1. Synthetic‐to‐real domain adaptation with deep learning for fitting the intravoxel incoherent motion model of diffusion‐weighted imaging
- Author
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Haoyuan Huang, Baoer Liu, Yikai Xu, and Wu Zhou
- Subjects
General Medicine - Abstract
Intravoxel incoherent motion (IVIM) is a type of diffusion-weighted imaging (DWI), and IVIM model parameters (water molecule diffusion rate Dt, pseudo diffusion coefficient Dp, and tissue perfusion fraction Fp) have been widely used in the diagnosis and characterization of malignant lesions.This study proposes a deep-learning model with synthetic-to-real domain adaptation to fit the IVIM model parameters of DWI.Ninety-eight consecutive patients diagnosed with hepatocellular carcinoma between January 2017 and September 2020 were included in the study, and routine IVIM-DWI serial examinations were performed using a 3.0 T magnetic resonance imaging system in preoperative MR imaging. The proposed method is mainly composed of two modules: a convolutional neural network-based IVIM model fitting network to map b-value images to the IVIM parameter maps and a domain discriminator to improve the accuracy of the IVIM parameter maps in the real data. The proposed method was compared with previously reported fitting methods, including the nonlinear least squares (NLS), IVIM-NETThe DWI reconstruction performance demonstrates that the proposed method has better reconstruction accuracy for DWI with a low signal-to-noise ratio, which implies that the proposed method improves the fitting accuracy of the IVIM parameters. Noise-corrupt experiments show that the proposed method is more robust against noise-corrupted signals. With the proposed method, no outliers were found in Dt, and outliers were reduced for Fp in the abnormal regions (proposed method: 1.85%; NLS: 5.90%; IVIM-NETIVIM parameters can be estimated using a synthetic-to-real domain-adaptation framework with deep learning, and the proposed method outperforms previously reported methods. This article is protected by copyright. All rights reserved.
- Published
- 2023