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

Deep learning methods (DLMs) have recently been proposed to generate pseudo-CT (pCT) for 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).

Thirty-nine patients received a VMAT for prostate cancer (78 Gy). T2-weighted MRIs were acquired in addition to planning CTs. The pCTs were generated from the MRIs using seven configurations: four GANs (L2, single-scale PL, multiscale PL, weighted multiscale PL), two U-Net (L2 and single-scale PL), and the PBM. The imaging endpoints were mean absolute error (MAE) and mean error (ME), in Hounsfield units (HU), between the reference CT (CTref) and the pCT. Dose uncertainties were quantified as mean absolute differences between the DVHs calculated from the CTref and pCT obtained by each method. 3D gamma indexes were analyzed.

Considering the image uncertainties in the whole pelvis, GAN L2 and U-Net L2 showed the lowest MAE (≤34.4 HU). The ME were not different than 0 (p≤0.05). The PBM provided the highest uncertainties. Very few DVH points differed when comparing GAN L2 or U-Net L2 DVHs and CTref DVHs (p≤0.05). Their dose uncertainties were: ≤0.6% for the prostate PTV V95%, ≤0.5% for the rectum V70Gy, and ≤0.1% for the bladder V50Gy. The PBM, U-Net PL and GAN PL presented the highest systematic dose uncertainties. The gamma passrates were >99% for all DLMs. The mean calculation time to generate one pCT was 15 s for the DLMs and 62 min for the PBM.

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.

International journal of radiation oncology, biology, physics. 2019 Sep 07 [Epub ahead of print]

Axel Largent, Anaïs Barateau, Jean-Claude Nunes, Eugenia Mylona, Joël Castelli, Caroline Lafond, Peter B Greer, Jason A Dowling, John Baxter, Hervé Saint-Jalmes, Oscar Acosta, Renaud de Crevoisier

Univ Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, F-35000 Rennes, France., School of Mathematical and Physical Sciences University of Newcastle/Newcastle/Australia; Department of Radiation Oncology, Calvary Mater, Newcastle, Australia., CSIRO Australian e-Health Research Centre, Herston/Queensland/Australia.