Reproducibility of computed tomography angiography data analysis using semiautomated plaque quantification software: Implications for the design of longitudinal studies

Reproducibility of the quantitative assessment of atherosclerosis by computed tomography coronary angiography (CTCA) is paramount for the design of longitudinal studies. The purpose of this study was to assess the inter- and intra-observer reproducibility using semiautomated CT plaque analysis software in symptomatic individuals. CTCA was performed in 10 symptomatic patients after percutaneous treatment of the culprit lesions and was repeated after 3 years. The plaque quantitative analysis was performed in untreated vessels with mild-tomoderate atherosclerosis and included geometrical and compositional characteristics using semiautomated CT plaque analysis software. A total of 945 matched crosssections from 21 segments were analyzed independently by a second reviewer to assess inter-observer variability; the first observer repeated all the analyses after 3 months to assess intra-observer variability. The observer variability was also compared to the absolute plaque changes detected over time. Agreement was evaluated by Bland-Altman analysis and co

A novel software tool for semi-automatic quantification of thoracic aorta dilatation on baseline and follow-up computed tomography angiography

A dedicated software package that could semi-automatically assess differences in aortic maximal cross-sectional diameters from consecutive CT scans would most likely reduce the post-processing time and effort by the physicians. The aim of this study was to present and assess the quality of a new tool for the semi-automatic quantification of thoracic aorta dilation dimensions. Twenty-nine patients with two CTA scans of the thoracic aorta for which the official clinical report indicated an increase in aortic diameters were included in the study. Aortic maximal cross-sectional diameters of baseline and follow-up studies generated semi-automatically by the software were compared with corresponding manual measurements. The semi-automatic measurements were performed at seven landmarks defined on the baseline scan by two operators. Bias, Bland–Altman plots and intraclass correlation coefficients were calculated between the two methods and, for the semi-automatic software, also between two observers. The average time difference between the two scans of a single patient was 1188 ± 622 days. For the semi-automatic software, in 2 out of 29 patients, manual interaction was necessary; in the remaining 27 patients (93.1%), semi-automatic results were generated, demonstrating excellent intraclass correlation coefficients (all values ≥ 0.91) and small differences, especially for the proximal aortic arch (baseline: 0.19 ± 1.30 mm; follow-up: 0.44 ± 2.21 mm), the mid descending aorta (0.37 ± 1.64 mm; 0.37 ± 2.06 mm), and the diaphragm (0.30 ± 1.14 mm; 0.37 ± 1.80 mm). The inter-observer variability was low with all errors in diameters ≤ 1 mm, and intraclass correlation coefficients all ≥ 0.95. The semi-automatic tool decreased the processing time by 40% (13 vs. 22 min). In this work, a semi-automatic software package that allows the assessment of thoracic aorta diameters from baseline and follow-up CTs (and their differences), was presented, and demonstrated high accuracy and low inter-observer variability

A model-guided method for improving coronary artery tree extractions from CCTA images

PurposeAutomatically extracted coronary artery trees (CATs) from coronary computed tomography angiography images could contain incorrect extractions which require manual corrections before they can be used in clinical practice. A model-guided method for improving the extracted CAT is described to automatically detect potential incorrect extractions and improve them.MethodsThe proposed method is a coarse-to-fine approach. A coarse improvement is first applied on all vessels in the extracted CAT, and then a fine improvement is applied only on vessels with higher clinical significance. Based upon a decision tree, the proposed method automatically and iteratively performs improvement operations for the entire extracted CAT until it meets the stop criteria. The improvement in the extraction quality obtained by the proposed method is measured using a scoring system. 18 datasets were used to determine optimal values for the parameters involved in the model-guided method and 122 datasets were used for evaluation.ResultsCompared to the initial automatic extractions, the proposed method improves the CATs for 122 datasets from an average quality score of 876 to 93 +/- 4. The developed method is able to run within 2min on a typical workstation. The difference in extraction quality after automatic improvement is negatively correlated with the initial extraction quality (R=-0.694, P<0.001).Conclusion Without deteriorating the initially extracted CATs, the presented method automatically detects incorrect extractions and improves the CATs to an average quality score of 93 guided by anatomical statistical models.Cardiovascular Aspects of Radiolog

Automatic Coronary Plaque Thickness Differences Calculation between Baseline and Follow-up CCTA Images

Cardiovascular Aspects of Radiolog

Automatic coronary artery plaque thickness comparison between baseline and follow‐up CCTA images

Purpose Currently, coronary plaque changes are manually compared between a baseline and follow-up coronary computed tomography angiography (CCTA) images for long-term coronary plaque development investigation. We propose an automatic method to measure the plaque thickness change over time. Methods We model the lumen and vessel wall for both the baseline coronary artery tree (CAT-BL) and follow-up coronary artery tree (CAT-FU) as smooth three-dimensional (3D) surfaces using a subdivision fitting scheme with the same coarse meshes by which the correspondence among these surface points is generated. Specifically, a rigid point set registration is used to transform the coarse mesh from the CAT-FU to CAT-BL. The plaque thickness and the thickness difference is calculated as the distance between corresponding surface points. To evaluate the registration accuracy, the average distance between manually defined markers on clinical scans is calculated. Artificial CAT-BL and CAT-FU pairs were created to simulate the plaque decrease and increase over time. Results For 116 pairs of markers from nine clinical scans, the average marker distance after registration was 0.95 +/- 0.98 mm (two times the voxel size). On the 10 artificial pairs of datasets, the proposed method successfully located the plaque changes. The average of the calculated plaque thickness difference is the same as the corresponding created value (standard deviation +/- 0.1 mm). Conclusions The proposed method automatically calculates local coronary plaque thickness differences over time and can be used for 3D visualization of plaque differences. The analysis and reporting of coronary plaque progression and regression will benefit from an automatic plaque thickness comparison.Cardiovascular Aspects of Radiolog

Automatic coronary artery plaque thickness comparison between baseline and follow-up CCTA images

Purpose Currently, coronary plaque changes are manually compared between a baseline and follow-up coronary computed tomography angiography (CCTA) images for long-term coronary plaque development investigation. We propose an automatic method to measure the plaque thickness change over time. Methods We model the lumen and vessel wall for both the baseline coronary artery tree (CAT-BL) and follow-up coronary artery tree (CAT-FU) as smooth three-dimensional (3D) surfaces using a subdivision fitting scheme with the same coarse meshes by which the correspondence among these surface points is generated. Specifically, a rigid point set registration is used to transform the coarse mesh from the CAT-FU to CAT-BL. The plaque thickness and the thickness difference is calculated as the distance between corresponding surface points. To evaluate the registration accuracy, the average distance between manually defined markers on clinical scans is calculated. Artificial CAT-BL and CAT-FU pairs were created to simulate the plaque decrease and increase over time. Results For 116 pairs of markers from nine clinical scans, the average marker distance after registration was 0.95 +/- 0.98 mm (two times the voxel size). On the 10 artificial pairs of datasets, the proposed method successfully located the plaque changes. The average of the calculated plaque thickness difference is the same as the corresponding created value (standard deviation +/- 0.1 mm). Conclusions The proposed method automatically calculates local coronary plaque thickness differences over time and can be used for 3D visualization of plaque differences. The analysis and reporting of coronary plaque progression and regression will benefit from an automatic plaque thickness comparison

Automatic coronary artery plaque thickness comparison between baseline and follow-up CCTA images

Purpose Currently, coronary plaque changes are manually compared between a baseline and follow-up coronary computed tomography angiography (CCTA) images for long-term coronary plaque development investigation. We propose an automatic method to measure the plaque thickness change over time. Methods We model the lumen and vessel wall for both the baseline coronary artery tree (CAT-BL) and follow-up coronary artery tree (CAT-FU) as smooth three-dimensional (3D) surfaces using a subdivision fitting scheme with the same coarse meshes by which the correspondence among these surface points is generated. Specifically, a rigid point set registration is used to transform the coarse mesh from the CAT-FU to CAT-BL. The plaque thickness and the thickness difference is calculated as the distance between corresponding surface points. To evaluate the registration accuracy, the average distance between manually defined markers on clinical scans is calculated. Artificial CAT-BL and CAT-FU pairs were created to simulate the plaque decrease and increase over time. Results For 116 pairs of markers from nine clinical scans, the average marker distance after registration was 0.95 +/- 0.98 mm (two times the voxel size). On the 10 artificial pairs of datasets, the proposed method successfully located the plaque changes. The average of the calculated plaque thickness difference is the same as the corresponding created value (standard deviation +/- 0.1 mm). Conclusions The proposed method automatically calculates local coronary plaque thickness differences over time and can be used for 3D visualization of plaque differences. The analysis and reporting of coronary plaque progression and regression will benefit from an automatic plaque thickness comparison.Cardiovascular Aspects of Radiolog

Automatic Quantification and Characterization of Coronary Plaque with Computed Tomography Coronary Angiography: A Comparison with Intravascular Ultrasound Virtual Histology

Cardiovascular Aspects of Radiolog

Quantification of coronary low-attenuation plaque volume for long-term prediction of cardiac events and reclassification of patients

Background: To investigate the incremental prognostic value of low-attenuation plaque volume (LAPV) from coronary CT angiography datasets.Methods: Quantification of LAPV was performed using dedicated software equipped with an adaptive plaque tissue algorithm in 1577 patients with suspected CAD. A combination of death and acute coronary syndrome was defined as primary endpoint. To assess the incremental prognostic value of LAPV, parameters were added to a baseline model including clinical risk and obstructive coronary artery disease (CAD), a baseline model including clinical risk and calcium scoring (CACS) and a baseline model including clinical risk and segment involvement score (SIS).Results: Patients were followed for 5.5 years either by telephone contact, mail or clinical visits. The primary endpoint occurred in 30 patients. Quantified LAPV provided incremental prognostic information beyond clinical risk and obstructive CAD (c-index 0.701 vs. 0.767, p<.001), clinical risk and CACS (c-index 0.722 vs. 0.771, p<.01) and clinical risk and SIS (c-index 0.735 vs. 0.771, p<.01. A combined approach using quantified LAPV and clinical risk significantly improved the stratification of patients into different risk categories compared to clinical risk alone (categorical net reclassification index 0.69 with 95% CI 0.27 and 0.96, p<.001). The combined approach classified 846 (53.6%) patients as low risk (annual event rate 0.04%), 439 (27.8%) patients as intermediate risk (annual event rate 0.5%) and 292 (18.5%) patients as high risk (annual event rate 0.99%).Conclusion: Quantification of LAPV provides incremental prognostic information beyond established CT risk patterns and permits improved stratification of patients into different risk categories.Cardiovascular Aspects of Radiolog

First experiences with model based iterative reconstructions influence on quantitative plaque volume and intensity measurements in coronary computed tomography angiography

Cardiovascular Aspects of Radiolog