A practical guide to reading CT coronary angiograms--how to avoid mistakes when assessing for coronary stenoses

Quantitative coronary computed tomography assessment for differentiating between total occlusions and severe stenoses

BACKGROUNDS

The impact of quantitative assessment to differentiate total occlusions (TOs) from severe stenoses on coronary computed tomography angiography (CCTA) remains unknown.

OBJECTIVE

This study investigated whether quantitative characteristics assessed on CCTA could help differentiate a TO from a severe stenosis on invasive coronary angiography (ICA).

METHODS

This study is a sub-analysis of the FASTTRACK CABG (NCT04142021) in which both CCTA and ICA were routinely performed. Quantitative analysis was performed with semi-automated CCTA plaque-analysis software. Blinded analysts compared TOs on CCTA, defined as a complete lack of contrast opacification within the coronary occlusion, with corresponding ICA.

RESULTS

Eighty-four TOs were seen on CCTA in 59 of the 114 patients enrolled in the trial. The concordance in diagnosing a TO between ICA and CCTA was 56.0% (n ​= ​47). Compared to severe stenoses, TOs had a significantly longer lesion length (25.1 ​± ​23.0 ​mm vs 9.4 ​± ​11.2 ​mm, P ​< ​0.001). The best cut-off value to differentiate a TO from severe stenosis was a lesion length of 5.5 ​mm (area under the curve 0.77, 95% CI: 0.66-0.87), with a 91.1% sensitivity and 61.1% specificity. Dense calcium percentage atheroma volume (PAV) was significantly higher in TOs compared to severe stenoses (18.7 ​± ​19.6% vs. 6.6 ​± ​13.0%, P ​< ​0.001), whilst the opposite was seen for fibro-fatty PAV (31.3 ​± ​14.2% vs. 19.5 ​± ​10.5%, P ​< ​0.001). On a multivariable logistic regression analysis, lesion length (>5.5 ​mm) was the only parameter associated with differentiating a TO from a severe stenosis.

CONCLUSION

In quantitative CCTA analysis, a lesion length >5.5 ​mm was the only independent predictor differentiating a TO from a severe stenosis.

NCT REGISTRATION NUMBER

NCT04142021.

Calcium evaluation using coronary computed tomography in combination with optical coherence tomography

Optical coherence tomography (OCT) can assess calcium thickness, a key factor for predicting good stent expansion; however, it underestimates coronary calcium severity due to its penetration limitation. This study aimed to evaluate computed tomography (CT) and OCT images to assess calcification. We investigated 25 left anterior descending arteries of 25 patients, using coronary CT and OCT, and assessed their calcification. Of the 25 vessels, 1811 pairs of CT and OCT cross-sectional images were co-registered. Of the 1811 cross-sectional CT images, calcification was not detectable in 256 (14.1%) of the corresponding OCT images due to limited penetration. In the 1555 OCT calcium-detectable images, the maximum calcium thickness was not detectable in 763 (49.1%) images compared to the CT images. In CT images of slices corresponding to undetected calcium in OCT images, the angle, thickness, and maximum density of calcium were significantly smaller compared to slices corresponding to detected calcium in OCT. Calcium with an undetectable maximum thickness in the corresponding OCT image had a significantly greater calcium angle, thickness, and density than calcium with a detectable maximum thickness. There was an excellent correlation between CT and OCT with respect to calcium angle ( R= 0.82, P < 0.001). The calcium thickness on the OCT image had a stronger correlation with the maximum density on the corresponding CT image (R = 0.73, P < 0.001) than with the calcium thickness on the CT image (R = 0.61, P < 0.001). Cross-sectional CT imaging allows for pre-procedural assessment of calcium morphology and severity and could complement the lack of information on calcium severity in OCT-guided percutaneous coronary intervention.

Visual Ordinal Scoring of Coronary Artery Calcium on Contrast-Enhanced and Non-Contrast Chest CT: A Retrospective Study of Diagnostic Performance and Prognostic Utility

Background: Current guidelines recommend visual evaluation of coronary artery calcium (CAC) on all non-gated non-contrast chest CT examinations. However, chest CT examinations are often performed with contrast material administration. Objective: To evaluate diagnostic performance, prognostic utility, and interobserver agreement of visual CAC assessment on chest CT performed for other indications. Methods: This retrospective study included 260 patients (mean age, 60±11 years; 158 male, 102 female) who underwent both non-gated chest CT (contrast-enhanced in 116 patients; non-contrast in 144 patients) and cardiac calcium-score CT within a 12-month interval. A cardiothoracic radiologist visually assessed CAC on chest CT using an ordinal scale (absent, mild, moderate, or severe). Cardiac CT Agatston calcium scores were quantified according to established guidelines and categorized as absent (0), mild (1-99), moderate (100-299), or severe (≥300). Diagnostic performance of chest CT for presence of CAC was assessed using cardiac CT as reference standard. Major adverse cardiac events (MACE) were assessed as a composite of cardiovascular death and myocardial infarction and evaluated using Cox proportional hazards models. A second cardiothoracic radiologist performed visual CAC assessments in a random subset of 50 chest CT examinations to assess interobserver agreement. Results: For presence of any CAC on cardiac CT, contrast-enhanced and non-contrast chest CT had sensitivity of 83% [62/75] and 90% [85/95] (p=.20) and specificity of 100% [41/41] and 100% [49/49] (p=.99). CAC present on cardiac CT was misclassified as absent on 13 contrast-enhanced and 10 non-contrast chest CT examinations; Agatston score was less than 30 in all such patients, and none experienced MACE. Visual ordinal CAC score was associated with MACE for contrast-enhanced [hazard ratio (HR)=4.5 [95% CI 1.2, 16.4], p=.02) and non-contrast (HR=3.4 [95% CI 1.5, 7.8], p=.003) chest CT. Interobserver agreement was excellent for contrast-enhanced (κ =0.95) and non-contrast (κ =0.89) chest CT. Conclusions: Visual ordinal CAC assessment on both contrast-enhanced and non-contrast chest CT has high diagnostic performance, prognostic utility, and interobserver agreement. Clinical Impact: Routine reporting of CAC on all chest CT examinations regardless of clinical indication and contrast material administration could identify a large number of patients with previously unknown CAC who might benefit from preventive treatment.

Incremental improvement of diagnostic performance of coronary CT angiography for the assessment of coronary stenosis in the presence of calcium using a dual-layer spectral detector CT: validation by invasive coronary angiography

To investigate value of spectral reconstructions for the quantification of coronary stenosis in the presence of calcified or partially calcified plaques using a dual-layer spectral detector CT (SDCT). Seventy-two consecutive patients were retrospectively enrolled. Conventional 120 kVp images, eight virtual monoenergetic images (VMI) (70 to 140 keV), the effective atomic number (Z effective) and iodine no water images were reconstructed. Invasive coronary angiography was used as the reference standard. Parallel and serial testing were used to assess the incremental diagnostic value of Z effective and iodine no water images to the best VMI series. 122 coronary lesions of 72 patients (49 men and 23 women; 63.7 ± 10.2 years) were enrolled in analysis. Reconstruction at 100 keV yielded optimal diagnostic performance, the sensitivity, specificity, PPV, NPV and diagnostic accuracy to identify stenosis ≥ 50% or ≥ 70% were 84%, 70%, 80%, 76%, 79% and 78%, 98%, 93%, 91%, 92%, respectively. A serial combination (100 keV VMI followed by Z effective images) resulted in an improved specificity (from 70 to 80%) with a moderate loss of sensitivity (81% from 84%) in identifying ≥ 50% stenosis (P = 0.021). For patients with high Agatston score, this combination could further reduce false positive cases and improve diagnostic accuracy. 100 keV VMI provide optimal diagnostic performance for the detection of coronary stenosis in the presence of calcified or partially calcified plaques using a dual-layer SDCT, with further improvements obtained with the combined use of Z effective images.

Deep learning powered coronary CT angiography for detecting obstructive coronary artery disease: The effect of reader experience, calcification and image quality

OBJECTIVES

To investigate the effect of reader experience, calcification and image quality on the performance of deep learning (DL) powered coronary CT angiography (CCTA) in automatically detecting obstructive coronary artery disease (CAD) with invasive coronary angiography (ICA) as reference standard.

METHODS

A total of 165 patients (680 vessels and 1505 segments) were included in this study. Three sessions were performed in order: (1) The artificial intelligence (AI) software automatically processed CCTA images, stenosis degree and processing time were recorded for each case; (2) Six cardiovascular radiologists with different experiences (low/ intermediate/ high experience) independently performed image post-processing and interpretation of CCTA, (3) AI + human reading was performed. Luminal stenosis ≥50% was defined as obstructive CAD in ICA and CCTA. Diagnostic performances of AI, human reading and AI + human reading were evaluated and compared on a per-patient, per-vessel and per-segment basis with ICA as reference standard. The effects of calcification and image quality on the diagnostic performance were also studied.

RESULTS

The average post-processing and interpretation times of AI was 2.3 ± 0.6 min per case, reduced by 76%, 72%, 69% compared with low/ intermediate/ high experience readers (all P < 0.001), respectively. On a per-patient, per-vessel and per-segment basis, with ICA as reference method, the AI overall diagnostic sensitivity for detecting obstructive CAD were 90.5%, 81.4%, 72.9%, the specificity was 82.3%, 93.9%, 95.0%, with the corresponding areas under the curve (AUCs) of 0.90, 0.90, 0.87, respectively. Compared to human readers, the diagnostic performance of AI was higher than that of low experience readers (all P < 0.001). The diagnostic performance of AI + human reading was higher than human reading alone, and AI + human readers' ability to correctly reclassify obstructive CAD was also improved, especially for low experience readers (Per-patient, the net reclassification improvement (NRI) = 0.085; per-vessel, NRI = 0.070; and per-segment, NRI = 0.068, all P < 0.001). The diagnostic performance of AI was not significantly affected by calcification and image quality (all P > 0.05).

CONCLUSIONS

AI can substantially shorten the post-processing time, while AI + human reading model can significantly improve the diagnostic performance compared with human readers, especially for inexperienced readers, regardless of calcification severity and image quality.

Lower extremity CT angiography in peripheral arterial disease: from the established approach to evolving technical developments

With the advent of multidetector computed tomography (CT), CT angiography (CTA) has gained widespread popularity for noninvasive imaging of the arterial vasculature. Peripheral extremity CTA can nowadays be performed rapidly with high spatial resolution and a decreased amount of both intravenous contrast and radiation exposure. In patients with peripheral artery disease (PAD), this technique can be used to delineate the bilateral lower extremity arterial tree and to determine the amount of atherosclerotic disease while differentiating between acute and chronic changes. This article provides an overview of several imaging techniques for PAD, specifically discusses the use of peripheral extremity CTA in patients with PAD, clinical indications, established technical considerations and novel technical developments, and the effect of postprocessing imaging techniques and structured reporting.

Artificial Intelligence in plastic surgery: What is it? Where are we now? What is on the horizon?

INTRODUCTION

An increasing quantity of data is required to guide precision medicine and advance future healthcare practices, but current analytical methods often become overwhelmed. Artificial intelligence (AI) provides a promising solution. Plastic surgery is an innovative surgical specialty expected to implement AI into current and future practices. It is important for all plastic surgeons to understand how AI may affect current and future practice, and to recognise its potential limitations.

METHODS

Peer-reviewed published literature and online content were comprehensively reviewed. We report current applications of AI in plastic surgery and possible future applications based on published literature and continuing scientific studies, and detail its potential limitations and ethical considerations.

FINDINGS

Current machine learning models using convolutional neural networks can evaluate breast mammography and differentiate benign and malignant tumours as accurately as specialist doctors, and motion sensor surgical instruments can collate real-time data to advise intraoperative technical adjustments. Centralised big data portals are expected to collate large datasets to accelerate understanding of disease pathogeneses and best practices. Information obtained using computer vision could guide intraoperative surgical decisions in unprecedented detail and semi-autonomous surgical systems guided by AI algorithms may enable improved surgical outcomes in low- and middle-income countries. Surgeons must collaborate with computer scientists to ensure that AI algorithms inform clinically relevant health objectives and are interpretable. Ethical concerns such as systematic biases causing non-representative conclusions for under-represented patient groups, patient confidentiality and the limitations of AI based on the quality of data input suggests that AI will accompany the plastic surgeon, rather than replace them.

Implementing Precision Medicine and Artificial Intelligence in Plastic Surgery: Concepts and Future Prospects

Precision medicine, or the individualization of evidence-based medicine, is forthcoming. As surgeons, we must be prepared for the integration of patient and system factors. Plastic surgeons regard themselves as innovators and early adopters. As such, we need our adaptability now more than ever to implement digital advancements and precision medicine into our practices. The integration of artificial intelligence (AI) technology and the capture of big data techniques should foster the next great leaps in medicine and surgery, allowing us to capture the detailed minutiae of precision medicine. The algorithmic process of artificial neural networks will guide large-scale analysis of data, including features such as pattern recognition and rapid quantification, to organize and distribute data to surgeons seamlessly. This vast digital collection of information, commonly termed “big data,” is only one potential application of AI. By incorporating big data, the cognitive abilities of a surgeon can be complemented by the computer to improve patient-centered care. Furthermore, the use of AI will provide individual patients with increased access to the broadening world of precision medicine. Therefore, plastic surgeons must learn how to use AI within the contexts of our practices to keep up with an evolving field in medicine. Although rudimentary in its practice, we present a glimpse of the potential applications of AI in plastic surgery to incorporate the practice of precision medicine into the care that we deliver.

Heavily Calcified Coronary Arteries: Advanced Calcium Subtraction Improves Luminal Visualization and Diagnostic Confidence in Dual-Energy Coronary Computed Tomography Angiography

OBJECTIVES

The aim of this study was to evaluate a prototype dual-energy computed tomography calcium subtraction algorithm and its impact on luminal visualization in patients with heavily calcified coronary arteries.

MATERIALS AND METHODS

Twenty-nine patients (62% male; mean age, 64 ± 7 years) who had undergone dual-energy coronary computed tomography angiography were retrospectively included in this institutional review board-approved, Health Insurance Portability and Accountability Act-compliant study. Linearly blended (M_0.6) and calcium-subtracted images were reconstructed. Two independent observers assessed luminal visualization of the coronary arteries in a segment-based analysis, subjective image quality, and diagnostic confidence using 5-point Likert scales. Contrast-to-noise ratios for both data sets were calculated. Wilcoxon testing and Cohen's κ were used for statistical comparisons.

RESULTS

Calcium-subtracted image series showed improved lumen visualization of the coronary arteries (P = 0.008), with excellent interreader agreement (mean score, 3.3; κ = 0.82), compared with M_0.6 series (mean score, 2.9; κ = 0.77). The calcium subtraction algorithm improved diagnostic confidence compared with the M_0.6 reconstructions (mean scores, 4.0 and 3.1, respectively; all P ≤ 0.002). The image quality analysis showed no significant differences between calcium-subtracted and M_0.6 data sets (subjectively: mean scores, 4.1 and 4.2, respectively, P = 0.442; objectively: mean contrast-to-noise ratio, 37.0 and 38.2, respectively, P = 0.733).

CONCLUSIONS

A prototype algorithm for calcium subtraction improves coronary lumen visualization and diagnostic confidence in patients with heavy coronary calcifications without differences in conventional subjective and objective measures of image quality.

Selective Inferior Mesenteric Artery Embolization during Endovascular Abdominal Aortic Aneurysm Repair to Prevent Type II Endoleak

PURPOSE

The purpose of this study was to evaluate the efficacy of simultaneous IMA (s-IMA) embolization during the endovascular abdominal aortic aneurysm repair (EVAR).

MATERIALS AND METHOD

From July 2007 to January 2011, 189 patients in the no embolization (NE) group underwent EVAR without the indication for s-IMA embolization. Since February 2011 to April 2014, 143 patients have undergone EVAR. Among these patients, 26 patients underwent s-IMA embolism under a predefined indication and constituted the simultaneous embolization (SE) group. The indications for s-IMA embolization were defined by preoperative computed tomography (CT) findings, as follows: (1) the diameter was greater than 2.5 mm and (2) no stenosis due to thrombus or calcification at its orifice.

RESULTS

The incidence of a type II endoleak from the IMA was 3.4% (5/143) in the SE group patients and 13.2% (25/189) in the NE group patients (p = 0.013), and the incidence of a type II endoleak from all branches (i.e., IMA, lumbar, medial sacral arteries) was 15.4% (22/143) in the SE group patients and 32.3% (61/189) in the NE group patients (p = 0.0003). During the follow-up period (range, 6-72 months; mean: 28 months), the reintervention rate for a type II endoleak from the IMA and/or other branches was 9.5% (18/189) in the NE group and 0.6% (1/143) in the SE group (p = 0.0001).

CONCLUSION

In selected patients, performing an s-IMA embolization, based on CT findings, decreased the incidence of a type II endoleak and reintervention from the IMA and from all branches.

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

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.

Quantification of coronary arterial stenoses by multidetector CT angiography in comparison with conventional angiography methods, caveats, and implications

Multidetector computed tomography (MDCT) is a rapidly evolving technology for performing noninvasive coronary angiography. Despite good sensitivity and specificity for detecting significant coronary artery disease in patients, disagreement on individual coronary arterial stenosis severity is common between MDCT and the current gold standard, conventional angiography. The reasons for such disagreement are numerous, but are at least partly inherent to MDCT's modest spatial and temporal resolution at present. Less well acknowledged, however, is the fact that MDCT and conventional angiography are fundamentally different technologies, rendering good agreement on the degree of lumen narrowing rather unrealistic, given both of their respective limitations. Discrepant stenosis assessment by MDCT and conventional angiography receives remarkable attention, whereas its significance for patient outcome is less certain. On the other hand, the ability to noninvasively assess coronary arterial plaque characteristics and composition in addition to lumen obstruction shows strong promise for improved risk assessment and may at last enable us to move beyond mere coronary stenosis assessment for the management of patients with coronary artery disease.

Quantitative assessment of image quality in 64-slice-computed tomography of coronary arteries in subjects undergoing screening for coronary artery disease

Accurate and consistent visualization of the entire coronary system with high-grade imaging quality is crucial for routine applications of multi-detector-computed tomography (MDCT) coronary angiography. To determine the imaging quality of 64-slice-MDCT coronary angiography, we respectively explored the quantitative parameters of imaging quality in 105 consecutive subjects (71 men, 34 women; aged 58.66 +/- 10.62 years) who underwent 64-slice-MDCT coronary angiography to screen for coronary disease. The interobserver agreement for semi-quantitative image quality, visible length, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the coronary arteries was good. The SNR and CNR of the proximal segments of the coronary arteries were superior to that of the distal segments of coronary arteries (p < 0.001). The visible length of the stenosed right coronary artery was significantly shorter than that of the non-stenosed right coronary artery (p = 0.03). The SNR and CNR of the stenosed and non-stenosed coronary arteries revealed no significant difference (p > 0.05). Body weight and body mass index were inversely related to the SNR and CNR of the aorta (p < 0.001). In conclusion, 64-slice-MDCT coronary angiography can provide excellent imaging quality of coronary arteries in subjects undergoing screening for coronary disease, although the SNR and CNR were relatively low at the distal segments of coronary arteries.

Automated computer-aided stenosis detection at coronary CT angiography: initial experience

OBJECTIVE

To evaluate the performance of a computer-aided algorithm for automated stenosis detection at coronary CT angiography (cCTA).

METHODS

We investigated 59 patients (38 men, mean age 58 +/- 12 years) who underwent cCTA and quantitative coronary angiography (QCA). All cCTA data sets were analyzed using a software algorithm for automated, without human interaction, detection of coronary artery stenosis. The performance of the algorithm for detection of stenosis of 50% or more was compared with QCA.

RESULTS

QCA revealed a total of 38 stenoses of 50% or more of which the algorithm correctly identified 28 (74%). Overall, the automated detection algorithm had 74%/100% sensitivity, 83%/65% specificity, 46%/58% positive predictive value, and 94%/100% negative predictive value for diagnosing stenosis of 50% or more on per-vessel/per-patient analysis, respectively. There were 33 false positive detection marks (average 0.56/patient), of which 19 were associated with stenotic lesions of less than 50% on QCA and 14 were not associated with an atherosclerotic surrogate.

CONCLUSION

Compared with QCA, the automated detection algorithm evaluated has relatively high accuracy for diagnosing significant coronary artery stenosis at cCTA. If used as a second reader, the high negative predictive value may further enhance the confidence of excluding significant stenosis based on a normal or near-normal cCTA study.

Initial evaluation of coronary images from 320-detector row computed tomography

PURPOSE

To evaluate image quality and contrast opacification from coronary images acquired from 320-detector row computed tomography (CT). Patient dose is estimated for prospective and retrospective ECG-gating; initial correlation between 320-slice CT and coronary catheterization is illustrated.

METHODS

Retrospective image evaluation from forty consecutive patients included subjective assessment of image quality and contrast opacification (80 ml iopamidol 370 mg I/ml followed by 40 ml saline). Region of interest opacification measurements at the ostium and at 2.5 mm diameter were used to determine the gradient of contrast opacification (defined as the proximal minus distal HU measurements) in coronary arteries imaged in a single heartbeat. Estimated effective dose was compared for prospective versus retrospective ECG-gating, two body mass index categories (30 kg/m(2) cutoff), and single versus two heartbeat acquisition. When available, CT findings were correlated with those from coronary catheterization.

RESULTS

Over 89% of arterial segments (15 segment model) had excellent image quality. The most common reason for image degradation was cardiac motion. One segment in one patient was considered unevaluable. Contrast opacification was almost universally considered excellent. The mean Hounsfield units (HU) was greater than 350; the coronary contrast opacification gradient was 30-50 HU. Patient doses were greater for retrospective ECG-gating, larger patients, and those imaged with two heartbeats. For the most common (n=25) protocol (120 kV, 400 mA, prospective ECG-gating, 60-100% phase window, 16 cm craniocaudal coverage, single heartbeat), the mean dose was 6.8+/-1.4 mSv. All CT findings were confirmed in the four patients who underwent coronary catheterization.

CONCLUSION

Initial 320-detector row coronary CT images have consistently excellent quality and iodinated contrast opacification. These patients were scanned with conservative protocols with respect to iodine load, prospective ECG-gating phase window, and craniocaudal coverage. Future work will focus on lowering contrast and radiation dose while maintaining image quality.