Abstract #4938

CAMWARE: Cascaded Multi-level Wavelet Refine Neural Network for Accelerated Whole-brain MR Vessel Wall Imaging

Zhehao Hu^1,2, Alexander Lerner¹, Roy Poblete³, and Zhaoyang Fan^1,4,5

¹Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States, ²Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States, ³Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States, ⁴Department of Radiation Oncology, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States, ⁵Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States

Synopsis

3D MR vessel wall imaging (VWI) is a non-invasive imaging modality for directly assessing intracranial arterial wall diseases. A typical intracranial VWI protocol requires 6-12 minutes per scan to obtain adequate spatial coverage and resolution. Such a long scan time hinders widespread use of VWI in clinical settings. We have developed a novel intracranial vessel-dedicated CAscaded Multi-level WAvelet REfine (CAMWARE) network that enables a VWI scan within 4 minutes. The proposed network achieved significant improvement in vessel wall delination over conventional compressed sensing reconstruction, and several state-of-the-art deep neural networks, such as U-Net and multi-level wavelet U-Net.

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