The self-supervised fitting method based on similar neighborhood information of voxels for intravoxel incoherent motion diffusion-weighted MRI

• Published in: Medical physics (Lancaster) Vol. 52; no. 7; p. e17825
• Main Authors: Luo, Lingfeng, Ye, Chen, Li, Tianxian, Zhong, Ming, Wang, Lihui, Zhu, Yuemin
• Format: Journal Article
• Language: English
• Published: United States 01.07.2025
• Subjects: Algorithms; Brain - diagnostic imaging; Brain Neoplasms - diagnostic imaging; Diffusion Magnetic Resonance Imaging; Humans; Image Processing, Computer-Assisted - methods; Motion; Movement; Neural Networks, Computer; parameter estimation; similar neighborhoods of voxels; cluster analysis; convolutional neural network; intravoxel incoherent motion
• ISSN: 2473-4209; 1522-8541; 0094-2405; 2473-4209
• DOI: 10.1002/mp.17825

The intravoxel incoherent motion (IVIM) parameter estimation is affected by noise, while existing CNN-based fitting methods utilize neighborhood spatial features around voxels to obtain more robust parameters. However, due to the heterogeneity of tissue, neighborhood features with low similarity can lead to excessively smooth parameter maps and even loss of tissue details. To propose a novel neural network fitting approach, IVIM-CNN , which utilizes similar neighborhood information of voxels to assist in the estimation of IVIM parameters in diffusion-weighted imaging (DWI). The proposed fitting model is based on convolutional neural network (CNN), which first identifies the similar neighborhoods of voxels through cluster analysis and then uses CNN to learn the spatial features of similar neighborhoods to reduce the impact of noise on the parameter estimation of the voxel. To evaluate the performance of the proposed method, comparisons were conducted with the least squares (LSQ), Bayesian, PI-DNN, and IVIM-CNN algorithms on both simulated and in vivo brains, including 23 healthy brains and three brain tumors, in terms of root mean square error (RMSE) of IVIM parameters and the parameter contrast ratio between the tumor and normal regions. The CNN-based methods, such as IVIM-CNN and IVIM-CNN , yield smoother parameter maps compared to voxel-based methods like nonlinear least squares, segmented nonlinear least squares, Bayesian, and PI-DNN. Additionally, the IVIM-CNN retains more local tissue details while maintaining smoothness of parameter maps compared to the IVIM-CNN . In simulated experiments, IVIM-CNN outperforms IVIM-CNN in terms of parameter estimation accuracy (SNR = 30; RMSE [ ] = 0.0168 vs. 0.0253; RMSE ( ) = 0.0001 vs. 0.0002; RMSE [ ] = 0.0266 vs. 0.0416). In addition, compared with other methods, the proposed IVIM-CNN is more robust to noise, which is reflected in the lower RMSE of each parameter at different SNRs. For in vivo brains, compared to other methods, IVIM-CNN achieved the highest PCR for most parameters when comparing the normal and tumor regions. The IVIM-CNN method uses similar neighborhood information to assist IVIM parameter fitting by reducing the impact of noise on voxel parameter estimation, thereby improving the accuracy of parameter estimation and increasing the potential for IVIM clinical application.