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Comparison of deep learning-based and patch-based methods for pseudo-CT generation in MRI-based prostate dose planning

Abstract : Purpose - Deep learning methods (DLMs) have recently been proposed to generate pseudo-CT (pCT) for magnetic resonance imaging (MRI) based dose planning. This study aims to evaluate and compare DLMs (U-Net and generative adversarial network [GAN]) using various loss functions (L2, single-scale perceptual loss [PL], multiscale PL, weighted multiscale PL) and a patch-based method (PBM). Methods and materials - Thirty-nine patients received a volumetric modulated arc therapy for prostate cancer (78 Gy). T-weighted MRIs were acquired in addition to planning CTs. The pCTs were generated from the MRIs using 7 configurations: 4 GANs (L2, single-scale PL, multiscale PL, weighted multiscale PL), 2 U-Net (L2 and single-scale PL), and the PBM. The imaging endpoints were mean absolute error and mean error, in Hounsfield units, between the reference CT (CT) and the pCT. Dose uncertainties were quantified as mean absolute differences between the dose volume histograms (DVHs) calculated from the CT and pCT obtained by each method. Three-dimensional gamma indexes were analyzed. Results - Considering the image uncertainties in the whole pelvis, GAN L2 and U-Net L2 showed the lowest mean absolute error (≤34.4 Hounsfield units). The mean errors were not different than 0 (P ≤ .05). The PBM provided the highest uncertainties. Very few DVH points differed when comparing GAN L2 or U-Net L2 DVHs and CT DVHs (P ≤ .05). Their dose uncertainties were ≤0.6% for the prostate planning target Volume V, ≤0.5% for the rectum V, and ≤0.1% for the bladder V. The PBM, U-Net PL, and GAN PL presented the highest systematic dose uncertainties. The gamma pass rates were >99% for all DLMs. The mean calculation time to generate 1 pCT was 15 s for the DLMs and 62 min for the PBM. Conclusions - Generating pCT for MRI dose planning with DLMs and PBM provided low-dose uncertainties. In particular, the GAN L2 and U-Net L2 provided the lowest dose uncertainties together with a low computation time.
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Contributor : Laurent Jonchère <>
Submitted on : Thursday, October 3, 2019 - 11:10:43 AM
Last modification on : Thursday, February 18, 2021 - 11:28:41 AM




Axel Largent, Anais Barateau, Jean-Claude Nunes, Eugenia Mylona, Joel Castelli, et al.. Comparison of deep learning-based and patch-based methods for pseudo-CT generation in MRI-based prostate dose planning. International Journal of Radiation Oncology - Biology - Physics, Elsevier, 2019, 105 (5), pp.1137-1150. ⟨10.1016/j.ijrobp.2019.08.049⟩. ⟨hal-02304378⟩



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