MRI-only treatment planning: benefits and challenges

Owrangi, Amir; Greer, Peter B.; Glide-Hurst, Carri K

doi:10.1088/1361-6560/aaaca4

Cited by 187 publications

(191 citation statements)

References 150 publications

(246 reference statements)

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“…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%

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: Introductionmentioning

confidence: 99%

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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“…In this regard, magnetic resonance imaging (MRI), as a structural imaging modality, offers improved soft‐tissue contrast and organ visualization compared to CT images. Beside excellent soft‐tissue contrast, advantages of MRI‐guided radiation therapy (RT) planning include no exposure to ionizing radiation and cost reduction as no planning CT needs to be acquired, multi‐parametric imaging possibilities offered by MRI, and elimination of uncertainties raised by imperfect coregistration . The fact that MRI does not directly provide electron density information owing to different physical principles, challenges the calculation of dose distribution based on MRI and is, aside from engineering aspects, one of the major limitations of combining an MR scanner with a linear accelerator …”

Section: Introductionmentioning

confidence: 99%

Comparative study of algorithms for synthetic CT generation from MRI: Consequences for MRI‐guided radiation planning in the pelvic region

et al. 2018

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Overall, machine learning and advanced atlas-based methods exhibited promising performance by achieving reliable organ segmentation and synthetic CT generation. DCNN appears to have slightly better performance by achieving accurate automated organ segmentation and relatively small dosimetric errors (followed closely by advanced atlas-based methods, which in some cases achieved similar performance). However, the DCNN approach showed higher vulnerability to anatomical variation, where a greater number of outliers was observed with this method. Considering the dosimetric results obtained from the evaluated methods, the challenge of electron density estimation from MR images can be resolved with a clinically tolerable error.

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Section: Introductionmentioning

confidence: 99%

“…To streamline the workflow by implementing MRI alone in the planning process, several methods have been proposed for generating synthetic CT from MRI in various body sites. These methods have been reviewed comprehensively by Edmund and Nyholm, 1 Johnstone et al, 2 and Owrangi et al 3 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 [4][5][6][7][8][9][10] and pelvis [11][12][13][14][15] radiation therapy.…”

Section: Introductionmentioning

confidence: 99%

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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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MRI-only treatment planning: benefits and challenges

Cited by 187 publications

References 150 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

Comparative study of algorithms for synthetic CT generation from MRI: Consequences for MRI‐guided radiation planning in the pelvic region

A technique to generate synthetic CT from MRI for abdominal radiotherapy

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