MR-only Radiotherapy Research Articles

The first patients treated with MR-CBCT soft-tissue matching in a MR-only prostate radiotherapy pathway

IntroductionMagnetic Resonance (MR)-only radiotherapy for prostate cancer has previously been reported using fiducial markers for on-treatment verification. MR-Cone Beam Computed Tomography (CBCT) soft-tissue matching does not require invasive fiducial markers and enables MR-only treatments to other pelvic cancers. This study evaluated the first clinical implementation of MR-only prostate radiotherapy using MR-CBCT soft-tissue matching. MethodsTwenty prostate patients were treated with MR-only radiotherapy using a synthetic (s)CT-optimised plan with MR-CBCT soft-tissue matching. Two MR sequences were acquired: small Field Of View (FOV) for target delineation and large FOV for organs at risk delineation, sCT generation and on-treatment verification. Patients also received a CT for validation. The prostate was independently contoured on the small FOV MR, copied to the registered CT and modified if there were MR-CT soft-tissue alignment differences (MR-CT volume). This was compared to the MR-only volume with a paired t-test. The treatment plan was recalculated on CT and the doses compared. Independent offline CT-CBCT matches for 5/20 fractions were performed by three therapeutic radiographers using the MR-only contours and compared to the online MR-CBCT matches using two one-sided paired t-tests for equivalence within ±1 mm. ResultsThe MR-only volumes were significantly smaller than MR-CT (p = 0.003), with a volume ratio 0.92 ± 0.02 (mean ± standard error). The sCT isocentre dose difference to CT was 0.2 ± 0.1%. MR-CBCT soft-tissue matching was equivalent to CT-CBCT (p < 0.001), with differences of 0.1 ± 0.2 mm (vertical), −0.1 ± 0.2 mm (longitudinal) and 0.0 ± 0.1 mm (lateral). ConclusionsMR-only radiotherapy with soft-tissue matching has been successfully clinically implemented. It produced significantly smaller target volumes with high dosimetric and on-treatment matching accuracy. Implications for practiceMR-only prostate radiotherapy can be safely delivered without using invasive fiducial markers. This enables MR-only radiotherapy to be extended to other pelvic cancers where fiducial markers cannot be used.

QACL: Quartet attention aware closed-loop learning for abdominal MR-to-CT synthesis via simultaneous registration.

Synthesis of computed tomography (CT) images from magnetic resonance (MR) images is an important task to overcome the lack of electron density information in MR-only radiotherapy treatment planning (RTP). Some innovative methods have been proposed for abdominal MR-to-CT synthesis. However, it is still challenging due to the large misalignment between preprocessed abdominal MR and CT images and the insufficient feature information learned by models. Although several studies have used the MR-to-CT synthesis to alleviate the difficulty of multi-modal registration, this misalignment remains unsolved when training the MR-to-CT synthesis model. In this paper, we propose an end-to-end quartet attention aware closed-loop learning (QACL) framework for MR-to-CT synthesis via simultaneous registration. Specifically, the proposed quartet attention generator and mono-modal registration network form a closed-loop to improve the performance of MR-to-CT synthesis via simultaneous registration. In particular, a quartet-attention mechanism is developed to enlarge the receptive fields in networks to extract the long-range and cross-dimension spatial dependencies. Experimental results on two independent abdominal datasets demonstrate that our QACL achieves impressive results with MAE of 55.30±10.59 HU, PSNR of 22.85±1.43 dB, and SSIM of 0.83±0.04 for synthesis, and with Dice of 0.799±0.129 for registration. The proposed QACL outperforms the state-of-the-art MR-to-CT synthesis and multi-modal registration methods.

Organs-at-Risk Segmentation on T2-Weighted Magnetic Resonance Imaging Using a Transformer-Based Model

<h3>Purpose/Objective(s)</h3> The scarcity of high-quality, annotated MRI data for MR-only radiotherapy (RT) treatment planning remains a limitation for training automatic segmentation algorithms. In this study, we develop a framework first to synthesize T₂-weighted MRI from CT using unpaired input images, and subsequently train a volumetric transformer-based segmentation model with propagated CT contours to perform segmentation on acquired T₂-weighted MRI. <h3>Materials/Methods</h3> A retrospective database including 46 patients with locally advanced was identified. For synthesis, a 3D patch-based Cycle-GAN model (patch size of 96 × 96 × 96) was trained and validated on 13 and 5 patients respectively. Then, a transformer-based segmentation algorithm was trained and validated on synthetic MR images from 32 and 7 patients respectively to segment the femoral heads, bladder, rectum, bowel and sigmoid (binary cross entropy loss + Dice loss). <h3>Results</h3> The results from automatic contouring of OARs on synthetic images revealed that our framework delineated the femoral heads and the bladder with average Dice scores and Hausdorff distance of >0.9 and <10mm respectively. However, the segmented contours were less accurate in bowel and rectum due to their variable geometry and difficulty in segregating from adjacent soft tissues. The predictions of these organs on acquired T₂-weighted MR images also showed acceptable accuracy with visual inspection. <h3>Conclusion</h3> Our framework showed the feasibility of transferring anatomical knowledge from a pre-existing labelled CT data for RT to synthetic T₂-weighted MR images, particularly in the femoral heads and bladder. This approach provides significant clinical value as it uses an unsupervised approach for data augmentation and/or domain adaptation Future studies will quantitatively evaluate our framework's performance on larger acquired MRI cohorts and explore weakly-supervised techniques to assess performance improvements on more challenging soft tissues (e.g., bowel, rectum) with inclusion of several labelled MRI input datasets.

A high-performance method of deep learning for prostate MR-only radiotherapy planning using an optimized Pix2Pix architecture

PurposeThe first aim was to generate and compare synthetic-CT (sCT) images using a conditional generative adversarial network (cGAN) method (Pix2Pix) for MRI-only prostate radiotherapy planning by testing several generators, loss functions, and hyper-parameters. The second aim was to compare the optimized Pix2Pix model with five other architectures (bulk-density, atlas-based, patch-based, U-Net, and GAN). MethodsFor 39 patients treated by VMAT for prostate cancer, T2-weighted MRI images were acquired in addition to CT images for treatment planning. sCT images were generated using the Pix2Pix model. The generator, loss function, and hyper-parameters were tuned to improve sCT image generation (in terms of imaging endpoints). The final evaluation was performed by 3-fold cross-validation. This method was compared to five other methods using the following imaging endpoints: the mean absolute error (MAE) and mean error (ME) between sCT and reference CT images (rCT) of the whole pelvis, bones, prostate, bladder, and rectum. For dose planning analysis, the dose-volume histogram metric differences and 3D gamma analysis (local, 1 %/1 mm) were calculated using the sCT and reference CT images. ResultsCompared with the other architectures, Pix2Pix with Perceptual loss function and generator ResNet 9 blocks showed the lowest MAE (29.5, 107.7, 16.0, 13.4, and 49.1 HU for the whole pelvis, bones, prostate, bladder, and rectum, respectively) and the highest gamma passing rates (99.4 %, using the 1 %/1mm and 10 % dose threshold criterion). Concerning the DVH points, the mean errors were −0.2% for the planning target volume V95%, 0.1 % for the rectum V70Gy, and −0.1 % for the bladder V50Gy. ConclusionThe sCT images generated from MRI data with the Pix2Pix architecture had the lowest image errors and similar dose uncertainties (in term of gamma pass-rate and dose-volume histogram metric differences) than other deep learning methods.

Synthetic computed tomography for low-field magnetic resonance-guided radiotherapy in the abdomen.

The requirement of computed tomography (CT) for radiotherapy planning may be bypassed by synthetic CT (sCT) generated from magnetic resonance (MR), which has recently led to the clinical introduction of MR-only radiotherapy for specific sites. Further developments are required for abdominal sCT, mostly due to the presence of mobile air pockets affecting the dose calculation. In this study we aimed to overcome this limitation for abdominal sCT at a low field (0.35 T) hybrid MR-Linac. A retrospective analysis was conducted enrolling 168 patients corresponding to 215 MR-CT pairs. After the exclusion criteria, 152 volumetric images were used to train the cycle-consistent generative adversarial network (CycleGAN) and 34 to test the sCT. Image similarity metrics and dose recalculation analysis were performed. The generated sCT faithfully reproduced the original CT and the location of the air pockets agreed with the MR scan. The dose calculation did not require manual bulk density overrides and the mean deviations of the dose-volume histogram dosimetric points were within 1 % of the CT, without any outlier above 2 %. The mean gamma passing rates were above 99 % for the 2 %/ 2 mm analysis and no cases below 95 % were observed. This study presented the implementation of CycleGAN to perform sCT generation in the abdominal region for a low field hybrid MR-Linac. The sCT was shown to correctly allocate the electron density for the mobile air pockets and the dosimetric analysis demonstrated the potential for future implementation of MR-only radiotherapy in the abdomen.

Structurally-constrained optical-flow-guided adversarial generation of synthetic CT for MR-only radiotherapy treatment planning

The rapid progress in image-to-image translation methods using deep neural networks has led to advancements in the generation of synthetic CT (sCT) in MR-only radiotherapy workflow. Replacement of CT with MR reduces unnecessary radiation exposure, financial cost and enables more accurate delineation of organs at risk. Previous generative adversarial networks (GANs) have been oriented towards MR to sCT generation. In this work, we have implemented multiple augmented cycle consistent GANs. The augmentation involves structural information constraint (StructCGAN), optical flow consistency constraint (FlowCGAN) and the combination of both the conditions (SFCGAN). The networks were trained and tested on a publicly available Gold Atlas project dataset, consisting of T2-weighted MR and CT volumes of 19 subjects from 3 different sites. The network was tested on 8 volumes acquired from the third site with a different scanner to assess the generalizability of the network on multicenter data. The results indicate that all the networks are robust to scanner variations. The best model, SFCGAN achieved an average ME of 0.9 5.9 HU, an average MAE of 40.4 4.7 HU and 57.2 1.4 dB PSNR outperforming previous research works. Moreover, the optical flow constraint between consecutive frames preserves the consistency across all views compared to 2D image-to-image translation methods. SFCGAN exploits the features of both StructCGAN and FlowCGAN by delivering structurally robust and 3D consistent sCT images. The research work serves as a benchmark for further research in MR-only radiotherapy.

PD-0320 Two-ways validation of the feasibility of AI-based synthetic CT for MR-only brain radiotherapy

PD-0320 Two-ways validation of the feasibility of AI-based synthetic CT for MR-only brain radiotherapy

Multisequence MR-generated sCT is promising for HNC MR-only RT: A comprehensive evaluation of previously developed sCT generation networks.

To verify the feasibility of our in-house developed multisequence magnetic resonance (MR)-generated synthetic computed tomography (sCT) for accurate dose calculation and fractional positioning for head and neck MR-only radiation therapy (RT). Forty-five patients with nasopharyngeal carcinoma were retrospectively studied. By applying our previously in-house developed network, a patient's sCT can rapidly be generated with respect to feeding the sole T1 image, T1C image, T1DixonC image, T2 image, and their combination (five pipelines in total). A k(5)-fold strategy was implemented during model establishment. Dose recalculation was performed for each pipeline generation to attain a dosimetric feasibility evaluation. Fractional positioning evaluation was performed by calculating the digitally reconstructed radiograph (DRR) of the sCT and planning CT and their offset to the portal image. The dose mean absolute error values were (0.47±0.16)%, (0.48±0.15)% (p<0.05), (0.50±0.16)% (p<0.05), (0.50±0.15)% (p<0.05), and (0.45±0.16)% (p<0.05) for the T1, T1C, T1Dixon C, T2, and 4-channel generated sCT to the prescription dose, respectively. The 4-channel-generated sCT outperforms any other single-sequence pipeline. Among the single-sequence MR imaging-generated sCTs, the T1-generated sCT shows the most accurate HU image quality and provides a reliable dose result. Quantified positioning errors with calculation of the difference to the planning CT offsets are (-0.26±0.50) mm, (-0.58±0.52) mm (p<0.05), (-0.27±0.57) mm (p>0.05), (-0.31±0.44) mm (p>0.05), and (-0.19±0.37) mm (p>0.05) at LNG and (0.34±0.53) mm, (0.48±0.56) mm (p>0.05), (0.55±0.56) mm (p>0.05), (0.37±0.61) mm (p>0.05), and (0.24±0.43) mm (p>0.05) at LAT of the anterior-posterior direction for the five pipelines. Multisequence MR-generated sCT allows for accurate dose calculation and fractional positioning for head and neck MR-only RT.

Synthetic CT Generation From Multi-Sequence MR Images for Head and Neck MRI-Only Radiotherapy via Cycle-Consistent Generative Adversarial Network

Synthetic CT (sCT) generation with MR image plays an essential role in MR-only radiotherapy, which can be used for electron density estimation and dose calculation in treatment planning. Currently, the reliability of deep learning-based methods for sCT generation depends on two aspects: the MR-CT registration errors, which can eliminate by a cycle-consistent generative adversarial network (CycleGAN) with unpaired training data, and the different anatomy information and contract divergence of various tissue in different sequences MRI which will significantly affect the nonlinear mapping of MR-CT. The purpose of this study is to investigate the effect of different sequence MRI by evaluating the image quality of corresponded sCTs.Multi-sequence MR images (T1, T2, T1C) and plan CT images of 151 patients with nasopharyngeal carcinoma were acquired on the same day in the radiation treatment position. 126 cases were randomly selected as the training set with the same preprocessing. Three unsupervised CycleGAN-based models were respectively trained with different sequence MRI with Architecture, which consists of 2 ResUnet-based generators and 2 Patch-GAN-based discriminators. The remaining 25 CT-MR cases with non-rigid registration were selected as the independent paired testing set. The mean error (ME) and mean absolute error (MAE) between plan CT and sCT of each model were used to measure the CT value estimation accuracy, which was calculated within air (Hounsfield Unit, HU < 100), soft tissue (-100 < HU < 150), bone (HU > 150) and whole body, respectively. Structural similarity index (SSIM) was applied to evaluate the similarity between error (plan CT and sCT.The MEs calculated between plan CT and T1, T2, T1C-based sCT images, were (-0.62 ± 3.06) vs. (-1.81 ± 3.13) vs. (0.13 ± 2.56) within air, (154.92 ± 87.93) vs. (173.27 ± 97.62) vs. (262.78 ± 91.74) within soft tissue, (1363.99 ± 294.94) vs. (337.21 ± 50.4) vs. (661.63 ± 168.43) within bone, (22.12 ± 29.47) vs. (17.05 ± 10.51) vs. (35.28 ± 20.75) within the whole body. The MAEs correspondingly were (4.49 ± 3.13) vs. (4.6 ± 3.47) vs. (4.6 ± 3.47) within air, (186.41 ± 61.75) vs. (197.33 ± 58.62) vs. (293.56 ± 72.72) within soft tissue, (186.41 ± 61.75) vs. (197.33 ± 58.62) vs. (293.56 ± 72.72) within bone, (31.28 ± 28.1) vs. (27.64 ± 10.41) vs. (42.6 ± 23.82) within the whole body. The quantitative analysis between T1, T2, T1C-sCTs indicates that the T1C based generated model achieves higher accuracy than the other models in nasopharyngeal carcinoma.The T1C MRI can provide sufficient anatomy information and tissue contract in synthetic CT prediction of head and neck region, which offers a substantial potential for density estimation and dose calculation in radiotherapy. The generation method and evaluation will investigate synthetic CT prediction with more complex tissue in various body parts.

Synthetic Computed Tomography Generation from 0.35T Magnetic Resonance Images for Magnetic Resonance–Only Radiation Therapy Planning Using Perceptual Loss Models

PurposeMagnetic resonance imaging (MRI) provides excellent soft-tissue contrast, which makes it useful for delineating tumor and normal structures in radiation therapy planning, but MRI cannot readily provide electron density for dose calculation. Computed tomography (CT) is used but introduces registration uncertainty between MRI and CT. Previous studies have shown that synthetic CTs (sCTs) can be generated directly from MRI images with deep learning methods. However, mainly high-field MRI images have been validated. This study tested whether acceptable sCTs for MR-only radiation therapy planning can be synthesized using an integrated MR-guided linear accelerator at 0.35T, using MRI images and treatment plans in the liver region. Methods and MaterialsTwo models were investigated in this study: a convolutional neural network (Unet) with conventional mean square error (MSE) loss and a Unet using a secondary convolutional neural network for perceptual loss. A total of 37 cases were used in this study with 10-fold cross validation, and 37 treatment plans were generated and evaluated for target coverage and dose to organs at risk (OARs) in the MSE loss model, perceptual loss model, and original CT. ResultsThe sCTs predicted by the perceptual loss model had improved subjective visual quality compared with those predicted by the MSE loss model, but both were similar in mean absolute error (MAE), peak-signal-to-noise ratio (PSNR), and normalized cross-correlation (NCC). The MAE, PSNR, and NCC for the perceptual loss model were 35.64, 24.11, and 0.9539, respectively, and those for the MSE loss model were 35.67, 24.36, and 0.9566, respectively. No significant differences in target coverage and dose to OARs were found between the sCT predicted by the perceptual loss model or by the MSE model and the original CT image. ConclusionsThis study indicated that a Unet with both MSE loss and perceptual loss models can be used for generating sCT images from a 0.35T integrated MR linear accelerator.

PD-0753 Unsupervised AutoConfidence estimation for deep-learning synthetic-CT in MR-only liver radiotherapy

PD-0753 Unsupervised AutoConfidence estimation for deep-learning synthetic-CT in MR-only liver radiotherapy

OC-0093 Automated organ at risk delineation in T2w head and pelvis MR images for MR-only radiation therapy

OC-0093 Automated organ at risk delineation in T2w head and pelvis MR images for MR-only radiation therapy

PO-1657 Generation of synthetic CT with 3D deep convolutional neural networks for brain MR-only radiotherapy

PO-1657 Generation of synthetic CT with 3D deep convolutional neural networks for brain MR-only radiotherapy

OC-0092 Evaluation of MRI sequences for image guided radiotherapy in a MR-only radiotherapy planning pathway

OC-0092 Evaluation of MRI sequences for image guided radiotherapy in a MR-only radiotherapy planning pathway

OC-0524 Evidence supporting anal and rectal cancer MR-only radiotherapy planning clinical implementation

OC-0524 Evidence supporting anal and rectal cancer MR-only radiotherapy planning clinical implementation

Deep learning-based synthetic CT generation for MR-only radiotherapy of prostate cancer patients with 0.35T MRI linear accelerator.

PurposeTo develop a deep learning model to generate synthetic CT for MR‐only radiotherapy of prostate cancer patients treated with 0.35 T MRI linear accelerator.Materials and MethodsA U‐NET convolutional neural network was developed to translate 0.35 T TRUFI MRI into electron density map using a novel cost function equalizing the contribution of various tissue types including fat, muscle, bone, and background air in training. The impact of training time, dataset size, image standardization, and data augmentation approaches was also quantified. Mean absolute error (MAE) between synthetic and planning CTs was calculated to measure the goodness of the model.ResultsWith 20 patients in training, our U‐NET model has the potential to generate synthetic CT with a MAE of about 29.68 ± 4.41, 16.34 ± 2.67, 23.36 ± 2.85, and 105.90 ± 22.80 HU over the entire body, fat, muscle, and bone tissues, respectively. As expected, we found that the number of patients used for training and MAE are nonlinearly correlated. Data augmentation and our proposed loss function were effective to improve MAE by ~9% and ~18% in bony voxels, respectively. Increasing the training time and image standardization did not improve the accuracy of the model.ConclusionA U‐NET model has been developed and tested numerically to generate synthetic CT from 0.35T TRUFI MRI for MR‐only radiotherapy of prostate cancer patients. Dosimetric evaluation using a large and independent dataset warrants the validity of the proposed model and the actual number of patients needed for the safe usage of the model in routine clinical workflow.

Are cone beam CT image matching skills transferrable from planning CT to planning MRI for MR-only prostate radiotherapy?

Objectives:Treatment verification for MR-only planning has focused on fiducial marker matching, however, these are difficult to identify on MR. An alternative is using the MRI for soft-tissue matching with cone beam computed tomography images (MR-CBCT). However, therapeutic radiographers have limited experience of MRI. This study aimed to assess transferability of therapeutic radiographers CT-CBCT prostate image matching skills to MR-CBCT image matching.Methods:23 therapeutic radiographers with 3 months–5 years’ experience of online daily CT-CBCT soft-tissue matching prostate cancer patients participated. Each observer completed a baseline assessment of 10 CT-CBCT prostate soft-tissue image matches, followed by 10 MR-CBCT prostate soft-tissue image match assessment. A MRI anatomy training intervention was delivered and the 10 MR-CBCT prostate soft-tissue image match assessment was repeated. Limits of agreement were calculated as the disagreement of the observers with mean of all observers.Results:Limits of agreement at CT-CBCT baseline were 2.8 mm, 2.8 mm, 0.7 mm (vertical, longitudinal, lateral). MR-CBCT matches prior to training were 3.3 mm, 3.1 mm, 0.9 mm, and after training 2.6 mm, 2.4 mm, 1.1 mm (vertical, longitudinal, lateral). Results show similar limits of agreement across the assessments, and variation reduced following the training intervention.Conclusion:This suggests therapeutic radiographers’ prostate CBCT image matching skills are transferrable to a MRI planning scan, since MR-CBCT matching has comparable observer variation to CT-CBCT matching.Advances in knowledge:This is the first publication assessing interobserver MR-CBCT prostate soft tissue matching in an MR-only pathway.

Synthesizing CT images from MR images with deep learning: model generalization for different datasets through transfer learning

Background and purpose. Replacing CT imaging with MR imaging for MR-only radiotherapy has sparked the interest of many scientists and is being increasingly adopted in radiation oncology. Although many studies have focused on generating CT images from MR images, only models on data with the same dataset were tested. Therefore, how well the trained model will work for data from different hospitals and MR protocols is still unknown. In this study, we addressed the model generalization problem for the MR-to-CT conversion task. Materials and methods. Brain T2 MR and corresponding CT images were collected from SZSPH (source domain dataset), brain T1-FLAIR, T1-POST MR, and corresponding CT images were collected from The University of Texas Southwestern (UTSW) (target domain dataset). To investigate the model’s generalizability ability, four potential solutions were proposed: source model, target model, combined model, and adapted model. All models were trained using the CycleGAN network. The source model was trained with a source domain dataset from scratch and tested with a target domain dataset. The target model was trained with a target domain dataset and tested with a target domain dataset. The combined model was trained with both source domain and target domain datasets, and tested with the target domain dataset. The adapted model used a transfer learning strategy to train a CycleGAN model with a source domain dataset and retrain the pre-trained model with a target domain dataset. MAE, RMSE, PSNR, and SSIM were used to quantitatively evaluate model performance on a target domain dataset. Results. The adapted model achieved best quantitative results of 74.56 ± 8.61, 193.18 ± 17.98, 28.30 ± 0.83, and 0.84 ± 0.01 for MAE, RMSE, PSNR, and SSIM using the T1-FLAIR dataset and 74.89 ± 15.64, 195.73 ± 31.29, 27.72 ± 1.43, and 0.83 ± 0.04 for MAE, RMSE, PSNR, and SSIM using the T1-POST dataset. The source model had the poorest performance. Conclusions. This work indicates high generalization ability to generate synthetic CT images from small training datasets of MR images using pre-trained CycleGAN. The quantitative results of the test data, including different scanning protocols and different acquisition centers, indicated the proof of this concept.

Cone beam computed tomography for dose calculation quality assurance for magnetic resonance-only radiotherapy.

Magnetic Resonance (MR)-only prostate radiotherapy using synthetic Computed Tomography (sCT) algorithms with high dose accuracy has been clinically implemented. MR images can suffer from geometric distortions so Quality Assurance (QA) using an independent, geometrically accurate, image could be required. The first-fraction Cone Beam CT (CBCT) has demonstrated potential but has not been evaluated in a clinical MR-only pathway. This study evaluated the clinical use of CBCT for dose accuracy QA of MR-only radiotherapy. A total of 49 patients treated with MR-only prostate radiotherapy were divided into two cohorts. Cohort 1 (20 patients) received a back-up CT, whilst Cohort 2 (29 patients) did not. All patients were planned using the sCT and received daily CBCT imaging with MR-CBCT soft-tissue matching. Each CBCT was calibrated using a patient-specific stepwise Hounsfield Units-to-mass density curve. The treatment plan was recalculated on the first-fraction CBCT using the clinically applied soft-tissue match and the doses compared. For Cohort 1 the sCT was rigidly registered to the back-up CT, the plan recalculated and doses compared. Mean sCT-CBCT dose difference across both cohorts was (standard error of the mean, range ), with 47/49 patients within [ ]. The sCT-CBCT dose difference was systematically lower than the sCT-CT by ( limits of agreement). The mean sCT-CBCT gamma pass rate ( ) was ( ). CBCT-based dose accuracy QA for MR-only radiotherapy appears clinically feasible. There was a small systematic sCT-CBCT dose difference implying asymmetric tolerances of [ ] would be appropriate.

Comparison of state-of-the-art atlas-based bone segmentation approaches from brain MR images for MR-only radiation planning and PET/MR attenuation correction

Comparison of state-of-the-art atlas-based bone segmentation approaches from brain MR images for MR-only radiation planning and PET/MR attenuation correction