Highly‐accelerated CEST MRI using frequency‐offset‐dependent k‐space sampling and deep‐learning reconstruction

作者：Chuyu Liu, Zhongsen Li, Zhensen Chen, Benqi Zhao, Zhuozhao Zheng, Xiaolei Song · 发表于：Magnetic Resonance in Medicine · 年份：2024 · DOI：10.1002/mrm.30089 · 被引用次数：15 · 研究领域：Advanced MRI Techniques and Applications、Lanthanide and Transition Metal Complexes、Advanced NMR Techniques and Applications

Abstract Purpose To develop a highly accelerated CEST Z‐spectral acquisition method using a specifically‐designed k‐space sampling pattern and corresponding deep‐learning‐based reconstruction. Methods For k‐space down‐sampling, a customized pattern was proposed for CEST, with the randomized probability following a frequency‐offset‐dependent (FOD) function in the direction of saturation offset. For reconstruction, the convolution network (CNN) was enhanced with a Partially Separable (PS) function to optimize the spatial domain and frequency domain separately. Retrospective experiments on a self‐acquired human brain dataset (13 healthy adults and 15 brain tumor patients) were conducted using k‐space resampling. The prospective performance was also assessed on six healthy subjects. Results In retrospective experiments, the combination of FOD sampling and PS network (FOD + PSN) showed the best quantitative metrics for reconstruction, outperforming three other combinations of conventional sampling with varying density and a regular CNN (nMSE and SSIM, p < 0.001 for healthy subjects). Across all acceleration factors from 4 to 14, the FOD + PSN approach consistently outperformed the comparative methods in four contrast maps including MTR asym , MTR rex , as well as the Lorentzian Difference maps of amide and nuclear Overhauser effect (NOE). In the subspace replacement experiment, the error distribution demonstrated the denoising benefits achieved in the spatial subspace. Finally,...