A deep learning model to generate synthetic CT for prostate MR-only radiotherapy dose planning: a multicenter study

IntroductionFor radiotherapy based solely on magnetic resonance imaging (MRI), generating synthetic computed tomography scans (sCT) from MRI is essential for dose calculation. The use of deep learning (DL) methods to generate sCT from MRI has shown encouraging results if the MRI images used for training the deep learning network and the MRI images for sCT generation come from the same MRI device. The objective of this study was to create and evaluate a generic DL model capable of generating sCTs from various MRI devices for prostate radiotherapyMaterials and methodsIn total, 90 patients from three centers (30 CT-MR prostate pairs/center) underwent treatment using volumetric modulated arc therapy for prostate cancer (PCa) (60 Gy in 20 fractions). T2 MRI images were acquired in addition to computed tomography (CT) images for treatment planning. The DL model was a 2D supervised conditional generative adversarial network (Pix2Pix). Patient images underwent preprocessing steps, including nonrigid registration. Seven different supervised models were trained, incorporating patients from one, two, or three centers. Each model was trained on 24 CT-MR prostate pairs. A generic model was trained using patients from all three centers. To compare sCT and CT, the mean absolute error in Hounsfield units was calculated for the entire pelvis, prostate, bladder, rectum, and bones. For dose analysis, mean dose differences of D99% for CTV, V95% for PTV, Dmax for rectum and bladder, and 3D gamma analysis (local, 1%/1 mm) were calculated from CT and sCT. Furthermore, Wilcoxon tests were performed to compare the image and dose results obtained with the generic model to those with the other trained models.ResultsConsidering the image results for the entire pelvis, when the data used for the test comes from the same center as the data used for training, the results were not significantly different from the generic model. Absolute dose differences were less than 1 Gy for the CTV D99% for every trained model and center. The gamma analysis results showed nonsignificant differences between the generic and monocentric models.ConclusionThe accuracy of sCT, in terms of image and dose, is equivalent to whether MRI images are generated using the generic model or the monocentric model. The generic model, using only eight MRI-CT pairs per center, offers robust sCT generation, facilitating PCa MRI-only radiotherapy for routine clinical use

Développement et validation expérimentale d'un outil de détermination de la dose hors-champ en radiothérapie

Depuis deux décennies, les nombreux développements des techniques de radiothérapie par modulation d intensité (RCMI) ont permis de mieux conformer la dose au volume cible et ainsi, d augmenter les taux de réussite des traitements des cancers. Ces techniques ont souvent l avantage de réduire la dose aux organes à risque proches de la zone traitée, mais elles ont l inconvénient d apporter un niveau de dose périphérique plus important que les techniques basiques sans modulation d intensité. Dans ce contexte, l augmentation du taux de survie des patients qui en résulte, accroît également la probabilité de manifestation d effets iatrogènes dus aux doses périphériques (tels que les cancers secondaires). Aujourd hui, la dose périphérique n est pas considérée lors de la planification du traitement et il n existe aucun outil numérique fiable pour sa prédiction. Il devient cependant indispensable de prendre en compte le dépôt de dose périphérique lors de la planification du traitement, notamment dans les cas pédiatriques. Cette étude doctorale a permis la réalisation de plusieurs étapes du développement d un outil numérique, précis et rapide, de prédiction de la dose hors-champ fondé sur le code Monte Carlo PENELOPE. Dans cet objectif, nous avons démontré la capacité du code PENELOPE à estimer la dose périphérique en comparant ses résultats avec des mesures de référence réalisées à partir de deux configurations expérimentales (métrologique et pré-clinique). Ces travaux expérimentaux ont notamment permis la mise en place d un protocole d utilisation des dosimètres OSL pour la mesure des faibles doses. En parallèle, nous avons pu mettre en évidence la convergence lente et rédhibitoire du calcul en vue d une utilisation clinique. Par conséquent nous avons réalisé un travail d accélération du code en implémentant une nouvelle technique de réduction de variance appelée transport pseudo-déterministe spécifiquement dédiée à l amélioration de la convergence dans des zones lointaines du faisceau principal. Ces travaux ont permis d améliorer l efficacité des estimations dans les deux configurations de validation définies (gain d un facteur 20) pour atteindre des temps de calcul raisonnables pour une application clinique. Des travaux d optimisation du code restent à entreprendre de façon à améliorer encore la convergence de l outil pour ensuite en envisager une utilisation clinique.Over the last two decades, many technical developments have been achieved on intensity modulated radiotherapy (IMRT) and allow a better conformation of the dose to the tumor and consequently increase the success of cancer treatments. These techniques often reduce the dose to organs at risk close to the target volume; nevertheless they increase peripheral dose levels. In this situation, the rising of the survival rate also increases the probability of secondary effects expression caused by peripheral dose deposition (second cancers for instance). Nowadays, the peripheral dose is not taken into account during the treatment planification and no reliable prediction tool exists. However it becomes crucial to consider the peripheral dose during the planification, especially for pediatric cases. Many steps of the development of an accurate and fast Monte Carlo out-of-field dose prediction tool based on the PENELOPE code have been achieved during this PhD work. To this end, we demonstrated the ability of the PENELOPE code to estimate the peripheral dose by comparing its results with reference measurements performed on two experimental configurations (metrological and pre-clinical). During this experimental work, we defined a protocol for low doses measurement with OSL dosimeters. In parallel, we highlighted the slow convergence of the code for clinical use. Consequently, we accelerated the code by implementing a new variance reduction technique called pseudo-deterministic transport which is specifically with the objective of improving calculations in areas far away from the beam. This step improved the efficiency of the peripheral doses estimation in both validation configurations (by a factor of 20) in order to reach reasonable computing times for clinical application. Optimization works must be realized in order improve the convergence of our tool and consider a final clinical use.PARIS11-SCD-Bib. électronique (914719901) / SudocSudocFranceF

Automatic Multiorgan Segmentation in Pelvic Region with Convolutional Neural Networks on 0.35 T MR-Linac Images

MR-Linac is a recent device combining a linear accelerator with an MRI scanner. The improved soft tissue contrast of MR images is used for optimum delineation of tumors or organs at risk (OARs) and precise treatment delivery. Automatic segmentation of OARs can contribute to alleviating the time-consuming process for radiation oncologists and improving the accuracy of radiation delivery by providing faster, more consistent, and more accurate delineation of target structures and organs at risk. It can also help reduce inter-observer variability and improve the consistency of contouring while reducing the time required for treatment planning. In this work, state-of-the-art deep learning techniques were evaluated based on 2D and 2.5D training strategies to develop a comprehensive tool for the accurate segmentation of pelvic OARs dedicated to 0.35 T MR-Linac. In total, 103 cases with 0.35 T MR images of the pelvic region were investigated. Experts considered and contoured the bladder, rectum, and femoral heads as OARs and the prostate as the target volume. For the training of the neural network, 85 patients were randomly selected, and 18 were used for testing. Multiple U-Net-based architectures were considered, and the best model was compared using both 2D and 2.5D training strategies. The evaluation of the models was performed based on two metrics: the Dice similarity coefficient (DSC) and the Hausdorff distance (HD). In the 2D training strategy, Residual Attention U-Net (ResAttU-Net) had the highest scores among the other deep neural networks. Due to the additional contextual information, the configured 2.5D ResAttU-Net performed better. The overall DSC were 0.88 ± 0.09 and 0.86 ± 0.10, and the overall HD was 1.78 ± 3.02 mm and 5.90 ± 7.58 mm for 2.5D and 2D ResAttU-Net, respectively. The 2.5D ResAttU-Net provides accurate segmentation of OARs without affecting the computational cost. The developed end-to-end pipeline will be merged with the treatment planning system for in-time automatic segmentation

Characterization of the Photoneutron Flux Emitted by an Electron Accelerator Using an Activation Detector

Symposium on Radiation Measurements and Applications (SORMA), Oakland, CA, MAY 14-17, 2012International audienceElectron accelerators are intense photoneutron sources for nuclear waste packages characterization and homeland security applications. Moreover, when considering medical accelerators, photoneutrons can cause secondary cancers for patients and also expose hospital staff to deactivation gammas after the irradiation. In this study, we investigate photoneutrons properties according to average emission intensity, energy spectrum and spatial distribution. The latter parameter was measured using an activation detector and the other two parameters were determined by simulation, and then, evaluated by comparing absolute activation simulations and measurements. Our methodology was applied to characterize the photoneutron flux generated by two electron accelerators. Our study has shown that there is a strong probability for photonuclear cross-sections to be undervalued in the energy range studied

DataSheet_1_Deep learning application for abdominal organs segmentation on 0.35 T MR-Linac images.pdf

IntroductionLinear accelerator (linac) incorporating a magnetic resonance (MR) imaging device providing enhanced soft tissue contrast is particularly suited for abdominal radiation therapy. In particular, accurate segmentation for abdominal tumors and organs at risk (OARs) required for the treatment planning is becoming possible. Currently, this segmentation is performed manually by radiation oncologists. This process is very time consuming and subject to inter and intra operator variabilities. In this work, deep learning based automatic segmentation solutions were investigated for abdominal OARs on 0.35 T MR-images.MethodsOne hundred and twenty one sets of abdominal MR images and their corresponding ground truth segmentations were collected and used for this work. The OARs of interest included the liver, the kidneys, the spinal cord, the stomach and the duodenum. Several UNet based models have been trained in 2D (the Classical UNet, the ResAttention UNet, the EfficientNet UNet, and the nnUNet). The best model was then trained with a 3D strategy in order to investigate possible improvements. Geometrical metrics such as Dice Similarity Coefficient (DSC), Intersection over Union (IoU), Hausdorff Distance (HD) and analysis of the calculated volumes (thanks to Bland-Altman plot) were performed to evaluate the results.ResultsThe nnUNet trained in 3D mode achieved the best performance, with DSC scores for the liver, the kidneys, the spinal cord, the stomach, and the duodenum of 0.96 ± 0.01, 0.91 ± 0.02, 0.91 ± 0.01, 0.83 ± 0.10, and 0.69 ± 0.15, respectively. The matching IoU scores were 0.92 ± 0.01, 0.84 ± 0.04, 0.84 ± 0.02, 0.54 ± 0.16 and 0.72 ± 0.13. The corresponding HD scores were 13.0 ± 6.0 mm, 16.0 ± 6.6 mm, 3.3 ± 0.7 mm, 35.0 ± 33.0 mm, and 42.0 ± 24.0 mm. The analysis of the calculated volumes followed the same behavior.DiscussionAlthough the segmentation results for the duodenum were not optimal, these findings imply a potential clinical application of the 3D nnUNet model for the segmentation of abdominal OARs for images from 0.35 T MR-Linac.</p

Characterisation of a split gradient coil design induced systemic imaging artefact on 0.35 T MR-linac systems

International audienceAbstract Objective . The aim of this work was to highlight and characterize a systemic ‘star-like’ artefact inherent to the low field 0.35 T MRIdian MR-linac system, a magnetic resonance guided radiotherapy device. This artefact is induced by the original split gradients coils design. This design causes a surjection of the intensity gradient in Z (or head-feet) direction. This artefact appears on every sequence with phase encoding in the head-feet direction. Approach . Basic gradient echo sequence and clinical mandatory bSSFP sequence were used. Three setups using manufacturer provided QA phantoms were designed: two including the linearity control grid used for the characterisation and a third including two homogeneity control spheres dedicated to the artefact management in a more clinical like situation. The presence of the artefact was checked in four different MRidian sites. The tested parameters based on the literature were: phase encoding orientation, slab selectivity, excitation bandwidth (BW RF ), acceleration factor (R) and phase/slab oversampling (PO/SO). Main results . The position of this artefact is constant and reproducible over the tested MRIdian sites. The typical singularity saturated dot or star is visible even with the 3D slab-selection enabled. A management is proposed by decreasing the BW RF , the R in head-feet direction and increasing the PO/SO. The oversampling can be optimized using a formula to anticipate the location of artefact in the field of view. Significance . The star-like artefact has been well characterised. A manageable solution comes at the cost of acquisition time. Observed in clinical cases, the artefact may degrade the images used for the RT planning and repositioning during the treatment unless corrected

Inferring postimplant dose distribution of salvage permanent prostate implant (PPI) after primary PPI on CT images

International audiencePURPOSE:To evaluate the dose distribution of additional radioactive seeds implanted during salvage permanent prostate implant (sPPI) after a primary permanent prostate implant (pPPI).METHODS AND MATERIALS:Patients with localized prostate cancer were primarily implanted with iodine-125 seeds and had a dosimetric assessment based on day 30 postimplant CT (CT1). After an average of 6 years, these patients underwent sPPI followed by the same CT-based evaluation of dosimetry (CT2). Radioactive seeds on each CT were detected. The detected primary seeds on CT1 and CT2 were registered and then removed from CT2 referred as a modified CT2 (mCT2). Dosimetry evaluations (D90 and V100) of sPPI were performed with dedicated planning software on CT2 and mCT2. Indeed, prostate volume, D90, and V100 differences between CT2 and either CT1 or mCT2 were calculated, and values were expressed as mean (standard deviation).RESULTS:The mean prostate volume difference between sPPI and pPPI over the 6 patients was 9.85 (7.32) cm3. The average D90 and V100 assessed on CT2 were 486.5 Gy (58.9) and 100.0% (0.0), respectively, whereas it was 161.3 Gy (47.5) and 77.3% (25.2) on mCT2 (p = 0.031 each time). The average D90 the day of sPPI [145.4 Gy (11.2)] was not significantly different from that observed on mCT2 (p = 0.56).CONCLUSION:Postimplant D90 and V100 of sPPI after pPPI can be estimated on CT images after removing the primary seeds.Copyright © 2018 American Brachytherapy Society. Published by Elsevier Inc. All rights reserved

Indirect deformable image registration using synthetic image generated by unsupervised deep learning

International audienceBackground and purpose: 3D image registration is now common in many medical domains. Multimodal registration implies the use of different imaging modalities, which results in lower accuracy compared to monomodal registration. The aim of this study was to propose a novel approach for deformable image registration (DIR) that incorporates an unsupervised deep learning (DL)-based generation step. The objective was to reduce the challenge of multimodal registration to monomodal registration. Material and methods: Two datasets from prostate radiotherapy patients were used to evaluate the proposed method. The first dataset consisted of Computed Tomography (CT)/ Cone Beam Computed Tomography (CBCT) pairs from 23 patients using different CBCT devices. The second dataset included Magnetic Resonance Imaging (MRI)/CT pairs from two different care centers, utilizing different MRI devices (0.35 T MRIdian MR-Linac, 1.5 T GE lightspeed MRI). Following a preprocessing step essential for ensuring DL synthesis accuracy and standardizing the database, synthetic CTs ( sCT reg ) were generated using an unsupervised conditional Generative Adversarial Network (cGAN). The generated sCTs from CBCT or MRI were then utilized for deformable registration with CT scans. This registration method was compared to three standard methods: rigid registration, Elastix registration based on BSplines, and VoxelMorph-based registration (applied exclusively to CBCT/CT). The endpoints of comparison were the dice coefficients calculated between delineated structures for both datasets. Results: For both datasets, intermediary sCT generation provided the highest dice coefficients. Dices reached 0.85, 0.85 and 0.75 for the prostate, bladder and rectum for the dataset 1 and 0.90, 0.95 and 0.87 respectively for the dataset 2. When the sCT were not used, dices reached 0.66, 0.78, 0.66 for the dataset 1 and 0.93, 0.87 and 0.84 for the dataset 2. Furthermore, the evaluation of the impact of registration on sCT generation showed that lower Mean Absolute Errors were obtained when the registration was conducted with a sCT. Conclusions: Using unsupervised deep learning to synthesize intermediate sCT has led to improved registration accuracy in radiotherapy applications employing two distinct imaging modalities

Characterisation of a split gradient coil design induced systemic imaging artefact on 0.35 T MR-linac systems

International audienceAbstract Objective . The aim of this work was to highlight and characterize a systemic ‘star-like’ artefact inherent to the low field 0.35 T MRIdian MR-linac system, a magnetic resonance guided radiotherapy device. This artefact is induced by the original split gradients coils design. This design causes a surjection of the intensity gradient in Z (or head-feet) direction. This artefact appears on every sequence with phase encoding in the head-feet direction. Approach . Basic gradient echo sequence and clinical mandatory bSSFP sequence were used. Three setups using manufacturer provided QA phantoms were designed: two including the linearity control grid used for the characterisation and a third including two homogeneity control spheres dedicated to the artefact management in a more clinical like situation. The presence of the artefact was checked in four different MRidian sites. The tested parameters based on the literature were: phase encoding orientation, slab selectivity, excitation bandwidth (BW RF ), acceleration factor (R) and phase/slab oversampling (PO/SO). Main results . The position of this artefact is constant and reproducible over the tested MRIdian sites. The typical singularity saturated dot or star is visible even with the 3D slab-selection enabled. A management is proposed by decreasing the BW RF , the R in head-feet direction and increasing the PO/SO. The oversampling can be optimized using a formula to anticipate the location of artefact in the field of view. Significance . The star-like artefact has been well characterised. A manageable solution comes at the cost of acquisition time. Observed in clinical cases, the artefact may degrade the images used for the RT planning and repositioning during the treatment unless corrected