Stochastic optimization of three‐dimensional non‐Cartesian sampling trajectory

Author:

Wang Guanhua1ORCID,Nielsen Jon‐Fredrik12ORCID,Fessler Jeffrey A.12ORCID,Noll Douglas C.1ORCID

Affiliation:

1. Biomedical Engineering University of Michigan Ann Arbor Michigan United States

2. EECS University of Michigan Ann Arbor Michigan United States

Abstract

PurposeOptimizing three‐dimensional (3D) k‐space sampling trajectories is important for efficient MRI yet presents a challenging computational problem. This work proposes a generalized framework for optimizing 3D non‐Cartesian sampling patterns via data‐driven optimization.MethodsWe built a differentiable simulation model to enable gradient‐based methods for sampling trajectory optimization. The algorithm can simultaneously optimize multiple properties of sampling patterns, including image quality, hardware constraints (maximum slew rate and gradient strength), reduced peripheral nerve stimulation (PNS), and parameter‐weighted contrast. The proposed method can either optimize the gradient waveform (spline‐based freeform optimization) or optimize properties of given sampling trajectories (such as the rotation angle of radial trajectories). Notably, the method can optimize sampling trajectories synergistically with either model‐based or learning‐based reconstruction methods. We proposed several strategies to alleviate the severe nonconvexity and huge computation demand posed by the large scale. The corresponding code is available as an open‐source toolbox.ResultsWe applied the optimized trajectory to multiple applications including structural and functional imaging. In the simulation studies, the image quality of a 3D kooshball trajectory was improved from 0.29 to 0.22 (NRMSE) with Stochastic optimization framework for 3D NOn‐Cartesian samPling trajectorY (SNOPY) optimization. In the prospective studies, by optimizing the rotation angles of a stack‐of‐stars (SOS) trajectory, SNOPY reduced the NRMSE of reconstructed images from 1.19 to 0.97 compared to the best empirical method (RSOS‐GR). Optimizing the gradient waveform of a rotational EPI trajectory improved participants' rating of the PNS from “strong” to “mild.”ConclusionSNOPY provides an efficient data‐driven and optimization‐based method to tailor non‐Cartesian sampling trajectories.

Funder

National Institutes of Health

National Science Foundation of Sri Lanka

Publisher

Wiley

Subject

Radiology, Nuclear Medicine and imaging

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Model‐based reconstruction for looping‐star MRI;Magnetic Resonance in Medicine;2024-01-28

2. New clinical opportunities of low-field MRI: heart, lung, body, and musculoskeletal;Magnetic Resonance Materials in Physics, Biology and Medicine;2023-10-30

3. [MRI] 2. Recent Research on MR Image Reconstruction Using Artificial Intelligence;Japanese Journal of Radiological Technology;2023-08-20

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