Comprehensive dose evaluation of a Deep Learning based synthetic Computed Tomography algorithm for pelvic Magnetic Resonance-only radiotherapy

Wyatt, J.; Kaushik, Sandeep; Cozzini, C.; Pearson, R.; Петит, С.; Capala, Marta E.; Hernández-Tamames, Juan Antonio; Hideghéty, Katalin; Maxwell, Ross J.; McCallum, H.

doi:10.1016/j.radonc.2023.109692

Cited by 9 publications

(2 citation statements)

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“…With the development of artificial intelligence, studies have focused on deep learning approaches using multiple architectures (10). These approaches were initially developed from diagnostic (high field) MRI data (11)(12)(13)(14). However, the low-field MR linac, the MRIDian (ViewRay, Inc., Oakwood Village, Ohio, USA), uses a True Fast Imaging with Steady State-Free Precession (TrueFISP) sequence for data acquisition (15).…”

Section: Introductionmentioning

confidence: 99%

A multi-centric evaluation of self-learning GAN based pseudo-CT generation software for low field pelvic magnetic resonance imaging

Prunaretty,

Güngör,

Gevaert

et al. 2023

Front. Oncol.

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Purpose/objectivesAn artificial intelligence-based pseudo-CT from low-field MR images is proposed and clinically evaluated to unlock the full potential of MRI-guided adaptive radiotherapy for pelvic cancer care.Materials and methodIn collaboration with TheraPanacea (TheraPanacea, Paris, France) a pseudo-CT AI-model was generated using end-to-end ensembled self-supervised GANs endowed with cycle consistency using data from 350 pairs of weakly aligned data of pelvis planning CTs and TrueFisp-(0.35T)MRIs. The image accuracy of the generated pCT were evaluated using a retrospective cohort involving 20 test cases coming from eight different institutions (US: 2, EU: 5, AS: 1) and different CT vendors. Reconstruction performance was assessed using the organs at risk used for treatment. Concerning the dosimetric evaluation, twenty-nine prostate cancer patients treated on the low field MR-Linac (ViewRay) at Montpellier Cancer Institute were selected. Planning CTs were non-rigidly registered to the MRIs for each patient. Treatment plans were optimized on the planning CT with a clinical TPS fulfilling all clinical criteria and recalculated on the warped CT (wCT) and the pCT. Three different algorithms were used: AAA, AcurosXB and MonteCarlo. Dose distributions were compared using the global gamma passing rates and dose metrics.ResultsThe observed average scaled (between maximum and minimum HU values of the CT) difference between the pCT and the planning CT was 33.20 with significant discrepancies across organs. Femoral heads were the most reliably reconstructed (4.51 and 4.77) while anal canal and rectum were the less precise ones (63.08 and 53.13). Mean gamma passing rates for 1%1mm, 2%/2mm, and 3%/3mm tolerance criteria and 10% threshold were greater than 96%, 99% and 99%, respectively, regardless the algorithm used. Dose metrics analysis showed a good agreement between the pCT and the wCT. The mean relative difference were within 1% for the target volumes (CTV and PTV) and 2% for the OARs.ConclusionThis study demonstrated the feasibility of generating clinically acceptable an artificial intelligence-based pseudo CT for low field MR in pelvis with consistent image accuracy and dosimetric results.

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Section: Introductionmentioning

confidence: 99%

A multi-centric evaluation of self-learning GAN based pseudo-CT generation software for low field pelvic magnetic resonance imaging

Prunaretty,

Güngör,

Gevaert

et al. 2023

Front. Oncol.

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“…Linked to this, the time it takes for these algorithms to perform the corrections also becomes an important part of the evaluation. Besides image quality and geometrical correctness, a dose/volume-based evaluation by (re)calculation of treatment plans on synthetic CT scans is currently seen as one of the standard methods to quantify the accuracy of a synthetic CT generation [17] , [18] .…”

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confidence: 99%

Current and future developments of synthetic computed tomography generation for radiotherapy

van Elmpt,

Trier Taasti,

Redalen

2023

Physics and Imaging in Radiation Oncology

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A study of Bayesian deep network uncertainty and its application to synthetic CT generation for MR‐only radiotherapy treatment planning

Law,

Tse,

et al. 2023

Medical Physics

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BackgroundThe use of synthetic computed tomography (CT) for radiotherapy treatment planning has received considerable attention because of the absence of ionizing radiation and close spatial correspondence to source magnetic resonance (MR) images, which have excellent tissue contrast. However, in an MR‐only environment, little effort has been made to examine the quality of synthetic CT images without using the original CT images.PurposeTo estimate synthetic CT quality without referring to original CT images, this study established the relationship between synthetic CT uncertainty and Bayesian uncertainty, and proposed a new Bayesian deep network for generating synthetic CT images and estimating synthetic CT uncertainty for MR‐only radiotherapy treatment planning.Methods and MaterialsA novel deep Bayesian network was formulated using probabilistic network weights. Two mathematical expressions were proposed to quantify the Bayesian uncertainty of the network and synthetic CT uncertainty, which was closely related to the mean absolute error (MAE) in Hounsfield Unit (HU) of synthetic CT. These uncertainties were examined to demonstrate the accuracy of representing the synthetic CT uncertainty using a Bayesian counterpart. We developed a hybrid Bayesian architecture and a new data normalization scheme, enabling the Bayesian network to generate both accurate synthetic CT and reliable uncertainty information when probabilistic weights were applied. The proposed method was evaluated in 59 patients (13/12/32/2 for training/validation/testing/uncertainty visualization) diagnosed with prostate cancer, who underwent same‐day pelvic CT‐ and MR‐acquisitions. To assess the relationship between Bayesian and synthetic CT uncertainties, linear and non‐linear correlation coefficients were calculated on per‐voxel, per‐tissue, and per‐patient bases. For accessing the accuracy of the CT number and dosimetric accuracy, the proposed method was compared with a commercially available atlas‐based method (MRCAT) and a U‐Net conditional‐generative adversarial network (UcGAN).ResultsThe proposed model exhibited 44.33 MAE, outperforming UcGAN 52.51 and MRCAT 54.87. The gamma rate (2%/2 mm dose difference/distance to agreement) of the proposed model was 98.68%, comparable to that of UcGAN (98.60%) and MRCAT (98.56%). The per‐patient and per‐tissue linear correlation coefficients between the Bayesian and synthetic CT uncertainties ranged from 0.53 to 0.83, implying a moderate to strong linear correlation. Per‐voxel correlation coefficients varied from −0.13 to 0.67 depending on the regions‐of‐interest evaluated, indicating tissue‐dependent correlation. The R2 value for estimating MAE solely using Bayesian uncertainty was 0.98, suggesting that the uncertainty of the proposed model was an ideal candidate for predicting synthetic CT error, without referring to the original CT.ConclusionThis study established a relationship between the Bayesian model uncertainty and synthetic CT uncertainty. A novel Bayesian deep network was proposed to generate a synthetic CT and estimate its uncertainty. Various metrics were used to thoroughly examine the relationship between the uncertainties of the proposed Bayesian model and the generated synthetic CT. Compared with existing approaches, the proposed model showed comparable CT number and dosimetric accuracies. The experiments showed that the proposed Bayesian model was capable of producing accurate synthetic CT, and was an effective indicator of the uncertainty and error associated with synthetic CT in MR‐only workflows.

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Comprehensive dose evaluation of a Deep Learning based synthetic Computed Tomography algorithm for pelvic Magnetic Resonance-only radiotherapy

Cited by 9 publications

References 30 publications

A multi-centric evaluation of self-learning GAN based pseudo-CT generation software for low field pelvic magnetic resonance imaging

A multi-centric evaluation of self-learning GAN based pseudo-CT generation software for low field pelvic magnetic resonance imaging

Current and future developments of synthetic computed tomography generation for radiotherapy

A study of Bayesian deep network uncertainty and its application to synthetic CT generation for MR‐only radiotherapy treatment planning

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