Subtracted Computed Tomography Angiography in the Evaluation of Coronary Arteries With Severe Calcification or Stents Using a 320-Row Computed Tomography Scanner

Subtraction Improves the Accuracy of Coronary CT Angiography in Patients with Severe Calcifications in Identifying Moderate and Severe Stenosis: A Multicenter Study

RATIONALE AND OBJECTIVES

To investigate the diagnostic accuracy of subtraction coronary computed tomographic angiography (CCTAsub) in identifying ≥ 50% and ≥ 70% coronary stenosis in patients with different degrees of calcification.

MATERIALS AND METHODS

In this study, 180 patients with coronary calcified plaques who underwent both coronary CT angiography and invasive coronary angiography (ICA) were prospectively enrolled at five centers. Patients were divided into three groups according to the Agatston score: group A (low to moderate, < 400), group B (high, 400-999), and group C (very high, ≥ 1000). Diagnostic accuracies estimated by area under the receiver operating characteristic curve (AUC) were compared between conventional CCTA (CCTAcon) and CCTAsub, with ICA as a reference standard.

RESULTS

There were 86 patients in group A, 44 in group B, and 50 in group C. In identifying ≥ 70% coronary stenosis, subtraction improved the diagnostic accuracies on a per-segment basis in group B (AUC: 0.80 vs 0.92, p = 0.001) and group C (AUC: 0.75 vs 0.84, p = 0.001) after subtraction. When identifying ≥ 50% coronary stenosis, the per-segment AUC of CCTAsub in group B and C were significantly higher than that in CCTAcon (group B: 0.81 vs 0.92, p < 0.001; group C: 0.77 vs 0.88, p < 0.001). However, no improvement was observed in group A.

CONCLUSION

Subtraction achieved better diagnostic accuracy in patients with Agatston score ≥ 400, both in identifying ≥ 50% and ≥ 70% coronary stenosis, which was instructive for the application of subtraction in clinical practice.

Diagnostic performance of deep learning-based vessel extraction and stenosis detection on coronary computed tomography angiography for coronary artery disease: a multi-reader multi-case study

BACKGROUND

Post-processing and interpretation of coronary CT angiography (CCTA) imaging are time-consuming and dependent on the reader's experience. An automated deep learning (DL)-based imaging reconstruction and diagnosis system was developed to improve diagnostic accuracy and efficiency.

METHODS

Our study including 374 cases from five sites, inviting 12 radiologists, assessed the DL-based system in diagnosing obstructive coronary disease with regard to diagnostic performance, imaging post-processing and reporting time of radiologists, with invasive coronary angiography as a standard reference. The diagnostic performance of DL system and DL-assisted human readers was compared with the traditional method of human readers without DL system.

RESULTS

Comparing the diagnostic performance of human readers without DL system versus with DL system, the AUC was improved from 0.81 to 0.82 (p < 0.05) at patient level and from 0.79 to 0.81 (p < 0.05) at vessel level. An increase in AUC was observed in inexperienced radiologists (p < 0.05), but was absent in experienced radiologists. Regarding diagnostic efficiency, comparing the DL system versus human reader, the average post-processing and reporting time was decreased from 798.60 s to 189.12 s (p < 0.05). The sensitivity and specificity of using DL system alone were 93.55% and 59.57% at patient level and 83.23% and 79.97% at vessel level, respectively.

CONCLUSIONS

With the DL system serving as a concurrent reader, the overall post-processing and reading time was substantially reduced. The diagnostic accuracy of human readers, especially for inexperienced readers, was improved. DL-assisted human reader had the potential of being the reading mode of choice in clinical routine.

Impact of coronary computed tomography angiography-derived fractional flow reserve based on deep learning on clinical management

Background

To examine the value of coronary computed tomography angiography (CCTA)-derived fractional flow reserve based on deep learning (DL-FFRCT) on clinical practice and analyze the limitations of the application of DL-FFRCT.

Methods

This is an observational, retrospective, single-center study. Patients with suspected coronary artery disease (CAD) were enrolled. The patients underwent invasive coronary angiography (ICA) examination within 1 months after CCTA examination. And quantitative coronary angiography (QCA) was performed to evaluate the area stenosis rate. The CCTA data of these patients were retrospectively analyzed to calculate the FFRCT value.

Results

A total of 485 lesions of coronary arteries in 229 patients were included in the analysis. Of the lesions, 275 (56.7%) were ICA-positive, and 210 (43.3%) were FFRCT-positive. The discordance rate of the risk stratification of FFRCT for ICA-positive lesions was 33.1% (91) and that for ICA-negative lesions was 12.4% (26). 14.6% (7/48) patients with mild to moderate coronary stenosis in ICA have functional ischemia according to FFRCT positive indications. In addition, hemodynamic analysis of severely calcified, occluded, or small (< 2 mm in diameter) coronary arteries by DL-FFRCT is not so reliable.

Conclusion

This study revealed that most patients with ICA negative did not require further invasive FFR. Besides, some patients with mild to moderate coronary stenosis in ICA may also have functional ischemia. However, for severely calcified, occluded, or small coronary arteries, treatment strategy should be selected based on ICA in combination with clinical practice.

Comparison of Triple-Rule-Out Prospectively ECG-triggered Systolic and Diastolic Acquisition Protocol in Patients With Acute Chest Pain

PURPOSE

The purpose of this study was to compare image quality and coronary interpretability of triple-rule-out systolic and diastolic protocols in patients with acute chest pain.

MATERIALS AND METHODS

From March 2016 to October 2017 the authors prospectively enrolled patients with undifferentiated acute chest pain, who were at low to intermediate cardiovascular risk. Those with heart rate >75 bpm underwent a systolic prospectively triggered acquisition (systolic triggering [ST]), and in those with ≤75 bpm, end-diastolic triggering (DT) was instead performed. Examinations were evaluated for coronary artery disease, aortic dissection, and pulmonary embolism. Image quality was assessed using a Likert scale. Coronary arteries interpretability was evaluated both on a per-vessel and a per segment basis. The occurrence of major adverse cardiovascular events was investigated.

RESULTS

The final study population was 189 patients. Fifty-two patients (27.5%) underwent systolic acquisition and 137 (72.5%) underwent diastolic acquisition. No significant differences in overall image quality were observed between DT and ST groups (median score 5 [interquartile ranges 4 to 5] vs. 4 [interquartile ranges 4 to 5], P =0.074). Although both DT and ST protocols showed low percentages of noninterpretable coronary arteries on a per-vessel (1.5% and 6.7%, respectively) and per-segment analysis (1% and 4.7%, respectively), these percentages resulted significantly higher for ST groups ( P <0.001). Obstructive coronary stenosis was observed in 18 patients. Only one case of pulmonary embolism was diagnosed and no cases of aortic dissection were found in our population. No death or major adverse cardiovascular events were observed during follow-up among the 2 groups.

CONCLUSIONS

Results showed that triple-rule-out computed tomography angiography is a reliable technique in patients with acute chest pain and that an ST acquisition protocol could be considered an alternative acquisition protocol in patients with higher heart rate, reaching a good image quality.

Influence of heart rate and coronary artery calcification on image quality and diagnostic performance of coronary CT angiography: comparison between 96-row detector dual source CT and 256-row multidetector CT

BACKGROUND

CT-derived fractional flow reserve (FFRCT) and diagnostic accuracy rely on good image quality during coronary CT angiography (CCTA).

OBJECTIVE

To investigate whether heart rate (HR) and coronary artery calcium (CAC) score decrease image quality and diagnostic performance of two advanced CT scanners including 96-row detector dual source CT (DSCT) and 256-row multidetector CT (MDCT).

METHODS

First, 79 patients who underwent CCTA (42 with DSCT and 37 with MDCT) and invasive coronary angiography (ICA) are enrolled. Next, coronary segments with excellent image quality are evaluated and the percentage is calculated. Then, diagnostic accuracy in detecting significant diameter stenosis is presented with ICA as the reference standard.

RESULTS

Compared with the DSCT, the percentage of coronary segments with excellent image quality is lower (P = 0.010) while diagnostic accuracy on per-segment level is improved (P = 0.037) using MDCT. CAC score≥400 is the only independent factor influencing the percentage of coronary segments with excellent image quality [odds ratio (OR): DSCT, 3.096 and MDCT, 1.982] and segmental diagnostic accuracy (OR: DSCT, 2.630 and MDCT, 2.336) for both scanners. HR≥70 bpm (OR: 5.506) is the independent factor influencing the percentage of coronary segments with excellent image quality with MDCT.

CONCLULSION

During CCTA, CAC score≥400 still decreases the proportion of coronary segments with excellent image quality and diagnostic accuracy with advanced CT scanners. HR≥70 bpm is another factor causing image quality decreasing with MDCT.

Deep learning analysis in coronary computed tomographic angiography imaging for the assessment of patients with coronary artery stenosis

BACKGROUND AND OBJECTIVE

Recently, deep convolutional neural network has significantly improved image classification and image segmentation. If coronary artery disease (CAD) can be diagnosed through machine learning and deep learning, it will significantly reduce the burdens of the doctors and accelerate the critical patient diagnoses. The purpose of the study is to assess the practicability of utilizing deep learning approaches to process coronary computed tomographic angiography (CCTA) imaging (termed CCTA-artificial intelligence, CCTA-AI) in coronary artery stenosis.

MATERIALS AND METHODS

A CCTA reconstruction pipeline was built by utilizing deep learning and transfer learning approaches to generate auto-reconstructed CCTA images based on a series of two-dimensional (2D) CT images. 150 patients who underwent successively CCTA and digital subtraction angiography (DSA) from June 2017 to December 2017 were retrospectively analyzed. The dataset was divided into two parts comprising training dataset and testing dataset. The training dataset included the CCTA images of 100 patients which are trained using convolutional neural networks (CNN) in order to further identify various plaque classifications and coronary stenosis. The other 50 CAD patients acted as testing dataset that is evaluated by comparing the auto-reconstructed CCTA images with traditional CCTA images on the condition that DSA images are regarded as the reference method. Receiver operating characteristic (ROC) analysis was used for statistical analysis to compare CCTA-AI with DSA and traditional CCTA in the aspect of detecting coronary stenosis and plaque features.

RESULTS

AI significantly reduces time for post-processing and diagnosis comparing to the traditional methods. In identifying various degrees of coronary stenosis, the diagnostic accuracy of CCTA-AI is better than traditional CCTA (AUC_AI = 0.870, AUC_CCTA = 0.781, P < 0.001). In identifying ≥ 50% stenotic vessels, the accuracy, sensitivity, specificity, positive predictive value and negative predictive value of CCTA-AI and traditional method are 86% and 83%, 88% and 59%, 85% and 94%, 73% and 84%, 94% and 83%, respectively. In the aspect of identifying plaque classification, accuracy of CCTA-AI is moderate compared to traditional CCTA (AUC = 0.750, P < 0.001).

CONCLUSION

The proposed CCTA-AI allows the generation of auto-reconstructed CCTA images from a series of 2D CT images. This approach is relatively accurate for detecting ≥50% stenosis and analyzing plaque features compared to traditional CCTA.