Affiliations: [a] School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China | [b] High Field Magnetic Resonance Brain Imaging Laboratory of Sichuan, University of Electronic Science and Technology of China, Chengdu, Sichuan, China | [c] Key Laboratory for Neuro Information, Ministry of Education, University of Electronic Science and Technology of China, Chengdu, Sichuan, China | [d] Data Recovery Key Laboratory of Sichuan Province, College of Computer Science and AI, Neijiang Normal University, Neijiang, Sichuan, China
Correspondence:
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Corresponding author: Tao Zhang, School of Life Science and Technology, University of Electronic %****␣thc-30-thc213149_temp.tex␣Line␣125␣**** Science and Technology of China, Chengdu, Sichuan, China. Tel.: +86 18684018908; E-mail: zhang@uestc.edu.cn.
Note: [1] These authors contributed equally to this work.
Abstract: BACKGROUND: Addressing intensity inhomogeneity is critical in magnetic resonance imaging (MRI) because associated errors can adversely affect post-processing and quantitative analysis of images (i.e., segmentation, registration, etc.), as well as the accuracy of clinical diagnosis. Although several prior methods have been proposed to eliminate or correct intensity inhomogeneity, some significant disadvantages have remained, including alteration of tissue contrast, poor reliability and robustness of algorithms, and prolonged acquisition time. OBJECTIVE: In this study, we propose an intensity inhomogeneity correction method based on volume and surface coils simultaneous reception (VSSR). METHODS: The VSSR method comprises of two major steps: 1) simultaneous image acquisition from both volume and surface coils and 2) denoising of volume coil images and polynomial surface fitting of bias field. Extensive in vivo experiments were performed considering various anatomical structures, acquisition sequences, imaging resolutions, and orientations. In terms of correction performance, the proposed VSSR method was comparatively evaluated against several popular methods, including multiplicative intrinsic component optimization and improved nonparametric nonuniform intensity normalization bias correction methods. RESULTS: Experimental results show that VSSR is more robust and reliable and does not require prolonged acquisition time with the volume coil. CONCLUSION: The VSSR may be considered suitable for general implementation.
Keywords: Intensity inhomogeneity correction, volume and surface coil simultaneously received, magnetic resonance imaging
