Dynamic CT myocardial perfusion imaging: performance of 3D semi-automated evaluation software

Ebersberger, Ullrich; Marcus, Roy; Schoepf, U. Joseph; Lo, Gladys; Wang, Yining; Blanke, Philipp; Geyer, Lucas L.; Gray, J. Cranston; McQuiston, Andrew D.; Cho, Young Jun; Scheuering, Michael; Canstein, Christian; Nikolaou, Konstantin; Hoffmann, Ellen; Bamberg, Fabian

doi:10.1007/s00330-013-2997-5

Cited by 40 publications

(24 citation statements)

References 17 publications

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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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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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“…107 Motion correction can significantly increase spatial alignment and reduce imaging noise. 108 Semi-automated three-dimensional software has been used to produce quantification with substantially reduced processing times, 109 which is a beneficial step for integration into clinical workflow.…”

Section: Patlak Et Almentioning

confidence: 99%

Cardiac CT for myocardial ischaemia detection and characterization—comparative analysis

Bucher¹,

Cecco²,

Schoepf³

et al. 2014

BJR

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The assessment of patients presenting with symptoms of myocardial ischaemia remains one of the most common and challenging clinical scenarios faced by physicians. Current imaging modalities are capable of three-dimensional, functional and anatomical views of the heart and as such offer a unique contribution to understanding and managing the pathology involved. Evidence has accumulated that visual anatomical coronary evaluation does not adequately predict haemodynamic relevance and should be complemented by physiological evaluation, highlighting the importance of functional assessment. Technical advances in CT technology over the past decade have progressively moved cardiac CT imaging into the clinical workflow. In addition to anatomical evaluation, cardiac CT is capable of providing myocardial perfusion parameters. A variety of CT techniques can be used to assess the myocardial perfusion. The single energy firstpass CT and dual energy first-pass CT allow static assessment of myocardial blood pool. Dynamic cardiac CT imaging allows quantification of myocardial perfusion through time-resolved attenuation data. CT-based myocardial perfusion imaging (MPI) is showing promising diagnostic accuracy compared with the current reference modalities. The aim of this review is to present currently available myocardial perfusion techniques with a focus on CT imaging in light of recent clinical investigations. This article provides a comprehensive overview of currently available CT approaches of static and dynamic MPI and presents the results of corresponding clinical trials.

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“…Recently, stress myocardial CT perfusion (CTP) imaging is reported to be a promising option for detecting myocardial ischemia [8][9][10][11][12][13][14][15][16][17][18][19]. Dynamic CTP imaging allows for quantitative assessment of myocardial perfusion with myocardial blood flow (MBF) by analyzing the myocardial time attenuation curve (TAC) [13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%