Blooming Artifact Reduction in Coronary Artery Calcification by A New De-blooming Algorithm: Initial Study

Scientific Reports, May 2018

The aim of this study was to investigate the use of de-blooming algorithm in coronary CT angiography (CCTA) for optimal evaluation of calcified plaques. Calcified plaques were simulated on a coronary vessel phantom and a cardiac motion phantom. Two convolution kernels, standard (STND) and high-definition standard (HD STND), were used for imaging reconstruction. A dedicated de-blooming algorithm was used for imaging processing. We found a smaller bias towards measurement of stenosis using the de-blooming algorithm (STND: bias 24.6% vs 15.0%, range 10.2% to 39.0% vs 4.0% to 25.9%; HD STND: bias 17.9% vs 11.0%, range 8.9% to 30.6% vs 0.5% to 21.5%). With use of de-blooming algorithm, specificity for diagnosing significant stenosis increased from 45.8% to 75.0% (STND), from 62.5% to 83.3% (HD STND); while positive predictive value (PPV) increased from 69.8% to 83.3% (STND), from 76.9% to 88.2% (HD STND). In the patient group, reduction in calcification volume was 48.1 ± 10.3%, reduction in coronary diameter stenosis over calcified plaque was 52.4 ± 24.2%. Our results suggest that the novel de-blooming algorithm could effectively decrease the blooming artifacts caused by coronary calcified plaques, and consequently improve diagnostic accuracy of CCTA in assessing coronary stenosis.

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Blooming Artifact Reduction in Coronary Artery Calcification by A New De-blooming Algorithm: Initial Study

Abstract The aim of this study was to investigate the use of de-blooming algorithm in coronary CT angiography (CCTA) for optimal evaluation of calcified plaques. Calcified plaques were simulated on a coronary vessel phantom and a cardiac motion phantom. Two convolution kernels, standard (STND) and high-definition standard (HD STND), were used for imaging reconstruction. A dedicated de-blooming algorithm was used for imaging processing. We found a smaller bias towards measurement of stenosis using the de-blooming algorithm (STND: bias 24.6% vs 15.0%, range 10.2% to 39.0% vs 4.0% to 25.9%; HD STND: bias 17.9% vs 11.0%, range 8.9% to 30.6% vs 0.5% to 21.5%). With use of de-blooming algorithm, specificity for diagnosing significant stenosis increased from 45.8% to 75.0% (STND), from 62.5% to 83.3% (HD STND); while positive predictive value (PPV) increased from 69.8% to 83.3% (STND), from 76.9% to 88.2% (HD STND). In the patient group, reduction in calcification volume was 48.1 ± 10.3%, reduction in coronary diameter stenosis over calcified plaque was 52.4 ± 24.2%. Our results suggest that the novel de-blooming algorithm could effectively decrease the blooming artifacts caused by coronary calcified plaques, and consequently improve diagnostic accuracy of CCTA in assessing coronary stenosis. Introduction Coronary CT angiography (CCTA) is a widely used non-invasive modality enabling high diagnostic performance for the diagnosis of coronary artery disease (CAD)1,2,3,4,5,6. Despite high diagnostic value achieved with recently developed advanced CT scanners, CCTA still has moderate specificity in the assessment of calcified plaques due to the artifacts that result from significant calcification7. Calcification produces blooming and partial volume artifacts on CT imaging, which can cause erroneous enlargement of the appearance of calcification8. As a result blooming artifact prevents accurate evaluation of the coronary artery lumen and results in overestimation of the stenosis leading to false positive diagnosis9,10,11,12,13,14. Although CCTA is an excellent imaging modality for the assessment of patients with suspected CAD, calcified plaque presents a major challenge for CCTA scans. A new vendor-specific de-blooming algorithm is under investigation to help minimize the blooming artifact of coronary calcification, and this has not been well studied. We hypothesized that this brand new algorithm would allow for more accurate evaluation of coronary artery stenosis in the presence of calcified plaques. Thus, the aim of this study was to investigate the diagnostic value of the de-blooming algorithm for evaluation of calcified plaques based on an in vitro phantom study and a small group of patients. Results Phantom studyObjective image quality on phantom study For quantitative image quality analysis, the SNR of vessel models showed significantly higher in the group with use of the de-blooming algorithm than that of without de-blooming (STND: 52.6 ± 11.7 vs 67.9 ± 16.3, p = 0.003; HD STND: 26.8 ± 2.9 vs 33.5 ± 3.5; p = 0.005). The image noise was significantly higher in the group without use of the de-blooming algorithm (STND: 7.9 ± 2.0 vs 6.3 ± 1.9, p = 0.022; HD STND: 14.6 ± 2.8 vs 11.2 ± 2.4; p = 0.004). Correlation analysis of the stenosis The results of the Bland–Altman comparisons are shown in Fig. 1. In the STND group, representative Bland-Altman plots of OS-RS and DS-RS showed 95% confidence limits of 10.2% to 39.0% (with a mean of 24.6%) for OS-RS, and 4.0% to 25.9% (with a mean of 15.0%) for DS-RS, respectively (Fig. 1A,B). In the HD STND group, Bland-Altman plots of OS-RS and DS-RS showed 95% confidence limits of 8.9% to 30.6% (with a mean of 19.7%) for OS-RS and 0.5% to 21.5% (with a mean of 11.0%) for DS-RS, respectively (Fig. 1C,D). Figure 1 (A–B) Bland and Altman showing the correlation of Reference stenosis and Original stenosis without and with de-blooming algorithm using standard mode reconstruction (STND); (C–D) showing Reference stenosis and De-blooming stenosis without and with de-blooming algorithm using high definition mode reconstruction (HD STND). Only few cases are outside the boundary line (beyond two SD). SD = Standard deviation. Full size image The Bland–Altman plots presented a distinct systematic overestimation of calcified plaque stenosis in CCTA, but the de-blooming stenosis is closer to reference standard than original stenosis. Diagnostic performance of CCTA Figure 2 provides a representative example of the difference in calcified plaque with and without using de-blooming algorithm. Diagnostic performance of CCTA for detecting ≥50% stenosis and ≥70% stenosis is summarized in Table 1. Figure 2 Axial CT images of the coronary vessel phantom of nine stenosis models with calcified plaques. Clockwise, in turn, is 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, and 90% stenosis. (A) nine different stenoses of calcified plaques without de-blooming algorithm using standard mode (...truncated)


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Ping Li, Lei Xu, Lin Yang, Rui Wang, Jiang Hsieh, Zhonghua Sun, Zhanming Fan, Jonathon A. Leipsic. Blooming Artifact Reduction in Coronary Artery Calcification by A New De-blooming Algorithm: Initial Study, Scientific Reports, 2018, Issue: 8, DOI: 10.1038/s41598-018-25352-5