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- Citation Author(s):
- Submitted by:
- xiangyu xiong
- Last updated:
- Mon, 11/04/2024 - 14:36
- DOI:
- 10.21227/00b7-mw76
- License:
276 Views- Categories:
- Keywords:
Abstract
Contrast-enhanced computed tomography (CE-CT) is the gold standard for diagnosing AD. However, contrast agents can cause allergic reactions or renal failure in some patients. Moreover, AD diagnosis by radiologists using non- contrast-enhanced CT (NCE-CT) images has poor sensitivity. To address this issue, a novel deep learning methos was proposed for AD detection using NCE-CT volumes. It may have great potential to reduce the misdiagnosis of AD using NCE-CT in clinical practice.
All datasets were collected from two medical centers using three vendors with kilovolt peak (KVP) of 100 ∼ 120. The datasets consisted of 207 subjects (i.e., 78 AD patients and 129 non-AD patients with other cardiovascular diseases). We sequentially performed Non-contrast enhanced CT and Contrast enhanced CT on each subject with the same scan conditions, including position, coverage, and parameters.
