Deep learning-based synthetic CT generation for paediatric brain MR-only photon and proton radiotherapy

Maspero, Matteo; Bentvelzen, Laura G.; Savenije, M.H.F.; Guerreiro, Filipa; Seravalli, Enrica; Janssens, Geert O.; Berg, Cornelis A.T. van den; Philippens, M.E.P.

doi:10.1016/j.radonc.2020.09.029

Cited by 76 publications

(125 citation statements)

References 46 publications

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“…This simplification may compromise the dosimetric accuracy as currently robust conversion approaches from MR to CT are lacking. To overcome this, several groups have recently proposed deep learning models for the calculation of synthetic CT data sets based on anatomical MR imaging which has shown to be a timeefficient and robust approach (45)(46)(47)(48). Dosimetric evaluations have shown promising results in terms of dose differences of 0-0.5% (49,50).…”

Section: Mr-only Planningmentioning

confidence: 99%

Technical Challenges of Real-Time Adaptive MR-Guided Radiotherapy

Thorwarth

Low

2021

Front. Oncol.

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In the past few years, radiotherapy (RT) has experienced a major technological innovation with the development of hybrid machines combining magnetic resonance (MR) imaging and linear accelerators. This new technology for MR-guided cancer treatment has the potential to revolutionize the field of adaptive RT due to the opportunity to provide high-resolution, real-time MR imaging before and during treatment application. However, from a technical point of view, several challenges remain which need to be tackled to ensure safe and robust real-time adaptive MR-guided RT delivery. In this manuscript, several technical challenges to MR-guided RT are discussed. Starting with magnetic field strength tradeoffs, the potential and limitations for purely MR-based RT workflows are discussed. Furthermore, the current status of real-time 3D MR imaging and its potential for real-time RT are summarized. Finally, the potential of quantitative MR imaging for future biological RT adaptation is highlighted.

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Section: Mr-only Planningmentioning

confidence: 99%

Technical Challenges of Real-Time Adaptive MR-Guided Radiotherapy

Thorwarth

Low

2021

Front. Oncol.

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“…Most of the deep learning approaches presented in the literature for synthetic CT generation has been focused on the head and neck anatomy 11 , 12 , 13 , 14 , 15 , 16 , 17 and less studies have been conducted on the pelvis region. 8 , 9 , 10 In most of these works, U‐NET was the main deep learning architecture used in the model development step.…”

Section: Introductionmentioning

confidence: 99%

“…However, there are some works using ResNet 17 as the base convolutional neural network (CNN). General adversarial networks (GAN) have been also utilized 10 , 13 , 14 , 15 , 16 , 17 to enhance the performance of the U‐NET architecture. In GAN techniques, two CNN models, named generator and discriminator, are simultaneously trained wherein the generator is the predictive architecture such as U‐NET while discriminator is a simpler network whose role is to distinguish between the synthetic (fake) and planning (real) CT.…”

Section: Introductionmentioning

confidence: 99%

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

Farjam

Nagar

Zhou

et al. 2021

J Applied Clin Med Phys

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“…A major focus and active area of research within MRI-only RT is the development of methods that convert the MRI into a synthetic computed tomography (sCT) needed for dose planning and possible image guidance (IGRT) purposes [5] , [6] . Following the trends in related areas such as computer vision and medical imaging, much attention has recently been given to deep learning convolution neural network techniques [7] , [8] , [9] , [10] and commercial solutions are currently available for clinical use [11] , [12] .…”

Section: Introductionmentioning

confidence: 99%

Cone beam computed tomography based image guidance and quality assessment of prostate cancer for magnetic resonance imaging-only radiotherapy in the pelvis

Edmund

Andreasen

Leemput

2021

Physics and Imaging in Radiation Oncology

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Background and purpose: Radiotherapy (RT) based on magentic resonance imaging (MRI) only is currently used clinically in the pelvis. A synthetic computed tomography (sCT) is needed for dose planning. Here, we investigate the accuracy of cone beam CT (CBCT) based MRI-only image guided RT (IGRT) and sCT image quality. Materials and methods: CT, MRI and CBCT scans of ten prostate cancer patients were included. The MRI was converted to a sCT using a multi-atlas approach. The sCT, CT and MR images were auto-matched with the CBCT on the bony anatomy. Paired sCT-CT and sCT-CBCT data were created. CT numbers were converted to relative electron (RED) and mass densities (DES) using a standard calibration curve for the CT and sCT. For the CBCT RED/DES conversion, a phantom and paired CT-CBCT population based calibration curve was used. For the latter, the CBCT numbers were averaged in 100 HU bins and the known RED/DES of the CT were assigned. The paired sCT-CT and sCT-CBCT data were averaged in bins of 10 HU or 0.01 RED/DES. The median absolute error (MeAE) between the sCT-CT and sCT-CBCT bins was calculated. Wilcoxon rank-sum tests were carried out for the IGRT and MeAE study. Results: The mean sCT or MR IGRT difference from CT was ≤ 2 mm but significant differences were observed. A CBCT HU or phantom-based RED/DES MeAE did not estimate the sCT quality similar to a CT based MeAE but the CBCT population-based RED/DES MeAE did. Conclusions: MRI-only CBCT-based IGRT seems feasible but caution is advised. A MeAE around 0.1 DES could call for sCT quality inspection.

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Deep learning-based synthetic CT generation for paediatric brain MR-only photon and proton radiotherapy

Cited by 76 publications

References 46 publications

Technical Challenges of Real-Time Adaptive MR-Guided Radiotherapy

Technical Challenges of Real-Time Adaptive MR-Guided Radiotherapy

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

Cone beam computed tomography based image guidance and quality assessment of prostate cancer for magnetic resonance imaging-only radiotherapy in the pelvis

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