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Automatic aortic valve landmark localization in coronary CT angiography using colonial walk.

ABSTRACT: The minimally invasive transcatheter aortic valve implantation (TAVI) is the most prevalent method to treat aortic valve stenosis. For pre-operative surgical planning, contrast-enhanced coronary CT angiography (CCTA) is used as the imaging technique to acquire 3-D measurements of the valve. Accurate localization of the eight aortic valve landmarks in CT images plays a vital role in the TAVI workflow because a small error risks blocking the coronary circulation. In order to examine the valve and mark the landmarks, physicians prefer a view parallel to the hinge plane, instead of using the conventional axial, coronal or sagittal view. However, customizing the view is a difficult and time-consuming task because of unclear aorta pose and different artifacts of CCTA. Therefore, automatic localization of landmarks can serve as a useful guide to the physicians customizing the viewpoint. In this paper, we present an automatic method to localize the aortic valve landmarks using colonial walk, a regression tree-based machine-learning algorithm. For efficient learning from the training set, we propose a two-phase optimized search space learning model in which a representative point inside the valvular area is first learned from the whole CT volume. All eight landmarks are then learned from a smaller area around that point. Experiment with preprocedural CCTA images of TAVI undergoing patients showed that our method is robust under high stenotic variation and notably efficient, as it requires only 12 milliseconds to localize all eight landmarks, as tested on a 3.60 GHz single-core CPU.

SUBMITTER: Al WA

PROVIDER: S-EPMC6059446 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Automatic aortic valve landmark localization in coronary CT angiography using colonial walk.

Al Walid Abdullah WA Jung Ho Yub HY Yun Il Dong ID Jang Yeonggul Y Park Hyung-Bok HB Chang Hyuk-Jae HJ

PloS one 20180725 7

The minimally invasive transcatheter aortic valve implantation (TAVI) is the most prevalent method to treat aortic valve stenosis. For pre-operative surgical planning, contrast-enhanced coronary CT angiography (CCTA) is used as the imaging technique to acquire 3-D measurements of the valve. Accurate localization of the eight aortic valve landmarks in CT images plays a vital role in the TAVI workflow because a small error risks blocking the coronary circulation. In order to examine the valve and ...[more]

PMID: 30044802

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Project description:BackgroundTranscatheter aortic valve implantation (TAVI) is a well-established treatment for patients with severe aortic valve stenosis. This procedure requires pre-operative planning by assessment of aortic dimensions on CT Angiography (CTA). It is well-known that the aortic root dimensions vary over the heart cycle. However, sizing is commonly performed at either mid-systole or end-diastole only, which has resulted in an inadequate understanding of its full dynamic behavior.Study goalWe studied the variation in annulus measurements during the cardiac cycle and determined if this variation is dependent on the amount of calcification at the annulus.MethodsWe measured and compared aortic root annular dimensions and calcium volume in CTA acquisitions at 10 cardiac cycle phases in 51 aortic stenosis patients. Sub-group analysis was performed based on the volume of calcium by splitting the population into mildly and severely calcified valves subgroups.ResultsFor most annulus measurements, the largest differences were found between 10% and 70 to 80% cardiac cycle phases. Mean difference (±standard deviation) in annular minimum diameter, maximum diameter, area, and aspect ratio between mid-systole and end-diastole phases were 1.0 ± 0.29 mm (p = 0.065), 0.30 ± 0.24 mm (p = 0.7), 24.1 ± 7.6 mm2 (p < 0.001), and 0.041 ± 0.012 (p = 0.039) respectively. Calcium volume measurements varied strongly during the cardiac cycle. The dynamic annulus area was behaving differently between mildly and severely calcified subgroups (p = 0.02). Furthermore, patients with severe aortic calcification were associated with larger annulus diameters.ConclusionThere is a significant variation of annulus area and calcium volume measurement during the cardiac cycle. In our measurements, only the dynamic variation of the annulus area is dependent on the severity of the aortic calcification. For TAVI candidates, the annulus area is significantly larger in mid-systole compared to end-diastole.

Project description:Precise definition of the mitral valve plane (VP) during segmentation of the left ventricle for SPECT myocardial perfusion imaging (MPI) quantification often requires manual adjustment, which affects the quantification of perfusion. We developed a machine learning approach using support vector machines (SVM) for automatic VP placement. Methods: A total of 392 consecutive patients undergoing 99mTc-tetrofosmin stress (5 min; mean ± SD, 350 ± 54 MBq) and rest (5 min; 1,024 ± 153 MBq) fast SPECT MPI attenuation corrected (AC) by CT and same-day coronary CT angiography were studied; included in the 392 patients were 48 patients who underwent invasive coronary angiography and had no known coronary artery disease. The left ventricle was segmented with standard clinical software (quantitative perfusion SPECT) by 2 experts, adjusting the VP if needed. Two-class SVM models were computed from the expert placements with 10-fold cross validation to separate the patients used for training and those used for validation. SVM probability estimates were used to compute the best VP position. Automatic VP localizations on AC and non-AC images were compared with expert placement on coronary CT angiography. Stress and rest total perfusion deficits and detection of per-vessel obstructive stenosis by invasive coronary angiography were also compared. Results: Bland-Altman 95% confidence intervals (CIs) for VP localization by SVM and experts for AC stress images (bias, 1; 95% CI, -5 to 7 mm) and AC rest images (bias, 1; 95% CI, -7 to 10 mm) were narrower than interexpert 95% CIs for AC stress images (bias, 0; 95% CI, -8 to 8 mm) and AC rest images (bias, 0; 95% CI, -10 to 10 mm) (P < 0.01). Bland-Altman 95% CIs for VP localization by SVM and experts for non-AC stress images (bias, 1; 95% CI, -4 to 6 mm) and non-AC rest images (bias, 2; 95% CI, -7 to 10 mm) were similar to interexpert 95% CIs for non-AC stress images (bias, 0; 95% CI, -6 to 5 mm) and non-AC rest images (bias, -1; 95% CI, -9 to 7 mm) (P was not significant [NS]). For regional detection of obstructive stenosis, ischemic total perfusion deficit areas under the receiver operating characteristic curve for the 2 experts (AUC, 0.79 [95% CI, 0.7-0.87]; AUC, 0.81 [95% CI, 0.73-0.89]) and the SVM (0.82 [0.74-0.9]) for AC data were the same (P = NS) and were higher than those for the unadjusted VP (0.63 [0.53-0.73]) (P < 0.01). Similarly, for non-AC data, areas under the receiver operating characteristic curve for the experts (AUC, 0.77 [95% CI, 0.69-0.89]; AUC, 0.8 [95% CI, 0.72-0.88]) and the SVM (0.79 [0.71-0.87]) were the same (P = NS) and were higher than those for the unadjusted VP (0.65 [0.56-0.75]) (P < 0.01). Conclusion: Machine learning with SVM allows automatic and accurate VP localization, decreasing user dependence in SPECT MPI quantification.

Project description:PurposeTo determine the impact of low tube voltage coronary CT angiography on detection of subclinical atherosclerosis.Materials and methodsRetrospective sampling of an emergency department coronary CT angiography registry was performed. All patients in the registry underwent a noncontrast coronary artery calcium (CAC) scoring scan at 120 kV before coronary CT angiography. The study sample (n = 264) constituted patients with subclinical atherosclerosis (Coronary Artery Disease Reporting and Data System™ [CAD-RADS] 1 or 2) randomly mixed one-to-one with patients without atherosclerosis (CAD-RADS 0). The patients with coronary CT angiography performed at 70-90 kV were considered the low tube voltage group (n = 159) and patients with coronary CT angiography performed at 100-120 kV were considered the standard tube voltage group (n = 105). The number of coronary plaques and overall CAD-RADS classification (per patient) were evaluated twice: initially, by reading coronary CT angiography alone, and then, by coronary CT angiography in combination with a CAC scan. Considering the combined reading (CT angiography plus CAC scan) as the reference standard, the performance of coronary CT angiography alone was assessed for plaque detection and appropriate CAD-RADS (per patient) classification. The comparisons were made between the low tube voltage and standard tube voltage groups by using a Fisher exact test and χ2 test for proportions and a Mann-Whitney test and Kruskal-Wallis test for means.ResultsIn total, 455 plaques were identified in 118 patients (70 of 159 patients in the low tube voltage group; 48 of 105 in the standard tube voltage group). When reading coronary CT angiographic images alone, 97 of 455 (21%) plaques were missed that led to an incorrect CAD-RADS classification in 16 of 264 (6%) studies (interpreted as CAD-RADS 0 instead of CAD-RADS 1 or 2). Missed plaques were significantly more frequent in the low tube voltage group versus the standard tube voltage group (41% [85 of 206] vs 5% [12 of 249], respectively; P < .001). Incorrect CAD-RADS classification was also seen more commonly in the low tube voltage group (8.8% [14 of 159] vs 2% [two of 105]; P = .01), typically at low plaque burden (median CAC score, 1; range, 1-4). Calcified plaques that appeared isodense to luminal contrast material attenuation were seen more frequently in the low tube voltage group compared with the standard tube voltage group (20% [32 of 159] vs 7.6% [eight of 105], respectively; P = .005).ConclusionCoronary artery plaques may be missed at low tube voltage coronary CT angiographic examination performed without a concomitant CAC scan.© RSNA, 2019Supplemental material is available for this article.See also the commentary by Truong in this issue.

Dataset Information

Automatic aortic valve landmark localization in coronary CT angiography using colonial walk.

Publications

Automatic aortic valve landmark localization in coronary CT angiography using colonial walk.

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