitors can free-ride. For example, the combination of physical showrooms with an online site by one retailer may provide necessary information for consumers to comparison shop for a luxury good. The retailer making such investments may seek an exclusivity agreement, at least for certain parts of the product line or in certain countries. Without such exclusivity, other retailers will free-ride on the information it provided to consumers and undercut its prices. In such circumstances, consumers may be better off with some vertical restraints, as long as inter-brand competition among luxury good suppliers is sufficient to preserve competition. In other circumstances, if the upstream supplier has market power, those agreements may weaken competition downstream.
As illustrated by the differing positions of EU and US agencies, vertical restraints on end-customer prices may be the most difficult to evaluate. The efficiency motives cited for resale price maintenance and other pricing restraints generally fall into two categories – coordination of decision-making and signalling. Suppliers and retailers often have distinctly different information: local preferences may or may not reflect market-wide preferences and the prices charged by one retailer may not be optimal from a market perspective (e.g., if too many units are sold at depressed prices in select markets, supplier profitability and long-term investments may be adversely affected). Furthermore, given that price is often a signal of quality, suppliers offering premium products may seek to control prices, retail discounting and the channels of distribution to maintain their reputation. However, when suppliers directly limit intra-brand price competition, upstream market power is highly relevant. Without sufficient competition among suppliers, such contractual arrangements may, on balance, be anticompetitive.
While these effects are well known, e-commerce challenges the analysis of some vertical restraints. For instance, territorial exclusivity clauses may be procompetitive when they constrain free-riding; however, e-commerce may jeopardise any procompetitive effects and result in heightened concerns for such clauses. Similarly, selective distribution – whereby a supplier applies criteria to admit retailers to its distribution network – is common and well understood. However, e-commerce has made such clauses more common as suppliers try to protect their brand image on the internet. An extreme version of such clauses is the outright ban of online sales. A supplier may prefer to forgo sales on the internet because of information asymmetry (consumers are not able to physically see and try the product) or the fear that retailers will have insufficient incentives to invest in quality.
Recent antitrust enforcement
Apple eBooks
In 2011, the US Department of Justice (DOJ) accused Apple and five of the largest US publishers of conspiring to increase the price for e-books above Amazon's US$9.99 price in the run-up to the launch of the iPad in 2010.[17] The DOJ claimed that Apple and the publishers had cost consumers millions of dollars by colluding to raise the price for e-books. The companies allegedly achieved this by simultaneously agreeing to switch to an agency sales model, which gave the power to publishers, rather than retailers, to set the price for the consumer. The publishers denied wrongdoing, but all eventually chose to settle. Apple, however, vigorously defended its actions, claiming that it was simply a new entrant breaking the hold of an existing monopolist (Amazon). Ultimately, after multiple appeals and the Supreme Court declining to hear the case, Apple was found guilty in 2016 and required to re-negotiate all e-book distribution deals and pay US$450 million in damages to consumers. The central issues of this case centred around two fundamental components of antitrust law and how to apply them in the case of a digital platform.
First, the courts needed to decide whether market conditions in the early days of the e-book industry were sufficiently unique to warrant a review of Apple's actions under the rule of reason standard. In cases where the economic impact of the defendant's actions is not immediately obvious, the plaintiffs are required to prove a concerted action and harm to competition. Alternatively, if Apple's alleged actions were per se anticompetitive, then the plaintiffs would only have to prove that an agreement existed. This more lenient standard is meant to be applied 'only after courts have had considerable experience with the type of restraint at issue.'[18]
The DOJ argued that the court did have experience with cases like Apple's, and that the economics of bookselling through traditional physical platforms, such as brick-and-mortar bookstores, was well understood. Apple contended that digitisation of both book content and the platform had fundamentally changed the market. Both publishers and retailers were struggling with how to price books that cost substantially less to produce but delivered the same content as the physical copies. Further, Amazon's market power in the e-book market was allowing it to engage in potentially anticompetitive behaviour by pricing below marginal cost. While the 2nd Circuit judge did say she was 'mindful of Apple's argument that the nascent e-book industry has some new and unusual features,' she ruled that the DOJ could apply the per se standard. This ruling sent a warning to other digital platforms that new digital markets may not be considered sufficiently different from physical markets to warrant differential treatment. To consider Apple's actions per se anticompetitive would also be tied to whether Apple's agreements should be characterised as vertical or whether Apple was effectively the facilitator of horizontal agreements. As noted, horizontal agreements among competitors are 'prohibited despite the reasonableness of the particular prices agreed upon,' while the legality of vertical price restraints depends on the companies' motives and the outcome of their actions.[19] Apple argued that it had engaged in 'inherently vertical, novel conduct' with the procompetitive objective of launching the iBooks Store to compete with Amazon, which owned 90 per cent of the e-book market, while the DOJ claimed that Apple's behaviour was targeted at horizontal coordination.[20] However, the courts sided with the DOJ in accusing Apple of facilitating a horizontal scheme, reasoning that Apple had informed publishers of one another's intentions and had made it clear that multi-publisher sign-on was essential. This ruling brings to light the challenge of evaluating the nature of coordination efforts when a platform is established.
In re: Online Travel Company Hotel Booking Antitrust Litigation
In 2012, a consolidated class action lawsuit was filed in the US District Court for the Northern District of Texas against 12 major hotel chains and nine online travel agencies (OTAs) for allegedly engaging in an industry-wide conspiracy to uniformly enact vertical agreements between hotels and OTAs to eliminate intra-brand competition between online hotel booking sites.[21] In the vertical agreements, the hotels would publish the best available rate or lowest rate of their hotel rooms for the OTA to use on their website (another example of an RPM agreement). The agreements also contained most favoured nation (MFN) clauses to ensure that the published rate for each OTA was not lower than that for any other OTA.
Critically, the plaintiffs did not allege that any individual vertical agreement was anticompetitive; rather, the 'Plaintiffs' antitrust claims rested entirely on the circumstantial facts purportedly showing that Defendants entered into an "express or tacit" industry-wide conspiracy not to compete.'[22] As such, the plaintiffs claimed that the matter 'must be considered holistically, for without either [horizontal or vertical] component, the scheme would fail'.[23]
The plaintiffs alleged both a per se violation and a violation under the rule of reason. Using the rule of reason framework, the parties disagreed on the correct market definition. The plaintiffs defined the relevant product market as 'the direct online sale of hotel room reservations' in the US. The defendants argued that the product market should be wider, including other methods of booking hotel rooms such as brick-and-mortar travel agencies. The defendants also argued that the market should be considered at a local level, as consumers substitute among hotels at their desired location, not among all hotels in the US.
The plaintiffs and defendants also used different methods to evaluate market power. The plaintiffs alleged that the defendants collectively had significant market power, citing the OTA defendants' 94 per cent market share. The defendants argued that market power should be evaluated separately for each defendant, rather than collectively.
The defendants argued that the RPM agreements had procompetitive benefits. The defendants relied on the argument set forth in the Leegin decision that RPMs between the hotels and OTAs (which reduce intra-brand competition) allow for an increase in inter-brand competition among hotel companies. They also noted that the agreements allow hotels to communicate a consistent message to their customers and more efficiently deliver their products to the public.
In 2014, the court dismissed the lawsuit, primarily on the grounds that the plaintiffs did not provide sufficient facts to demonstrate an industry-wide conspiracy. The court concluded that the agreements could be explained by economic forces: a hotel could reasonably want to control the price of its rooms, for example, to protect the hotel's brand, and an OTA would want an MFN clause in exchange for giving up the right to discount hotel rooms. Additionally, the court found that the plaintiffs' 'factual enhancements' to their case (such as potential communication among competitors at industry conferences and the existence of government investigations into the matter in the EU) did not suggest a conspiracy or were irrelevant. Because the case was framed holistically as an industry-wide conspiracy, rather than focusing on the legality of the individual vertical agreements, the court did not complete a rule of reason analysis of the RPM agreements in its decision. As such, the case did not set further precedent on how to apply Leegin in e-commerce industries.
Vertical restraints in e-commerce raise important questions for antitrust regulators around the globe. Given the reach of many e-commerce sites, the regulatory framework applied to various vertical restraints must be carefully considered by businesses when crafting agreements. Meanwhile, regulators must consider the risks of weakened competition in the context of efficiencies and market expansion. Because e-commerce creates a natural complementarity between the aggregation of information and resources, and the distribution of goods and services, there is an increasing likelihood for firms to engage in both horizontal and vertical relationships simultaneously. These arrangements may change the antitrust analysis of vertical restraints, and particularly require the evaluation of both supply and demand side substitution patterns. The traditional tools of antitrust analysis are up to the challenge, so long as the time horizon over which effects are considered is correctly matched to the dynamism and uncertainty that accompanies e-commerce business models.
[1] Rebecca Kirk Fair is a managing principal, and Nikita Piankov and Emmanuel Frot are vice presidents, of Analysis Group, an economics consulting firm.
[2] Wallace, Tracey, 'The 19 Ecommerce Trends + 147 Online Shopping Stats Fueling Sales Growth in 2018,' BigCommerce, https://www.bigcommerce.com/blog/ecommerce-trends/ (last accessed 17 October 2018).
[3] 'US Census Bureau News,' US Department of Commerce, Economic Indicators Division, 17 August 2018; 'E-commerce share of total retail sales in China from 2014 to 2019,' Statista (eMarketer, InsideRetail Hong Kong), https://www.statista.com/statistics/379087/e-commerce-share-of-retail-sales-in-china/, (last accessed 17 October 2018).
[4] Orendorff, Aaron, 'B2B in Ecommerce: How the Best Succeed in a $7.6 Trillion Online Industry,' ShopifyPlus, 17 July 2017, https://www.shopify.com/enterprise/b2b-ecommerce (last accessed 17 October 2018).
[5] Opinion of the Court, Leegin Creative Leather Products, Inc. v. PSKS, Inc., US Supreme Court, No. 06-480, 28 June 2007. In Europe, RPM is largely seen as a restriction of competition 'by object.' The European Commission's Guidelines on Vertical Restraints (2010) consider it a 'hardcore restriction,' although they also note that 'RPM may not only restrict competition but may also, in particular where it is supplier driven, lead to efficiencies, which will be assessed under Article 101(3) [TFEU].' In practice, RPM is an area of substantial antitrust intervention among EU national antitrust agencies. The European Commission itself added to this enforcement trend with its own recent decisions in July 2018, against four manufacturers that were found to have engaged in RPM with respect to the online sale of electronic goods (see http://europa.eu/rapid/press-release_IP-18-4601_en.htm).
[6] For example, Wal-Mart has faced only increasing competition from Amazon, as the types of items frequently purchased on Amazon continue to expand, including fresh food and drugstore items. See Stone, Brad and Matthew Boyle, 'Can Wal-Mart's Expensive New E-Commerce Operation Compete With Amazon?' Bloomberg, https://www.bloomberg.com/news/features/2017-05-04/can-wal-mart-s-expensive-new-e-commerce-operation-compete-with-amazon.
[7] See Stucke, Maurice and Allen Grunes, Big Data and Competition Policy, 2016, Oxford University Press; Lambrecht, Anja, and Catherine E Tucker, 'Can Big Data Protect a Firm from Competition?,' Competition Policy International Antitrust Chronicle, January 2017.
[8] Economists find positive but modest costs, usually lower than the value of the time it would take consumers to travel to just one offline seller. See Bajari, Patrick and Ali Hortaçsu, 'The Winner's Curse, Reserve Prices, and Endogenous Entry: Empirical Insights from eBay Auctions,' The RAND Journal of Economics, 34(2), 329-355, 2003; Brynjolfsson, Erik, Astrid A Dick and Michael D Smith, 'A Nearly Perfect Market? Differentiation vs. Price in Consumer Choice,' Quantitative Marketing and Economics, 8, 1-33, 2010; and Hong, Han and Matthew Shum, 'Using Price Distributions to Estimate Search Costs,' The RAND Journal of Economics, 37(2), 257-275, 2006.
[9] See Brynjolfsson, Erik, Yu (Jeffrey) Hu and Michael D Smith, 'Consumer Surplus in the Digital Economy: Estimating the Value of Increased Product Variety at Online Booksellers,' Management Science, 49(11), 1580-1596, 2003. They find that online book retailers offer 23 times as many titles as a typical brick-and-mortar firm does. Welfare gains generated by the greater variety are estimated to be 7 to 10 times larger than the gains from increased competition.
[10] A more recent example is Amazon's interest in offering advertising. See Creswell, Julie, 'Amazon Sets Its Sights on the $88 Billion Online Ad Market,' The New York Times, 3 September 2018.
[11] Opinion of the Court, Leegin Creative Leather Products, Inc. v. PSKS, Inc., US Supreme Court, No. 06-480, 28 June 2007.
[12] Shim, Sunghee and Juwon Kwak, 'Tying to Foreclose in Two-Sided Markets,' Journal of Economic Analysis and Policy, 15(4), 1919-1937, 2015. See also Michael D Whinston, 'Tying, Foreclosure, and Exclusion,' American Economic Review, 80(4), 837-859, September 1990.
[13] The Herfindahl-Hirschman Index, which is used to measure market concentration.
[14] The Upward Pricing Pressure Index, which is used to assess the risk of price increases.
[15] Feinstein, Deborah L, 'Revising the Merger Guidelines: Looking Back to Move Forward,' Competition Policy International Antitrust Chronicle, December 2009 (1).
[16] Jacobs v. Tempur-Pedic Int'l., an antitrust action claiming that the defendant illegally enforced vertical RPM agreements, was dismissed by the US 11th Circuit court in 2010 because of the lack of both a defined, relevant market and any harm to competition. Further, in June 2018 the US Supreme Court ruled in Ohio et al. v. American Express Co. et al. that the rules in American Express's merchant contracts, which barred merchants from encouraging American Express cardholders to use competing credit cards to pay for purchases, did not violate antitrust laws because of the services American Express provided to both sides of the platform.
[17] The publishers charged were Hachette Book Group Inc, HarperCollins Publishers LLC, Penguin Group USA Inc, Simon & Schuster Inc and Holtzbrinck Publishers LLC.
[18] Opinion of the Court, Leegin Creative Leather Products, Inc v. PSKS, Inc, US Supreme Court, No. 06-480, 28 June 2007, p. 2.
[19] Starr v. Sony BMG Music Entertainment, US Court of Appeals, 2nd Circuit, 592 F.3d 314, January 2010.
[20] United States of America v. Apple Inc, US Court of Appeals, 2nd Circuit, 13-3741, June 2015 (DLC 2013).
[21] EyeforTravel, Ltd, a travel industry news company based in the UK, was also a defendant because it allegedly facilitated the price fixing conspiracy through its annual industry conferences. See In re Online Travel Company (OTC) Hotel Booking Antitrust Litigation, 997 F. Supp. 2d 526 (N.D. Tex. 2014).
[22] 'Memorandum Opinion and Order,' In re Online Travel Company (OTC) Hotel Booking Antitrust Litigation, Case No. 3:12-cv-3513-B.
Rebecca Kirk Fair
Nikita Piankov
Emmanuel Frot
Introduction – Why E-commerce?
Claire Jeffs
Slaughter and May
Part I: Europe
European Union – Key Developments
Guillaume Loriot
Directorate General for Competition, European Commission
European Union – Restrictions of Online Sales, including Geo-blocking and Geo-filtering
Nelson Jung
Clifford Chance LLP
European Union – E-commerce: Most Favoured Nation Clauses
Philippe Chappatte, Kerry O'Connell and Sarah de Morant
European Union – Algorithmic pricing under Article 101 TFEU
Pierre Honoré and Guillaume Fabre
Bredin Prat
European Union – Data-related Abuse of Dominance
Thorsten Mäger and Philipp Otto Neideck
Hengeler Mueller - Partnerschaft von Rechtsanwälten mbB
European Union – Data and Privacy in Merger Control
Miranda Cole
Covington & Burling LLP
European Union – Access to Online Platforms and Competition Law
Thomas Graf and Henry Mostyn
Cleary Gottlieb Steen & Hamilton
European Union – Two-Sided Markets, Platforms and Network Effects
Joshua White, Antoine Chapsal and Aaron Yeater
Part II: Americas
United States – E-commerce and Big Data: Merger Control
Daniel S Bitton
Axinn, Veltrop & Harkrider
United States – E-commerce, Big Data and Algorithms: Antitrust
Paul Eckles and Jeremy Koegel
Micah Wood, Laura Weinrib, Kevin Macdonald and David Dueck
Blake, Cassels & Graydon LLP
Carlos Mena-Labarthe, Jorge Kargl Pavia and Aleine S Obregón Natera
Creel Garcia-Cuéllar, Aiza y Enríquez SC
Amadeu Ribeiro, Michelle Machado, Paula Camara and Ana Paula Tavassi
Mattos Filho Veiga Filho Marrey Jr e Quiroga Advogados
Part III: Asia-Pacific
Australian Competition and Consumer Commission
Rod Sims
Janet Yung Yung Hui, Xuefei Bai and Huting Li
JunHe LLP
India – Competition Commission of India
Smita Jhingran
Competition Commission of India
Nisha Kaur Uberoi and Shravani Shekhar
Japan Fair Trade Commission
Hideki Utsunomiya and Yusuke Takamiya
Mori Hamada & Matsumoto
Young-Jin Jung, In-Sang Kim and Hee Won Marina Moon
Kim & Chang
Competition and Consumer Commission of Singapore
Lee Pei Rong Rachel and Leow Rui Ping
Daren Shiau and Elsa Chen

redpajama_set_name: RedPajamaCommonCrawl


text:
Prince Edward is an area between Tong Mi and Mong Kok surrounding Prince Edward station in Kowloon, Hong Kong. Named after Prince Edward Road West, the Prince Edward station of the MTR rapid transit system is an interchange station on the Tsuen Wan and the Kwun Tong lines.

Location
Prince Edward is located between southern part of Tong Mi and the northern part of Mong Kok on either side of Nathan Road, administratively under the Yau Tsim Mong District (until the mid-1990s part of the Mong Kok District). Prince Edward contains the northern end of Nathan Road. Prince Edward is shown on Google Maps as bounded by:

 Boundary Street to the North
 Waterloo Road to the East
 Prince Edward Road West, Sai Yee Street and Bute Street to the South
 Canton Road and Lai Chi Kok Road to the West

Transport
The area is served by the Prince Edward Station of the MTR rapid transit system on both the Tsuen Wan line and the Kwun Tong line. Access to Mong Kok East station includes an entrance via the MOKO shopping centre on Prince Edward Road West.

Policing
Mong Kok Police Station is on Prince Edward Road West. The police station is adjacent to Prince Edward MTR station exit B1.

Sport and recreation
Mong Kok Stadium () is a sports venue with capacity of 6,769. It hosts Hong Kong Premier League football matches, with Southern and Kitchee currently ground-sharing the venue as their home ground. The stadium is run by the Leisure and Cultural Services Department of Hong Kong.

Markets
Although Prince Edward area is perceived as part of Mong Kok the following markets are closer to the Prince Edward station:
 Flower Market Road ()
 Goldfish Street () or Goldfish Market — Centered around the section of Tung Choi Street
 Yuen Po Street Bird Garden ()
 Ladies' Street (, Ladies' market on Tung Choi Street)

See also

 List of buildings, sites and areas in Hong Kong
 2019 Prince Edward station attack

References

Areas of Hong Kong
Mong Kok
Yau Tsim Mong District

redpajama_set_name: RedPajamaWikipedia


Just like other self-supported winter ultra events, Fat Viking is about racing ethics, sportsmanship, respect, honour, common sense, etiquette and safety.
Fat Viking is created, organized and run by unpaid volunteers. They sacrifice their spare time and put a lot of energy into this event. They are doing their best. Be nice to them!
All participants must be registered in the correct class and for the correct distance.
The starting fee and license must be paid prior to the pre-race meeting.
Attending of the pre-race meeting is mandatory. It includes gear check (see the separate Mandatory Gear List) and signing of the disclaimer (egenerklæring).
The Race number is mandatory for all racers. It must be attached at the front of the bike.
Racers will receive a tracking device. This has to be carried at all times and it must be returned to the race organisation after the race!
Racers must follow the exact route given to them. Racers are not allowed to take short cuts. A racer may leave the route (for example to buy food or to fix the bike), but racers must enter the route again at the same location.
Racers MUST check in at ALL checkpoints along their course. Make sure the volunteer writes down your race number. There is a mandatory 5 minute stop at all checkpoints. No gear or clothes can be left at a Checkpoint.
Cut-off times for the checkpoints will be announced at the pre race meeting.
Fat Viking is a self-supported event. No outside support is allowed. No service crew, friends, family with food, clothing, spares etc. anywhere! No arranged/ hidden depots.
Racers may assist each other. Otherwise, only race officials (on the trail and at the Checkpoints) are allowed to help. Racers may use commercial services that they come across (serviced hut, shop, restaurant) as well as the Checkpoints.
Spectators are strongly encouraged, but they mustn't help/ assist any racer.
Our checkpoints are serviced, and we supply some basic food.
Other trail users – Respect the trail!
Some of the race course consists of cross country skiing tracks. Do NOT ride on the classic ski tracks.
Be aware that cross country skiers may not know about the race. They may not see you and might be going fast. Be cautious on the descents, be friendly, say hello and give them room.
Grooming machines and snow machines may be on the course. If a grooming machine approaches you must get off the trail entirely (grooming machines are wider than you may think).
Some of the race course runs on mushing trails. Dogsleds always have the right of way. You need to make room for them.
Don't even think about leaving litter on the trail. Racers will be disqualified for littering.
Leave no trace. You must finish with all gear and clothes that you started with.
Nothing can be left at a Checkpoint or on the course.
What you start with is what you finish with.
We want all the racers to rest at the checkpoints.
If you choose to camp or bivy between Checkpoints you must inform the race director (mobile telephone). You must get off the trail. You must face your red blinky safety light towards the trail to let others know you are out there and safe.
There are toilets at the Checkpoints. If nature calls while on route, get off the trail and keep it clean! Dig a hole, and bury all waste. No yellow snow marks.
MUST be worn at all times when you are on your bike.
If you need to be hauled off the course for any reason, you may have to cover (some of) the cost for that.
No (hidden) motors, no doping!


At turns randy and sweet, this romantic comedy is surprisingly honest about the trials of long-distance relationships. Justin Long and Drew Barrymore fall for one another before their respective careers place them on opposite coasts. They explore all the real frustrations of having your beloved only reachable via phone for months on end. Going the Distance presents two likeable leads with an affable chemistry, and the real kicker is that they genuinely love each other. Nobody is a man-child or a shrew. The real villain is the distance. While the film doesn't know if it wants to be a Judd Apatow-style raunchy comedy or a saccharine romantic comedy, there is a strong rooting interest in our couple. The supporting characters aren't too wacky, the situations feel more authentic than contrived, and our couple makes seriously difficult decisions in the end that are downright adult. Going the Distance is a true surprise of a film. It's got enough laugh-out-loud lines and situations to recommend as a comedy and enough emotional involvement to recommend as a relationship drama. It's a little unnecessarily vulgar at times, like a fascinated kid who has just discovered the power of dirty words. While it may not go the full distance, this cheeky rom-com will nicely get you to a pleasant place.


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