Stress Myocardial Perfusion CT in Patients Suspected of Having Coronary Artery Disease: Visual and Quantitative Analysis—Validation by Using Fractional Flow Reserve

Yang, Dong Hyun; Kim, Young‐Hak; Roh, Jae‐Hyung; Kang, Joon‐Won; Han, Duck‐Jong; Jung, Joonho; Kim, Namkug; Lee, Jung Bok; Ahn, Jung‐Min; Lee, Jong‐Young; Park, Duk‐Woo; Kang, Soo–Jin; Lee, Seung-Whan; Lee, Cheol Whan; Park, Seong-Wook; Lim, Tae‐Hwan

doi:10.1148/radiol.2015141126

Cited by 58 publications

(38 citation statements)

References 22 publications

(31 reference statements)

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“…In this study, although resting CTP improved the detection of ACS, the results showed limited value for exclusion of ACS. In another study, Yang et al 28 reported the discriminatory power of CTP for predicting hemodynamically significant stenosis. The findings of that study indicated that visual analysis of stress CTP provides incremental value over CCTA.…”

Section: Discussionmentioning

confidence: 99%

Incremental role of resting myocardial computed tomography perfusion for predicting physiologically significant coronary artery disease: A machine learning approach

Han

Lee

Rizvi

et al. 2018

Journal of Nuclear Cardiology

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Background. Evaluation of resting myocardial computed tomography perfusion (CTP) by coronary CT angiography (CCTA) might serve as a useful addition for determining coronary artery disease. We aimed to evaluate the incremental benefit of resting CTP over coronary stenosis for predicting ischemia using a computational algorithm trained by machine learning methods.Methods. 252 patients underwent CCTA and invasive fractional flow reserve (FFR). CT stenosis was classified as 0%, 1-30%, 31-49%, 50-70%, and >70% maximal stenosis. Significant ischemia was defined as invasive FFR < 0.80. Resting CTP analysis was performed using a gradient boosting classifier for supervised machine learning.Results. On a per-patient basis, accuracy, sensitivity, specificity, positive predictive, and negative predictive values according to resting CTP when added to CT stenosis (>70%) for predicting ischemia were 68.3%, 52.7%, 84.6%, 78.2%, and 63.0%, respectively. Compared with CT stenosis [area under the receiver operating characteristic curve (AUC): 0.68, 95% confidence interval (CI) 0.62-0.74], the addition of resting CTP appeared to improve discrimination (AUC: 0.75, 95% CI 0.69-0.81, P value .001) and reclassification (net reclassification improvement: 0.52, P value < .001) of ischemia.Conclusions. The addition of resting CTP analysis acquired from machine learning techniques may improve the predictive utility of significant ischemia over coronary stenosis. (J Nucl Cardiol 2018;25:223-33.)

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Section: Discussionmentioning

confidence: 99%

Incremental role of resting myocardial computed tomography perfusion for predicting physiologically significant coronary artery disease: A machine learning approach

Han

Lee

Rizvi

et al. 2018

Journal of Nuclear Cardiology

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“…Ko et al found static rest and stress CT-MPI assets visually were of incremental value to coronary CT angiography [24]. More recently Yang et al published visual static CT-MPI assessment performed better than the transmural perfusion ratio, validated by FFR [25]. In these studies a segmental-based TPR was calculated while for the epicardial layer the entire circumferential attenuation was averaged.…”

Section: Discussionmentioning

confidence: 99%

Diagnostic value of transmural perfusion ratio derived from dynamic CT-based myocardial perfusion imaging for the detection of haemodynamically relevant coronary artery stenosis

et al. 2016

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ObjectivesTo investigate the additional value of transmural perfusion ratio (TPR) in dynamic CT myocardial perfusion imaging for detection of haemodynamically significant coronary artery disease compared with fractional flow reserve (FFR).MethodsSubjects with suspected or known coronary artery disease were prospectively included and underwent a CT-MPI examination. From the CT-MPI time-point data absolute myocardial blood flow (MBF) values were temporally resolved using a hybrid deconvolution model. An absolute MBF value was measured in the suspected perfusion defect. TPR was defined as the ratio between the subendocardial and subepicardial MBF. TPR and MBF results were compared with invasive FFR using a threshold of 0.80.ResultsForty-three patients and 94 territories were analysed. The area under the receiver operator curve was larger for MBF (0.78) compared with TPR (0.65, P = 0.026). No significant differences were found in diagnostic classification between MBF and TPR with a territory-based accuracy of 77 % (67-86 %) for MBF compared with 70 % (60-81 %) for TPR. Combined MBF and TPR classification did not improve the diagnostic classification.ConclusionsDynamic CT-MPI-based transmural perfusion ratio predicts haemodynamically significant coronary artery disease. However, diagnostic performance of dynamic CT-MPI-derived TPR is inferior to quantified MBF and has limited incremental value.Key Points• The transmural perfusion ratio from dynamic CT-MPI predicts functional obstructive coronary artery disease • Performance of the transmural perfusion ratio is inferior to quantified myocardial blood flow • The incremental value of the transmural perfusion ratio is limited

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“…It helps physicians to recognize various quantitative parameters of the LV at a glance, such as the intensity, intensity difference, and transmural perfusion ratio and thickness 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 . This 2D polar plot was generated from 17 myocardial segments on 3D polar coordinates.…”

Section: Methodsmentioning

confidence: 99%

Patient‐specific 17‐segment myocardial modeling on a bull's‐eye map

Jung

Kim

et al. 2016

J Applied Clin Med Phys

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The purpose of this study was to develop and validate cardiac computed tomography (CT) quantitative analysis software with a patient‐specific, 17‐segment myocardial model that uses electrocardiogram (ECG)‐gated cardiac CT images to differentiate between normal controls and severe aortic stenosis (AS) patients. ECG‐gated cardiac CT images from 35 normal controls and 144 AS patients were semiautomatically segmented to create a patient‐specific, 17‐segment myocardial model. Two experts then manually determined the anterior and posterior interventricular grooves to be boundaries between the 1st and 2nd segments and between the 3rd and 4th segments, respectively, to correct the model. Each segment was automatically identified as follows. The outer angle of two boundaries was divided to differentiate the 1st, 4th, 5th, and 6th segments in the basal plane, whereas the inner angle divided the 2nd and 3rd segments. The segments of the midplane were similarly divided. Segmental area distributions were quantitatively evaluated on the bull's‐eye map on the basis of the morphological boundaries by measuring the area of each segment. Segmental areas of severe AS patients and normal controls were significantly different (t‐test, all p‐values<0.011) in the proposed model because the septal regions of the severe AS patients were smaller than those of normal controls and the difference was enough to divide the two groups. The capabilities of the 2D segmental areas (p<0.011) may be equivalent to those of 3D segmental analysis (all p‐values<0.001) for differentiating the two groups (t‐test, all p‐values<0.001). The proposed method is superior to the conventional 17‐segment in relation to reflection of patient‐specific morphological variation and allows to obtain a more precise mapping between segments and the AHA recommended nomenclature. It can be used to differentiate severer AS patients and normal controls and also helps to understand the left ventricular morphology at a glance.PACS number(s): 87.57.N‐, 87.57.R‐, 87.57.qp

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Stress Myocardial Perfusion CT in Patients Suspected of Having Coronary Artery Disease: Visual and Quantitative Analysis—Validation by Using Fractional Flow Reserve

Abstract: Stress myocardial perfusion CT provides incremental value over CT angiography in patients with a high calcium score for the detection of myocardial ischemia as defined by FFR.

Cited by 58 publications

References 22 publications

Incremental role of resting myocardial computed tomography perfusion for predicting physiologically significant coronary artery disease: A machine learning approach

Incremental role of resting myocardial computed tomography perfusion for predicting physiologically significant coronary artery disease: A machine learning approach

Diagnostic value of transmural perfusion ratio derived from dynamic CT-based myocardial perfusion imaging for the detection of haemodynamically relevant coronary artery stenosis

Patient‐specific 17‐segment myocardial modeling on a bull's‐eye map

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