Open AccessNO-QSM: combining convolutional neural network with numerical optimization algorithm for Quantitative Susceptibility Mapping reconstruction
Author(s) -
Qianqian Zhang,
Yihao Guo,
Wufan Chen
Publication year - 2024
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2024.3368518
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
In gradient echo MRI, quantitative susceptibility mapping (QSM) quantifies the magnetic susceptibility distributions of tissues, which has great potential in detecting brain diseases. However, QSM reconstruction is an ill-conditional inversion problem because of the zeros in the frequency domain of the dipole kernel. The intrinsic nature of the ill-posedness would affect the accuracy of quantifying tissue susceptibility. Recently, deep learning-based methods have been proposed to improve accuracy by suppressing the streaking artifacts. In this work, we proposed a hybrid architecture to enforce data consistency by involving numerical optimization blocks within convolutional neural networks (CNN), which aimed to reconstruct high-quality QSM images, referred to as NO-QSM. The Calculation of Susceptibility through Multiple Orientation Sampling (COSMOS) QSM maps were used as labels for training. The performance of the proposed method was evaluated on two healthy volunteers and brain images of patients with diseases. Our experiments showed that the proposed method achieved good performance in terms of quantitative metrics and could effectively suppress artifacts in reconstructed QSM images, demonstrating its potential for future applications. For experiments on patients with multiple sclerosis (MS), the proposed method could better detect lesion regions in the results of NO-QSM.