Comparison of Diagnostic Value of a Novel Noninvasive Coronary Computed Tomography Angiography Method Versus Standard Coronary Angiography for Assessing Fractional Flow Reserve

Renker, Matthias; Schoepf, U. Joseph; Wu, Rui; Meinel, Felix G.; Rier, Jeremy; Bayer, Richard R.; Möllmann, Helge; Hamm, Christian W.; Steinberg, Daniel; Baumann, Stefan

doi:10.1016/j.amjcard.2014.07.064

Cited by 179 publications

(102 citation statements)

References 18 publications

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“…Hence, imaging artifacts such as blooming caused by large coronary calcifications, stents, and cardiac motion may compromise the accuracy of F F R CT (Koo, 2014). Itu et al (2012) presented a technique to estimate FFR in the coronary artery tree using a CCTA scan by simulating blood flow, which was later evaluated by Renker et al (2014) and Coenen et al (2015). The method uses a patient-specific parametric lumped heart model, while modeling the hemodynamics in both healthy and stenotic vessel tree.…”

Section: Introductionmentioning

confidence: 99%

Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis

Zreik

Leßmann

Hamersvelt

et al. 2018

Medical Image Analysis

166

124

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In patients with coronary artery stenoses of intermediate severity, the functional significance needs to be determined. Fractional flow reserve (FFR) measurement, performed during invasive coronary angiography (ICA), is most often used in clinical practice. To reduce the number of ICA procedures, we present a method for automatic identification of patients with functionally significant coronary artery stenoses, employing deep learning analysis of the left ventricle (LV) myocardium in rest coronary CT angiography (CCTA).The study includes consecutively acquired CCTA scans of 166 patients who underwent invasive FFR measurements. To identify patients with a functionally significant coronary artery stenosis, analysis is performed in several stages. First, the LV myocardium is segmented using a multiscale convolutional neural network (CNN). To characterize the segmented LV myocardium, it is subsequently encoded using unsupervised convolutional autoencoder (CAE). As ischemic changes are expected to appear locally, the LV myocardium is divided into a number of spatially connected clusters, and statistics of the encodings are computed as features. Thereafter, patients are classified according to the presence of functionally significant stenosis using an SVM classifier based on the extracted features.Quantitative evaluation of LV myocardium segmentation in 20 images resulted in an average Dice coefficient of 0.91 and an average mean absolute distance between the segmented and reference LV boundaries of 0.7 mm. Twenty CCTA images were used to train the LV myocardium encoder. Classification of patients was evaluated in the remaining 126 CCTA scans in 50 10-fold cross-validation experiments and resulted in an area under the receiver operating characteristic curve of 0.74 ± 0.02. At sensitivity levels 0.60, 0.70 and 0.80, the corresponding specificity was 0.77, 0.71 and 0.59, respectively.The results demonstrate that automatic analysis of the LV myocardium in a single CCTA scan acquired at rest, without assessment of the anatomy of the coronary arteries, can be used to identify patients with functionally significant coronary artery stenosis. This might reduce the number of patients undergoing unnecessary invasive FFR measurements.

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Section: Introductionmentioning

confidence: 99%

Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis

Zreik

Leßmann

Hamersvelt

et al. 2018

Medical Image Analysis

166

124

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show abstract

“…Recently, technological innovation allows to calculate coronary flow and pressure based on anatomic MDCT image data without hyperemia, thereby measuring FFR noninvasively (35,36). Several studies demonstrated the feasibility and diagnostic performance of FFR evaluation on MDCT (FFR CT ) (37)(38)(39)(40)(41). Recent multicenter international study investigated the ability of FFR CT for diagnosis of ischemia compared to an invasive FFR measurement in 252 stable patients with suspected or known CAD (37).…”

Section: Fractional Flow Reserve (Ffr)mentioning

confidence: 99%

Noninvasive imaging modalities to visualize atherosclerotic plaques

Shishikura¹

2016

Cardiovasc. Diagn. Ther.

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“…Thus, FFRCT may be useful for identifying patients who may benefit from revascularization and for prediction and optimization of PCI outcomes. [32][33][34] …”

Section: Accuracy Of Ffrctmentioning

confidence: 99%

CT Determination of Fractional Flow Reserve in Coronary Lesions

Mester¹,

Chițu

Raț

et al. 2016

Journal of Interdisciplinary Medicine

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Invasively determined fractional flow reserve (FFR) represents the gold-standard method for the functional evaluation of coronary lesions. Coronary computed tomography angiography (CCTA) provides characterization of the coronary anatomy, with important morphological information on the atherosclerotic plaques, but does not offer a hemodynamic evaluation of coronary artery lesions. CT evaluation of FFR (FFRCT) is a new noninvasive diagnostic method, which provides anatomical and functional assessment of the whole coronary tree, based on computational techniques, with no more radiation or hyperemic agent administration compared with routine CCTA. Recent studies demonstrated the safety and accuracy of FFRCT and its therapeutic use and cost benefits in real-world clinical use.

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Comparison of Diagnostic Value of a Novel Noninvasive Coronary Computed Tomography Angiography Method Versus Standard Coronary Angiography for Assessing Fractional Flow Reserve

Cited by 179 publications

References 18 publications

Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis

Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis

Noninvasive imaging modalities to visualize atherosclerotic plaques

CT Determination of Fractional Flow Reserve in Coronary Lesions

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