The present invention is directed in general to magnetic resonance tomography (MRT) as employed in medicine for examining patients. The present invention is particularly directed to a magnetic resonance tomography apparatus as well as to a method for operating such an apparatus wherein data are acquired by a technique known as xe2x80x9cpartially parallel acquisitionxe2x80x9d (PPA).
MRT is based on the physical phenomenon of nuclear magnetic resonance and has been successfully utilized as an imaging method in medicine and in biophysics for more than 15 years. In this examination method, the subject is exposed to a strong, constant magnetic field. As a result, the nuclear spins of the atoms in the subject, which were previously irregularly oriented, are aligned. Radio frequency waves can then excite these xe2x80x9corderedxe2x80x9d nuclear spins to a specific oscillation. This oscillation generates the actual measured signal in MRT that is picked up with suitable reception coils. By utilizing non-uniform magnetic fields generated by gradient coils, signals from the examination subject can thereby be spatially encoded in all three spatial directions. The method allows a free selection of the slice to be imaged, so that tomograms of the human body can be registered in all directions. MRT as a tomographic method in medical diagnostics is mainly distinguished as a xe2x80x9cnon-invasivexe2x80x9d examination method on the basis of a versatile contrast capability. Due to the excellent presentation of soft tissue, MRT has developed into a method that is often superior to X-ray computed tomography (CT). MRT is currently based on the application of spin echo and gradient echo sequences that enable an excellent image quality given measurement times on the order of magnitude of minutes.
The constant technical improvement of the components of MRT apparatus and the introduction of fast imaging sequences have increased the areas of employment in medicine for MRT. Real-time imaging for supporting minimally invasive surgery, functional imaging in neurology and perfusion measurement in cardiology represent only a few examples. Despite the technical progress designing the components of an MRT apparatus, the exposure time of an MRT image remains the limiting factor for many applications of MRT in medical diagnostics. A limit is placed on a further enhancement of the performance of MRT apparatus from a technical point of view (feasibility) and for reasons of patient protection (stimulation and tissue heating). In recent years, many efforts therefore were made to develop and establish new approaches in order to achieve further shortening of the image measurement time.
One approach for shortening the acquisition time is to reduce the quantity of image data to be recorded. In order to obtain a complete image from such a reduced dataset, either the missing data must be reconstructed with suitable algorithms or the faulty image from the reduced data must be corrected. The registration of the data in MRT occurs in an arrangement referred to as k-space (synonym: frequency domain). The MRT image in the image domain is obtained by an operation on the MRT data in the k-space by means of Fourier transformation. The location coding of the subject that arises the k-space occurs by means of gradients in all three spatial directions. A distinction is made between the slice selection (determines an exposure slice in the subject, usually the z-axis), the frequency coding (determines a direction in the slice, usually the x-axis), and the phase coding (defines the second dimension within the slice, usually the y-axis). Without limitation placed on the universality, a Cartesian k-space is assumed below, this being sampled row-by-row. The data of a single k-space row are frequency-coded with a gradient when read out. Each row in the k-space has the spacing xcex94ky that is generated by a phase-coding step. Since the phase coding requires a long time compared to the other location codings, most methods for shortening the image measurement time are based on a reduction in the number of time-consuming phase coding steps. All method of the type known as xe2x80x9cpartially parallel acquisitionxe2x80x9d (referred to below as PPA) are based on this approach.
The basic idea in PPA imaging is that the k-space data are not registered by a single coil but by, for example, a linear arrangement of component coils, a coil array. Each of the spatially independent coils of the array carries certain spatial information that is used in order to achieve a complete location coding by a combination of the simultaneously acquired coil data. This means that a number of shifted data rows in the k-space that are omitted, i.e. not acquired, can be identified from a single registered k-space row.
PPA methods thus employ spatial information that is contained in (represented by) the components of the coil arrangement in order to partially replace the time-consuming phase coding that is normally generated employing a phase gradient. As a result, the image measurement time is reduced corresponding to the ratio of the number of rows of the reduced dataset to the number of rows of the conventional (i.e. complete) dataset. Compared to the conventional acquisition, only a fraction (xc2xd, ⅓, xc2xc, etc.) of the phase coding rows are acquired in a typical PPA acquisition. A specific reconstruction is then applied to the data in order to reconstruct the missing k-space rows, and thus to obtain the full field-of-view (FOV) image in a fraction of the time.
Some of these PPA techniques (SMASH, SENSE, GSMASH) are successfully utilized in many areas of MRT. The most noteworthy is the SMASH method that was invented by Sodickson in 1997 (D. K. Sodickson, W. J. Manning, Simultaneous Acquisition of Spatial Harmonics (SMASH): Fast Imaging with Radio frequency Coil Arrays, Magn. Reson. Med. 38:591-603 (1997)) that is described in brief below.
SMASH stands for xe2x80x9cSimultaneous Acquisition of Spatial Harmonicsxe2x80x9d. As mentioned above, this is a PPA method. Data are simultaneously acquired from spatially separate and independent coils that are arranged in the phase-coding direction. By a linear combination of these coil data, a spatial modulation of the signal that is achieved in conventional methods by activating a phase-coding gradient is achieved, with time-consuming phase-coding steps being saved as a result. Only a reduced k-space is thus registered, and the exposure time is shortened by an amount corresponding to the reduction of this k-space. This missing data are then reconstructed using suitable linear combinations of the coil datasets only after the actual data acquisition.
Sodickson et al. thus shows that a row of the k-space can be reconstructed upon employing of linear combinations of the signals that have been acquired by an arrangement of coils according to the SMASH technique whenever                                           ∑                          l              =              1                        L                    ⁢                                    n              l                              (                m                )                                      ⁢                                          i                l                            ⁡                              (                y                )                                                    =                  ⅇ                      ⅈ            ⁢                          xe2x80x83                        ⁢            m            ⁢                          xe2x80x83                        ⁢            Δ            ⁢                          xe2x80x83                        ⁢                          k              y                                                          (        1        )            
applies. The exponential term describes a sinusoidal modulation of the real part and of the imaginary part of the data. The number of oscillations of this modulation over the FOV is defined by the number m. For m=0, 1, 2, . . . , the spatial harmonic of the 0th, 1st, 2nd, . . . order of the coil sensitivities is referred to in this context.
The quantity i1(y) is the coil sensitivity of coil 1 from a total of L coils. Further, n1(m) SMASH weighting factors are required for the linear combination of the coil sensitivities in order to generate spatial harmonics of the order m. The coil sensitivity profiles i1(y) are normally determined by a separate exposure using a proton density-weighted FLASH sequence or similar sequence. When the coil sensitivities are known, the spatial harmonics can be calculated therewith in a purely mathematical manner. Only the weighting factors n1(m) thus remain as the sole unknown quantity in Equation (1). The determination of these coefficients is implemented such that the sensitivity profiles are fitted to the profiles of the spatial harmonics. Using these coil weighting factors, various rows can now be reconstructed from a single acquired row; this is established by                               S          ⁡                      (                                          k                ⁢                                  xe2x80x83                                ⁢                y                            +                              m                ⁢                                  xe2x80x83                                ⁢                Δ                ⁢                                  xe2x80x83                                ⁢                k                ⁢                                  xe2x80x83                                ⁢                y                                      )                          =                              ∑                                          k                y                            =                                                N                  y                                /                2                                                                                      N                  y                                /                2                            -              1                                ⁢                                    ∑                              l                =                1                            L                        ⁢                                          n                l                                  (                  m                  )                                            ⁢                                                i                  l                                ⁡                                  (                  y                  )                                            ⁢                              p                ⁡                                  (                  y                  )                                            ⁢                              ⅇ                                  ⅈ                  ⁢                                      xe2x80x83                                    ⁢                                      k                    y                                    ⁢                  y                                                                                        (        2        )                                          S          ⁡                      (                                          k                ⁢                                  xe2x80x83                                ⁢                y                            +                              m                ⁢                                  xe2x80x83                                ⁢                Δ                ⁢                                  xe2x80x83                                ⁢                k                ⁢                                  xe2x80x83                                ⁢                y                                      )                          =                              ∑                                          k                y                            =                                                N                  y                                /                2                                                                                      N                  y                                /                2                            -              1                                ⁢                                    p              ⁡                              (                y                )                                      ⁢                                          ⅇ                                  ⅈ                  ⁢                                      xe2x80x83                                    ⁢                                      (                                                                  k                        y                                            +                                              m                        ⁢                                                  xe2x80x83                                                ⁢                        Δ                        ⁢                                                  xe2x80x83                                                ⁢                                                  k                          y                                                                                      )                                    ⁢                  y                                            .                                                          (        3        )            
wherein p(y) denotes the spin density of the image to be ultimately reconstructed along the y-axis (the x-dependency of the image was neglected for clarity). The procedure in the reconstruction is schematically shown in FIG. 2, this showing how an individual row is reconstructed from a different, acquired row.
In the SMASH method, the exact knowledge of the coil sensitivity distribution i1(y) of every coil along the y-direction is required, this usually being determined in a separate exposure. As a rule, it is very difficult to determine this distribution exactly, due to disturbances as a result of noise and spin density fluctuations within the subject. According to FIG. 2, an external coil card is employed in order to determine the complex coefficients for the linear combination of each of the datasets from coil 1 through coil L of each harmonic m (left). This makes it possible to reconstruct at least one offset row from a normally acquired row. At least two linear combinations are implemented, which leads to two shifted datasets 23 that are combined to form a complete dataset. This dataset is then Fourier-transformed in order to produce the ultimate image. This image has the composite sensitivity and the signal-to-noise ration S/R of a phase-sum image 24.
A significant disadvantage of SMASH and other PPA methods is that an attempt is made to develop the exponential function or cosine functions and sine functions by means of the coil sensitivity function. This incurs considerable limitations since the coil sensitivity functions ultimately must be of such a nature that the aforementioned functions can be described optimally well by such functions. In practice, this usually means that the required plurality of coils must be greater by a multiple than the degree of the harmonic that one wants to approximatexe2x80x94or conversely: with a given number of coils, only a limited, smaller number of k-rows can be reconstructed, and thus the exposure time for obtaining an MR image of a given image quality is limited.
It is an object of the present invention to shorten the exposure time for obtaining an MR image of a given image quality, or to enhance the image quality for a given exposure time.
This object is inventively achieved in a method having the following steps for magnetic resonance imaging or an interconnected region of a human body on the basis of a partially parallel acquisition (PPA) by exciting nuclear spins and measuring the radio frequency signals indicating the excited spins:
a) exciting the nuclear spins in the region,
b) measuring the radio frequency response signals of the excited nuclear spins in the form of a simultaneous measurement of a radio frequency response signal of each component coil that has a characteristic sensitivity over the region,
c) forming a number of different signal combinations from the number of radio frequency response signals on the basis of combinations of the component coil sensitivities defined for at least one spin excitation,
d) applying the signal combinations of one or more spin excitations for entries into an ordered dataset,
and
e) implementing a spatial transformation of the filled and ordered dataset for generating a magnetic resonance image of the interconnected region.
Advantageously, the number of k-rows to be determined in a readout cycle corresponds to the number of coils, resulting in greater efficiency compared to conventional PPA methods, i.e., the exposure time for obtaining an MR image is shortened for a given image quality, or the image quality is enhanced given the same exposure time.
The method steps are executed run in a first sequence according to
1) spin excitation according to step a),
2) radio frequency response signal measurement according to step b),
3) forming the signal combinations according to step c),
4) repeating the steps 1) through 3) until a dataset of defined size has been obtained, and
5) implementing the steps d) and e),
or alternatively in a second sequence employing
1xe2x80x2) spin excitation according to step a),
2xe2x80x2) radio frequency response signal measurement according to step b),
3xe2x80x2) forming the signal combination according to step c),
4xe2x80x2) applying the signal combinations from step c) according to step d),
5xe2x80x2) repeating the steps 1xe2x80x2) through 4xe2x80x2) until a dataset of defined size has been obtained, and
6xe2x80x2) implementing the step e).
Advantageously, the combination of the coil sensitivities of the component coils is approximated by a Fourier row.
The spatial transformation likewise represents a Fourier transformation.
Further, the above object is achieved according to the invention in an apparatus for magnetic resonance imaging having a magnet for generating a uniform magnetic field, a number of component coils for exciting nuclear spins in an interconnected region of a human body, as well as measuring the radio frequency response signals of the excited spins, with each component coil having a characteristic sensitivity over the region, and a radio frequency response signal of each coil is simultaneously measured. A system computer forming a number of different signal combinations from the number of response signals of a spin excitation on the basis of combinations of the coil sensitivities defined for at least one spin excitation. The signal combinations of a spin excitation or of a number of spin excitations are entered into the ordered dataset, and the computer implements a spatial transformation of the filled and ordered dataset into a magnetic resonance image over the interconnected region.
Advantageously, the spin excitation, the radio frequency response signal measurement and the formation of the signal combinations are repeated in the apparatus in this sequence until a dataset of a defined size has been obtained, whereupon the signal combination and the spatial transformation are implemented.
In a second embodiment of the invention, the spin excitation, the radio frequency response signal measurement, the forming of the signal combinations and the entry of the signal combinations are repeated in this sequence until a dataset of defined size has been obtained, whereupon the spatial transformation is implemented.
Preferably, the spatial transformation is a Fourier transformation.
The component coils preferably form a regular arrangement.
The component coils preferably form a linear arrangement.