Meeting Banner
Abstract #3760

Deep learning imaging-based reconstruction improved the image quality of synthetic high b-value DWI for prostate lesion detecting

Li Fan1, Xiuxiu Zhou1, Hanxiao Zhang1, Jiankun Dai2, Jie Shi2, Song Jiang1, Lingling Gu1, and Pei Zhang1
1Second Affiliated Hospital of Naval Medical University, Shanghai, China, 2MR Research, GE Healthcare, Beijing, China

Synopsis

Keywords: Synthetic MR, Prostate, Deep learning reconstruction, diffusion weighted imaging, cancer

Motivation: Synthetic-DWI (SyDWI) at high-b-value, derived from low-b-value DWI, might be beneficial for prostate cancer evaluation due to better conspicuity of lesions. Relative to conventional reconstruction (ConR), a vendor-provided deep learning reconstruction (DLR) has been reported for improving imaging quality in aspects of higher SNR and imaging sharpness.

Goal(s): Investigate the impact of DLR on the image quality of SyDWI for prostate lesion detection.

Approach: Low-b-value DWI was reconstructed with DLR and ConR, separately. SyDWIs were generated from DWI_DLR and DWI_ConR. The image quality and lesion conspicuity were compared.

Results: SyDWI generated from DWI_DLR showed improved image quality and enhanced prostate lesion detection.

Impact: The enhancement of prostate lesion detection would be beneficial for clinical examination.

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