Segmentation of parotid glands from registered CT and MR images

PurposeTo develop an automatic multimodal method for segmentation of parotid glands (PGs) from pre-registered computed tomography (CT) and magnetic resonance (MR) images and compare its results to the results of an existing state-of-the-art algorithm that segments PGs from CT images only.MethodsMagnetic resonance images of head and neck were registered to the accompanying CT images using two different state-of-the-art registration procedures. The reference domains of registered image pairs were divided on the complementary PG regions and backgrounds according to the manual delineation of PGs on CT images, provided by a physician. Patches of intensity values from both image modalities, centered around randomly sampled voxels from the reference domain, served as positive or negative samples in the training of the convolutional neural network (CNN) classifier. The trained CNN accepted a previously unseen (registered) image pair and classified its voxels according to the resemblance of its patches to the patches used for training. The final segmentation was refined using a graph-cut algorithm, followed by the dilate-erode operations.ResultsUsing the same image dataset, segmentation of PGs was performed using the proposed multimodal algorithm and an existing monomodal algorithm, which segments PGs from CT images only. The mean value of the achieved Dice overlapping coefficient for the proposed algorithm was 78.8%, while the corresponding mean value for the monomodal algorithm was 76.5%.ConclusionsAutomatic PG segmentation on the planning CT image can be augmented with the MR image modality, leading to an improved RT planning of head and neck cancer.     

Automatic lung segmentation in COVID-19 patients: Impact on quantitative computed tomography analysis

PurposeTo assess the impact of lung segmentation accuracy in an automatic pipeline for quantitative analysis of CT images.MethodsFour different platforms for automatic lung segmentation based on convolutional neural network (CNN), region-growing technique and atlas-based algorithm were considered. The platforms were tested using CT images of 55 COVID-19 patients with severe lung impairment. Four radiologists assessed the segmentations using a 5-point qualitative score (QS). For each CT series, a manually revised reference segmentation (RS) was obtained. Histogram-based quantitative metrics (QM) were calculated from CT histogram using lung segmentationsfrom all platforms and RS. Dice index (DI) and differences of QMs (ΔQMs) were calculated between RS and other segmentations.ResultsHighest QS and lower ΔQMs values were associated to the CNN algorithm. However, only 45% CNN segmentations were judged to need no or only minimal corrections, and in only 17 cases (31%), automatic segmentations provided RS without manual corrections. Median values of the DI for the four algorithms ranged from 0.993 to 0.904. Significant differences for all QMs calculated between automatic segmentations and RS were found both when data were pooled together and stratified according to QS, indicating a relationship between qualitative and quantitative measurements. The most unstable QM was the histogram 90th percentile, with median ΔQMs values ranging from 10HU and 158HU between different algorithms.ConclusionsNone of tested algorithms provided fully reliable segmentation. Segmentation accuracy impacts differently on different quantitative metrics, and each of them should be individually evaluated according to the purpose of subsequent analyses.     

Dosimetric impact of contrast-enhanced 4d computed tomography for stereotactic body radiation therapy of hepatocelluar carcinoma

BackgroundA purpose of the study was to investigate the dosimetric impact of contrast media on dose calculation using average 4D contrast-enhanced computed tomography (4D-CECT) and delayed 4D-CT (d4D-CT) images caused by CT simulation contrast agents for stereotactic body radiation therapy (SBRT) of liver cases.Materials and methodsFifteen patients of liver SBRT treated using the volumetric modulated arc therapy (VMAT) technique were selected retrospectively. 4D-CECT, and d4D-CT were acquired with the Anzai gating system and GE CT. For all patients, gross target volume (GTV) was contoured on the ten phases after rigid registration of both the contrast and delayed scans and merged to generate internal target volume (ITV) on average CT images. Region of interest (ROI) was drawn on contrast images and then copied to the delayed images after rigid registration of two average CT datasets. The treatment plans were generated for contrast enhanced average CT, delayed average CT and contrast enhanced average CT with electron density of the heart overridden.ResultsNo significant dosimetric difference was observed in plans parameters (mean HU value of the liver, total monitor units, total control points, degree of modulation and average segment area) except mean HU value of the aorta amongst the three arms. All the OARs were evaluated and resulted in statistically insignificant variation (p > 0.05) using one way ANOVA analysis.ConclusionsContrast enhanced 4D-CT is advantageous in accurate delineation of tumors and assessing accurate ITV. The treatment plans generated on average 4D-CECT and average d4D-CT have a clinically insignificant effect on dosimetric parameters.     

Combined PET/CT for IMRT treatment planning of NSCLC: Contrast-enhanced CT images for Monte Carlo dose calculation

PurposeCombined PET/CT imaging has been proposed as an integral part of radiotherapy treatment planning (TP). Contrast-enhanced CT (ceCT) images are frequently acquired as part of the PET/CT examination to support target delineation. The aim of this dosimetric planning study was to investigate the error introduced by using a ceCT for intensity modulated radiotherapy (IMRT) TP with Monte Carlo dose calculation for non-small cell lung cancer (NSCLC).Material and methodsNine patients with NSCLC prior to chemo-RT were included in this retrospective study. For each patient non-enhanced, low-dose CT (neCT), ceCT and [¹⁸F]-FDG-PET emission data were acquired within a single examination. Manual contouring and TP were performed on the ceCT. An additional set of independent target volumes was auto-segmented in PET images. Dose distributions were recalculated on the neCT. Differences in dosimetric parameters were evaluated.ResultsDose differences in PTV and lungs were small for all patients. The maximum difference in all PTVs when using ceCT images for dose calculation was ?2.1%, whereas the mean difference was less than ?1.7%. Maximum differences in the lungs ranged from ?1.8% to 2.1% (mean: ?0.1%). In four patients an underestimation of the maximum spinal cord dose between 2% and 3.2% was observed, but treatment plans remained clinically acceptable.ConclusionsMonte Carlo based IMRT planning for NSCLC patients using ceCT allows for correct dose calculation. A direct comparison to neCT-based treatment plans revealed only small dose differences. Therefore, ceCT-based TP is clinically safe as long as the maximum acceptable dose to organs at risk is not approached.     

Multi-Atlas Based Adaptive Active Contour Model with Application to Organs at Risk Segmentation in Brain MR Images

BackgroundAccurate delineation of organs at risk (OARs) is critical in radiotherapy. Manual delineation is tedious and suffers from both interobserver and intraobserver variability. Automatic segmentation of brain MR images has a wide range of applications in brain tumor radiotherapy. In this paper, we propose a multi-atlas based adaptive active contour model for OAR automatic segmentation in brain MR images.MethodsThe proposed method consists of two parts: multi-atlas based OAR contour initiation and an adaptive edge and local region based active contour evolution. In the adaptive active contour model, we define an energy functional with an adaptive edge intensity fitting force which is responsible for evaluating contour inwards or outwards, and a local region intensity fitting force which guides the evolution of the contour.ResultsExperimental results show that the proposed method achieved more accurate segmentation results in brainstem, eyes and lens automatic segmentation with the Dice Similar Coefficient (DSC) value of 87.19%, 91.96%, 77.11% respectively. Besides, the dosimetric parameters also demonstrate the high consistency of the manual OAR delineations and the auto segmentation results of the proposed method in brain tumor radiotherapy.ConclusionsThe geometric and dosimetric evaluations show the desirable performance of the proposed method on the application of OARs segmentations in brain tumor radiotherapy.     

Evaluation of a multi-atlas CT synthesis approach for MRI-only radiotherapy treatment planning

Background and purposeComputed tomography (CT) imaging is the current gold standard for radiotherapy treatment planning (RTP). The establishment of a magnetic resonance imaging (MRI) only RTP workflow requires the generation of a synthetic CT (sCT) for dose calculation. This study evaluates the feasibility of using a multi-atlas sCT synthesis approach (sCT_a) for head and neck and prostate patients.Material and methodsThe multi-atlas method was based on pairs of non-rigidly aligned MR and CT images. The sCT_a was obtained by registering the MRI atlases to the patient’s MRI and by fusing the mapped atlases according to morphological similarity to the patient. For comparison, a bulk density assignment approach (sCT_bda) was also evaluated. The sCT_bda was obtained by assigning density values to MRI tissue classes (air, bone and soft-tissue). After evaluating the synthesis accuracy of the sCTs (mean absolute error), sCT-based delineations were geometrically compared to the CT-based delineations. Clinical plans were re-calculated on both sCTs and a dose-volume histogram and a gamma analysis was performed using the CT dose as ground truth.ResultsResults showed that both sCTs were suitable to perform clinical dose calculations with mean dose differences less than 1% for both the planning target volume and the organs at risk. However, only the sCT_a provided an accurate and automatic delineation of bone.ConclusionsCombining MR delineations with our multi-atlas CT synthesis method could enable MRI-only treatment planning and thus improve the dosimetric and geometric accuracy of the treatment, and reduce the number of imaging procedures.     

Automated classification of urinary stones based on microcomputed tomography images using convolutional neural network

PurposeThe classification of urinary stones is important prior to treatment because the treatments depend on three types of urinary stones, i.e., calcium, uric acid, and mixture stones. We have developed an automatic approach for the classification of urinary stones into the three types based on microcomputed tomography (micro-CT) images using a convolutional neural network (CNN).Materials and methodsThirty urinary stones from different patients were scanned in vitro using micro-CT (pixel size: 14.96 μm; slice thickness: 15 μm); a total of 2,430 images (micro-CT slices) were produced. The slices (227 × 227 pixels) were classified into the three categories based on their energy dispersive X-ray (EDX) spectra obtained via scanning electron microscopy (SEM). The images of urinary stones from each category were divided into three parts; 66%, 17%, and 17% of the dataset were assigned to the training, validation, and test datasets, respectively. The CNN model with 15 layers was assessed based on validation accuracy for the optimization of hyperparameters such as batch size, learning rate, and number of epochs with different optimizers. Then, the model with the optimized hyperparameters was evaluated for the test dataset to obtain classification accuracy and error.ResultsThe validation accuracy of the developed approach with CNN with optimized hyperparameters was 0.9852. The trained CNN model achieved a test accuracy of 0.9959 with a classification error of 1.2%.ConclusionsThe proposed automated CNN-based approach could successfully classify urinary stones into three types, namely calcium, uric acid, and mixture stones, using micro-CT images.     

Low-tube-voltage selection for non-contrast-enhanced CT: Comparison of the radiation dose in pediatric and adult phantoms

PurposeWe used pediatric and adult anthropomorphic phantoms to compare the radiation dose of low- and standard tube voltage chest and abdominal non-contrast-enhanced computed tomography (CT) scans. We also discuss the optimal low tube voltage for non-contrast-enhanced CT.MethodsUsing a female adult- and three differently-sized pediatric anthropomorphic phantoms we acquired chest and abdominal non-contrast-enhanced scans on a 320-multidetector CT volume scanner. The tube voltage was set at 80-, 100-, and 120 kVp. The tube current was automatically assigned on the CT scanner in response to the set image noise level. On each phantom and at each tube voltage we measured the surface and center dose using high-sensitivity metal-oxide-semiconductor field-effect transistor detectors.ResultsThe mean surface dose of chest and abdominal CT scans in 5-year olds was 4.4 and 5.3 mGy at 80 kVp, 4.5 and 5.4 mGy at 100 kV, and 4.0 and 5.0 mGy at 120 kVp, respectively. These values were similar in our 3-pediatric phantoms (p > 0.05). The mean surface dose in the adult phantom increased from 14.7 to 19.4 mGy for chest- and from 18.7 to 24.8 mGy for abdominal CT as the tube voltage decreased from 120 to 80 kVp (p < 0.01).ConclusionCompared to adults, the surface and center dose for pediatric patients is almost the same despite a decrease in the tube voltage and the low tube voltage technique can be used for non-contrast-enhanced chest- and abdominal scanning.     

Repositioning accuracy of a novel thermoplastic mask for head and neck cancer radiotherapy

PurposeThe aim of this study was to assess the reproducibility of patient shoulder position immobilized with a novel and innovative prototype mask (E-Frame, Engineering System).MethodsThe E-frame mask fixes both shoulders and bisaxillary regions compared with that of a commercial mask (Type-S, CIVCO). Thirteen and twelve patients were immobilized with the Type-S and E-Frame mask systems, respectively. For each treatment fraction, cone-beam CT (CBCT) images of the patient were acquired and retrospectively analyzed. The CBCT images were registered to the planning CT based on the cervical spine, and then the displacements of the acromial extremity of the clavicle were measured.ResultsThe systematic and random errors between the two mask systems were evaluated. The differences of the systematic errors between the two mask systems were not statistically significant. The mean random errors in the three directions (AP, SI and LR) were 2.7 mm, 3.1 mm and 1.5 mm, respectively for the Type-S mask, and 2.8 mm 2.5 mm and 1.4 mm, respectively for the E-Frame mask. The random error of the E-Frame masks in the SI direction was significantly smaller than that of the Type-S. The number of cases showing displacements exceeding 10 mm in the SI direction for at least one fraction was eight (61% of 13 cases) and three (25% of 12 cases) for Type-S and E-Frame masks, respectively.ConclusionsThe E-Frame masks reduced the random displacements of patient’s shoulders in the SI direction, effectively preventing large shoulder shifts that occurred frequently with Type-S masks.     

Effects of metal implants and validation of four treatment planning methods used for radiotherapy dose calculation

BackgroundThe radiotherapy treatment planning process involves target delineation and dose calculation, both of which directly depend on image quality and Hounsfield unit (HU) accuracy of computed tomography (CT) images. CT images of patients having metal implants undergo image quality deterioration and show inaccurate HU values due to various artifacts. Metal artifact reduction (MAR) is used to improve the image quality. In this study, four treatment planning methods with and without MAR, in combination with actual and assigned HU values, were analyzed for dose calculation accuracy. The aim was to study the effects of metal implants on planning CT and to evaluate the dose calculation accuracy of four treatment planning methods for radiotherapy.Materials and methodsTwo phantoms with six different metal inserts were scanned in the extended HU mode, with and without MAR. Geometry verification and HU analysis of the metals and the surrounding region were carried out. Water equivalent distance (WED) measurements and dose calculation for each metal insert were done in the treatment planning system (TPS) using the anisotropic analytical algorithm (AAA). Point dose and two-dimensional dose distribution were studied. Percentage variation analysis between calculated and measured doses and gamma evaluation were conducted to determine the most suitable method for treatment planning.ConclusionThis study concludes that an MARCT image with an assigned HU similar to that of the metal implant is better for contouring and high dose calculation accuracy. If MAR is not available, the actual HU value from the extended HU CT for the metal should be used for dose calculation.     

Accuracy of target delineation by positron emission tomography-based auto-segmentation methods after deformable image registration: A phantom study

PurposeDiagnostic positron emission tomography and computed tomography (PET/CT) images can be fused to the planning CT images by a deformable image registration (DIR). The aim of this study was to evaluate the standardized uptake value (SUV) and target delineation on deformed PET images.MethodsWe used a cylindrical phantom and removable inserts of four spheres (16–38 mm in diameter) and three ellipsoids with a volume equal to the 38-mm-diameter sphere (S38) in each. S38 was filled with 18F-fluorodeoxyglucose activity, and then PET/CT images were acquired. The contours of S38 were generated using original PET images by PET auto-segmentation (PET-AS) methods of (1) SUV2.5, (2) 40% of maximum SUV (SUV40%max), and (3) gradient-based (GB), and were deformed to the other inserts by DIR. We compared the volumes and the SUV_max with the generated contours using the deformed PET images.ResultsThe SUV_max was slightly decreased by DIR; the mean absolute difference was −0.10 ± 0.04. For SUV2.5 and SUV40%max, the differences in S38 volumes between the original and deformed PET images were less than 5%, regardless of deformation type. For the GB, the contoured volumes obtained from deformed PET images were larger than those of the original PET images for the deformation type of ellipsoids. When the S38 was deformed to the 16-mm-diameter sphere, the maximum volume difference was −22.8%.ConclusionsAlthough SUV fluctuations by DIR were negligible, the target delineation on deformed PET images by the GB should be carefully considered owing to the distortion of intensity profiles.     

Estimation and evaluation of pseudo-CT images using linear regression models and texture feature extraction from MRI images in the brain region to design external radiotherapy planning

AimThe aim of this study is to construct and evaluate Pseudo-CT images (P-CTs) for electron density calculation to facilitate external radiotherapy treatment planning.BackgroundDespite numerous benefits, computed tomography (CT) scan does not provide accurate information on soft tissue contrast, which often makes it difficult to precisely differentiate target tissues from the organs at risk and determine the tumor volume. Therefore, MRI imaging can reduce the variability of results when registering with a CT scan.Materials and methodsIn this research, a fuzzy clustering algorithm was used to segment images into different tissues, also linear regression methods were used to design the regression model based on the feature extraction method and the brightness intensity values. The results of the proposed algorithm for dose-volume histogram (DVH), Isodose curves, and gamma analysis were investigated using the RayPlan treatment planning system, and VeriSoft software. Furthermore, various statistical indices such as Mean Absolute Error (MAE), Mean Error (ME), and Structural Similarity Index (SSIM) were calculated.ResultsThe MAE of a range of 45–55 was found from the proposed methods. The relative difference error between the PTV region of the CT and the Pseudo-CT was 0.5, and the best gamma rate was 95.4% based on the polar coordinate feature and proposed polynomial regression model.ConclusionThe proposed method could support the generation of P-CT data for different parts of the brain region from a collection of MRI series with an acceptable average error rate by different evaluation criteria.     

Metal artifact reduction on cervical CT images by deep residual learning

Background

Cervical cancer is the fifth most common cancer among women, which is the third leading cause of cancer death in women worldwide. Brachytherapy is the most effective treatment for cervical cancer. For brachytherapy, computed tomography (CT) imaging is necessary since it conveys tissue density information which can be used for dose planning. However, the metal artifacts caused by brachytherapy applicators remain a challenge for the automatic processing of image data for image-guided procedures or accurate dose calculations. Therefore, developing an effective metal artifact reduction (MAR) algorithm in cervical CT images is of high demand.

Methods

A novel residual learning method based on convolutional neural network (RL-ARCNN) is proposed to reduce metal artifacts in cervical CT images. For MAR, a dataset is generated by simulating various metal artifacts in the first step, which will be applied to train the CNN. This dataset includes artifact-insert, artifact-free, and artifact-residual images. Numerous image patches are extracted from the dataset for training on deep residual learning artifact reduction based on CNN (RL-ARCNN). Afterwards, the trained model can be used for MAR on cervical CT images.

Results

The proposed method provides a good MAR result with a PSNR of 38.09 on the test set of simulated artifact images. The PSNR of residual learning (38.09) is higher than that of ordinary learning (37.79) which shows that CNN-based residual images achieve favorable artifact reduction. Moreover, for a 512?×?512 image, the average removal artifact time is less than 1 s.

Conclusions

The RL-ARCNN indicates that residual learning of CNN remarkably reduces metal artifacts and improves critical structure visualization and confidence of radiation oncologists in target delineation. Metal artifacts are eliminated efficiently free of sinogram data and complicated post-processing procedure.

     

Image quality assessment of an iterative reconstruction algorithm applied to abdominal CT imaging

PurposeTo compare the noise and accuracy on images of the whole porcine liver acquired with iterative reconstruction (IMR, Philips Healthcare, Cleveland, OH, USA) and filtered back projection (FBP) methods.Materials and methodsWe used non-enhanced porcine liver to simulate the human liver and acquired it 100 times to obtain the average FBP value as the ground-truth. The mean and the standard deviation (“inter-scan SD”) of the pixel values on the 100 image acquisitions were calculated for FBP and for three levels of IMR (L1, L2, and L3). We also calculated the noise power spectrum (NPS) and the normalized NPS for the 100 image acquisitions.ResultsThe spatial SD for the porcine liver parenchyma on these slices was 9.92, 4.37, 3.63, and 2.30 Hounsfield units with FBP, IMR-L1, IMR-L2, and IMR-L3, respectively. The detectability of small faint features was better on single IMR than single FBP images. The inter-scan SD value for IMR-L3 images was 53% larger at the liver edges than at the liver parenchyma; it was only 10% larger on FBP images. Assessment of the normalized NPS showed that the noise on IMR images was comprised primarily of low-frequency components.ConclusionIMR images yield the same structure informations as FBP images and image accuracy is maintained. On level 3 IMR images the image noise is more strongly suppressed than on IMR images of the other levels and on FBP images.     

Conditional generative adversarial networks to generate pseudo low monoenergetic CT image from a single-tube voltage CT scanner

PurposeTo generate pseudo low monoenergetic CT images of the abdomen from 120-kVp CT images with cGAN.Materials and MethodsWe retrospectively included 48 patients who underwent contrast-enhanced abdominal CT using dual-energy CT. We reconstructed paired data sets of 120 kVp CT images and virtual low monoenergetic (55-keV) CT images. cGAN was prepared to generate pseudo 55-keV CT images from 120-kVp CT images. The pseudo 55 keV CT images in epoch 10, 50, 100, and 500 were compared to the 55 keV images generated using peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM).ResultsThe PSNRs were 28.0, 28.5, 28.6, and 28.8 at epochs 10, 50, 100, and 500, respectively. The SSIM was approximately constant from epochs 50 to 500.ConclusionPseudo low monoenergetic abdominal CT images were generated from 120-kVp CT images using cGAN, and the images had good quality similar to that of monochromatic images obtained with DECT software.     

Dosimetric study of Hounsfield number correction effect in areas influenced by contrast product in lungs case

BackgroundThe aim of the study was dosimetric effect quantification of exclusive computed tomography (CT) use with an intravenous (IV) contrast agent (CA ), on dose distribution of 3D-CRT treatment plans for lung cancer. Furthermore, dosimetric advantage investigation of manually contrast-enhanced region overriding, especially the heart.Materials and methodsTen patients with lung cancer were considered. For each patient two planning CT sets were initially taken with and without CA. Treatment planning were optimized based on CT scans without CA. All plans were copied and recomputed on scans with CA. In addition, scans with IV contrast were copied and density correction was performed for heart contrast enhanced. Same plans were copied and replaced to undo dose calculation errors that may be caused by CA. Eventually, dosimetric evaluations based on dose volume histograms (DVHs) of planning target volumes (PTV) and organs at-risk were studied and analyzed using the Wilcoxon’s signed rank test.ResultsThere is no statistically significant difference in dose calculation for the PTV maximum, mean, minimum doses, spinal cord maximum doses and lung volumes that received 20 and 30 Gy, between planes calculated with and without contrast scans (p > 0.05) and also for contrast scan, with manual regions overriding.ConclusionsDose difference caused by the contrast agent is negligible and not significant. Therefore, there is no justification to perform two scans, and using an IV contrast enhanced scan for dose calculation is sufficient.     

Geometric inaccuracy and co-registration errors for CT,DynaCT and MRI images used in robotic stereotactic radiosurgery treatment planning

PurposeTo measure the combined errors due to geometric inaccuracy and image co-registration on secondary images (dynamic CT angiography (dCTA), 3D DynaCT angiography (DynaCTA), and magnetic resonance images (MRI)) that are routinely used to aid in target delineation and planning for stereotactic radiosurgery (SRS).MethodsThree phantoms (one commercial and two in-house built) and two different analysis approaches (commercial and MATLAB based) were used to quantify the magnitude of geometric image distortion and co-registration errors for different imaging modalities within CyberKnife’s MultiPlan treatment planning software. For each phantom, the combined errors were reported as a mean target registration error (TRE). The mean TRE’s for different intramodality imaging parameters (e.g., mAs, kVp, and phantom set-ups) and for dCTA, DynaCTA, and MRI systems were measured.ResultsOnly X-ray based imaging can be performed with the commercial phantom, and the mean TRE ± standard deviation values were large compared to the in-house analysis using MATLAB. With the 3D printed phantom, even drastic changes in treatment planning CT imaging protocols did not greatly influence the mean TRE (<0.5 mm for a 1 mm slice thickness CT). For all imaging modalities, the largest mean TRE was found on DynaCT, followed by T2-weighted MR images (albeit all <1 mm).ConclusionsThe user may overestimate the mean TRE if the commercial phantom and MultiPlan were used solely. The 3D printed phantom design is a sensitive and suitable quality assurance tool for measuring 3D geometric inaccuracy and co-registration errors across all imaging modalities.     

Validation of dose distribution computation on sCT images generated from MRI scans by Philips MRCAT

AimTo evaluate calculation of treatment plans based on synthetic-CT (sCT) images generated from MRI.BackgroundBecause of better soft tissue contrast, MR images are used in addition to CT images for radiotherapy planning. However, registration of CT and MR images or repositioning between scanning sessions introduce systematic errors, hence suggestions for MRI-only therapy. The lack of information on electron density necessary for dose calculation leads to sCT (synthetic CT) generation. This work presents a comparison of dose distribution calculated on standard CT and sCT.Materials and methods10 prostate patients were included in this study. CT and MR images were collected for each patient and then water equivalent (WE) and MRCAT images were generated. The radiation plans were optimized on CT and then recalculated on MRCAT and WE data. 2D gamma analysis was also performed.ResultsThe mean differences in the majority of investigated DVH points were in order of 1% up to 10%, including both MRCAT and WE dose distributions. Mean gamma pass for acceptance criteria 1%/1 mm were greater than 82.5%. Prescribed doses for target volumes and acceptable doses for organs at risk were met in almost all cases.ConclusionsThe dose calculation accuracy on MRCAT was not significantly compromised in the majority of clinical relevant DVH points. The introduction of MRCAT into practise would eliminate systematic errors, increase patients’ comfort and reduce treatment expenses. Institutions interested in MRCAT commissioning must, however, consider changes to established workflow.