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Abstract #2298

Clinical feasibility of accelerated diffusion weighted imaging with deep learning reconstruction for patients with acute neurologic symptoms

Younghee Yim1, Sang Ik Park1, Jung Bin Lee1, and Min-young Park2
1Chung-Ang University Hospital, Seoul, Korea, Republic of, 2Severance Hospital, Seoul, Korea, Republic of

Synopsis

Keywords: Stroke, Ischemia

Motivation: Timely diagnosis of patient with acute neurologic symptom is critical. Detecting small lesions on DWI can be challenging and experience plays a significant role, especially when motion artifacts affect image quality.

Goal(s): Our goal was to shorten acquisition time and provide highly sensitive images of small lesions, particularly for emergency clinicians unfamiliar with DWI.

Approach: We included 80 patients, comparing quantitative and qualitative analyses between conventional and deep-learning DWI. We assessed diagnostic performance among experienced neuroradiologists and primary care physician.

Results: Results showed similar image quality between two sequences, but deep-learning DWI exhibited superior lesion conspicuity. Diagnostic accuracy remained consistent between the two.

Impact: Deep-learning DWI offers comparable image quality with significant shorter acquisition time. It also enhances detection of tiny brain lesion, providing diagnostic confidence to less experienced clinicians in emergency situation.

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Keywords