Detection Of Coronary Artery Calcification Research Articles

Enhanced CT imaging artificial neural network coronary artery calcification score assisted diagnosis.

The study of coronary artery calcification (CAC) may assist in identifying additional coronary artery problem protective factors. On the contrary side, due to the wide variety of CAC as individuals, CAC research is difficult. Due to this, evaluating data for investigation is becoming complicated. To use a multi-layer perceptron, we investigated the accuracy and reliability of synthetic CAC coursework or hazard classification in pre or alors chest computerized tomography (CT) of arrangements resolutions in this analysis. Photographs of the chest from similar individuals as well as calcium-just and non-gated pictures were incorporated. This cut thickness ordered CT pictures (bunch A: 1 mm; bunch B: 3 mm). The CAC rating was determined utilizing calcification score picture information, and became standard for tests. While the control treatment's machine learning program was created using 170 computed tomography pictures and evaluated using 144 scans, group A's machine learning algorithm was created using 150 chest CT diagnostic tests. 334 external related pictures (100 μm: 117; 0.5 mm x: 117) of 117 individuals and 612 inside design organizing (1 mm: 294; mm3: 314) of 406 patients were surveyed. Pack B had 0.94, however, tests An and b had 0.90 (95% CI: 0.85-0.93) ICCs between significant learning and gold expenses (0.92-0.96). Dull Altman plots agreed well. A machine teaching approach successfully identified 71% of cases in category A is 81% of patients in section B again for cardiac risk class. Regression risk evaluation algorithms could assist in categorizing cardiorespiratory individuals into distinct risk groups and conveniently personalize the treatments to the patient's circumstances. The models would be based on information gathered through CAC. On both 1 and 3-mm scanners, the automatic determination of a CAC value and cardiovascular events categorization that used a depth teaching approach was reliable and precise. The layer thickness of 0.5 mm on chest CT was slightly less accurate in CAC detection and risk evaluation.

Artificial Intelligence in Coronary Artery Calcium Scoring Detection and Quantification.

Coronary artery calcium (CAC) is a marker of coronary atherosclerosis, and the presence and severity of CAC have been shown to be powerful predictors of future cardiovascular events. Due to its value in risk discrimination and reclassification beyond traditional risk factors, CAC has been supported by recent guidelines, particularly for the purposes of informing shared decision-making regarding the use of preventive therapies. In addition to dedicated ECG-gated CAC scans, the presence and severity of CAC can also be accurately estimated on non-contrast chest computed tomography scans performed for other clinical indications. However, the presence of such "incidental" CAC is rarely reported. Advances in artificial intelligence have now enabled automatic CAC scoring for both cardiac and non-cardiac CT scans. Various AI approaches, from rule-based models to machine learning algorithms and deep learning, have been applied to automate CAC scoring. Convolutional neural networks, a deep learning technique, have had the most successful approach, with high agreement with manual scoring demonstrated in multiple studies. Such automated CAC measurements may enable wider and more accurate detection of CAC from non-gated CT studies, thus improving the efficiency of healthcare systems to identify and treat previously undiagnosed coronary artery disease.

Coronary artery calcium scoring assessment in ultra-low-dose chest computed tomography

ObjectivesTo investigate the effect of non-electrocardiogram (ECG) -triggered ultra-low-dose CT (ULD-CT) with different reconstruction protocols on coronary artery calcium (CAC) scoring assessment, compared with ECG-triggered CAC CT (CAC-CT). MethodsThis prospective study included 115 patients who underwent CAC-CT and ULD-CT scans under the same topogram images. CAC-CT adopted a prospective ECG-triggered sequential acquisition with a tube potential of 120 kV, and the reconstruction protocol was standard Qr36 + slice 3 mm (CACQr-3mm group). ULD-CT adopted a non-ECG-triggered high-pitch acquisition with a tube potential of Sn100 kV, and four groups of images (named ULDQr-3mm, ULDSa-3mm, ULDQr-1.5mm, and ULDSa-1.5mm) were reconstructed using different reconstruction algorithms (standard Qr36, kV-independent Sa36) and slice thicknesses (3 mm, 1.5 mm). The accuracy of CAC detection by ULD-CT was calculated. The agreement of the CAC score between ULD-CT and CAC-CT scans was assessed using intraclass correlation coefficients (ICC) and Bland-Altman plot, and the agreement of risk categorization was assessed using weighted kappa. ResultsThe sensitivity and specificity of the ULDSa-1.5mm group for detecting positive CAC were 100% and 97.4%, respectively (k = 0.980). The CAC score for the ULDSa-3mm and ULDSa-1.5mm groups demonstrated excellent agreement with the CACQr-3mm group (ICC = 0.992, 0.990, respectively), with a mean difference of −12.3 and − 12.4. The agreement of risk categorization based on absolute and percentile CAC score between the ULDSa-1.5mm and CACQr-3mm groups was excellent (weighted k = 0.954, 0.983, respectively), and risk reclassification rates were low (3.5%, 2.8%, respectively). The effective dose was reduced by approximately 77.2% for the ULD-CT compared to the CAC-CT (0.18 mSv vs. 0.79 mSv, p < 0.001). ConclusionReconstruction with a 1.5-mm slice thickness and kV-independent iterative algorithmic protocol in ULD-CT yielded excellent agreement in CAC score quantification and risk categorization compared with ECG-triggered CAC-CT.

Cardiovascular Applications of Photon-Counting CT Technology: A Revolutionary New Diagnostic Step.

Photon-counting computed tomography (PCCT) is an emerging technology that can potentially transform clinical CT imaging. After a brief description of the PCCT technology, this review summarizes its main advantages over conventional CT: improved spatial resolution, improved signal and contrast behavior, reduced electronic noise and artifacts, decreased radiation dose, and multi-energy capability with improved material discrimination. Moreover, by providing an overview of the existing literature, this review highlights how the PCCT benefits have been harnessed to enhance and broaden the diagnostic capabilities of CT for cardiovascular applications, including the detection of coronary artery calcifications, evaluation of coronary plaque extent and composition, evaluation of coronary stents, and assessment of myocardial tissue characteristics and perfusion.

Application of an artificial intelligence ensemble for detection of important secondary findings on lung ventilation and perfusion SPECT-CT

RationaleSingle-photon-emission-computerized-tomography/computed-tomography(SPECT/CT) is commonly used for pulmonary disease. Scant work has been done to determine ability of AI for secondary findings using low-dose-CT(LDCT) attenuation correction series of SPECT/CT. Methods120 patients with ventilation-perfusion-SPECT/CT from 9/1/21–5/1/22 were included in this retrospective study. AI-RAD companion(VA10A,Siemens-Healthineers, Erlangen, Germany), an ensemble of deep-convolutional-neural-networks was evaluated for the detection of pulmonary nodules, coronary artery calcium, aortic ectasia/aneurysm, and vertebral height loss. Accuracy, sensitivity, specificity was measured for the outcomes. Inter-rater reliability were measured. Inter-rater reliability was measured using the intraclass correlation coefficient (ICC) by comparing the number of nodules identified by the AI to radiologist. ResultsOverall per-nodule accuracy, sensitivity, and specificity for detection of lung nodules were 0.678(95%CI 0.615–0.732), 0.956(95%CI 0.900–0.985), and 0.456(95%CI 0.376–0.543), respectively, with an intraclass correlation coefficient (ICC) between AI and radiologist of 0.78(95%CI 0.71–0.83). Overall per-patient accuracy for AI detection of coronary artery calcium, aortic ectasia/aneurysm, and vertebral height loss was 0.939(95%CI 0.878–0.975), 0.974(95%CI 0.925–0.995), and 0.857(95%CI 0.781–0.915), respectively. Sensitivity for coronary artery calcium, aortic ectasia/aneurysm, and vertebral height loss was 0.898(95%CI 0.778–0.966), 1 (95%CI 0.958–1), and 1 (95%CI 0.961–1), respectively. Specificity for coronary artery calcium, aortic ectasia/aneurysm, and vertebral height loss was 0.969(95% CI 0.893–0.996), 0.897 (95% CI 0.726–0.978), and 0.346 (95% CI 0.172–0.557), respectively. ConclusionAI ensemble was accurate for coronary artery calcium and aortic ectasia/aneurysm, while sensitive for aortic ectasia/aneurysm, lung nodules and vertebral height loss on LDCT attenuation correction series of SPECT/CT.

CADNet: an advanced architecture for automatic detection of coronary artery calcification and shadow border in intravascular ultrasound (IVUS) images.

Intravascular Ultrasound (IVUS) is a medical imaging modality widely used for the detection and treatment of coronary heart disease. The detection of vascular structures is extremely important for accurate treatment procedures. Manual detection of lumen and calcification is very time-consuming and requires technical experience. Ultrasound imaging suffers from the generation of artifacts which obstructs the clear delineation among structures. Considering, the need, to provide special attention to crucial areas, convolutional block attention modules (CBAM) is integrated into an encoder-decoder-based U-Net architecture along with Atrous Spatial Pyramid Pooling (ASPP) to detect vessel components: lumen, calcification and shadow borders. The attention modules prove effective in dealing with areas of special attention by assigning additional weights to crucial channels and preserving spatial features. The IVUS data of 12 patients undergoing the treatment is taken for this study. The novelty of the model design is such that it is able to detect the lumen area in the presence/absence of calcification and bifurcation artifacts too. Also, the model efficiently detects the calcification area even in case of severely complex lesions with shadows behind them. The main contribution of the work is that IVUS images of varying degrees of calcification till 360° are also considered in this work, which is usually neglected in previous studies. The experimental results of 1097 IVUS images of 12 patients resulted in meanIoU (0.7894 ± 0.011), Dice Coefficient (0.8763 ± 0.070), precision (0.8768 ± 0.069) and recall (0.8774 ± 0.071) of the proposed model CADNet which show the model's effectiveness relative to other state-of-the art methods.

A deep convolutional neural network ensemble for composite identification of pulmonary nodules and incidental findings on routine PET/CT

To evaluate primary and secondary pathologies of interest using an artificial intelligence (AI) platform, AI-Rad Companion, on low-dose computed tomography (CT) series from integrated positron-emission tomography (PET)/CT to detect CT findings that might be overlooked. One hundred and eighty-nine sequential patients who had undergone PET/CT were included. Images were evaluated using an ensemble of convolutional neural networks (AI-Rad Companion, Siemens Healthineers, Erlangen, Germany). The primary outcome was detection of pulmonary nodules for which the accuracy, identity, and intra-rater reliability was calculated. For secondary outcomes (binary detection of coronary artery calcium, aortic ectasia, vertebral height loss), accuracy and diagnostic performance were calculated. The overall per-nodule accuracy for detection of lung nodules was 0.847. The overall sensitivity and specificity for detection of lung nodules was 0.915 and 0.781. The overall per-patient accuracy for AI detection of coronary artery calcium, aortic ectasia, and vertebral height loss was 0.979, 0.966, and 0.840, respectively. The sensitivity and specificity for coronary artery calcium was 0.989 and 0.969. The sensitivity and specificity for aortic ectasia was 0.806 and 1. The neural network ensemble accurately assessed the number of pulmonary nodules and presence of coronary artery calcium and aortic ectasia on low-dose CT series of PET/CT. The neural network was highly specific for the diagnosis of vertebral height loss, but not sensitive. The use of the AI ensemble can help radiologists and nuclear medicine physicians to catch CT findings that might be overlooked.

Development of a Web Application for the Detection of Coronary Artery Calcium from Computed Tomography

Coronary atherosclerosis is the most common form of cardiovascular diseases, which represent the leading global cause of mortality in the adult population. The amount of coronary artery calcium (CAC) is a robust predictor of this disease that can be measured using the medical workstations of computed tomography (CT) equipment or specialized tools included in commercial software for DICOM viewers, which is not available for all operating systems. This manuscript presents a web application that semiautomatically quantifies the amount of coronary artery calcium (CAC) on the basis of the coronary calcium score (CS) using the Agatston technique through digital image processing. To verify the correct functioning of this web application, 30 CTCSs were analyzed by a cardiologist and compared to those of commercial software (OsiriX DICOM Viewer).All the scans were correctly classified according to the cardiovascular event risk group, with an average error in the calculation of CS of 1.9% and a Pearson correlation coefficient r = 0.9997, with potential clinical application.

Abstract 13215: Accuracy of Visual Qualitative Estimates of CAC Incidentally Found on Chest CT Compared With Formal CAC Scoring

Introduction: While coronary artery calcium (CAC) scoring improves CVD risk assessment, it remains underutilized and is often not covered by insurance. Coronary calcium can be detected on chest CT scans performed for other indications and may allow identification of higher-risk people without a dedicated CAC scan. However, qualitative CAC scoring on non-gated chest CT scans is not widely validated. Methods: Since 2016, radiologists at two large health systems in Dallas (an academic medical system and a large safety net hospital) have routinely documented the presence and severity of CAC (none, mild, moderate, large) on all chest CT scans. We identified all adults who underwent both dedicated CAC scanning and a separate chest CT scan within 6 months. We compared qualitative visual CAC by CT scan versus a CAC score from a dedicated, gated coronary CT scan. Results: From 2016-2021, 891 patients (mean age 60 ± 12y, 43% male, 61% White, 35% Black, 24% Hispanic) underwent both a CT scan and a CAC score. Of these, 424 had no CAC, 268 small, 137 moderate, and 62 large CAC on CT scan. Qualitative CAC estimate from non-gated CT scans were highly correlated with formal CAC scores (Kendalls tau-b=0.75, p&lt;0.0001, figure). To detect CAC (score &gt;0), qualitative assessment on CT had a sensitivity of 82%, specificity of 95%, positive predictive value (PPV) of 97%, and negative predictive value (NPV) of 77%. Only 6% with small, 1% with moderate, and 0% with large CAC on CT had a CAC score = 0. For detection of CAC ≥ 100, moderate to large CAC on CT had a sensitivity of 62%, specificity of 96%, PPV of 89%, and NPV of 84%. Discussion: At two different hospitals serving a diverse population, visual estimates of CAC on routine CT scans were strongly correlated with formal CAC scores from dedicated, gated CT scans. Incidental identification of CAC on routine CT scans should prompt more aggressive cardiovascular risk reduction, particularly when moderate to large CAC burden is seen.

Prognostic impact of coronary artery calcifications in patients with newly diagnosed prostate cancer

Abstract Background Patients diagnosed with prostate cancer (PCa) are known to be at increased risk of cardiovascular events. Because of the ageing population and better cancer treatment, the number of these patients is increasing. A prostate-specific membrane antigen positron emission tomography (PSMA-PET) is guideline recommended as part of the diagnostic workup. This scan includes a non-ECG-gated computed tomography (CT) of the thorax, allowing assessment of coronary artery calcifications (CAC). It is not well-established, whether estimation of CAC based on this CT scan can identify patients with increased risk of major adverse cardiac events (MACE) and thus possibly improve therapy and prognosis. Purpose This study aims to assess CAC and evaluate whether high calcium scores predict the risk of MACE in patients newly diagnosed with PCa. Methods We identified 634 consecutive patients diagnosed with PCa and undergoing PSMA-PET from 2016 to 2019 in an area of 300.000 residents. In total, 63 patients were excluded due to prior revascularization. The remaining 571 patients (mean age 69.9±6.7 years) were included. Patients were followed until June 2021 or until an event occurred (mean follow-up 962±372 days). We used a standard cardiac CT software to assess CAC. Information on MACE defined as all-cause mortality, stroke and myocardial infarction were obtained from electronic patient files. We divided patients into five groups based on their coronary artery calcium score (CACS): 0–10, 11–99, 100–399, 400–999, and + 1000. Cox regression analysis was used to compute the hazard ratios (HR) for MACE with CACS 0–10 as reference while adjusting for age, hypertension diabetes, and treatment with aspirin and statins. Results We found a significantly increasing risk of MACE with increasing CACS after adjustment for risk factors. For CACS 100–399 (n=136): HR 2.10 (95% CI: 0.98–4.51, p=0.056), for CACS 400–999 (n=79): HR of 2.99 (95% CI: 1.35–6.59, p=0.007) and for CACS +1000 (n=150): HR 3.06 (95% CI: 1.37–6.85, p=0.006) (Figure 1). Cardiovascular disease prevention treatment with statins and aspirin was 53% and 16% in patients with CACS 400–999 and 52% and 19% in patients with CACS + 1000. Conclusion In patients with PCa, estimating coronary calcium score based on non-ECG-gated CT scans, which are performed as part of the diagnostic workup, predicts the occurrence of MACE. In the patient group with the highest CAC, a four-year event rate of 24% was found. In combination with the finding of infrequent use of statin and aspirin, this emphasizes the possibility of early detection of high CAC in patients with PCa and illustrates the potential for more extensive use of preventive treatment. Future studies should investigate the effect of preventive treatment being initiated in patients with PCa and high CAC. Funding Acknowledgement Type of funding sources: None.

Extra-coronary Calcification and Cardiovascular Events: What Do We Know and Where Are We Heading?

The coronary artery calcium score is a guideline-endorsed aid for further risk stratification in the primary prevention of atherosclerotic cardiovascular disease. The non-contrast scan performed for detection of coronary artery calcium also gives an opportunity to visualize calcifications in the thoracic aorta and in the heart valves, at no additional cost or radiation exposure. The purpose of this review was to discuss the potential clinical value of measuring thoracic aortic calcification, aortic valve calcification, and mitral annulus calcification. After two decades of active research, all three calcifications have been extensively evaluated, across various cohorts. We discuss classic and recent studies, current knowledge gaps, and future directions in this space. The added value of these measurements has traditionally been considered modest at best, and they are not currently discussed in relevant primary prevention guidelines in North America and Europe. However, recent studies evaluating high thoracic calcification thresholds and younger populations have further enriched this space. Specifically, some studies suggest that detection of severe thoracic aortic calcification may be helpful in further risk assessment and that detection of aortic valve calcifications may have important prognostic implications in younger individuals. Although more research is needed, particularly in larger young-to-middle-aged cohorts, future guidelines might consider including these features as risk-enhancing factors.

Automatic coronary artery calcium scoring on routine chest computed tomography (CT): comparison of a deep learning algorithm and a dedicated calcium scoring CT.

The aim of this study was to investigate the reliability and accuracy of automatic coronary artery calcium (CAC) scoring and risk classification in non-gated, non-contrast chest computed tomography (CT) of different slice thicknesses using a deep learning algorithm. This retrospective study was performed at 2 tertiary hospitals. Paired, dedicated calcium-scoring CT scans and non-gated, non-contrast chest CT scans taken within a month from the same patients were included. Chest CT images were grouped according to the slice thickness (group A: 1 mm; group B: 3 mm). For internal scans, the CAC score manually measured on dedicated calcium scoring CT images was used as the gold standard. The deep learning algorithm for group A was trained using 150 chest CT scans and tested using 144 scans, and that for group B was trained using 170 chest CT scans and tested using 144 scans. The intraclass correlation coefficient (ICC) was used to evaluate the correlation between the algorithm and the gold standard. Agreement between the deep learning algorithm, the manual results on chest CT, and the gold standard was determined by Bland-Altman analysis. Cardiac risk categories were compared. External validation was performed on 334 paired scans from a different organization. A total of 608 internal paired scans (1 mm: 294; 3 mm: 314) of 406 individuals and 334 external paired scans (1 mm: 117; 3 mm: 117) of 117 individuals were included in the analysis. The ICCs between the deep learning algorithm and the gold standard were excellent in both group A (0.90; 95% CI: 0.85-0.93) and group B (0.94; 95% CI: 0.92-0.96). The Bland-Altman plots showed good agreement in both groups. For the cardiovascular risk category, the deep learning algorithm accurately classified 71% of cases in group A and 81% of cases in group B. The Kappa values for risk classification were 0.72 in group A and 0.82 in group B. External validation yielded equally good results. The automatic calculation of CAC score and cardiovascular risk stratification on non-gated chest CT using a deep learning algorithm was reliable and accurate on both 1 and 3 mm scans. Chest CT with a slice thickness of 3 mm was slightly more accurate in CAC detection and risk classification.

Breast arterial calcifications on mammography: a new marker of cardiovascular risk in asymptomatic middle age women?

To determine whether breast arterial calcification (BAC) detected on mammography can predict the presence of coronary artery calcification (CAC) on CT in women. Women explored with both mammography and thoracic CT from 2009 to 2018 were retrospectively included. Women were separated in 3 categories (no BAC, few BAC, and marked BAC) using a specific 12-point scale. Similar scale was used to evaluate the amount of CAC on CT. The mean sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy of BAC for the detection of CAC were calculated. Statistical significance was assessed with Pearson's chi-squared test and Fisher's exact test as appropriate. A total of 507 women (mean age: 62 years ± 16) were included. Patients with high amount of BAC were older (72 ± 11 vs. 59 ± 15 years old; p < .0001), were more frequently hypertensive (66% vs. 31%; p < .0001), and had more frequently renal failure (21% vs. 6%; p < .0003) than patients without BAC. The sensitivity, specificity, PPV, NPV, and diagnostic accuracy of BAC for the detection of women with marked CAC were 53.1%, 87.6%, 55.0%, 86.7%, and 79.9%, respectively. The highest diagnostic accuracy was obtained in patients under 60 years: 84.2% for detection of CAC and 93.2% for detection of women with marked CAC. The presence of BAC on mammography was linked to the presence of CAC and may be used as a cardiovascular marker in patient less than 60 years. • The diagnostic accuracy of breast arterial calcification (BAC) to detect the presence of coronary artery calcification (CAC) was 70.4% and reached to 79.9% to detect women with high amount of CAC. • Highest diagnostic accuracy of BAC to detect CAC (93.2%) was noticed in women under 60 years. • The presence of BAC on mammography may be used as a cardiovascular risk marker in women, especially under 60 years.

Improved coronary calcium detection and quantification with low-dose full field-of-view photon-counting CT: a phantom study.

The aim of the current study was to systematically assess coronary artery calcium (CAC) detection and quantification for spectral photon-counting CT (SPCCT) in comparison to conventional CT and, in addition, to evaluate the possibility of radiation dose reduction. Routine clinical CAC CT protocols were used for data acquisition and reconstruction of two CAC containing cylindrical inserts which were positioned within an anthropomorphic thorax phantom. In addition, data was acquired at 50% lower radiation dose by reducing tube current, and slice thickness was decreased. Calcifications were considered detectable when three adjacent voxels exceeded the CAC scoring threshold of 130 Hounsfield units (HU). Quantification of CAC (as volume and mass score) was assessed by comparison with known physical quantities. In comparison with CT, SPCCT detected 33% and 7% more calcifications for the small and large phantoms, respectively. At reduced radiation dose and reduced slice thickness, small phantom CAC detection increased by 108% and 150% for CT and SPCCT, respectively. For the large phantom size, noise levels interfered with CAC detection. Although comparable between CT and SPCCT, routine protocols CAC quantification showed large deviations (up to 134%) from physical CAC volume. At reduced radiation dose and slice thickness, physical volume overestimations decreased to 96% and 72% for CT and SPCCT, respectively. In comparison with volume scores, mass score deviations from physical quantities were smaller. CAC detection on SPCCT is superior to CT, and was even preserved at a reduced radiation dose. Furthermore, SPCCT allows for improved physical volume estimation. • In comparison with conventional CT, increased coronary artery calcium detection (up to 156%) for spectral photon-counting CT was found, even at 50% radiation dose reduction. • Spectral photon-counting CT can more accurately measure physical volumes than conventional CT, especially at reduced slice thickness and for high-density coronary artery calcium. • For both conventional and spectral photon-counting CT, reduced slice thickness reconstructions result in more accurate physical mass approximation.

Automated coronary artery calcium scoring using nested U-Net and focal loss

Coronary artery calcium (CAC) is a great risk predictor of the atherosclerotic cardiovascular disease and CAC scores can be used to stratify the risk of heart disease. Current clinical analysis of CAC is performed using onsite semiautomated software. This semiautomated CAC analysis requires experienced radiologists and radiologic technologists and is both demanding and time-consuming. The purpose of this study is to develop a fully automated CAC detection model that can quantify CAC scores. A total of 1,811 cases of cardiac examinations involving contrast-free multidetector computed tomography were retrospectively collected. We divided the database into the Training Data Set, Validation Data Set, Testing Data Set 1, and Testing Data Set 2. The Training, Validation, and Testing Data Set 1 contained cases with clinically detected CAC; Testing Data Set 2 contained those without detected calcium. The intraclass correlation coefficients between the overall standard and model-predicted scores were 1.00 for both the Training Data Set and Testing Data Set 1. In Testing Data Set 2, the model was able to detect clinically undetected cases of mild calcium. The results suggested that the proposed model’s automated detection of CAC was highly consistent with clinical semiautomated CAC analysis. The proposed model demonstrated potential for clinical applications that can improve the quality of CAC risk stratification.

Machine learning for coronary artery calcification detection and labeling using only native computer tomography.

In recent decades, the World Health Organization has found an increase in the death rate due to cardiovascular disease. Calcifications of the coronary arteries are the main sign of any cardiovascular event. Each individual's calcium score helps estimate the severity of the disease. However, the score for each artery is more significant. This study aims to research the segmentation, the labeling, and then the complete and partial quantification of calcium using only native coronary computed tomography with the help of machine-learning algorithms. Our semi-automatic system limited the region of interest by applying a defined preprocessing step. We then implemented two random forest classifiers; the first separated true coronary artery calcification (CAC) from the noise, and the second labeled CAC into the right coronary artery, left coronary artery, left anterior descending artery, and left circumflex artery using specific features. Agatston score and volume score of each CAC, each artery, and all of the arteries were calculated. This method gave promising results, comparable to those found in the literature, with the accuracy of 99.98% and 100% for CAC detection and labeling respectively.

Abstract 9550: Sex and Ethnicity Specific Burden of Coronary Artery Calcium Among Middle-Aged and Older Adults: Multi-Ethnic Study of Atherosclerosis

Introduction: Detection of coronary artery calcium (CAC) at a low score is the ideal phase of the atherosclerotic process to start primary prevention therapies. A better understanding of how the proportion of individuals with zero, low, or moderate/high CAC changes with age, sex, and race/ethnicity may aid in determining the optimal age to perform general CAC scoring before there is a severe burden of atherosclerosis. Methods: We calculated the proportion of individuals with 1) CAC ≥1 and CAC ≥100 based on population percentiles from 6,110 participants without diabetes from the Multi-Ethnic Study of Atherosclerosis (MESA) who had a CAC scan at Visit 1 (2000-02). Results: There was an approximately 12 year age difference for when 25% of men had CAC ≥1 compared to CAC ≥100 (&lt;45-51 years old vs 58-63 years old) compared to an approximately 14 year age difference for women (54-59 years old vs 69-75 years old). Women had a 50% prevalence of CAC ≥1 by age 69 years old (71 for Black women vs 67 for White women) compared to age 57 years old for men (62 for Black men vs 53 for White men). White men had a 50% prevalence of CAC ≥100 at age 68 years old, which was between 8-15 years younger than men of other races/ethnicities while women did not have a 50% prevalence of CAC ≥100 until age 80 years or older. In order to have at least a 25 to 50% probability of CAC ≥100, testing should be considered at ages of 55-64 for White men, 65-74 for men of other races/ethnicities and White women, and 75-84 years old for women of other races/ethnicities. Conclusion: Women developed CAC ≥1 approximately 12 years later in life than men. White men developed CAC ≥100 at a much younger age than men of other races/ethnicities, while a majority of women did not develop CAC ≥100 until age ≥80 years old. These findings may provide additional information to aid in the clinician-patient decision making process on the ideal age to perform CAC scoring, especially for those individuals who wish to rule out the presence of a moderate or high CAC score.

Deep-Learning-Based Coronary Artery Calcium Detection from CT Image.

One of the most common methods for diagnosing coronary artery disease is the use of the coronary artery calcium score CT. However, the current diagnostic method using the coronary artery calcium score CT requires a considerable time, because the radiologist must manually check the CT images one-by-one, and check the exact range. In this paper, three CNN models are applied for 1200 normal cardiovascular CT images, and 1200 CT images in which calcium is present in the cardiovascular system. We conduct the experimental test by classifying the CT image data into the original coronary artery calcium score CT images containing the entire rib cage, the cardiac segmented images that cut out only the heart region, and cardiac cropped images that are created by using the cardiac images that are segmented into nine sub-parts and enlarged. As a result of the experimental test to determine the presence of calcium in a given CT image using Inception Resnet v2, VGG, and Resnet 50 models, the highest accuracy of 98.52% was obtained when cardiac cropped image data was applied using the Resnet 50 model. Therefore, in this paper, it is expected that through further research, both the simple presence of calcium and the automation of the calcium analysis score for each coronary artery calcium score CT will become possible.

Coronary calcium scoring potential of large field-of-view spectral photon-counting CT: a phantom study

ObjectiveThe aim of the current study was, first, to assess the coronary artery calcium (CAC) scoring potential of spectral photon-counting CT (SPCCT) in comparison with computed tomography (CT) for routine clinical protocols. Second, improved CAC detection and quantification at reduced slice thickness were assessed.MethodsRaw data was acquired and reconstructed with several combinations of reduced slice thickness and increasing strengths of iterative reconstruction (IR) for both CT systems with routine clinical CAC protocols for CT. Two CAC-containing cylindrical inserts, consisting of CAC of different densities and sizes, were placed in an anthropomorphic phantom. A specific CAC was detectable when 3 or more connected voxels exceeded the CAC scoring threshold of 130 Hounsfield units (HU). For all reconstructions, total CAC detectability was compared between both CT systems. Significant differences in CAC quantification (Agatston and volume scores) were assessed with Mann-Whitney U tests. Furthermore, volume scores were compared with the known CAC physical.ResultsCAC scores for routine clinical protocols were comparable between SPCCT and CT. SPCCT showed 34% and 4% higher detectability of CAC for the small and large phantom, respectively. At reduced slice thickness, CAC detection increased by 142% and 169% for CT and SPCCT, respectively. In comparison with CT, volume scores from SPCCT were more comparable with the physical volume of the CAC.ConclusionCAC scores using routine clinical protocols are comparable between conventional CT and SPCCT. The increased spatial resolution of SPCCT allows for increased detectability and more accurate CAC volume estimation.Key Points• Coronary artery calcium scores using routine clinical protocols are comparable between conventional CT and spectral photon-counting CT.• In comparison with conventional CT, increased coronary artery calcium detectability was shown for spectral photon-counting CT due to increased spatial resolution.• Volumes scores were more accurately determined with spectral photon-counting CT.

Warranty Period of a Calcium Score of Zero: Comprehensive Analysis From MESA

ObjectivesThis study sought to quantify and model conversion of a normal coronary artery calcium (CAC) scan to an abnormal CAC scan. BackgroundAlthough the absence of CAC is associated with excellent prognosis, progression to CAC >0 confers increased risk. The time interval for repeated scanning remains poorly defined. MethodsThis study included 3,116 participants from the MESA (Multi-Ethnic Study of Atherosclerosis) with baseline CAC = 0 and follow-up scans over 10 years after baseline. Prevalence of incident CAC, defined by thresholds of CAC >0, CAC >10, or CAC >100, was calculated and time to progression was derived from a Weibull parametric survival model. Warranty periods were modeled as a function of sex, race/ethnicity, cardiovascular risk, and desired yield of repeated CAC testing. Further analysis was performed of the proportion of coronary events occurring in participants with baseline CAC = 0 that preceded and followed repeated CAC testing at different time intervals. ResultsMean participants’ age was 58 ± 9 years, with 63% women, and mean 10-year cardiovascular risk of 14%. Prevalence of CAC >0, CAC >10, and CAC >100 was 53%, 36%, and 8%, respectively, at 10 years. Using a 25% testing yield (number needed to scan [NNS] = 4), the estimated warranty period of CAC >0 varied from 3 to 7 years depending on sex and race/ethnicity. Approximately 15% of participants progressed to CAC >10 in 5 to 8 years, whereas 10-year progression to CAC >100 was rare. Presence of diabetes was associated with significantly shorter warranty period, whereas family history and smoking had small effects. A total of 19% of all 10-year coronary events occurred in CAC = 0 prior to performance of a subsequent scan at 3 to 5 years, whereas detection of new CAC >0 preceded 55% of future events and identified individuals at 3-fold higher risk of coronary events. ConclusionsIn a large population of individuals with baseline CAC = 0, study data provide a robust estimation of the CAC = 0 warranty period, considering progression to CAC >0, CAC >10, and CAC >100 and its impact on missed versus detectable 10-year coronary heart disease events. Beyond age, sex, race/ethnicity, diabetes also has a significant impact on the warranty period. The study suggests that evidence-based guidance would be to consider rescanning in 3 to 7 years depending on individual demographics and risk profile.