Abstract #5052

Optimizing Accelerated Protocols for Clinical T1w Vessel Wall Imaging: A Comparison of Deep Learning, Compressed Sensing, and CAIPI Methods

Shraddha Pandey¹, Manuel Taso², Rob Sellers², Zhaoyang Fan³, Konstanze Guggenberger⁴, Shawn Lyo¹, Colbey Freeman¹, Chengcheng Zhu⁵, Jae Song¹, and M. Dylan Tisdall¹

¹Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, United States, ²Siemens Medical Solutions USA Inc, Malvern, PA, United States, ³Department of Radiology, Keck School of Medicine of USC, Los Angeles, CA, United States, ⁴Department of Radiology, University Hospital of Würzburg, Würzburg, Germany, ⁵Department of Radiology, University of Washington, Medical Center, Seattle, WA, United States

Synopsis

Keywords: Stroke, Acquisition Methods, Vessel Wall Imaging, Deep Learning, Compressed Sensing, Protocol Optimization

Motivation: Clinical intracranial VWI requires high spatial resolution and effective blood/CSF suppression, leading to long scan times, motion artifacts, and increased patient burden.

Goal(s): To evaluate optimized protocols for intracranial VWI using accelerated 3D T1w SPACE sequences.

Approach: We evaluated DL, CS, and CAIPI acceleration on 20- and 64-channel coils to reduce scan times, comparing them to the clinical GRAPPA protocol.

Results: The 64-channel CS protocol with 6x acceleration matched clinical image quality and SNR with reduced scan time. CS and DL sequences had similar image quality and SNR on the 64-channel coil, with DL showing better noise suppression on the 20-channel coil.

Impact: This study optimizes intracranial VWI using 3D T1w SPACE with DL, CS, and CAIPI for 20- and 64-channel coils, achieving reduced scan times. The 64-channel CS (6x) matched clinical image quality, while DL provided superior noise suppression on 20-channel coils.

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