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- Title
Deep Learning Imaging Reconstruction Algorithm for Carotid Dual Energy CT Angiography: Opportunistic Evaluation of Cervical Intervertebral Discs—A Preliminary Study.
- Authors
Jiang, Chenyu; Zhang, Jingxin; Li, Wenhuan; Li, Yali; Ni, Ming; Jin, Dan; Zhang, Yan; Jiang, Liang; Yuan, Huishu
- Abstract
Thus, the aim of this study is to evaluate the performance of deep learning imaging reconstruction (DLIR) algorithm in different image sets derived from carotid dual-energy computed tomography angiography (DECTA) for evaluating cervical intervertebral discs (IVDs) and compare them with those reconstructed using adaptive statistical iterative reconstruction-Veo (ASiR-V). Forty-two patients who underwent carotid DECTA were included in this retrospective analysis. Three types of image sets (70 keV, water-iodine, and water-calcium) were reconstructed using 50% ASiR-V and DLIR at medium and high levels (DLIR-M and DLIR-H). The diagnostic acceptability and conspicuity of IVDs were assessed using a 5-point scale. Hounsfield Units (HU) and water concentration (WC) values of the IVDs; standard deviation (SD); and coefficient of variation (CV) were calculated. Measurement parameters of the 50% ASIR-V, DLIR-M, and DLIR-H groups were compared. The DLIR-H group showed higher scores for diagnostic acceptability and conspicuity, as well as lower SD values for HU and WC than the ASiR-V and DLIR-M groups for the 70 keV and water-iodine image sets (all p <.001). However, there was no significant difference in scores and SD among the three groups for the water-calcium image set (all p >.005). The water-calcium image set showed better diagnostic accuracy for evaluating IVDs compared to the other image sets. The inter-rater agreement using ASiR-V, DLIR-M, and DLIR-H was good for the 70 keV image set, excellent for the water-iodine and water-calcium image sets. DLIR improved the visualization of IVDs in the 70 keV and water-iodine image sets. However, its improvement on color-coded water-calcium image set was limited.
- Subjects
IODINE analysis; RESEARCH funding; COMPUTED tomography; RETROSPECTIVE studies; DESCRIPTIVE statistics; CALCIUM; DEEP learning; INTERVERTEBRAL disk; MEDICAL records; ACQUISITION of data; WATER; DIGITAL image processing; CERVICAL vertebrae; ALGORITHMS
- Publication
Journal of Digital Imaging, 2024, Vol 37, Issue 4, p1960
- ISSN
0897-1889
- Publication type
Article
- DOI
10.1007/s10278-024-01016-x