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Jung Y, Lee H, Jun H, Cho S. Evaluation of Motion Artifact Correction Technique for Cone-Beam Computed Tomography Image Considering Blood Vessel Geometry. J Clin Med 2024; 13:2253. [PMID: 38673526 PMCID: PMC11050711 DOI: 10.3390/jcm13082253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Revised: 03/07/2024] [Accepted: 04/09/2024] [Indexed: 04/28/2024] Open
Abstract
Background: In this study, we present a quantitative method to evaluate the motion artifact correction (MAC) technique through the morphological analysis of blood vessels in the images before and after MAC. Methods: Cone-beam computed tomography (CBCT) scans of 37 patients who underwent transcatheter chemoembolization were obtained, and images were reconstructed with and without the MAC technique. First, two interventional radiologists selected the blood vessels corrected by MAC. We devised a motion-corrected index (MCI) metric that analyzed the morphology of blood vessels in 3D space using information on the centerline of blood vessels, and the blood vessels selected by the interventional radiologists were quantitatively evaluated using MCI. In addition, these blood vessels were qualitatively evaluated by two interventional radiologists. To validate the effectiveness of the devised MCI, we compared the MCI values in a blood vessel corrected by MAC and one non-corrected by MAC. Results: The visual evaluation revealed that motion correction was found in the images of 23 of 37 patients (62.2%), and a performance evaluation of MAC was performed with 54 blood vessels in 23 patients. The visual grading analysis score was 1.56 ± 0.57 (radiologist 1) and 1.56 ± 0.63 (radiologist 2), and the proposed MCI was 0.67 ± 0.11, indicating that the vascular morphology was well corrected by the MAC. Conclusions: We verified that our proposed method is useful for evaluating the MAC technique of CBCT, and the MAC technique can correct the blood vessels distorted by the patient's movement and respiration.
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Affiliation(s)
- Yunsub Jung
- Department of Materials and Production, Aalborg University, 9220 Aalborg East, Denmark;
| | - Ho Lee
- Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul 03722, Republic of Korea;
| | - Hoyong Jun
- Department of Radiology, College of Medicine, Ewha Womans University, Seoul 03760, Republic of Korea;
| | - Soobuem Cho
- Department of Radiology, College of Medicine, Ewha Womans University, Seoul 03760, Republic of Korea;
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Zhang Z, Liu Z, Hong N, Chen L. Effect of a second-generation motion correction algorithm on image quality and measurement reproducibility of coronary CT angiography in patients with a myocardial bridge and mural coronary artery. Clin Radiol 2024; 79:e462-e467. [PMID: 38135576 DOI: 10.1016/j.crad.2023.11.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 11/14/2023] [Accepted: 11/20/2023] [Indexed: 12/24/2023]
Abstract
AIM To determine the effect of second-generation motion correction (MC2) on image quality and measurement reproducibility of cardiac CT images in patients with a myocardial bridge and mural coronary artery (MB-MCA) compared to standard (STD) images without motion correction and with first-generation motion correction (MC1). MATERIALS AND METHODS A total of 66 patients with MB-MCA in the left anterior descending branch who underwent 256-detector CT with single-heartbeat acquisition were included. Images were reconstructed at 45% and 75% R-R intervals using STD, MC1, and MC2 algorithms. Image quality for MB-MCA was assessed by two observers on a four-point scale (1 = poor and 4 = excellent) and compared among STD, MC1, and MC2. Depth and length of MB, lumen area, and minimal diameter of MCA were measured and compared. RESULTS At 45% R-R interval, image quality scores were 1.59 ± 0.78, 2.21 ± 0.97, and 3.21 ± 0.62 for MCA, and 2.48 ± 0.79, 2.76 ± 0.75, and 3.58 ± 0.58 for MB with STD, MC1 and MC2, respectively. At 75% R-R interval, these values were 2.26 ± 0.60, 3.03 ± 0.89, and 3.59 ± 0.55 for MCA and 3.00 ± 0.93, 3.17 ± 0.83, and 3.80 ± 0.44 for MB. Although MC1 was superior to STD in displaying MCA, there was no statistical difference between the two algorithms for MB (p>0.05). Compared with STD and MC1, MC2 statistically improved image quality and interpretability for both MCA and MB and had narrower limits in interobserver agreement for measurements at both 45% and 75% R-R intervals. CONCLUSION MC2 improves CT image quality and measurement reproducibility in patients with MB-MCA compared to STD and MC1.
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Affiliation(s)
- Z Zhang
- Department of Radiology, Peking University People's Hospital, Beijing, 100044, China
| | - Z Liu
- Department of Radiology, Peking University People's Hospital, Beijing, 100044, China
| | - N Hong
- Department of Radiology, Peking University People's Hospital, Beijing, 100044, China
| | - L Chen
- Department of Radiology, Peking University People's Hospital, Beijing, 100044, China.
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Lecomte A, Serrand A, Marteau L, Carlier B, Manigold T, Letocart V, Warin Fresse K, Nguyen JM, Serfaty JM. Coronary artery assessment on pre transcatheter aortic valve implantation computed tomography may avoid the need for additional coronary angiography. Diagn Interv Imaging 2023; 104:547-551. [PMID: 37331824 DOI: 10.1016/j.diii.2023.06.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 06/07/2023] [Accepted: 06/09/2023] [Indexed: 06/20/2023]
Abstract
PURPOSE The purpose of this study was to evaluate the percentage of coronary angiography that can be securely avoided by the interpretation of coronary arteries on pre transcatheter aortic valve implantation CT (TAVI-CT), using CT images obtained with deep-learning reconstruction and motion correction algorithms. MATERIAL AND METHOD All consecutive patients who underwent TAVI-CT and coronary angiography, from December 2021 to July 2022 were screened for inclusion in the study. Patients who had previous coronary artery revascularization or who did not undergo TAVI were excluded. All TAVI-CT examinations were obtained using deep-learning reconstruction and motion correction algorithms. On TAVI-CT examinations, quality and stenosis of coronary artery were analyzed retrospectively. When insufficient image quality and/or when diagnosis or doubt of one significant coronary artery stenosis, patients were considered as having possible coronary artery stenosis. The results of coronary angiography were used as the standard of reference for significant CAS. RESULTS A total of 206 patients (92 men; mean age, 80.6 years) were included; of these 27/206 (13%) had significant coronary artery stenosis on coronary angiography and were referred for potential revascularization. Sensitivity, specificity, negative predictive value, positive predictive value, and accuracy of TAVI-CT to identify patients requiring coronary artery revascularization was 100% (95% confidence interval [CI]: 87.2-100%), 100% (95% CI: 96.3-100%), 54% (95% CI: 46.6-61.6), 25% (95% CI: 17.0-34.0%) and 60% (95% CI: 53.1-66.9%) respectively. Intra- and inter observer variability was substantial agreement for quality and decision to recommend coronary angiography. Mean reading time was 2 ± 1.2 (standard deviation) min (range: 1-5 min). Overall, TAVI-CT could potentially rule out indication for revascularization for 97 patients (47%). CONCLUSION Analysis of coronary artery on TAVI-CT using deep-learning reconstruction and motion correction algorithms can potentially safely avoid coronary angiography in 47% of patients.
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Affiliation(s)
- Adrien Lecomte
- Department of Cardiovascular Radiology, Nantes Université, CHU Nantes, 44000 Nantes, France.
| | - Aude Serrand
- Department of Cardiovascular Radiology, Nantes Université, CHU Nantes, 44000 Nantes, France
| | - Lara Marteau
- Department of Cardiovascular Radiology, Nantes Université, CHU Nantes, 44000 Nantes, France; Department of Cardiology, Nantes Université, CHU Nantes, Institut du thorax, 44000 Nantes, France; Department of Biostatistics and Epidemiology, CRCINA, INSERM U1232 Team2, CHU Nantes, 44000 Nantes, France; Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du thorax, F-44000 Nantes, France
| | - Baptiste Carlier
- Department of Cardiology, Nantes Université, CHU Nantes, Institut du thorax, 44000 Nantes, France
| | - Thibaut Manigold
- Department of Cardiology, Nantes Université, CHU Nantes, Institut du thorax, 44000 Nantes, France
| | - Vincent Letocart
- Department of Cardiology, Nantes Université, CHU Nantes, Institut du thorax, 44000 Nantes, France
| | - Karine Warin Fresse
- Department of Cardiovascular Radiology, Nantes Université, CHU Nantes, 44000 Nantes, France
| | - Jean-Michel Nguyen
- Department of Biostatistics and Epidemiology, CRCINA, INSERM U1232 Team2, CHU Nantes, 44000 Nantes, France
| | - Jean-Michel Serfaty
- Department of Cardiovascular Radiology, Nantes Université, CHU Nantes, 44000 Nantes, France; Department of Cardiology, Nantes Université, CHU Nantes, Institut du thorax, 44000 Nantes, France; Department of Biostatistics and Epidemiology, CRCINA, INSERM U1232 Team2, CHU Nantes, 44000 Nantes, France; Nantes Université, CHU Nantes, CNRS, INSERM, l'institut du thorax, F-44000 Nantes, France
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