LARO: Learned acquisition and reconstruction optimization to accelerate quantitative susceptibility mapping
Jinwei Zhang,
Pascal Spincemaille,
Hang Zhang,
Thanh D. Nguyen,
Chao Li,
Jiahao Li,
Ilhami Kovanlikaya,
Mert R. Sabuncu,
Yi Wang
Affiliations
Jinwei Zhang
Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA; Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
Pascal Spincemaille
Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
Hang Zhang
Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA; Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA
Thanh D. Nguyen
Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
Chao Li
Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA; Department of Applied Physics, Cornell University, Ithaca, NY, USA
Jiahao Li
Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA; Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
Ilhami Kovanlikaya
Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
Mert R. Sabuncu
Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA; Department of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA
Yi Wang
Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA; Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA; Corresponding author: Radiology, Weill Cornell Medicine, 407 E 61st St, New York, NY 10065, USA.
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-QSM.git.
