Abstract #2941

Denoising MRSI Data Using Atlas-Based Statistical Subspaces

Wen Jin^1,2, Yibo Zhao¹, Yudu Li^1,3,4, Rong Guo^1,5, Yuanbo Zhang^1,2, Ziyang Xu^1,2, Jie Luo⁶, Yao Li^6,7, and Zhi-Pei Liang^1,2

¹Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ²Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ³Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ⁴National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, United States, ⁵Siemens Medical Solutions USA, Inc., St Louis, MO, United States, ⁶School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, ⁷Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China

Synopsis

Keywords: Spectroscopy, Spectroscopy

Motivation: The ensemble statistics or population information of metabolic signals has not been effectively utilized for MRSI signal processing due to lack of high-quality training data.

Goal(s): To leverage the large high-quality MRSI datasets we have created for denoising MRSI signals.

Approach: A position-dependent statistical subspaces model was used to create a spectroscopic brain atlas that captured the population statistics of a large MRSI dataset. The statistical atlas was then used to denoise new MRSI data by solving a maximum-a-posterior estimation problem.

Results: The method was validated using both simulated and in vivo data acquired from healthy subjects, demonstrating excellent denoising performance.

Impact: This proposed method significantly improved the sensitivity of brain MRSI using atlas-based statistical subspaces. The method may further enhance the reliability and practical utility of high-resolution MRSI techniques.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.