• Users Online: 93
  • Print this page
  • Email this page


 
 
Table of Contents
REVIEW ARTICLE
Year : 2020  |  Volume : 5  |  Issue : 1  |  Page : 13-20

Noninvasive cardiac imaging technologies in detecting coronary artery disease: From research to clinical practice


Department of Cardiology, Chinese PLA General Hospital, Beijing, China

Date of Submission16-Feb-2020
Date of Acceptance11-Mar-2020
Date of Web Publication4-Apr-2020

Correspondence Address:
Dr. Junjie Yang
Department of Cardiology, Chinese PLA General Hospital, No. 28 Fuxing Road, Haidian District, Beijing
China
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/cp.cp_3_20

Get Permissions

  Abstract 


We can use several noninvasive cardiac imaging modalities in the diagnosis of coronary artery disease (CAD), and we have investigated these technologies in many clinical trials. Some have already become regular examinations in the assessment of CAD among large-scale hospitals. We can detect not only coronary artery anatomic stenosis but also functional myocardial ischemia according to the information provided by these methods. Based on the evaluation of versatile noninvasive cardiac imaging modalities, risk stratification and treatment management help improve the prognosis of patients with CAD. In this review, we summarize these techniques in the evaluation of myocardial ischemia in terms of principles, evidence, advantages, and limitations.

Keywords: Computed tomography, coronary artery disease, fractional flow reserve, magnetic resonance imaging, myocardial perfusion


How to cite this article:
Shan D, Yang J, Chen Y. Noninvasive cardiac imaging technologies in detecting coronary artery disease: From research to clinical practice. Cardiol Plus 2020;5:13-20

How to cite this URL:
Shan D, Yang J, Chen Y. Noninvasive cardiac imaging technologies in detecting coronary artery disease: From research to clinical practice. Cardiol Plus [serial online] 2020 [cited 2020 May 27];5:13-20. Available from: http://www.cardiologyplus.org/text.asp?2020/5/1/13/281939




  Introduction Top


Coronary artery disease (CAD) remains the leading cause of death globally.[1],[2] Early detection and diagnosis are essential for proactive secondary prevention and improved prognosis. Several noninvasive imaging diagnostic modalities exist to investigate patients presenting with chest pain and suspected CAD, such as imaging stress tests to assess for ischemia or coronary angiography to assess for the presence of atherosclerotic coronary stenosis. These cardiac imaging technologies assist in the clinical practice from anatomy evaluation to hemodynamic significance, as well as from diagnosis to guidance of decision-making. Thanks to the collaborative task force, a recent consensus has also been made concerning the noninvasive imaging examination pathways of stable CAD based on current situation of clinical practice in China [Table 1].[3] In this review, we summarize the recent research progress of noninvasive CAD imaging techniques to help physicians update frontier noninvasive imaging techniques and thus master the clinical values of these modalities in disease diagnosis, risk stratification, and treatment management.


  Cardiac Computed Tomography Top


Traditional coronary computed tomography angiography (CCTA) is an important method to evaluate coronary lesion noninvasively, providing the characteristics of calcification, plaque, and epicardial adipose tissue (EAT). According to the high negative predictive value, CCTA is considered as the “gatekeeper” of the catheter room for CAD. CCTA has emerged as afirst-line imaging modality for the evaluation of CAD, particularly in people with low-to-intermediate risk. Although CCTA has high diagnostic value, it also has a low specificity in the evaluation of the hemodynamic significance of the lesions. With the rapid development of computed tomography (CT) technology in recent years, several CT techniques such as CT perfusion (CTP) imaging and fractional flow reserve based on CT (CT-FFR) emerge and were used preliminary in clinical practice, making it possible to evaluate the hemodynamic significance of stenosis simultaneously and precisely. The development of these technologies promotes a comprehensive evaluation of CAD from anatomy to function.
Table 1: Evaluation table of cardiovascular noninvasive imaging examination

Click here to view


Evaluation of calcification and epicardial adipose tissue by computed tomography

CT can detect coronary artery calcification and parameters of EAT with low-dose radiation and no contrast agent injection. These indicators, which reflect arterial lesion and inflammatory activity, could help physicians to determine the probability of patients with obstructive CAD (lumen stenosis ≥50%), and provide information for further decision-making. Previous studies showed that coronary artery calcification score (CACS) measured by Agatston et al. method [4] is not only related to the progress of atherosclerosis but also the risk of the further cardiac outcome, which can be served as a marker of prognosis in patients with atherosclerosis. Large studies with long-term follow-up lend strength to the evidence that CACS improves outcomes, cost-effectiveness, and safety of primary prevention efforts. An observational outcome study, including 25,253 individuals, showed that, with an average follow-up of 6.8 ± 3.0 years, the death rate was 2% (510 deaths). CACS was an independent predictor of mortality in a multivariable model controlling for age, gender, ethnicity, and cardiac risk factors. Ten-year survival was 99.4% for a CACS of 0 and worsened to 87.8% for a CACS of >1000.[5] Another study also found that the prognostic value of CACS exceeds traditional cardiovascular risk factors. The presence of a high CACS burden, even among individuals without risk factor burden, is associated with an elevated event rate. CACS has the potential to further risk stratify asymptomatic individuals at the extremes of risk factor burden.[6]

Adipose tissue plays an essential role in the progression of CAD. Epicardial and perivascular adipose tissues release plenty of pro-inflammatory cytokines to the vascular and circulation. Clinicians could detect adipose tissue parameters (e.g., volume and attenuation) measured by CT scan to evaluate the inflammatory status of a coronary artery, risk of plaque progression, or cardiac event. Previous studies have shown that morbid EAT was associated with CAD,[7],[8],[9] major adverse cardiovascular event (MACE),[10],[11] and myocardial ischemia.[12],[13],[14] A previous study using integrated backscatter intravascular ultrasound has found that EAT volume was an independent risk predictor for the presence of significant noncalcified components (plaque burden ≥40%).[15] Noticeably, a recent study evaluated the predictive value of the perivascular fat attenuation index (FAI, calculated as local adipose attenuation) on all-cause mortality of patients with CAD. The results showed that FAI could predict all-cause and cardiac death (risk ratio 2.15) and could be used as a marker of coronary artery inflammation.[16] Furthermore, the same research team published the discovery in 2019 that the fat radiomic profile based on a machine learning algorithm of perivascular fat beyond inflammation could improve the cardiac risk prediction. These regularly provided new evidence for radiomic indices related to perivascular fat tissue as a risk stratification tool.[17] The above study suggests that EAT attenuation can be used as a new imaging index of inflammation, which can help identify real high-risk patients and guide clinicians to make a preventive strategy.

Evaluation of high-risk plaque by coronary computed tomography angiography

Acute myocardial infarction (MI) is often caused by the rupture of unstable atherosclerotic plaque, which could be detected by CCTA. Several studies have confirmed that the characteristics of a high-risk plaque displayed by CCTA include low-attenuation plaque, positive remodeling, napkin-ring sign, and spotty calcification [Figure 1].[18],[19] Therefore, it is of considerable significance to recognize unstable plaque features and strengthen early intervention to reduce future risk. CCTA can provide information about plaque features such as larger lipid core, spotty calcification, and neovascularization in vulnerable plaque, which could be recognized as corresponding pathological representation in the coronary ultrasound. Previous studies have found that patients with high-risk plaque represented a higher rate of adverse cardiac events (6.4%). After adjusted traditional cardiovascular risk factors and coronary lumen stenosis, high-risk plaque signs were still significantly related to cardiac events.[20] On the other hand, some novel features of high-risk plaques on CCTA imaging analysis have also attracted physicians' attention. CCTA radiomics analysis, includingfirst-order statistics, gray-level co-occurrence matrices, gray-level run-length matrices, and geometry-based statistics, showed a good-to-excellent diagnostic accuracy for the identification of plaque vulnerability and significant outperforming standard quantitative and qualitative high-risk plaque features.[21] For advanced atherosclerotic lesion identified by histologic cross-sections, a radiomics based on machine learning analysis (least angles regression) can improve the discriminatory power of CCTA in this identification.[22] Therefore, no matter the conventional or novel machine learning algorithm for the evaluation of vulnerable plaque, CCTA could be used as a valuable tool for clinical screening of high-risk plaque.
Figure 1: Characteristics of high-risk plaque. (a) positive remodeling: the ratio of lesion segment diameter to the mean diameter of the proximal and distal reference vessels was 1.3; (b) spotty calcification: calcified plaque <3 mm in diameter within a mixed plaque; (c) low-attenuation plaque: noncalcified plaque with internal attenuation <30 HU; (d) napkin-ring sign: central low-attenuation plaque with a peripheral rim of higher computed tomography attenuation

Click here to view


Hemodynamics geometry analysis by coronary computed tomography angiography

When hemodynamic stress factors exceed the plaque strength, coronary plaque ruptures, which leads to subsequent acute coronary syndrome (ACS).[23] Depending on computational fluid dynamics technology, it is possible to evaluate hemodynamics in vivo. Rapid blood flow produces circumferential stress, axial stress, and shear stress in the artery lumen, leading to progression and rupture of the lesion. The previous study applied computational fluid dynamics based on CCTA to calculated several hemodynamic parameters found that although there were no differences in CT-FFR, diameter stenosis, and wall shear stress pattern, the distribution of axial stress was different between upstream- and downstream-dominant lesions. Therefore, the combination of axial stress and geometric indices can help assess the risk of plaque rupture and has the potential to introduce a treatment strategy of patients with CAD.[24] A recent noninvasive hemodynamic study based on CCTA confirmed that, compared to a plaque with either high-risk anatomic characteristics or high-risk hydrodynamic characteristics, a plaque with both of them has a significantly increased risk of progression to ACS (hazard ratio: 3.22). The combination of high-risk anatomic and hydrodynamic characteristic assessment can significantly improve the prediction ability of adverse cardiac events and improve the stratification of patients.[25]

Myocardial computed tomography perfusion

CTP can make a qualitative evaluation of myocardial ischemia by observing myocardial perfusion defects by visual inspection. CTP evaluates thefirst pass of contrast medium through the myocardium at rest and during pharmacological stress, emphasized the differences in perfusion between normal and ischemic myocardium. This technique includes two methods: static and dynamic CTP. The former one acquired data during a single phase of thefirst pass of contrast medium through the myocardium, whereas the later one acquired data at multiple phases. The major advantage of CTP is that it can detect perfusion directly that is useful to assess the hemodynamic significance of coronary lesion.[26] Combined with previous CCTA procedures, CTP can not only obtain the anatomic information about the coronary artery but also functional of myocardial perfusion in an optimized one-stop-shop pattern, compared with the conventional myocardial perfusion imaging (MPI). In a recent study, combined quantitative coronary angiography (QCA) and single-photon emission CT (SPECT) or QCA alone used as a reference, the diagnostic performance of stress CTP in the detection of CAD was evaluated. The result revealed that the diagnostic performance of CTP is similar to that of perfusion magnetic resonance imaging (MRI). Per-patient diagnostic accuracy, sensitivity, and specificity were 82%, 90%, and 67%, respectively.[27] Another multicenter study, including 381 patients, also revealed that according to invasive angiography (ICA) and SPECT as a reference, for the combination of a CCTA stenosis ≥50% stenosis and a CTP perfusion deficit, the sensitivity and specificity were 80% and 74%, respectively.[28] Dynamic CTP, as opposed to the previously introduced static CTP, acquires a series of images during the transit of contrast medium through the myocardium, which allows quantification of myocardial blood flow (MBF) in a way resembling quantitative MBF acquired by positron emission tomography (PET) perfusion imaging [Figure 2]. Coenen et al.first investigated the individual and combined accuracy of dynamic CTP and CT-FFR for functionally relevant CAD. They found that combined with CT-FFR, the diagnostic performance of CTP was increased; the area under the curve (AUC) for CTP/CT-FFR was 0.85, higher than individual CTP (0.78) or CT-FFR (0.78) or CCTA (0.70).[29] Another randomized trial to assess the clinical value of combined CCTA and CTP examination has demonstrated that the diagnostic strategy of CCTA + CTP reduces the need for invasive examination and treatment in acute onset chest pain patients.[30] Therefore, CTP may be expected as a noninvasive technique for the accurate diagnosis of CAD. However, the procedure of CTP is different from conventional CCTA, high-level expertise, and advanced scanners make it not widely used. Stress drugs, such as adenosine and additional radiation exposure (3-16 mSv), may be necessary. Our recent study focused on a new CT scan protocol that integrated dynamic CTP and CCTA, supporting those new parameters, stress MBF ratio (SFR), can provide better accuracy than hyperemic MBF to identify flow-limiting coronary stenosis.[31] For each coronary territory, SFR was defined as the ratio of hyperemic MBF in an artery with stenosis to hyperemic MBF in a no diseased artery. However, before CTP is used in clinical practice, more research results are strengthened, and the procedure should be optimized in the future.
Figure 2: Myocardial blood flow in dynamic computed tomography perfusion. Perfusion defect can be found in the basal and intermediate inferior wall, and the absolute value of myocardial blood flow was relatively lower accordingly

Click here to view


Fractional flow reserve based on computed tomography technology

In recent years, CT-FFR has become a new focus in the field of CCTA research. CT-FFR calculates the FFR from CCTA data at rest using computational fluid dynamics principle to generate arithmetic model including flow, pressure, and resistance [Figure 3].[32] Because the response of coronary arteries to adenosine during ICA is predictable and could be used to create a 3-D model of the hyperemic state, we can solve the 3-D model equation to compute the flow and pressure across the coronary vascular bed.[33] The advantage of CT-FFR is that it can obtain the anatomy and functional information of the coronary artery at the same time by dedicated postprocessing software, which is valuable for decision-making for patients with stenosis 50%–70%. It does not require additional image acquisition and taking additional drugs except nitroglycerine. Several multicenter trials and a meta-analysis have shown that CT-FFR was useful in the diagnosis of selected CAD patients when referring to invasive FFR as the gold standard.[33],[34],[35],[36] Moreover, clinical care guided by CT-FFR could provide benefits with equivalent clinical outcomes with lower expenditure compared with routine clinical care over a 1-year follow-up (Platform trial).[37] The results revealed that 60% of patients in the CT-FFR arm canceled the subsequent ICA, and especially, only 12% of the patients in CT-FFR arm were found without obstructive CAD during ICA at 90 days, which was significantly lower than that of routine care arm (73%), yielding a risk difference of 61%. The research results indicated that clinical care guided by CT-FFR could reduce unnecessary ICA.[37] A recently published international prospective study (ADVANCE registry) enrolled 5083 patients further evaluated the reclassification of CT-FFR in the clinical flow of the real world. According to the results of CCTA/CT-FFR, 66.9% of the treatment strategy patients have been changed. The nonobstructive CAD was significantly lower in ICA patients with CT-FFR ≤0.80 compared to patients with CT-FFR >0.80 (14.4% vs. 43.8%). No death/MI occurred within 90 days in patients with CT-FFR >0.80, whereas 19 (0.6%) MACE and 14 (0.3%) death/MI occurred in patients with CT-FFR ≤0.80.[38] Different from conventional viewpoint, CT-FFR based on machine learning shows better diagnostic performance superior to CCTA alone regardless of calcium burden (CACS: 0–400, AUC: 0.85 vs. 0.64; CACS: ≥400, AUC: 0.71 vs. 0.55), which reflects the utility of this technology in a broad patient population with a wide range of CACS in real world.[39] Undeniably, CT-FFR has the disadvantage of underestimating the serial and diffuse lesion. Moreover, the CT-FFR value does not match perfectly with invasive FFR because where CT-FFR is measured is not always the exact location where invasive FFR is measured.[40]
Figure 3: Measurement of fractional flow reserve based on computed tomography. (a) Multiple planar reconstruction of a left anterior descending branch with a plaque in proximal of a branch.* Indicates the location of the plaque. Arrow indicates the location of fractional flow reserve based on computed tomography (approximately 2 mm from lesion); (b) 3-dimensional computational fluid dynamics model. * And arrow is as same as (a)

Click here to view


New computed tomography concepts

Medical CT is generally based on measuring and displaying the local X-ray attenuation coefficients in a slice of the patient. Phase-contrast CT is an alternative CT technique that does not measure the X-ray absorption but the phase shift of the X-rays by the measurement object. The phase-contrast CT images show the local X-ray phase shift coefficients on a grayscale. Phase-contrast imaging has recently gained considerable attention in the scientific literature as a new method with potential applications in medical imaging, in particular after its successful implementation using compact low-brilliance X-ray sources that are standard components of medical X-ray systems or CT systems.[41]

Another new CT technology is the novel photon-counting detector, which is currently being investigated directly converts the absorbed X-rays into electrical signals. This new detector is based on a semiconductor such as cadmium telluride or cadmium-zinc-telluride (CZT). The absorbed X-rays directly induce short current pulses that are individually counted as soon as they exceed a threshold.[42] The pulse height is proportional to the X-ray energy. A photon-counting detector can, therefore, provide energy-resolved signals. The individual detector pixels are defined by the high electric field between the common cathode and pixelated anodes - in contrast to conventional scintillation detectors, no additional separation layers between the pixels are necessary.[43] Therefore, the detector pixels can be made much smaller to improve spatial resolution.


  Cardiac Magnetic Resonance Imaging Top


Cardiac magnetic resonance (CMR) imaging serves as the gold standard for the evaluation of cardiac structure and function. Cine CMR and delay gadolinium enhancement imaging, cardiac structure, function, abnormal ventricular wall motion, myocardial ischemia, infarction, and viability can be evaluated with stress perfusion. Additional CMR sequences can also evaluate tissue characteristics, such as edema, fibrosis, and hemorrhage.[44] CMR perfusion imaging can display a reduction or deficiency of myocardial perfusion in a condition of positive results. In the clinical evaluation of MRI in CAD study, the sensitivity, specificity, and negative predictive value of multiparametric CMR were 86.5%, 83.4%, and 90.5%, respectively, and the sensitivity and negative predictive value of CMR are superior to SPECT.[45] In the recently published MR Perfusion Imaging to Guide Management of Patients With Stable Coronary Artery Disease Study (MR-INFORM), it was demonstrated that myocardial perfusion cardiovascular MRI was associated with a lower incidence of coronary revascularization than FFR and was noninferior to FFR with respect to MACE.[46] A recent study also revealed that compared with CT-FFR, CMR yielded similar overall diagnostic accuracy and has the highest specificity for the prediction of coronary revascularization in patients with stable chest pain.[47] CMR is not limited by the patient's body and has a high spatial resolution, temporal resolution, and soft-tissue contrast. Image quality may be affected by an artifact, and exercise is not routinely available due to the requirement of MRI-compatible equipment. For the past few years, whole-heart synchronized dynamic myocardial perfusion image can be obtained from high-resolution whole-heart 3D CMR perfusion, which improves the sensitivity of myocardial ischemia diagnosis and accurate quantitative analysis for myocardial ischemia.[48] In addition, the myocardium quantitative imaging (T1 mapping and T2 mapping) is applied to detect diffuse myocardial fibrosis and edema in clinical practice.[49],[50]


  Radionuclide Myocardial Perfusion Imaging Top


Radionuclide MPI includes the use of SPECT or PET techniques to detect and evaluate myocardial perfusion status and myocardial viability to provide useful information of the coronary arteries.[51] MPI has been widely used in the detection of CAD, including the diagnosis of myocardial ischemia and infarct and evaluation of the viability of the myocardium; ECG-gated imaging can also provide information of myocardial perfusion, segmental wall motion, mechanical synchronization, and cardiac function parameters at the same time.[52] The published literature with SPECT suggests that its average sensitivity for detecting >50% ICA stenosis is 87%, whereas the average specificity is 73%. With PET perfusion imaging, the reported average sensitivity for detecting >50% ICA stenosis is 91%, whereas the average specificity is 89%.[53]

The quantity of tracers taken in by the myocardium is related to regional blood flow to the myocardium. When stress MPI is reduced, and resting MPI is healthy or improved, the diagnosis of myocardial ischemia should be considered. In the case of transient left ventricular enlargement and decreased ejection fraction at the time of stress MPI, it is an essential indicator for predicting severe CAD. The PET MPI has higher spatial resolution and better diagnostic accuracy than SPECT. It can quantify absolute blood flow to detect microvascular diseases and measure MBF reserve. It can be used for early detecting CAD, especially for the patients with the microvascular disease, balanced multi-vessel CAD, and obesities.[54] Clinical applications of PET/CT are increasing gradually, and it is useful for the comprehensive assessment of coronary artery function and myocardial perfusion. Although the majority of technical advantages have been recognized for a long time, access to PET for routine detection of CAD remains somewhat limited. It has a higher cost for a new generator to produce 82 Rubidium or advanced cyclotron to produce 13 N-ammonia and is not widely used in China.

Fortunately, a new quantitative approach, such as measuring coronary flow reserve (CFR) using dynamic SPECT imaging, is being rendered as a better mode of diagnosing CAD than the visual assessment of stress and rest images from static SPECT images.[55] With the development of advanced technology, including hardware and software, the CZT detector can benefit in higher sensitivity, shorter acquisition time, and lower patient dose, which might permit dynamic cardiac SPECT to evaluate regional MBF and CFR based on time-varying information of radiotracer distribution.[56]


  Conclusion Top


Noninvasive cardiac imaging modalities could enable us to evaluate not only the stenosis and plaque feature but also the hemodynamic significance of stenosis and myocardial ischemia. These techniques make it possible to fulfill risk stratification for patients with CAD precisely and formulate subsequent optimized decision-making to improve prognosis further. It is reasonable to believe that noninvasive cardiac imaging technologies be more valuable in future clinical practice.

Financial support and sponsorship

This study was supported by grants from the National Key R & D Program of China (2016YFC1300304) and Beijing NOVA Program (Z181100006218055).

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Timmis A, Townsend N, Gale CP, Torbica A, Lettino M, Petersen SE, et al. European society of cardiology: Cardiovascular disease statistics 2019. Eur Heart J 2020;41:12-85.  Back to cited text no. 1
    
2.
Wong MC, Zhang DX, Wang HH. Rapid emergence of atherosclerosis in Asia: A systematic review of coronary atherosclerotic heart disease epidemiology and implications for prevention and control strategies. Curr Opin Lipidol 2015;26:257-69.  Back to cited text no. 2
    
3.
Chen YD, Fang WY, Chen JY, Fan ZM, Gao CY, Ge JB, et al. Chinese expert consensus on the non-invasive imaging examination pathways of stable coronary artery disease. J Geriatr Cardiol 2018;15:30-40.  Back to cited text no. 3
    
4.
Agatston AS, Janowitz WR, Hildner FJ, Zusmer NR, Viamonte M Jr, Detrano R. Quantification of coronary artery calcium using ultrafast computed tomography. J Am Coll Cardiol 1990;15:827-32.  Back to cited text no. 4
    
5.
Budoff MJ, Shaw LJ, Liu ST, Weinstein SR, Mosler TP, Tseng PH, et al. Long-term prognosis associated with coronary calcification: Observations from a registry of 25,253 patients. J Am Coll Cardiol 2007;49:1860-70.  Back to cited text no. 5
    
6.
Silverman MG, Blaha MJ, Krumholz HM, Budoff MJ, Blankstein R, Sibley CT, et al. Impact of coronary artery calcium on coronary heart disease events in individuals at the extremes of traditional risk factor burden: The multi-ethnic study of atherosclerosis. Eur Heart J 2014;35:2232-41.  Back to cited text no. 6
    
7.
Ueno K, Anzai T, Jinzaki M, Yamada M, Jo Y, Maekawa Y, et al. Increased epicardial fat volume quantified by 64-multidetector computed tomography is associated with coronary atherosclerosis and totally occlusive lesions. Circ J 2009;73:1927-33.  Back to cited text no. 7
    
8.
Konishi M, Sugiyama S, Sugamura K, Nozaki T, Ohba K, Matsubara J, et al. Association of pericardial fat accumulation rather than abdominal obesity with coronary atherosclerotic plaque formation in patients with suspected coronary artery disease. Atherosclerosis 2010;209:573-8.  Back to cited text no. 8
    
9.
Sinha SK, Thakur R, Jha MJ, Goel A, Kumar V, Kumar A, et al. Epicardial adipose tissue thickness and its association with the presence and severity of coronary artery disease in clinical setting: A cross-sectional observational study. J Clin Med Res 2016;8:410-9.  Back to cited text no. 9
    
10.
Gitsioudis G, Schmahl C, Missiou A, Voss A, Schüssler A, Abdel-Aty H, et al. Epicardial adipose tissue is associated with plaque burden and composition and provides incremental value for the prediction of cardiac outcome. A clinical cardiac computed tomography angiography study. PLoS One 2016;11:e0155120.  Back to cited text no. 10
    
11.
Iacobellis G, Corradi D, Sharma AM. Epicardial adipose tissue: Anatomic, biomolecular and clinical relationships with the heart. Nat Clin Pract Cardiovasc Med 2005;2:536-43.  Back to cited text no. 11
    
12.
Tanami Y, Jinzaki M, Kishi S, Matheson M, Vavere AL, Rochitte CE, et al. Lack of association between epicardial fat volume and extent of coronary artery calcification, severity of coronary artery disease, or presence of myocardial perfusion abnormalities in a diverse, symptomatic patient population: Results from the CORE320 multicenter study. Circ Cardiovasc Imaging 2015;8:e002676.  Back to cited text no. 12
    
13.
Tamarappoo B, Dey D, Shmilovich H, Nakazato R, Gransar H, Cheng VY, et al. Increased pericardial fat volume measured from noncontrast CT predicts myocardial ischemia by SPECT. JACC Cardiovasc Imaging 2010;3:1104-12.  Back to cited text no. 13
    
14.
Hell MM, Ding X, Rubeaux M, Slomka P, Gransar H, Terzopoulos D, et al. Epicardial adipose tissue volume but not density is an independent predictor for myocardial ischemia. J Cardiovasc Comput Tomogr 2016;10:141-9.  Back to cited text no. 14
    
15.
Alexopoulos N, McLean DS, Janik M, Arepalli CD, Stillman AE, Raggi P. Epicardial adipose tissue and coronary artery plaque characteristics. Atherosclerosis 2010;210:150-4.  Back to cited text no. 15
    
16.
Oikonomou EK, Marwan M, Desai MY, Mancio J, Alashi A, Hutt Centeno E, et al. Non-invasive detection of coronary inflammation using computed tomography and prediction of residual cardiovascular risk (the CRISP CT study): A post-hoc analysis of prospective outcome data. Lancet 2018;392:929-39.  Back to cited text no. 16
    
17.
Oikonomou EK, Williams MC, Kotanidis CP, Desai MY, Marwan M, Antonopoulos AS, et al. A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography. Eur Heart J 2019;40:3529-43.  Back to cited text no. 17
    
18.
Libby P. Mechanisms of acute coronary syndromes and their implications for therapy. N Engl J Med 2013;368:2004-13.  Back to cited text no. 18
    
19.
Cury RC, Abbara S, Achenbach S, Agatston A, Berman DS, Budoff MJ, et al. CAD-RADS™ coronary artery disease-reporting and data system. An expert consensus document of the society of cardiovascular computed tomography (SCCT), the American college of radiology (ACR) and the North American society for cardiovascular imaging (NASCI). Endorsed by the American college of cardiology. J Cardiovasc Comput Tomogr 2016;10:269-81.  Back to cited text no. 19
    
20.
Ferencik M, Mayrhofer T, Bittner DO, Emami H, Puchner SB, Lu MT, et al. Use of high-risk coronary atherosclerotic plaque detection for risk stratification of patients with stable chest pain: A secondary analysis of the PROMISE randomized clinical trial. JAMA Cardiol 2018;3:144-52.  Back to cited text no. 20
    
21.
Kolossváry M, Park J, Bang JI, Zhang J, Lee JM, Paeng JC, et al. Identification of invasive and radionuclide imaging markers of coronary plaque vulnerability using radiomic analysis of coronary computed tomography angiography. Eur Heart J Cardiovasc Imaging 2019;20:1250-8.  Back to cited text no. 21
    
22.
Kolossváry M, Karády J, Kikuchi Y, Ivanov A, Schlett CL, Lu MT, et al. Radiomics versus visual and histogram-based assessment to identify atheromatous lesions at coronary CT angiography: An ex vivo study. Radiology 2019;293:89-96.  Back to cited text no. 22
    
23.
Brown AJ, Teng Z, Evans PC, Gillard JH, Samady H, Bennett MR. Role of biomechanical forces in the natural history of coronary atherosclerosis. Nat Rev Cardiol 2016;13:210-20.  Back to cited text no. 23
    
24.
Choi G, Lee JM, Kim HJ, Park JB, Sankaran S, Otake H, et al. Coronary artery axial plaque stress and its relationship with lesion geometry: Application of computational fluid dynamics to coronary CT angiography. JACC Cardiovasc Imaging 2015;8:1156-66.  Back to cited text no. 24
    
25.
Lee JM, Choi G, Koo BK, Hwang D, Park J, Zhang J, et al. Identification of high-risk plaques destined to cause acute coronary syndrome using coronary computed tomographic angiography and computational fluid dynamics. JACC Cardiovasc Imaging 2019;12:1032-43.  Back to cited text no. 25
    
26.
Pontone G, Baggiano A, Andreini D, Guaricci AI, Guglielmo M, Muscogiuri G, et al. Dynamic stress computed tomography perfusion with a whole-heart coverage scanner in addition to coronary computed tomography angiography and fractional flow reserve computed tomography derived. JACC Cardiovasc Imaging 2019;12:2460-71.  Back to cited text no. 26
    
27.
Rief M, Chen MY, Vavere AL, Kendziora B, Miller JM, Bandettini WP, et al. Coronary artery disease: Analysis of diagnostic performance of CT perfusion and MR perfusion imaging in comparison with quantitative coronary angiography and SPECT-multicenter prospective Trial. Radiology 2018;286:461-70.  Back to cited text no. 27
    
28.
Rochitte CE, George RT, Chen MY, Arbab-Zadeh A, Dewey M, Miller JM, et al. Computed tomography angiography and perfusion to assess coronary artery stenosis causing perfusion defects by single photon emission computed tomography: The CORE320 study. Eur Heart J 2014;35:1120-30.  Back to cited text no. 28
    
29.
Coenen A, Rossi A, Lubbers MM, Kurata A, Kono AK, Chelu RG, et al. Integrating CT myocardial perfusion and CT-FFR in the work-up of coronary artery disease. JACC Cardiovasc Imaging 2017;10:760-70.  Back to cited text no. 29
    
30.
Sørgaard MH, Linde JJ, Kühl JT, Kelbæk H, Hove JD, Fornitz GG, et al. Value of myocardial perfusion assessment with coronary computed tomography angiography in patients with recent acute-onset chest pain. JACC Cardiovasc Imaging 2018;11:1611-21.  Back to cited text no. 30
    
31.
Yang J, Dou G, He B, Jin Q, Chen Z, Jing J, et al. Stress myocardial blood flow ratio by dynamic CT perfusion identifies hemodynamically significant CAD. JACC Cardiovasc Imaging 2019;S1936-878X(19)30601-1.[Epub ahead of print].  Back to cited text no. 31
    
32.
Taylor CA, Fonte TA, Min JK. Computational fluid dynamics applied to cardiac computed tomography for noninvasive quantification of fractional flow reserve: Scientific basis. J Am Coll Cardiol 2013;61:2233-41.  Back to cited text no. 32
    
33.
Koo BK, Erglis A, Doh JH, Daniels DV, Jegere S, Kim HS, et al. Diagnosis of ischemia-causing coronary stenoses by noninvasive fractional flow reserve computed from coronary computed tomographic angiograms. Results from the prospective multicenter DISCOVER-FLOW (diagnosis of ischemia-causing stenoses obtained via noninvasive fractional flow reserve) study. J Am Coll Cardiol 2011;58:1989-97.  Back to cited text no. 33
    
34.
Celeng C, Leiner T, Maurovich-Horvat P, Merkely B, de Jong P, Dankbaar JW, et al. Anatomical and functional computed tomography for diagnosing hemodynamically significant coronary artery disease: A meta-analysis. JACC Cardiovasc Imaging 2019;12:1316-25.  Back to cited text no. 34
    
35.
Nørgaard BL, Leipsic J, Gaur S, Seneviratne S, Ko BS, Ito H, et al. Diagnostic performance of noninvasive fractional flow reserve derived from coronary computed tomography angiography in suspected coronary artery disease: The NXT trial (analysis of coronary blood flow using CT angiography: Next Steps). J Am Coll Cardiol 2014;63:1145-55.  Back to cited text no. 35
    
36.
Min JK, Leipsic J, Pencina MJ, Berman DS, Koo BK, van Mieghem C, et al. Diagnostic accuracy of fractional flow reserve from anatomic CT angiography. JAMA 2012;308:1237-45.  Back to cited text no. 36
    
37.
Douglas PS, De Bruyne B, Pontone G, Patel MR, Norgaard BL, Byrne RA, et al. 1-year outcomes of FFRCT-guided care in patients with suspected coronary disease: The PLATFORM Study. J Am Coll Cardiol 2016;68:435-45.  Back to cited text no. 37
    
38.
Fairbairn TA, Nieman K, Akasaka T, Nørgaard BL, Berman DS, Raff G, et al. Real-world clinical utility and impact on clinical decision-making of coronary computed tomography angiography-derived fractional flow reserve: Lessons from the ADVANCE Registry. Eur Heart J 2018;39:3701-11.  Back to cited text no. 38
    
39.
Tesche C, Otani K, De Cecco CN, Coenen A, De Geer J, Kruk M, et al. Influence of coronary calcium on diagnostic performance of machine learning CT-FFR: Results from MACHINE registry. JACC Cardiovasc Imaging 2020;13:760-70.  Back to cited text no. 39
    
40.
Rabbat MG, Berman DS, Kern M, Raff G, Chinnaiyan K, Koweek L, et al. Interpreting results of coronary computed tomography angiography-derived fractional flow reserve in clinical practice. J Cardiovasc Comput Tomogr 2017;11:383-8.  Back to cited text no. 40
    
41.
Raupach R, Flohr T. Performance evaluation of x-ray differential phase contrast computed tomography (PCT) with respect to medical imaging. Med Phys 2012;39:4761-74.  Back to cited text no. 41
    
42.
Taguchi K. Energy-sensitive photon counting detector-based X-ray computed tomography. Radiol Phys Technol 2017;10:8-22.  Back to cited text no. 42
    
43.
Taguchi K, Iwanczyk JS. Vision 20/20: Single photon counting X-ray detectors in medical imaging. Med Phys 2013;40:100901/1-100901/19.  Back to cited text no. 43
    
44.
Coelho-Filho OR, Rickers C, Kwong RY, Jerosch-Herold M. MR myocardial perfusion imaging. Radiology 2013;266:701-15.  Back to cited text no. 44
    
45.
Greenwood JP, Maredia N, Younger JF, Brown JM, Nixon J, Everett CC, et al. Cardiovascular magnetic resonance and single-photon emission computed tomography for diagnosis of coronary heart disease (CE-MARC): A prospective trial. Lancet 2012;379:453-60.  Back to cited text no. 45
    
46.
Nagel E, Greenwood JP, McCann GP, Bettencourt N, Shah AM, Hussain ST, et al. Magnetic resonance perfusion or fractional flow reserve in coronary disease. N Engl J Med 2019;380:2418-28.  Back to cited text no. 46
    
47.
Rønnow Sand NP, Nissen L, Winther S, Petersen SE, Westra J, Christiansen EH, et al. Prediction of coronary revascularization in stable angina: Comparison of FFRCT With CMR stress perfusion imaging. JACC Cardiovasc Imaging 2019;S1936-878X(19)30636-9.[Epub ahead of print].  Back to cited text no. 47
    
48.
Motwani M, Jogiya R, Kozerke S, Greenwood JP, Plein S. Advanced cardiovascular magnetic resonance myocardial perfusion imaging: High-spatial resolution versus 3-dimensional whole-heart coverage. Circ Cardiovasc Imaging 2013;6:339-48.  Back to cited text no. 48
    
49.
Salerno M, Kramer CM. Advances in parametric mapping with CMR imaging. JACC Cardiovasc Imaging 2013;6:806-22.  Back to cited text no. 49
    
50.
François CJ. Current state of the art cardiovascular MR imaging techniques for assessment of ischemic heart disease. Radiol Clin North Am 2015;53:335-44.  Back to cited text no. 50
    
51.
Ritchie JL, Bateman TM, Bonow RO, Crawford MH, Gibbons RJ, Hall RJ, et al. Guidelines for clinical use of cardiac radionuclide imaging: A report of the American college of cardiology/American heart association task force on assessment of diagnostic and therapeutic cardiovascular procedures (committee on radionuclide imaging)--developed in collaboration with the american society of nuclear cardiology. J Nucl Cardiol 1995;2:172-92.  Back to cited text no. 51
    
52.
Bateman TM, Heller GV, McGhie AI, Friedman JD, Case JA, Bryngelson JR, et al. Diagnostic accuracy of rest/stress ECG-gated Rb-82 myocardial perfusion PET: Comparison with ECG-gated Tc-99m sestamibi SPECT. J Nucl Cardiol 2006;13:24-33.  Back to cited text no. 52
    
53.
Di Carli MF, Dorbala S, Meserve J, El Fakhri G, Sitek A, Moore SC. Clinical myocardial perfusion PET/CT. J Nucl Med 2007;48:783-93.  Back to cited text no. 53
    
54.
Di Carli MF, Hachamovitch R. New technology for noninvasive evaluation of coronary artery disease. Circulation 2007;115:1464-80.  Back to cited text no. 54
    
55.
Mitra D, Abdalah M, Boutchko R, Chang H, Shrestha U, Botvinick E, et al. Comparison of sparse domain approaches for 4D SPECT dynamic image reconstruction. Med Phys 2018;45:4493-509.  Back to cited text no. 55
    
56.
Sciammarella M, Shrestha UM, Seo Y, Gullberg GT, Botvinick EH. A combined static-dynamic single-dose imaging protocol to compare quantitative dynamic SPECT with static conventional SPECT. J Nucl Cardiol 2019;26:763-71.  Back to cited text no. 56
    


    Figures

  [Figure 1], [Figure 2], [Figure 3]
 
 
    Tables

  [Table 1]



 

Top
 
  Search
 
    Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
    Access Statistics
    Email Alert *
    Add to My List *
* Registration required (free)  

 
  In this article
Abstract
Introduction
Cardiac Computed...
Cardiac Magnetic...
Radionuclide Myo...
Conclusion
References
Article Figures
Article Tables

 Article Access Statistics
    Viewed750    
    Printed32    
    Emailed0    
    PDF Downloaded66    
    Comments [Add]    

Recommend this journal