***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: Head and neck cancers centered at the base of skull are better visualized on MRI than on CT . The purpose of this investigation was to investigate the accuracy of bulk density assignment in head and neck intensity‐modulated radiation therapy ( IMRT ) treatment plan optimization . Our study investigates dose calculation differences between density‐assigned MRI and CT , and identifies potential limitations related to dental implants and MRI geometrical distortion in the framework of MRI‐only‐based treatment planning . Bulk density assignment was performed and applied onto MRI to generate three MRI image sets with increasing levels of heterogeneity for seven patients : 1 ) MRIW : all water‐equivalent ; 2 ) MRIW + B : included bone with density of 1.53 g/cm3 ; and 3 ) MRIW + B + A : included bone and air . Using identical planning and optimization parameters , MRI‐based IMRT plans were generated and compared to corresponding , forward‐calculated , CT‐based plans on the basis of target coverage , isodose distributions , and dose‐volume histograms ( DVHs ) . Phantom studies were performed to assess the magnitude and spatial dependence of MRI geometrical distortion . MRIW‐based dose calculations overestimated target coverage by 16.1 % . Segmentation of bone reduced differences to within 2 % of the coverage area on the CT‐based plan . Further segmentation of air improved conformity near air – tissue interfaces . Dental artifacts caused substantial target coverage overestimation even on MRIW + B + A. Geometrical distortion was less than 1 mm in an imaging volume 20 × 20 × 20 cm3 around scanner isocenter , but up to 4 mm at 17 cm lateral to isocenter . Bulk density assignment in the framework of MRI‐only IMRT head and neck treatment planning is a feasible method with certain limitations . Bone and teeth account for the majority of density heterogeneity effects . While soft tissue is well visualized on MRI compared to CT , dental implants may not be visible on MRI and must be identified by other means and assigned appropriate density for accurate dose calculation . Far off‐center geometrical distortion of the body contour near the shoulder region is a potential source of dose calculation inaccuracy . PACS numbers : 87.61.‐c , Magnetic resonance ( MR ) images often provide superior anatomic and functional information over computed tomography ( CT ) images , but generally are not used alone without CT images for radiotherapy treatment planning and image guidance . This study aims to investigate the potential of probabilistic classification of voxels from multiple MRI contrasts to generate synthetic CT ( ' M RCT ' ) images . The method consists of ( 1 ) acquiring multiple MRI volumes : T1-weighted , T2-weighted , two echoes from a ultra-short echo time ( UTE ) sequence , and calculated fat and water image volumes using a Dixon method , ( 2 ) classifying tissues using fuzzy c-means clustering with a spatial constraint , ( 3 ) assigning attenuation properties with weights based on the probability of individual tissue classes being present in each voxel , and ( 4 ) generating a M RCT image volume from the sum of attenuation properties in each voxel . The capability of each MRI contrast to differentiate tissues of interest was investigated based on a retrospective analysis of ten patients . For one prospect i ve patient , the correlation of skull intensities between CT and MR was investigated , the discriminatory power of MRI in separating air from bone was evaluated , and the generated M RCT image volume was qualitatively evaluated . Our analyses showed that one MRI volume was not sufficient to separate all tissue types , and T2-weighted images was more sensitive to bone density variation compared to other MRI image types . The short echo UTE image showed significant improvement in contrasting air versus bone , but could not completely separate air from bone without false labeling . Generated M RCT and CT images showed similar contrast between bone and soft/solid tissues . These results demonstrate the potential of the presented method to generate synthetic CT images to support the workflow of radiation oncology treatment planning and image guidance Abstract Background and purpose . In radiotherapy ( RT ) , target soft tissues are best defined on MR images . In several cases , CT imaging is needed only for dose calculation and generation of digitally reconstructed radiographs ( DRRs ) . Image co- registration errors between MRI and CT can be avoided by using MRI-only based treatment planning , especially in the pelvis . Since electron density information can not be directly derived from the MRI , a method is needed to convert MRI data into CT like data . We investigated whether there is a relationship between MRI intensity and Hounsfield unit ( HU ) values for the pelvic bones . The aim was to generate a method to convert bone MRI intensity into HU data surrogate for RT treatment planning . Material and methods . The HU conversion model was generated for 10 r and omly chosen prostate cancer patients and independent validation was performed in another 10 patients . Data consisted of 800 image voxels chosen within the pelvic bones in both T1/T2 * -weighted gradient echo and CT images . Relation between MRI intensity and electron density was derived from calibrated HU-values . The proposed method was tested by constructing five “ pseudo”-CT series . Results . We found that the MRI intensity is related to the HU value within a HU range from 0 to 1400 within the pelvic bones . The mean prediction error of the conversion model was 135 HU . Dose calculation based on the pseudo-CT images was accurate and the generated DRRs were of good quality . Conclusions . The proposed method enables generation of clinical ly relevant pseudo-CT data for the pelvic bones from one MRI series . It is simpler than previously reported approaches which require either acquisition of several MRI series or T2 * maps with special imaging sequences . The method can be applied with commercial clinical image processing software . The application requires segmentation of the bones in the MR images Accurate separation of air and bone is critical for creating synthetic CT from MRI to support Radiation Oncology workflow . This study compares two different ultrashort echo-time sequences in the separation of air from bone , and evaluates post-processing methods that correct intensity nonuniformity of images and account for intensity gradients at tissue boundaries to improve this discriminatory power .  CT and MRI scans were acquired on 12 patients under an institution review board-approved prospect i ve protocol . The two MRI sequences tested were ultra-short TE imaging using 3D radial acquisition ( UTE ) , and using pointwise encoding time reduction with radial acquisition ( PETRA ) . Gradient nonlinearity correction was applied to both MR image volumes after acquisition . MRI intensity nonuniformity was corrected by vendor-provided normalization methods , and then further corrected using the N4itk algorithm . To overcome the intensity-gradient at air-tissue boundaries , spatial dilations , from 0 to 4 mm , were applied to threshold-defined air regions from MR images . Receiver operating characteristic ( ROC ) analyses , by comparing predicted ( defined by MR images ) versus ' true ' regions of air and bone ( defined by CT images ) , were performed with and without residual bias field correction and local spatial expansion . The post-processing corrections increased the areas under the ROC curves ( AUC ) from 0.944 ± 0.012 to 0.976 ± 0.003 for UTE images , and from 0.850 ± 0.022 to 0.887 ± 0.012 for PETRA images , compared to without corrections . When exp and ing the threshold-defined air volumes , as expected , sensitivity of air identification decreased with an increase in specificity of bone discrimination , but in a non-linear fashion . A 1 mm air mask expansion yielded AUC increases of 1 and 4 % for UTE and PETRA images , respectively . UTE images had significantly greater discriminatory power in separating air from bone than PETRA images . Post-processing strategies improved the discriminatory power of air from bone for both UTE and PETRA images , and reduced the difference between the two imaging sequences . Both post-processed UTE and PETRA images demonstrated sufficient power to discriminate air from bone to support synthetic CT generation from MRI data 
    Output:
    
This review showed that MRI-only RTP techniques could be grouped into 3 categories : ( 1 ) bulk density override ; ( 2 ) atlas-based ; and ( 3 ) voxel-based techniques , which all produce an sCT scan from MR images .
Clinical ly acceptable sCTs were reported , but atypical anatomy led to erroneous results in some cases .
High- quality sCTs were produced ; however , use of multiple sequences led to long scanning times increasing the chances of patient movement .