Deep Learning for the Acceleration of Magnetic Resonance Fingerprinting
Creators: name: Fang, Zhenghan
Publisher: name: The University of North Carolina at Chapel Hill University Libraries
Year: 2019

description: Magnetic resonance fingerprinting (MRF) is a quantitative imaging technique that can simultaneously measure multiple important tissue properties of the human body. Although MRF has demonstrated improved scan efficiency compared to conventional techniques, further acceleration is still desired for translation into routine clinical practice. The purpose of this work is to accelerate MRF acquisition by developing a new tissue quantification method for MRF that allows accurate quantification with less sampling data. Most existing approaches use the MRF signal evolution at each individual pixel to estimate tissue properties without considering the spatial association among neighboring pixels. In this report, I propose a spatially-constrained quantification method that uses signals at multiple neighboring pixels to better estimate tissue properties at the central pixel. Specifically, I have designed a unique two-step deep learning model to estimate the tissue property (T1 or T2) maps from the observed MRF signals, which is comprised of 1) a feature extraction module to reduce the dimension of signals by extracting a low-dimensional feature vector from the high-dimensional signal evolution and 2) a spatially-constrained quantification module to exploit the spatial information from the extracted feature maps to generate the final tissue property map. A corresponding two-step training strategy has been developed for network training. The proposed method was tested on highly undersampled MRF data acquired from human brains. The experimental results demonstrated that the proposed method can achieve accurate quantification for T1 and T2 relaxation times using only 1/4 time points of the original sequence (i.e., four times of acceleration for MRF acquisition).      Furthermore, a rapid 2D MRF technique with a sub-millimeter in-plane resolution was developed using deep-learning-based quantification approach for brain T1 and T2 quantification. Specifically, the 2D acquisition was performed using a FISP sequence and a spiral trajectory with 0.8 mm in-plane resolution. A novel network architecture, i.e., residual channel attention U-Net, was proposed to improve high resolution details in the estimated tissue maps. Quantitative brain imaging was performed with five adults and two pediatric subjects and the performance of the proposed approach was compared to several existing methods in the literature. In vivo measurements with both adult and pediatric subjects show that high quality T1 and T2 mapping with 0.8 mm in-plane resolution was achieved in 7.5 sec per slice. The proposed deep learning method outperformed existing algorithms in tissue quantification with improved accuracy. Compared to the standard U-Net, high resolution details in brain tissues were better preserved by the proposed residual channel attention U-Net. The experiments on pediatric subjects further demonstrated the potential of the proposed technique for fast pediatric neuroimaging. Alongside the reduced data acquisition time, five-fold acceleration in tissue property mapping was also achieved with the proposed method.