Improved visualization of the coronary arteries using motion correction during vasodilator stress CT myocardial perfusion imaging

Balaney, Bhavna; Vembar, Mani; Graß, Michael; Singh, Amita; Kawaji, Keigo; Landeras, Luis; Chung, Jonathan; Mor‐Avi, Victor; Patel, Amit R.

doi:10.1016/j.ejrad.2019.02.010

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…Regions of interest were also placed on the proximal RCA, LAD, and LCX to measure the CT attenuation value (CT vessel ) of each vessel and the noise of these vessels (SD vessel ). Then, the signal-to-noise ratio (SNR) of each vessel was calculated as CT vessel /SD vessel , and the contrast-to-noise ratio (CNR) was calculated as (CT vessel − CT fat )/SD fat 14,15 . Care was taken to avoid confounding structures, such as plaques and vessel wall, in each ROI.…”

Section: Methodsmentioning

confidence: 99%

“…Then, the signal-to-noise ratio (SNR) of each vessel was calculated as CT vessel /SD vessel , and the contrast-to-noise ratio (CNR) was calculated as (CT vessel − CT fat )/ SD fat . 14,15 Care was taken to avoid confounding structures, such as plaques and vessel wall, in each ROI. All measurements were conducted 3 times, and the average value was used for statistical analysis.…”

Section: Objective Image Quality Evaluationmentioning

confidence: 99%

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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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Section: Methodsmentioning

confidence: 99%

Section: Objective Image Quality Evaluationmentioning

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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“…Motion-correction algorithms have been investigated for single-source CT (SSCT), some of which are available on commercial scanners (Fuchs et al 2014, Symons et al 2016, Balaney et al 2019. These techniques can improve visualization of coronary arteries in some cases, but yield limited diagnostic quality in patients with high and irregular heart rates (Koplay et al 2016, Machida et al 2016, Liang et al 2017.…”

Section: Introductionmentioning

confidence: 99%

Motion artifact correction in cardiac CT using cross-phase temporospatial information and synergistic attention gate and spatial transformer sub-networks

Gong,

Ahmed,

Chang

et al. 2024

Phys. Med. Biol.

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Objectives: To improve quality of coronary CT angiography (CCTA) images using a generalizable motion-correction algorithm. Approach: A neural network with attention gate and spatial transformer (ATOM) was developed to correct coronary motion. Phantom and patient CCTA images (39 males, 32 females, age range 19 to 92, scan date 02/2020 to 10/2021) retrospectively collected from dual-source CT were used to create training, development, and testing sets corresponding to 140- and 75-ms temporal resolution, with 75-ms images as labels. To test generalizability, ATOM was deployed for locally adaptive motion-correction in both 140- and 75-ms patient images. Objective metrics were used to assess motion-corrupted and corrected phantom and patient images, including structural-similarity-index (SSIM), dice-similarity-coefficient (DSC), peak-signal-noise-ratio (PSNR), and normalized root-mean-square-error (NRMSE). In objective quality assessment, ATOM was compared with several baseline networks, including U-net, U-net plus attention gate, U-net plus spatial transformer, VDSR, and ResNet. Two cardiac radiologists independently interpreted motion-corrupted and -corrected images at 75 and 140 ms in a blinded fashion and ranked diagnostic image quality (worst to best: 1–4, no ties). Main results: ATOM improved quality metrics (p<0.05) before/after correction: In phantom, SSIM 0.87/0.95, DSC 0.85/0.93, PSNR 19.4/22.5, NRMSE 0.38/0.27; in patient images, SSIM 0.82 / 0.88, DSC 0.88/0.90, PSNR 30.0/32.0, NRMSE 0.16/0.12. ATOM provided more consistent improvement of objective image quality, compared to the presented baseline networks. The motion-corrected images received better ranks than un-corrected at the same temporal resolution (p<0.05): 140-ms images 1.65/2.25, and 75-ms images 3.1/3.2. The motion-corrected 75-ms images received the best rank in 65% of testing cases. A fair-to-good inter-reader agreement was observed (Kappa score 0.58). Significance: ATOM reduces motion artifacts, improving visualization of coronary arteries. This algorithm can be used to virtually improve temporal resolution in both single- and dual-source CT.

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The influence of motion-compensated reconstruction on coronary artery analysis for a dual-layer detector CT system: a dynamic phantom study

Dobrolinska,

Tetteroo,

Greuter

et al. 2024

Eur Radiol

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Improved visualization of the coronary arteries using motion correction during vasodilator stress CT myocardial perfusion imaging

Cited by 5 publications

References 22 publications

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

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

Motion artifact correction in cardiac CT using cross-phase temporospatial information and synergistic attention gate and spatial transformer sub-networks

The influence of motion-compensated reconstruction on coronary artery analysis for a dual-layer detector CT system: a dynamic phantom study

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