Several approaches have been proposed to generate pseudo computed tomography (pCT) from MR images for radiotherapy dose calculation. Quantification of errors in pCT has been reported using global scores disregarding spatial heterogeneity. The aim of this work was to propose a population voxel-based workflow allowing the local assessment of errors in the generation of pCTs from MRI. For the voxel-wise analysis to be anatomically meaningful, a robust customized inter-patient non-rigid registration method brought the population images to the same coordinate system. To illustrate the use of this methodology, four pCT generation methods were compared: atlas-based, patch-based, and two deep learning methods. Considering global and local scores, deep learning appeared widely superior. Main source of errors were found in the cortical bones. The proposed workflow paves the way for quality control procedures within the clinical workflow.
MR-only radiation treatment planning is attractive due to the superior soft tissue definition of MRI as compared to CT, and the elimination of the uncertainty introduced by CT-MRI registration. To facilitate MR-only radiation therapy planning, synthetic-CT (sCT) algorithms (for electron density correction) are required for dose calculation. Deep neural networks for sCT generation are useful due to their predictive power, but lack of uncertainty information is a concern for clinical implementatio...
#1Xin Tie(PolyU: Hong Kong Polytechnic University)H-Index: 1
#2Sai-kit Lam(PolyU: Hong Kong Polytechnic University)H-Index: 3
Last. Jing Cai(PolyU: Hong Kong Polytechnic University)H-Index: 26
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PURPOSE: To develop and evaluate a novel method for pseudo-CT generation from multi-parametric MR images using multi-channel multi-path generative adversarial network (MCMP-GAN). METHODS: Pre- and post-contrast T1-weighted (T1-w), T2-weighted (T2-w) MRI, and treatment planning CT images of 32 nasopharyngeal carcinoma (NPC) patients were employed to train a pixel-to-pixel MCMP-GAN. The network was developed based on a 5-level Residual U-Net (ResU-Net) with the channel-based independent feature ex...
#2Serena Monti(National Research Council)H-Index: 14
Last. Laura Cella(National Research Council)H-Index: 25
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Abstract Recently, 2D or 3D methods for dose distribution analysis have been proposed as evolutions of the Dose Volume Histogram (DVH) approaches. Those methods, collectively referred to as pixel- or voxel-based (VB) methods, evaluate local dose response patterns and go beyond the organ-based philosophy of Normal Tissue Complication Probability (NTCP) modelling. VB methods have been introduced in the context of radiation oncology in the very last years following the virtuous example of neuroimag...
#1A. Largent(French Institute of Health and Medical Research)H-Index: 5
#2A. Barateau(French Institute of Health and Medical Research)H-Index: 5
Last. Renaud de Crevoisier(French Institute of Health and Medical Research)H-Index: 28
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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)....
#1Jie Fu(UCLA: University of California, Los Angeles)H-Index: 5
#2Yingli Yang(UCLA: University of California, Los Angeles)H-Index: 16
Last. John H. Lewis(UCLA: University of California, Los Angeles)H-Index: 4
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PURPOSE The improved soft tissue contrast of magnetic resonance imaging (MRI) compared to computed tomography (CT) makes it a useful imaging modality for radiotherapy treatment planning. Even when MR images are acquired for treatment planning, the standard clinical practice currently also requires a CT for dose calculation and x-ray-based patient positioning. This increases workloads, introduces uncertainty due to the required inter-modality image registrations, and involves unnecessary irradiat...
#1Eugenia Mylona(French Institute of Health and Medical Research)H-Index: 4
#2Oscar Acosta(French Institute of Health and Medical Research)H-Index: 21
Last. Renaud de Crevoisier(French Institute of Health and Medical Research)H-Index: 28
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Purpose To apply a voxel-based analysis to identify urethrovesical symptom-related subregions (SRSs) associated with acute and late urinary toxicity in prostate cancer radiation therapy. Methods and Materials Two hundred seventy-two patients with prostate cancer treated with intensity-modulated radiation therapy/image-guided radiation therapy were analyzed prospectively. Each patient's computed tomography imaging was spatially normalized to a common coordinate system via nonrigid registration. T...
#1A. Largent(French Institute of Health and Medical Research)H-Index: 5
#2A. Barateau(French Institute of Health and Medical Research)H-Index: 5
Last. Renaud de Crevoisier(French Institute of Health and Medical Research)H-Index: 28
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Purpose Methods have been recently developed to generate pseudo–computed tomography (pCT) for dose calculation in magnetic resonance imaging (MRI)–only radiation therapy. This study aimed to propose an original nonlocal mean patch-based method (PBM) and to compare this PBM to an atlas-based method (ABM) and to a bulk density method (BDM) for prostate MRI-only radiation therapy. Materials and Methods Thirty-nine patients received a volumetric modulated arc therapy for prostate cancer. In addition...
The cycleGAN is becoming an influential method in medical image synthesis. However, due to a lack of direct constraints between input and synthetic images, the cycleGAN cannot guarantee structural consistency between these two images, and such consistency is of extreme importance in medical imaging. To overcome this, we propose a structure-constrained cycleGAN for brain MR-to-CT synthesis using unpaired data that defines an extra structure-consistency loss based on the modality independent neigh...
Purpose Interests have been rapidly growing in the field of radiotherapy to replace CT with magnetic resonance imaging (MRI), due to superior soft tissue contrast offered by MRI and the desire to reduce unnecessary radiation dose. MR-only radiotherapy also simplifies clinical workflow and avoids uncertainties in aligning MR with CT. Methods, however, are needed to derive CT-equivalent representations, often known as synthetic CT (sCT), from patient MR images for dose calculation and DRR-based pa...