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Differentiation of myocardial ischemia and infarction assessed by dynamic computed tomography perfusion imaging and comparison with cardiac magnetic resonance and single-photon emission computed tomography

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Abstract

Objectives

To evaluate the feasibility of myocardial blood flow (MBF) by computed tomography from dynamic CT perfusion (CTP) for detecting myocardial ischemia and infarction assessed by cardiac magnetic resonance (CMR) or single-photon emission computed tomography (SPECT).

Methods

Fifty-three patients who underwent stress dynamic CTP and either SPECT (n = 25) or CMR (n = 28) were retrospectively selected. Normal and abnormal perfused myocardium (ischemia/infarction) were assessed by SPECT/CMR using 16-segment model. Sensitivity and specificity of CT-MBF (mL/g/min) for detecting the ischemic/infarction and severe infarction were assessed.

Results

The abnormal perfused myocardium and severe infarction were seen in SPECT (n = 90 and n = 19 of 400 segments) and CMR (n = 223 and n = 36 of 448 segments). For detecting the abnormal perfused myocardium, sensitivity and specificity were 80 % (95 %CI, 71-90) and 86 % (95 %CI, 76-91) in SPECT (cut-off MBF, 1.23), and 82 % (95 %CI, 76-88) and 87 % (95 %CI, 80-92) in CMR (cut-off MBF, 1.25). For detecting severe infarction, sensitivity and specificity were 95 % (95 %CI, 52-100) and 72 % (95 %CI, 53-91) in SPECT (cut-off MBF, 0.92), and 78 % (95 %CI, 67-97) and 80 % (95 %CI, 58-86) in CMR (cut-off MBF, 0.98), respectively.

Conclusions

Dynamic CTP has a potential to detect abnormal perfused myocardium and severe infarction assessed by SPECT/CMR using comparable cut-off MBF.

Key Points

CT-MBF accurately reflects the severity of myocardial perfusion abnormality.

CT-MBF provides good diagnostic accuracy for detecting myocardial perfusion abnormalities.

CT-MBF may assist in stratifying severe myocardial infarction in abnormal perfusion myocardium.

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Abbreviations

ATP:

Adenosine triphosphate

CAD:

Coronary artery disease

CMR:

Cardiac magnetic resonance

CTA:

Computed tomography angiography

CTP:

Computed tomography perfusion

LGE:

Late gadolinium enhancement

LV:

Left ventricle

LVWT:

Left ventricular wall thickness

MBF:

Myocardial blood flow

MDCT:

Multi-detector row computed tomography

MPI:

Myocardial perfusion imaging

NPV:

Negative predictive value

PD:

Perfusion defect

PPV:

Positive predictive value

ROI:

region of interest

SPECT:

Single-photon emission computed tomography

SVD:

Singular value decomposition

TAC:

Time attenuation curve

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Acknowledgments

The scientific guarantor of this publication is Yuki Tanabe. The author Tsutomu Soma of this manuscript declares relationships with the following companies: FUJIFILM RI Pharma Co., Ltd., Tokyo, Japan. The other authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article. The authors state that this work has not received any funding.

One of the authors has significant statistical expertise. Institutional Review Board approval was obtained. Written informed consent was obtained from all subjects (patients) in this study. Methodology: retrospective, observational, performed at one institution.

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Correspondence to Yuki Tanabe.

Appendices

Appendix 1

Fig. 6
figure 6

Histograms of CT-MBF in SPECT and CMR group

Appendix 2

Relations between CT-MBF and LGE

Figures A and B show the relationships of CT perfusion-derived myocardial blood flow (CT-MBF) to the volume (%LGE-volume) and transmural extent (%LGE-TE) of myocardial infarction presented as late gadolinium enhancement (LGE) per segment. The medians of %LGE-volume and %LGE-TE were 27.9 % (16.4-42.2) and 42.4 % (16.1-59.2). Correlation coefficients of CT-MBF were 0.71 and 0.66 for %LGE-volume and %LGE-TE, respectively (P < 0.05, in each).

figure a

Appendix 3

Model-independent deconvolution method based on singular value decomposition

The relationship among the time attenuation curve (TAC) of contrast medium in tissue (Cmyo(t)) and feeding artery (C a (t)), and tissue response function (R(t)) can be described as convolution integral in the following Eq. (1).

$$ C\mathrm{m}\mathrm{y}\mathrm{o}(t)=R(t)\otimes Ca(t), $$
(1)

In this method, myocardial perfusion is quantified from the tissue response function estimated by deconvolution between Cmyo(t) and the arterial input function, i.e., C a (t) based on the central volume principal [32]. Myocardial blood flow (MBF) is calculated from the maximum value of R(t) (Fig. 7). In theory, this method is robust against the injection rate of the contrast medium, and easy to implement in clinical settings. Then, this method has been applied to the quantification of perfusion in various organs such as brain [33].

Fig. 7
figure 7

Concept of deconvolution analysis

This method can be used without prior assumptions on the local vasculature models; however, the deconvolution process is highly sensitive to statistical noise [34]. Then, singular value decomposition (SVD) is generally used to obtain the regularized solutions of ill-posed problems due to statistical noise. In this method, the robustness of MBF estimation is reinforced by adjusting the threshold value used in SVD, and the tissue response function obtained by deconvolution using the SVD depends largely on the threshold value [35].

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Tanabe, Y., Kido, T., Uetani, T. et al. Differentiation of myocardial ischemia and infarction assessed by dynamic computed tomography perfusion imaging and comparison with cardiac magnetic resonance and single-photon emission computed tomography. Eur Radiol 26, 3790–3801 (2016). https://doi.org/10.1007/s00330-016-4238-1

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