Abstract
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
Highlights
454 Jung et al.: Patient-specific 17-segment myocardial modelAssociation (AHA) in 2002(2) and is currently considered the conventional standard
The AHA noted that the name of the segments should define the location relative to the long axis of the heart and the circumferential location, and individual myocardial segments can be assigned to the three major coronaries by considering their anatomic variabilities.[2]. In addition, contrary to the conventional 17-segment model, several studies have reported, using singlephoton emission computed tomography (SPECT), cardiac magnetic resonance (CMR), and CT, that there are variations in the mapping of segments to left ventricular territories, due to their anatomic variations.[3,4,5] they proposed a revised mapping of segments considering anatomic variations, where some segments were assigned to multiple territories due to per-patient variability
Validation and statistical analysis To validate the usability of the patient-specific, 17-segment myocardial model, 2D quantitative parameters were evaluated from two 17-segment models on the bull’s-eye map and the 3D parameters were evaluated from volume data. We evaluated whether these parameters could differentiate between normal controls and severe AS patients on the basis of the clinical knowledge that the left ventricle (LV) response to aortic stenosis includes LV remodeling.[23,24,25,26,27,28] MATLAB R2013a (MathWorks, Natick, MA) was used to perform paired t-tests and a diagnostic classification test
Summary
454 Jung et al.: Patient-specific 17-segment myocardial modelAssociation (AHA) in 2002(2) and is currently considered the conventional standard. For the bull’s-eye map, Cain et al[6] presented a procedure for a quantitative polar representation of LV myocardial perfusion, function, and viability using SPECT and CMR They described how to generate polar plots using quantitative data based on the conventional 17-segment model. Oeltze et al[7] reported several novel visualization techniques that enabled the myocardial perfusion data of both rest and stress states to be shown in a single bull’s-eye plot based on the 17-segment model. Their model involved an interactive link between their bull’s-eye map and a 3D visualization showing the coronary artery branches. This study did not consider patient-specific anatomic variations in their bull’s-eye plot
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