MR-Only Brain Radiation Therapy: Dosimetric Evaluation of Synthetic CTs Generated by a Dilated Convolutional Neural Network

Dinkla, Anna M.; Wolterink, Jelmer M.; Maspero, Matteo; Savenije, M.H.F.; Verhoeff, J.; Seravalli, Enrica; Išgum, Ivana; Seevinck, Peter R.; Berg, Cornelis A.T. van den

doi:10.1016/j.ijrobp.2018.05.058

Cited by 111 publications

(146 citation statements)

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“…The sCTs obtained in this study had a MAE of 75 ± 9 HU and a ME of 9 ± 11 HU, which was comparable to what is found in literature . In our previous work, we obtained a MAE of 67 ± 11 HU and ME of 13 ± 9 HU, despite using a different network architecture and using head images for brain radiotherapy only .…”

Section: Discussionsupporting

confidence: 87%

“…Heterogeneity correction is required for accurate dose calculations, since bulk assignment of the whole body to water density can lead to large dosimetric errors for the head and neck region . To overcome the lack of electron densities on MRI, a so‐called pseudo‐CT or synthetic CT needs to be generated . Several methods have been proposed in literature to produce a synthetic computed tomography (sCT).…”

Section: Introductionmentioning

confidence: 99%

“…A relatively new approach in sCT generation is to train a convolutional neural network (CNN) to convert the MR images to CT . A CNN is a fully trainable model that can learn the mapping between input MR and output CT images.…”

Section: Introductionmentioning

confidence: 99%

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Dosimetric evaluation of synthetic CT for head and neck radiotherapy generated by a patch‐based three‐dimensional convolutional neural network

et al. 2019

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Purpose To develop and evaluate a patch‐based convolutional neural network (CNN) to generate synthetic computed tomography (sCT) images for magnetic resonance (MR)‐only workflow for radiotherapy of head and neck tumors. A patch‐based deep learning method was chosen to improve robustness to abnormal anatomies caused by large tumors, surgical excisions, or dental artifacts. In this study, we evaluate whether the generated sCT images enable accurate MR‐based dose calculations in the head and neck region. Methods We conducted a retrospective study on 34 patients with head and neck cancer who underwent both CT and MR imaging for radiotherapy treatment planning. To generate the sCTs, a large field‐of‐view T2‐weighted Turbo Spin Echo MR sequence was used from the clinical protocol for multiple types of head and neck tumors. To align images as well as possible on a voxel‐wise level, CT scans were nonrigidly registered to the MR (CTreg). The CNN was based on a U‐net architecture and consisted of 14 layers with 3 × 3 × 3 filters. Patches of 48 × 48 × 48 were randomly extracted and fed into the training. sCTs were created for all patients using threefold cross validation. For each patient, the clinical CT‐based treatment plan was recalculated on sCT using Monaco TPS (Elekta). We evaluated mean absolute error (MAE) and mean error (ME) within the body contours and dice scores in air and bone mask. Also, dose differences and gamma pass rates between CT‐ and sCT‐based plans inside the body contours were calculated. Results sCT generation took 4 min per patient. The MAE over the patient population of the sCT within the intersection of body contours was 75 ± 9 Hounsfield Units (HU) (±1 SD), and the ME was 9 ± 11 HU. Dice scores of the air and bone masks (CTreg vs sCT) were 0.79 ± 0.08 and 0.70 ± 0.07, respectively. Dosimetric analysis showed mean deviations of −0.03% ± 0.05% for dose within the body contours and −0.07% ± 0.22% inside the >90% dose volume. Dental artifacts obscuring the CT could be circumvented in the sCT by the CNN‐based approach in combination with Turbo Spin Echo (TSE) magnetic resonance imaging (MRI) sequence that typically is less prone to susceptibility artifacts. Conclusions The presented CNN generated sCTs from conventional MR images without adding scan time to the acquisition. Dosimetric evaluation suggests that dose calculations performed on the sCTs are accurate, and can therefore be used for MR‐only radiotherapy treatment planning of the head and neck.

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Section: Discussionsupporting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

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Dosimetric evaluation of synthetic CT for head and neck radiotherapy generated by a patch‐based three‐dimensional convolutional neural network

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“…The dilation rate does not affect the number of parameters in a kernel, but it does lower the number of layers. Hence, a large amount of context can be taken into account with a low number of trainable parameters, and hence a reduced chance of overfitting …”

Section: Methodsmentioning

confidence: 99%

Knowledge‐based and deep learning‐based automated chest wall segmentation in magnetic resonance images of extremely dense breasts

et al. 2019

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Purpose Segmentation of the chest wall, is an important component of methods for automated analysis of breast magnetic resonance imaging (MRI). Methods reported to date show promising results but have difficulties delineating the muscle border correctly in breasts with a large proportion of fibroglandular tissue (i.e., dense breasts). Knowledge‐based methods (KBMs) as well as methods based on deep learning have been proposed, but a systematic comparison of these approaches within one cohort of images is currently lacking. Therefore, we developed a KBM and a deep learning method for segmentation of the chest wall in MRI of dense breasts and compared their performances. Methods Two automated methods were developed, an optimized KBM incorporating heuristics aimed at shape, location, and gradient features, and a deep learning‐based method (DLM) using a dilated convolution neural network. A data set of 115 T1‐weighted MR images was randomly selected from MR images of women with extremely dense breasts (ACR BI‐RADS category 4) participating in a screening trial of women (mean age 56.6 yr, range 49.5–75.2 yr) with dense breasts. Manual segmentations of the chest wall, acquired under supervision of an experienced breast radiologist, were available for all data sets. Both methods were optimized using the same randomly selected 36 MRI data sets from a total of 115 data sets. Each MR data set consisted of 179 transversal images with voxel size 0.64 mm3 × 0.64 mm3 × 1.00 mm3. In the remaining 79 data sets, the results of both segmentation methods were qualitatively evaluated. A radiologist reviewed the segmentation results of both methods in all transversal images (n = 14 141) and determined whether the result would impact the ability to accurately determine the volume of fibroglandular and fatty tissue and whether segmentations masked breast regions that might harbor lesions. When no relevant deviation was detected, the result was considered successful. In addition, all segmentations were quantitatively assessed using the Dice similarity coefficient (DSC) and Hausdorff distance (HD), 95th percentile of the Hausdorff distance (HD95), false positive fraction (FPF), and false negative fraction (FNF) metrics. Results According to the radiologist's evaluation, the DLM had a significantly higher success rate than the KBM (81.6% vs 78.4%, P < 0.01). The success rate was further improved to 92.1% by combining both methods. Similarly, the DLM had significantly lower values for FNF (0.003 ± 0.003 vs 0.009 ± 0.011, P < 0.01) and HD95 (2.58 ± 1.78 mm vs 3.37 ± 2.11, P < 0.01). However, the KBM resulted in a significantly lower FPF than the DLM (0.018 ± 0.009 vs 0.030 ± 0.009, P < 0.01).There was no significant difference between the KBM and DLM in terms of DSC (0.982 ± 0.006 vs 0.984 ± 0.008, P = 0.08) or HD (24.14 ± 20.69 mm vs 12.81 ± 27.28 mm, P = 0.05). Conclusion Both optimized knowledge‐based and DLM showed good results to segment the pectoral muscle in women with dense breasts. Qualitatively assessed, the DLM was the most robust...

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“…Previous studies have shown promising results in terms of synthetic CT quality, and demonstrated the potential of using MRI alone in the workflow for head and pelvis radiation therapy. Despite encouraging results in head and pelvis, few studies investigated the potential of using MRI alone in the abdomen.…”

Section: Introductionmentioning

confidence: 99%

A technique to generate synthetic CT from MRI for abdominal radiotherapy

Hsu

DuPre

Peng

2020

J Applied Clin Med Phys

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Purpose To investigate a method to classify tissues types for synthetic CT generation using MRI for treatment planning in abdominal radiotherapy. Methods An institutional review board approved volunteer study was performed on a 3T MRI scanner. In‐phase, fat and water images were acquired for five volunteers with breath‐hold using an mDixon pulse sequence. A method to classify different tissue types for synthetic CT generation in the abdomen was developed. Three tissue clusters (fat, high‐density tissue, and spine/air/lungs) were generated using a fuzzy‐c means clustering algorithm. The third cluster was further segmented into three sub‐clusters that represented spine, air, and lungs. Therefore, five segments were automatically generated. To evaluate segmentation accuracy using the method, the five segments were manually contoured on MRI images as the ground truth, and the volume ratio, Dice coefficient, and Hausdorff distance metric were calculated. The dosimetric effect of segmentation accuracy was evaluated on simulated targets close to air, lungs, and spine using a two‐arc volumetric modulated arc therapy (VMAT) technique. Results The volume ratio of auto‐segmentation to manual segmentation was 0.88–2.1 for the air segment and 0.72–1.13 for the remaining segments. The range of the Dice coefficient was 0.24–0.83, 0.84–0.93, 0.94–0.98, 0.93–0.96, and 0.76–0.79 for air, fat, lungs, high‐density tissue, and spine, respectively. The range of the mean Hausdorff distance was 3–29.1 mm, 0.5–1.3 mm, 0.4–1 mm, 0.7–1.6 mm, and 1.2–1.4 mm for air, fat, lungs, high‐density tissue, and spine, respectively. Despite worse segmentation accuracy in air and spine, the dosimetric effect was 0.2% ± 0.2%, with a maximum difference of 0.8% for all target locations. Conclusion A method to generate synthetic CT in the abdomen was developed, and segmentation accuracy and its dosimetric effect were evaluated. Our results demonstrate the potential of using MRI alone for treatment planning in the abdomen.

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MR-Only Brain Radiation Therapy: Dosimetric Evaluation of Synthetic CTs Generated by a Dilated Convolutional Neural Network

Cited by 111 publications

References 40 publications

Dosimetric evaluation of synthetic CT for head and neck radiotherapy generated by a patch‐based three‐dimensional convolutional neural network

Dosimetric evaluation of synthetic CT for head and neck radiotherapy generated by a patch‐based three‐dimensional convolutional neural network

Knowledge‐based and deep learning‐based automated chest wall segmentation in magnetic resonance images of extremely dense breasts

A technique to generate synthetic CT from MRI for abdominal radiotherapy

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