Abstract #0048

Vessel wall imaging-dedicated deep learning (VWI-DL): Toward a 5-min clinically robust MR protocol

Pengcheng Wang^1,2, Junzhou Chen^1,3, Zhenjia Wang⁴, Nasim Sheikh-Bahaei¹, Steven Yong Cen¹, Qi Yang⁵, William Mack⁶, and Zhaoyang Fan^1,2

¹Department of Radiology, University of Southern California, Los Angeles, CA, United States, ²Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States, ³Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States, ⁴Department of Radiology, Anzhen Hospital, Beijing, China, ⁵Department of Radiology, Chaoyang Hospital, Beijing, China, ⁶Department of Neurological Surgery, University of Southern California, Los Angeles, CA, United States

Synopsis

Keywords: Vascular/Vessel Wall, Vessel Wall, Imaging acceleration, whole-brain vessel wall imaging

Motivation: Long scan times (8–12 minutes) in whole-brain MR vessel wall imaging (VWI) cause patient discomfort and motion artifacts, limiting clinical utilization. Faster VWI with preserved image quality is highly desirable.

Goal(s): To develop a VWI-dedicated deep learning model to substantially accelerate data acquisition without compromising image quality.

Approach: We developed a multi-view deep learning model using SwinIR as the backbone, trained it on 12-min VWI raw data, and used a voting mechanism across views to enhance consistency. Both retrospective and prospective testing were performed.

Results: The model reduced scan time to <6 minutes with PSNR and SSIM improvements of 9.69 and 0.28.

Impact: The VWI-dedicated deep learning model enables faster data acquisition with preserved image quality compared to standard clinical protocols, which may enhance VWI’s robustness and clinical throughput and thus promote its widespread clinical adoption.

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.