You will be presented with a collection of medical randomized controlled trial (RCT) reports addressing a shared clinical question -- for instance, the efficacy of a medical intervention on a disease or condition. Given the titles and abstracts of these reports, your task is to generate the conclusion of the systematic review article synthesizing the key findings. Your conclusion should be a few sentences long. In your response, include only the conclusion and no other text. The RCT reports are below.



Title 1:
Feasibility and limitations of bulk density assignment in MRI for head and neck IMRT treatment planning
Abstract 1:
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 ,


Title 2:
Investigation of a method for generating synthetic CT models from MRI scans of the head and neck for radiation therapy.
Abstract 2:
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


Title 3:
T1/T2*-weighted MRI provides clinically relevant pseudo-CT density data for the pelvic bones in MRI-only based radiotherapy treatment planning
Abstract 3:
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


Title 4:
Quantitative characterizations of ultrashort echo (UTE) images for supporting air-bone separation in the head.
Abstract 4:
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
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 .