Meeting Banner
Abstract #4042

Quantitative Evaluation of Deep Learning Reconstruction of Diffusion-weighted MRI using a DWI Phantom

Xiangchuang Kong1, Shengzhen Tao1, Eric H. Middlebrooks1, Thomas Benkert2, Xiangzhi Zhou1, and Chen Lin1
1Department of Radiology, Mayo Clinic, Jacksonville, FL, United States, 2Siemens Medical Solutions USA, Inc., Jacksonville, FL, United States, JACKSONVILLE, FL, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Diffusion/other diffusion imaging techniques, deep learning reconstruction; DWI phantomIn a quantitative phantom study, deep learning (DL) reconstruction is shown to improve the SNR ratio of DWI images while preserving measured ADC values. The average SNR gain is similar to that achieved with better gradient performance between Siemens Prisma and Vida. Such benefits of DL recon should allow better quality and/or shorter scanning of DWI in clinical applications.

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.

Keywords