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Improving Image Quality and Diagnostic Performance of CCTA in Patients with Challenging Heart Rate Conditions using a
Ziwei Wang1,2, Li Bao1, Sihua Zhong3
1Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.
Insights
A deep learning motion correction algorithm (MCA) significantly enhances Coronary Computed Tomography Angiography (CCTA) image quality and diagnostic performance in patients with challenging heart rates (HR) and heart rate variability (HRV). This improves vessel visualization and stenosis detection with low radiation exposure.
Area of Science:
- Medical Imaging
- Cardiovascular Imaging
- Artificial Intelligence in Medicine
Background:
- Elevated heart rate (HR) and heart rate variability (HRV) are primary causes of motion artifacts in Coronary Computed Tomography Angiography (CCTA).
- These artifacts degrade image quality and can impede accurate diagnosis of coronary artery disease.
- Motion artifacts pose a significant challenge in CCTA, particularly in patients with physiological conditions affecting heart rate.
Purpose of the Study:
- To evaluate the effectiveness of a deep learning-based motion correction algorithm (MCA) in mitigating motion artifacts in CCTA.
- To assess the impact of MCA on objective and subjective image quality in patients with challenging HR conditions.
- To determine the effect of MCA on the diagnostic performance of CCTA for detecting significant coronary stenosis.
Main Methods:
- Retrospective analysis of 240 CCTA scans from patients with elevated HR and HRV.
- Image reconstruction with and without the MCA.
- Objective image quality assessment using signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR).
- Subjective image quality evaluation by radiologists using a 5-point scale.
- Assessment of diagnostic performance for significant stenosis (≥50%) against invasive coronary angiography.
Main Results:
- MCA significantly improved subjective image quality, increasing vessel interpretability from 89.9% to 98.8% (p < 0.001).
- Diagnostic performance was significantly higher with MCA: patient-based AUC improved from 0.58 to 0.83 (p = 0.04), and vessel-based AUC from 0.81 to 0.92 (p < 0.001).
- Vessel-based accuracy increased from 79.4% to 91.2% (p = 0.01) with MCA; no significant difference in objective image quality was observed.
Conclusions:
- The deep learning-based MCA effectively reduces motion artifacts in CCTA for patients with challenging HR conditions.
- MCA significantly enhances subjective image quality and diagnostic performance without compromising objective image quality.
- The algorithm enables high-quality CCTA imaging and improved diagnostic accuracy with low radiation exposure.
Objective:
Challenging HR conditions, such as elevated Heart Rate (HR) and Heart Rate Variability (HRV), are major contributors to motion artifacts in Coronary Computed Tomography Angiography (CCTA). This study aims to assess the impact of a deep learning-based motion correction algorithm (MCA) on motion artifacts in patients with challenging HR conditions, focusing on image quality and diagnostic performance of CCTA.
Materials And Methods:
This retrospective study included 240 patients (mean HR: 88.1 ± 14.5 bpm; mean HRV: 32.6 ± 45.5 bpm) who underwent CCTA between June, 2020 and December, 2020. CCTA images were reconstructed with and without the MCA. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were measured to assess objective image quality. Subjective image quality was evaluated by two radiologists using a 5-point scale regarding vessel visualization, diagnostic confidence, and overall image quality. Moreover, all vessels with scores ≥ 3 were considered clinically interpretable. The diagnostic performance of CCTA with and without MCA for detecting significant stenosis (≥ 50%) was assessed in 34 patients at both per-vessel and per-patient levels, using invasive coronary angiography as the reference standard.
Results:
The MCA significantly improved subjective image quality, increasing the vessel interpretability from 89.9% (CI: 0.88-0.92) to 98.8% (CI: 0.98-0.99) (p < 0.001). The use of MCA resulted in significantly higher diagnostic performance in both patient-based (AUC: 0.83 vs. 0.58, p = 0.04) and vessel-based (AUC: 0.92 vs. 0.81, p < 0.001) analyses, with the vessel-based accuracy notably increased from 79.4% (CI: 0.72-0.86) to 91.2% (CI: 0.85-0.95) (p = 0.01). There were no significant differences in objective image quality between the two reconstructions. The mean effective dose in this study was 2.8 ± 1.1 mSv.
Conclusion:
The use of MCA allows for obtaining high-quality CCTA images and superior diagnostic performance with low radiation exposure in patients with elevated HR and HRV.
