Free-breathing coronary CT angiography using 16-cm wide-detector for challenging patients: comparison with invasive coronary angiography

Tao, Shuai; Deng, Liping; Pan, Yuning; Li, W.; Liao, Kai; Li, J.; Peng, Liqing; Li, Z.

doi:10.1016/j.crad.2018.06.023

Cited by 10 publications

(9 citation statements)

References 19 publications

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“…In comparison with several previous studies [ 4 , 13 – 16 ] on whole-heart coverage CT scanner, our overall segment scores are a little bit higher which could be due to faster rotation speed or radiologist assessment bias. But in general, the average score increases after motion correction is similar.…”

Section: Discussioncontrasting

confidence: 74%

“…But in general, the average score increases after motion correction is similar. In some works, phase selection before motion correction was done by an experienced radiologist [ 4 ] or details for phase selection were not provided [ 13 , 15 , 16 ]. In one study, the best cardiac phase and motion correction were both applied but this study focused on low kVp image quality and study population was relatively smaller (n = 30) [ 14 ].…”

Section: Discussionmentioning

confidence: 99%

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Image quality of automatic coronary CT angiography reconstruction for patients with HR ≥ 75 bpm using an AI-assisted 16-cm z-coverage CT scanner

et al. 2021

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Background Coronary CT angiography (CCTA) is a complicated CT exam in comparison to other CT protocols. Exam success highly depends on image assessment of experienced radiologist and the procedure is often time-consuming. This study aims to evaluate feasibility of automatic CCTA reconstruction in 0.25 s rotation time, 16 cm coverage CT scanner with best phase selection and AI-assisted motion correction. Methods CCTA exams of 90 patients with heart rates higher than 75 bpm were included in this study. Two image series were reconstructed—one at automatically selected phase and another with additional motion correction. All reconstructions were performed without manual interaction of radiologist. A four-point Likert scale rating system was used to evaluate the image quality of coronary artery segment by two experienced radiologists, according to the 18-segment model. Analysis was done on per-segment basis. Results Total 1194 out of the 1620 segments were identified for quality evaluation in 90 patients. After automatic best phase selection, 1172 segments (98.3%) were rated as having diagnostic image quality (scores 2–4) and the average score is 3.64 ± 0.55. When motion corrections were applied, diagnostic segment number increases to 1192 (99.8%) and the average score is 3.85 ± 0.37. Conclusions With the help of 0.25 s rotation speed, 16-cm z-coverage and AI-assisted motion correction algorithm, CCTA exam reconstruction could be performed with minimum radiologist involvement and still meet image quality requirement.

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Section: Discussioncontrasting

confidence: 74%

Section: Discussionmentioning

confidence: 99%

Image quality of automatic coronary CT angiography reconstruction for patients with HR ≥ 75 bpm using an AI-assisted 16-cm z-coverage CT scanner

et al. 2021

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“…CCTA images with a slice thickness of 0.625 mm and increment of 0.625 mm were reconstructed using the 80% adaptive statistical iterative reconstruction-V [ASIR-V80%, GE Healthcare] with the STND kernel at the optimal reconstruction phase automatically selected by the SmartPhase technique (GE Healthcare), and the cardiac motion correction technique (snapshot freeze, SSF) was also applied ( 12 , 15 , 17 ). The generated images were assigned as the routine group.…”

Section: Methodsmentioning

confidence: 99%

“…However, motion artifacts and image noise decrease CCTA image quality (4-7), and thus affect the diagnosis, management, and treatment of CAD (8). Notably, 16-cm-wide coverage-detector CT with motion-corrected reconstruction algorithm Snapshot Freeze (SSF) has been shown to reduce respiratory movement and motion artifacts (9)(10)(11)(12)(13)(14)(15)(16). However, the image noise due to insufficient photon quantity in low-dose CCTA still needs to be overcome (17).…”

Section: Introductionmentioning

confidence: 99%

Intraindividual evaluation of effects of image filter function on image quality in coronary computed tomography angiography

Jin

Gao

Wang

et al. 2022

Front. Cardiovasc. Med.

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ObjectivesTo evaluate whether applying image filters (smooth 3D+ and edge-2) improves image quality in coronary CT angiography (CCTA).MethodsNinety patients (routine group) with suspected coronary artery diseases based on 16-cm wide coverage detector CT findings were retrospectively enrolled at a chest pain center from December 2019 to September 2021. Two image filters, smooth 3D+ and edge-2 available on the Advantage Workstation (AW) were subsequently applied to the images to generate the research group (SE group). Quantitative parameters, including CT value, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), image sharpness and image quality score, and diagnostic accuracy were compared between the two groups.ResultsA total of 900 segments from 270 coronary arteries in 90 patients were analyzed. SNR, CNR, and image sharpness for vessels and image quality scores in the SE group were significantly better than those in the routine group (all p < 0.001). The SE group showed a slightly higher negative predictive value (NPV) on the left anterior descending artery and right coronary artery (RCA) stenosis evaluations, as well as total NPV. The SE group also showed slightly higher sensitivity and accuracy than the routine group on RCA stenosis evaluation.ConclusionThe use of an image filter combining smooth 3D+ and edge-2 on an AW could improve the image quality of CCTA and increase radiologists' diagnostic confidence.

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“…Over the past decade, coronary computed tomography angiography (CCTA) has been established as a reliable modality for the evaluation of coronary artery disease (CAD) 1,2 . However, the presence of motion-related artifacts due to the limited temporal resolution of computed tomography (CT) scanners, especially in patients with high heart rate (HR), degrades the image quality and diagnostic performance of CCTA 3,4 . For such reason, HR control plays a rather crucial role in obtaining satisfactory image quality of CCTA.…”

mentioning

confidence: 99%

Deep Learning–Based Motion Correction in Projection Domain for Coronary Computed Tomography Angiography: A Clinical Evaluation

Shuai,

Zhong,

Zhang

et al. 2023

J Comput Assist Tomogr

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Objective This study aimed to evaluate the clinical performance of a deep learning-based motion correction algorithm (MCA) in projection domain for coronary computed tomography angiography (CCTA). Methods A total of 192 patients who underwent CCTA examinations were included and divided into 2 groups based on the average heart rate (HR): group 1, 82 patients with HR of <75 beats per minute; group 2, 110 patients with HR of ≥75 beats per minute. The CCTA images were reconstructed with and without MCA. The subjective image quality was graded in terms of vessel visualization, sharpness, diagnostic confidence, and overall image quality using a 5-point scale, where cases with all scores of ≥3 were deemed interpretable. Objective image quality was measured through signal-to-noise ratio and contrast-to-noise ratio in regions relative to the vessels. The image quality scores for 2 reconstructions and effective dose between 2 groups were compared. Results The mean effective dose was similar between 2 groups. Neither group showed significant difference on objective image quality for 2 reconstructions. Images reconstructed with and without MCA were both found interpretable for group 1, whereas the subjective image quality was significantly improved by the MCA for all 4 metrics in group 2, with the interpretability increased from 80.91% to 99.09%. Compared with group 1, group 2 showed similar interpretability and diagnostic confidence, despite inferior overall image quality. Conclusions In CCTA examinations, the deep learning–based MCA is capable of improving the image quality and diagnostic confidence for patients with increased HR to a similar level as for those with low HR.

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Free-breathing coronary CT angiography using 16-cm wide-detector for challenging patients: comparison with invasive coronary angiography

Cited by 10 publications

References 19 publications

Image quality of automatic coronary CT angiography reconstruction for patients with HR ≥ 75 bpm using an AI-assisted 16-cm z-coverage CT scanner

Image quality of automatic coronary CT angiography reconstruction for patients with HR ≥ 75 bpm using an AI-assisted 16-cm z-coverage CT scanner

Intraindividual evaluation of effects of image filter function on image quality in coronary computed tomography angiography

Deep Learning–Based Motion Correction in Projection Domain for Coronary Computed Tomography Angiography: A Clinical Evaluation

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