Abstract #5110

Enabling 1-minute High-resolution Clinical Diffusion-weighted Imaging at 7 Tesla via Improved Non-local-PCA Denoising

Zhe Zhang¹, Xinyu Ye², Xiaodong Ma³, Yuan Li⁴, Decai Tian⁵, Hua Guo⁶, Xiaoping Wu⁷, and Jing Jing^1,5

¹Tiantan Neuroimaging Center of Excellence, Beijing Tiantan Hospital, Capital Medical University, Beijing, China, ²Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom, ³Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, United States, ⁴MR Research Collaboration Team, Siemens Healthineers, Beijing, China, ⁵Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China, ⁶Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China, ⁷Center for Magnetic Resonance Research, Radiology, Medical School, University of Minnesota, Minneapolis, MN, United States

Synopsis

Keywords: Diffusion Acquisition, Diffusion/other diffusion imaging techniques

Motivation: High-resolution diffusion-weighted imaging (DWI) is crucial for diagnosing neurological pathologies, but traditionally requires long scan times due to low SNR, hindering its application in clinical settings.

Goal(s): To evaluate how our new non-local principal-component-analysis (PCA)-based denoising method can help achieve high-resolution DWI at 7 Tesla within a clinically viable timeframe.

Approach: We compared our method to two existing local-PCA-based approaches by collecting whole-brain DWI at 1.2-mm isotropic resolutions from a healthy volunteer and a patient with multiple sclerosis.

Results: Our non-local PCA method provided improved denoising performances, producing quality DWI where the lesion was identifiable even with 1-minute acquisition.

Impact: Demonstrated capable of enabling high-resolution DWI under 1-minute scan at 7 Tesla, our non-local PCA method is believed to promote the utility of DWI in clinical settings while having the potential to improve many other neuroimaging applications.

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