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Identifying Coronary Artery Calcification on Non-gated Computed Tomography Scans
Published on: August 28, 2018
Diagnostic Improvements of Deep Learning-Based Image Reconstruction for Assessing Calcification-Related Obstructive
1State Key Laboratory of Complex Severe and Rare Diseases, Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Insights
Deep learning-based image reconstruction (DLR) significantly enhances coronary CT angiography (CCTA) image quality and diagnostic accuracy for obstructive coronary artery disease (CAD), particularly when combined with subtraction imaging.
Area of Science:
- Cardiovascular Imaging
- Radiology
- Artificial Intelligence in Medicine
Background:
- Coronary artery disease (CAD) poses a significant health burden, necessitating accurate diagnostic tools.
- Coronary CT angiography (CCTA) is a key imaging modality for CAD evaluation.
- Traditional image reconstruction methods may have limitations in visualizing calcified lesions.
Purpose of the Study:
- To evaluate the diagnostic performance of deep learning-based image reconstruction (DLR) compared to hybrid iterative reconstruction (HIR) for calcification-related obstructive CAD.
- To assess the utility of subtraction CCTA images generated from both DLR and HIR techniques.
- To compare image quality metrics between DLR and HIR reconstructed CCTA images.
Main Methods:
- Forty-two patients with suspected CAD underwent CCTA using a 320-row CT scanner.
- Images were reconstructed using DLR (CTA_DLR) and HIR (CTA_HIR).
- Subtraction CCTA images were created from both reconstruction methods (CTA_sDLR, CTA_sHIR).
- Image quality was assessed qualitatively (Likert score) and quantitatively (noise, SNR, CNR).
- Diagnostic performance for obstructive CAD was evaluated against invasive coronary angiography (ICA).
Main Results:
- DLR-based images (CTA_sDLR, CTA_DLR) demonstrated superior qualitative and quantitative image quality compared to HIR-based images (CTA_sHIR, CTA_HIR).
- Diagnostic accuracy for calcification-related obstructive diameter stenosis was highest with CTA_sDLR (83.73%).
- CTA_sDLR achieved a sensitivity of 90.91% and specificity of 83.23% for detecting ≥50% luminal diameter stenosis, with a lower false-positive rate (15%) compared to other methods.
Conclusions:
- Deep learning-based image reconstruction substantially improves CCTA image quality.
- DLR enhances the diagnostic performance for calcification-related obstructive CAD.
- The combination of DLR with subtraction imaging offers the most promising approach for accurate CAD evaluation.
Abstract:
Objectives: The objective of this study was to explore the diagnostic value of deep learning-based image reconstruction (DLR) and hybrid iterative reconstruction (HIR) for calcification-related obstructive coronary artery disease (CAD) evaluation by using coronary CT angiography (CCTA) images and subtraction CCTA images. Methods: Forty-two consecutive patients with known or suspected coronary artery disease who underwent coronary CTA on a 320-row CT scanner and subsequent invasive coronary angiography (ICA), which was used as the reference standard, were enrolled. The DLR and HIR images were reconstructed as CTADLR and CTAHIR, and, based on which, the corresponding subtraction CCTA images were established as CTAsDLR and CTAsHIR, respectively. Qualitative images quality comparison was performed by using a Likert 4 stage score, and quantitative images quality parameters, including image noise, signal-to-noise ratio, and contrast-to-noise ratio were calculated. Diagnostic performance on the lesion level was assessed and compared among the four CCTA approaches (CTADLR, CTAHIR, CTAsDLR, and CTAsHIR). Results: There were 166 lesions of 86 vessels in 42 patients (32 men and 10 women; 62.9 ± 9.3 years) finally enrolled for analysis. The qualitative and quantitative image qualities of CTAsDLR and CTADLR were superior to those of CTAsHIR and CTAHIR, respectively. The diagnostic accuracies of CTAsDLR, CTADLR, CTAsHIR, and CTAHIR to identify calcification-related obstructive diameter stenosis were 83.73%, 69.28%, 75.30%, and 65.66%, respectively. The false-positive rates of CTAsDLR, CTADLR, CTAsHIR, and CTAHIR for luminal diameter stenosis ≥50% were 15%, 31%, 24%, and 34%, respectively. The sensitivity and the specificity to identify ≥50% luminal diameter stenosis was 90.91% and 83.23% for CTAsDLR. Conclusion: Our study showed that deep learning-based image reconstruction could improve the image quality of CCTA images and diagnostic performance for calcification-related obstructive CAD, especially when combined with subtraction technique.
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