LARO: Learned Acquisition and Reconstruction Optimization to accelerate Quantitative Susceptibility Mapping

• URL: http://arxiv.org/abs/2211.00725v1
• Date: Tue, 1 Nov 2022 20:04:29 GMT
• Title: LARO: Learned Acquisition and Reconstruction Optimization to accelerate Quantitative Susceptibility Mapping
• Authors: Jinwei Zhang, Pascal Spincemaille, Hang Zhang, Thanh D. Nguyen, Chao Li, Jiahao Li, Ilhami Kovanlikaya, Mert R. Sabuncu, Yi Wang
• Abstract summary: Learned Acquisition and Reconstruction Optimization (LARO) aims to accelerate the multi-echo gradient echo (mGRE) pulse sequence for Quantitative susceptibility mapping (QSM) Our approach involves optimizing a Cartesian multi-echo k-space sampling pattern with a deep reconstruction network. Our ablation studies show that both the optimized sampling pattern and proposed reconstruction strategy help improve the quality of the multi-echo image reconstructions.
• Score: 24.665782241561185
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantitative susceptibility mapping (QSM) involves acquisition and reconstruction of a series of images at multi-echo time points to estimate tissue field, which prolongs scan time and requires specific reconstruction technique. In this paper, we present our new framework, called Learned Acquisition and Reconstruction Optimization (LARO), which aims to accelerate the multi-echo gradient echo (mGRE) pulse sequence for QSM. Our approach involves optimizing a Cartesian multi-echo k-space sampling pattern with a deep reconstruction network. Next, this optimized sampling pattern was implemented in an mGRE sequence using Cartesian fan-beam k-space segmenting and ordering for prospective scans. Furthermore, we propose to insert a recurrent temporal feature fusion module into the reconstruction network to capture signal redundancies along echo time. Our ablation studies show that both the optimized sampling pattern and proposed reconstruction strategy help improve the quality of the multi-echo image reconstructions. Generalization experiments show that LARO is robust on the test data with new pathologies and different sequence parameters. Our code is available at https://github.com/Jinwei1209/LARO.git.

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