Deep Learning Imaging Reconstruction Algorithm for Carotid Dual Energy CT Angiography: Opportunistic Evaluation of Cervical Intervertebral Discs—A Preliminary Study

• Published in: Journal of digital imaging Vol. 37; no. 4; pp. 1960 - 1968
• Main Authors: Jiang, Chenyu, Zhang, Jingxin, Li, Wenhuan, Li, Yali, Ni, Ming, Jin, Dan, Zhang, Yan, Jiang, Liang, Yuan, Huishu
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.08.2024; Springer Nature B.V
• Subjects: Acceptability; Adult; Aged; Algorithms; Angiography; Calcium; Carotid Arteries - diagnostic imaging; Cervical Vertebrae - diagnostic imaging; Coefficient of variation; Computed tomography; Computed Tomography Angiography - methods; Conspicuity; Deep Learning; Diagnostic systems; Female; Group theory; Humans; Image processing; Image Processing, Computer-Assisted - methods; Image reconstruction; Imaging; Intervertebral Disc - diagnostic imaging; Intervertebral discs; Iodine; Machine learning; Male; Medical imaging; Medicine; Medicine & Public Health; Middle Aged; Performance evaluation; Radiology; Retrospective Studies; Statistical analysis; Water; Deep learning; Dual-energy computed tomography; Intervertebral disc; Image reconstruction
• ISSN: 2948-2933; 0897-1889; 2948-2925; 2948-2933; 1618-727X
• DOI: 10.1007/s10278-024-01016-x

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.