Precise measurement of coronary stenosis diameter with CCTA using CT number calibration

Using Deep Learning and B-Splines to Model Blood Vessel Lumen from 3D Images

Accurate geometric modeling of blood vessel lumen from 3D images is crucial for vessel quantification as part of the diagnosis, treatment, and monitoring of vascular diseases. Our method, unlike other approaches which assume a circular or elliptical vessel cross-section, employs parametric B-splines combined with image formation system equations to accurately localize the highly curved lumen boundaries. This approach avoids the need for image segmentation, which may reduce the localization accuracy due to spatial discretization. We demonstrate that the model parameters can be reliably identified by a feedforward neural network which, driven by the cross-section images, predicts the parameter values many times faster than a reference least-squares (LS) model fitting algorithm. We present and discuss two example applications, modeling the lower extremities of artery-vein complexes visualized in steady-state contrast-enhanced magnetic resonance images (MRI) and the coronary arteries pictured in computed tomography angiograms (CTA). Beyond applications in medical diagnosis, blood-flow simulation and vessel-phantom design, the method can serve as a tool for automated annotation of image datasets to train machine-learning algorithms.

Coronary artery assessment on pre transcatheter aortic valve implantation computed tomography may avoid the need for additional coronary angiography

PURPOSE

The purpose of this study was to evaluate the percentage of coronary angiography that can be securely avoided by the interpretation of coronary arteries on pre transcatheter aortic valve implantation CT (TAVI-CT), using CT images obtained with deep-learning reconstruction and motion correction algorithms.

MATERIAL AND METHOD

All consecutive patients who underwent TAVI-CT and coronary angiography, from December 2021 to July 2022 were screened for inclusion in the study. Patients who had previous coronary artery revascularization or who did not undergo TAVI were excluded. All TAVI-CT examinations were obtained using deep-learning reconstruction and motion correction algorithms. On TAVI-CT examinations, quality and stenosis of coronary artery were analyzed retrospectively. When insufficient image quality and/or when diagnosis or doubt of one significant coronary artery stenosis, patients were considered as having possible coronary artery stenosis. The results of coronary angiography were used as the standard of reference for significant CAS.

RESULTS

A total of 206 patients (92 men; mean age, 80.6 years) were included; of these 27/206 (13%) had significant coronary artery stenosis on coronary angiography and were referred for potential revascularization. Sensitivity, specificity, negative predictive value, positive predictive value, and accuracy of TAVI-CT to identify patients requiring coronary artery revascularization was 100% (95% confidence interval [CI]: 87.2-100%), 100% (95% CI: 96.3-100%), 54% (95% CI: 46.6-61.6), 25% (95% CI: 17.0-34.0%) and 60% (95% CI: 53.1-66.9%) respectively. Intra- and inter observer variability was substantial agreement for quality and decision to recommend coronary angiography. Mean reading time was 2 ± 1.2 (standard deviation) min (range: 1-5 min). Overall, TAVI-CT could potentially rule out indication for revascularization for 97 patients (47%).

CONCLUSION

Analysis of coronary artery on TAVI-CT using deep-learning reconstruction and motion correction algorithms can potentially safely avoid coronary angiography in 47% of patients.

Quantitative analysis of small coronary artery calcium detectability with an accurate simulation and validation on a clinical CT scanner

BACKGROUND

The absence of coronary artery calcium (CAC) measured via CT is associated with very favorable prognosis, and current guidelines recommend low-density lipoprotein cholesterol (LDL-c) lowering therapy for individuals with any CAC. This motivates early detection of small granules of CAC; however, calcium scan sensitivity for detecting very low levels of calcium has not been quantified.

PURPOSE

In this work, the size limit of detectability of small calcium hydroxyapatite (CaHA) granules with clinical CAC scanning was assessed using validated simulations.

METHODS

CT projections of digital 3D mathematical phantoms containing small CaHA granules were simulated analytically; images were reconstructed using a filter designed to reproduce the point spread function of a specific commercial scanner, and a relationship of HU number versus diameter was derived. These simulation results were validated with experimental measurements of HU versus diameter from phantoms containing small granules of CaHA on a GE Revolution CT scanner in the clinic; ground truth measurements of the CaHA granule diameters were obtained using a Zeiss Xradia 510 Versa high-resolution 3D micro-CT imaging system. Using experimental measurements on the clinical CT scanner, detectability was quantified with a detectability index (d') using a non-prewhitened matched filter. The effect of changes to reconstruction slice thickness and reconstruction kernel on granule detectability was evaluated.

RESULTS

Under typical clinical calcium scanning and reconstruction conditions, the minimum detectable diameter of a simulated spherical calcium granule with a clinically relevant CaHA density was 0.76 mm. The minimum detectable volume was 2.4 times smaller on images reconstructed at a slice thickness of 0.625 mm compared to 2.5 mm. The detectability index d' increased by a factor of 1.7 when images were reconstructed with 0.625 mm slices compared to 2.5 mm slices. d' did not change when images were reconstructed with the high-resolution BONE filter compared to the less sharp STANDARD resolution filter on the GE Revolution CT.

CONCLUSIONS

We have quantified detectability versus size of small calcium granules at the resolution limit of a widely available clinical CT scanner. Detectability increased significantly with reduced slice thickness and did not change with a sharper reconstruction kernel. The simulation can be used to calculate the trade-off between dose and CAC detectability.

Pill or pump? Nitroglycerin 0.5 mg tablet vs 0.8 mg spray: Effect on proximal vessel diameters at Coronary CT Angiography (CCTA)

INTRODUCTION

The administration of sublingual Nitroglycerin (NTG) prior to CT coronary angiography (CCTA) can be perfomed using pump spray or tablets. Choice of method seems to be based on local preference, rather than published guidelines. This retrospective analysis tested whether proximal coronary diameters differed dependent on the sublingual administration of 0.5 mg Nitroglycerin (NTG) tablets or 0.8 mg NTG spray.

METHODS

287 ECG-gated CCTA studies with optimal image quality and Agatston scores<400 were included in this retrospective analysis. 143 of the patients were dosed with NTG tablets at a dose of 0.5 mg prior to CCTA. 144 patients received 2 puffs of 0.4 mg NTG spray for a total dose of 8 mg. All were scanned on a second-generation Dual Source CT. Diameters of proximal segments of Left Main (LM), Right (RCA), Left Anterior (LAD) and circumflex (CX) coronary arteries were measured using semi-automatic electronic callipers by two blinded readers. Results were summarised as the mean of maximum and minimum diameters. Sex-specific analysis of diameters was carried out using repeated-measures ANOVA for each vessel. Agreement between readers was examined with Bland-Altman analysis and intra-class-correlation coefficient (ICC).

RESULTS

No significant differences in coronary diameters were found except in the RCA for women and LM for men. In both cases, diameters were smaller in the spray group (11 and 9%, respectively). Reader agreement was excellent, with ICC>0.96 for all vessels, and no significant bias, except in CX (0.03 mm).

CONCLUSIONS

We found no evidence for the systematic superiority of either administration method in proximal coronary vessels.

IMPLICATIONS FOR PRACTICE

Choosing between tablet or spray NTG prior to CCTA can be guided by practical, economical and hygienic considerations alone.

Detection of left ventricular wall motion abnormalities from volume rendering of 4DCT cardiac angiograms using deep learning

Background

The presence of left ventricular (LV) wall motion abnormalities (WMA) is an independent indicator of adverse cardiovascular events in patients with cardiovascular diseases. We develop and evaluate the ability to detect cardiac wall motion abnormalities (WMA) from dynamic volume renderings (VR) of clinical 4D computed tomography (CT) angiograms using a deep learning (DL) framework.

Methods

Three hundred forty-three ECG-gated cardiac 4DCT studies (age: 61 ± 15, 60.1% male) were retrospectively evaluated. Volume-rendering videos of the LV blood pool were generated from 6 different perspectives (i.e., six views corresponding to every 60-degree rotation around the LV long axis); resulting in 2058 unique videos. Ground-truth WMA classification for each video was performed by evaluating the extent of impaired regional shortening visible (measured in the original 4DCT data). DL classification of each video for the presence of WMA was performed by first extracting image features frame-by-frame using a pre-trained Inception network and then evaluating the set of features using a long short-term memory network. Data were split into 60% for 5-fold cross-validation and 40% for testing.

Results

Volume rendering videos represent ~800-fold data compression of the 4DCT volumes. Per-video DL classification performance was high for both cross-validation (accuracy = 93.1%, sensitivity = 90.0% and specificity = 95.1%, κ: 0.86) and testing (90.9, 90.2, and 91.4% respectively, κ: 0.81). Per-study performance was also high (cross-validation: 93.7, 93.5, 93.8%, κ: 0.87; testing: 93.5, 91.9, 94.7%, κ: 0.87). By re-binning per-video results into the 6 regional views of the LV we showed DL was accurate (mean accuracy = 93.1 and 90.9% for cross-validation and testing cohort, respectively) for every region. DL classification strongly agreed (accuracy = 91.0%, κ: 0.81) with expert visual assessment.

Conclusions

Dynamic volume rendering of the LV blood pool combined with DL classification can accurately detect regional WMA from cardiac CT.

Accuracy of ultra-high-resolution computed tomography with a 0.3-mm detector for quantitative assessment of coronary artery stenosis grading in comparison with conventional computed tomography: A phantom study

BACKGROUND

The development of ultra-high-resolution CT (U-HRCT) is expected to improve the accuracy of coronary stenosis evaluation. This study aimed to evaluate the accuracy of the stenosis severities of coronary artery phantoms estimated using U-HRCT by comparing them to those estimated with conventional CT.

METHODS

Coronary artery phantoms with non-calcified and calcified lesions were scanned with conventional CT (64-row ​× ​0.625 ​mm) and U-HRCT (32-row ​× ​0.3125 ​mm). The coronary artery phantoms had lumen diameters of 2.0, 3.0, and 4.0 ​mm with non-calcified lesions representing 0%, 25%, 50%, and 75% stenosis and 3.0 and 4.0 ​mm with calcified lesions representing 0%, 25%, 50%, and 75% stenosis. The lumen diameters at the stenotic and non-stenotic regions were measured, and the stenosis severities were compared with the true values.

RESULTS

For non-calcified lesions, conventional CT significantly underestimated the stenosis severity in the phantom showing 75% stenosis with lumen diameters of 2.0 and 3.0 ​mm (p ​< ​0.05), while the estimated stenosis severities were not significantly different from the true values at all settings with U-HRCT. For the calcified lesions, conventional CT overestimated the stenosis severities at all settings (p ​< ​0.05), while U-HRCT yielded estimations closer to the true values, although still with some overestimation (p ​< ​0.05).

CONCLUSION

By using U-HRCT, the estimated stenosis severities of the coronary artery with non-calcified lesion become almost equal to the true value, while those with calcified lesion are still overestimated although they become closer to the true value.

Visualization of small visceral arteries on abdominal CT angiography using ultra-high-resolution CT scanner

PURPOSE

To evaluate the image quality and ability to delineate the small visceral arteries of high-resolution (HR) abdominal CT angiography (CTA) using an ultra-high-resolution computed tomography (UHR CT) scanner.

MATERIALS AND METHODS

Thirty-seven patients were enrolled who underwent abdominal CTA using a UHR CT scanner. The images were reconstructed with a matrix of 1024 × 1024 and 0.25 mm thickness for HR CTA and with a matrix of 512 × 512 and 0.5 mm thickness for normal resolution (NR) CTA. Maximum CT value, image quality, and delineation of the small arteries were compared between HR CTA and NR CTA.

RESULTS

HR CTA showed significantly higher maximum CT value, higher image quality, and better delineation of the small arteries than did NR CTA (P < .005).

CONCLUSION

HR CTA using a UHR CT scanner showed higher image quality than NR CTA and enhanced the delineation of visceral arteries.

Task-dependent estimability index to assess the quality of cardiac computed tomography angiography for quantifying coronary stenosis

Purpose: Quantifying stenosis in cardiac computed tomography angiography (CTA) images remains a difficult task, as image noise and cardiac motion can degrade image quality and distort underlying anatomic information. The purpose of this study was to develop a computational framework to objectively assess the precision of quantifying coronary stenosis in cardiac CTA.

Approach: The framework used models of coronary vessels and plaques, asymmetric motion point spread functions, CT image blur (task-based modulation transfer functions) and noise (noise-power spectrums), and an automated maximum-likelihood estimator implemented as a matched template squared-difference operator. These factors were integrated into an estimability index (e′) as a task-based measure of image quality in cardiac CTA. The e′ index was applied to assess how well it can to predict the quality of 132 clinical cases selected from the Prospective Multicenter Imaging Study for Evaluation of Chest Pain trial. The cases were divided into two cohorts, high quality and low quality, based on clinical scores and the concordance of clinical evaluations of cases by experienced cardiac imagers. The framework was also used to ascertain protocol factors for CTA Biomarker initiative of the Quantitative Imaging Biomarker Alliance (QIBA).

Results: The e′ index categorized the patient datasets with an area under the curve of 0.985, an accuracy of 0.977, and an optimal e′ threshold of 25.58 corresponding to a stenosis estimation precision (standard deviation) of 3.91%. Data resampling and training–test validation methods demonstrated stable classifier thresholds and receiver operating curve performance. The framework was successfully applicable to the QIBA objective.

Conclusions: A computational framework to objectively quantify stenosis estimation task performance was successfully implemented and was reflective of clinical results in the context of a prominent clinical trial with diverse sites, readers, scanners, acquisition protocols, and patients. It also demonstrated the potential for prospective optimization of imaging protocols toward targeted precision and measurement consistency in cardiac CT images.