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Coronary Computed Tomography vs. Cardiac Magnetic Resonance Imaging in the Evaluation of Coronary Artery Disease

Department of Cardiology, Angiology and Pneumology, University Hospital Heidelberg, Im Neuenheimer Feld 410, 69120 Heidelberg, Germany
DZHK (German Centre for Cardiovascular Research), Partner Site Heidelberg/Mannheim, 69120 Heidelberg, Germany
Author to whom correspondence should be addressed.
Diagnostics 2023, 13(1), 125;
Submission received: 28 November 2022 / Revised: 23 December 2022 / Accepted: 28 December 2022 / Published: 30 December 2022


Coronary artery disease (CAD) represents a widespread burden to both individual and public health, steadily rising across the globe. The current guidelines recommend non-invasive anatomical or functional testing prior to invasive procedures. Both coronary computed tomography angiography (cCTA) and stress cardiac magnetic resonance imaging (CMR) are appropriate imaging modalities, which are increasingly used in these patients. Both exhibit excellent safety profiles and high diagnostic accuracy. In the last decade, cCTA image quality has improved, radiation exposure has decreased and functional information such as CT-derived fractional flow reserve or perfusion can complement anatomic evaluation. CMR has become more robust and faster, and advances have been made in functional assessment and tissue characterization allowing for earlier and better risk stratification. This review compares both imaging modalities regarding their strengths and weaknesses in the assessment of CAD and aims to give physicians rationales to select the most appropriate modality for individual patients.

1. Introduction

Coronary artery disease (CAD) represents a widespread burden to both individual and public health, steadily rising across the globe [1]. Thus, safe and accurate tests for diagnosis and risk stratification are of paramount importance. In recent years, the European Society of Cardiology, American Heart Association and National Institute for Health and Care Excellence (NICE) updated their guidelines on the diagnosis and management of chronic coronary syndromes and in particular heightened the importance of non-invasive anatomical or functional testing in patients with suspected CAD [2,3,4]. Coronary computed tomography angiography (cCTA) is the imaging of choice for anatomical testing in most patients with suspected CAD. Cardiac magnetic resonance imaging (CMR) is widely recognized as an accurate, well-validated, non-ionizing imaging technique [5]. In comparison to other functional testing modalities, it uniquely incorporates a high diagnostic accuracy with the possibility to assess cardiac morphology, function, and tissue composition. This is why we chose it as representing modality to compare with cCTA, although many arguments may also hold true for other functional testing modalities such as nuclear imaging. Due to its high value in the recent guidelines, usage of both cCTA and stress CMR doubled in the last decade [6]. In this review, we compare the strengths and limitations of both imaging modalities and provide guidance on how to select the best suitable modality for each patient.

2. Diagnostic Performance of cCTA vs. CMR

Both cCTA and vasodilator stress CMR are robust imaging modalities with reported diagnostic image quality in >97% of cases [7,8].
However, to compare both in their diagnostic capabilities, comprehension of what both modalities primarily test for is indispensable. In general, cCTA is widely used for the assessment of the anatomical presence of coronary artery disease, whereas stress CMR tests for the functional presence of ischemia.
cCTA has the highest sensitivity (97%) of all non-invasive imaging modalities for the detection of anatomically significant CAD, but low specificity for anatomically (78%) or functionally (53%) significant CAD [9,10].
The latter is often due to partial volume or blooming artifacts which can lead to an enlargement of calcified plaques, thus resulting in an overestimation of stenosis severity [11]. Consequently, a high grade of calcification, as measured with the Agatston score, is the most relevant predictor of an uninterpretable cCTA. The same artifacts impair the assessment of coronary artery stents.
However, cCTA allows for the identification of patients in the early stage of CAD having non-obstructive coronary plaques. These patients have long been underrecognized since these plaques typically do not cause reduced blood flow and consequently most non-invasive tests assessing ischemia are inconspicuous. Yet, these patients are at increased risk since the rupture of non-obstructive plaques is the main cause of myocardial infarction [12,13]. Furthermore, cCTA is able to assess the overall plaque burden and to detect signs of plaque vulnerability such as low-attenuation, spotty calcifications, low-attenuation plaque, and the so-called “Napkin ring sign” (a plaque with a low attenuating necrotic core surrounded by a thin ring-like hyperattenuating rim) [14,15].
The identification of additional patients at risk by cCTA improves patient outcome significantly, as shown in the PROMISE, CONFIRM and SCOT-HEART trial [16,17,18,19]. For the PROMISE trial, 9,102 patients with intermediate pretest probability for obstructive CAD were randomly assigned to functional testing or anatomical testing with cCTA [17]. The primary endpoint of a major adverse cardiac event (MACE) was not statistically significant between groups, but cCTA was associated with fewer invasive angiographies showing no obstructive CAD [20]. In a post hoc analysis a normal cCTA, in contrast to a completely normal functional test, was highly unlikely to be associated with a MACE for at least 2 years and the authors attribute this effect to the identification of patients with non-obstructive CAD [17]. However, over-generalization of this study with regard to an inferiority of functional testing should be avoided, since patients had mostly atypical angina (77.8%), the Framingham risk score was intermediate or high in 77.1% and mostly nuclear perfusion stress (67.8%) and no stress CMR was used [17]. Additionally, 10% of patients had undergone exercise treadmill testing, which has low diagnostic accuracy [2]. Nevertheless, the PROMISE trial provides insights into how patient outcome might be improved apart from identifying ischemia alone.
In the SCOT-HEART trial, 4,146 patients with angina and suspected CAD were randomly assigned to standard care or standard care and cCTA [18]. In the cCTA group occurrences of fatal and nonfatal myocardial infarctions were halved as opposed to standard care [19]. The additional cCTA changed treatment significantly, with more appropriate use of invasive angiography, more preventive treatment, and lesser antianginal treatment [18,19]. Overall, 1 in 4 patients had changes to their treatment [18]. In the CONFIRM trial assessing 27,125 patients, statin use but not acetylsalicylic acid (ASA) use, was associated with a significant reduction in mortality for individuals with non-obstructive CAD, but not for individuals without CAD [16].
The same was observed by a registry study on 33,552 patients with <50% coronary stenoses on a cCTA, in which a new medication with a statin after cCTA was associated with a reduction in myocardial infarctions and all-cause death, directionally proportional to the observed CAD burden [21]. cCTA is, therefore, able to identify those patients in which LDL-lowering therapy is most beneficial and has been shown to significantly influence prescription rates of statins in vulnerable patients [22,23].
Apart from coronary stenosis assessment and plaque characterization, cCTA also allows for visualization and quantification of perivascular fat suggestive of coronary inflammation, which is shown to be a negative predictor for cardiac outcome [24,25].
In summary, cCTA is the diagnostic modality of choice to safely assess the presence of CAD, independently of its hemodynamic relevance, and, thus, guide further treatment such as the initiation of preventive medical therapies. cCTA can achieve non-inferior diagnostic accuracy in stable CAD patients without having the periprocedural risks of an invasive coronary angiography [26].
Stress CMR cannot safely exclude the presence of CAD. Its strength lies in the accurate assessment of the hemodynamic relevance of coronary stenosis, i.e., the testing for ischemia. Ischemia, however, is not a bivalent marker, but rather a continuum [27]. CAD with hemodynamically significant coronary artery stenosis leads to a decreased blood perfusion of the heart, which is dependent on the severity. During physiological or medication-induced stress, cell metabolism, diastolic function and systolic function are impaired, leading to ECG changes and ultimately symptoms such as angina or dyspnea [27]. As opposed to stress echocardiography, which usually screens for systolic wall motion deficits occurring relatively late in the ischemic cascade, perfusion stress CMR visualizes the decreased blood perfusion occurring early in the ischemic cascade [27]. In stress perfusion CMR, a vasodilator is applied, resulting in reduced myocardial perfusion in post-stenotic segments through a “steal phenomenon” (meaning that blood is predominantly directed through non-obstructed coronary arteries) and loss of autoregulation mechanisms, which is visualized by the application of a contrast agent [5,28]. Figure 1 shows an example of a perfusion deficit in perfusion stress CMR. Perfusion stress CMR has been shown to be non-inferior to invasive fractional flow reserve (FFR) with respect to major adverse cardiac events, to predict patient outcomes and to significantly reduce the need for both diagnostic and therapeutic invasive coronary angiography, thus guiding therapeutic decision making [29,30,31,32]. Apart from perfusion testing, CMR also offers an evaluation of systolic function and scar. Ejection fraction as measured by CMR is the current reference standard for systolic function due to its very low inter- and intra-rater variability (<3%) [33]. Late Gadolinium Enhancement (LGE) on the other hand is the reference standard for visualization of myocardial scar or tissue fibrosis. It may be used for targeted ablation in patients with atrial or ventricular arrhythmias or prediction of viability before revascularization [34,35,36]. Both have a significant correlation to patient outcome in a variety of entities and serve as early risk factors for adverse events [33,37,38,39,40]. The variety of sequences for tissue characterization as offered by CMR is of unparalleled importance in entities such as myocardial infarction with nonobstructive coronary arteries (MINOCA), myocarditis, storage diseases or cardiomyopathies [41,42,43].
In summary, CMR is the modality of choice to assess hemodynamic relevance, predict prognosis and for a thorough differential diagnosis in suspected CAD patients.

3. Advances in cCTA

Broad advances have been made in the technical capabilities of contemporary CT scanners. High-end CT detectors with ≥128 slices have become a standard in many academic and non-academic sites in developed countries [7].
While conventional CT utilized a single polychromatic X-ray beam received by a single detector, dual-energy CTs (DECT) show increasing popularity. Depending on the vendor, different techniques are used. One is a second tube and detector unit at a 90° angle (dual source CT when operated with two different energies). An alternative technology is the combination of a single x-ray source rapidly alternating between low and high energies (fast switching) with a single detector that registers information from both energies [44]. Another technology is a detector made of two layers (sandwich detector) simultaneously detecting two energy levels [44]. DECT offers the possibility for spectral imaging that can improve tissue characterization, aid in improved composition analysis of coronary plaques and may reduce artifacts and contrast agent dose. Each technique has its own strengths and limitations. There are no reports comparing radiation dose among vendors [44].
Dual source scanners stand out with improved temporal solution (when both sources emit identical energy levels) and therefore a reduction in motion artifacts, which is critical in the assessment of a moving structure such as the heart [45]. They can also be used to enable ultra-high pitch spirals to reduce radiation exposure.
Regarding unenhanced CT such as coronary calcium scoring, the application of a tin filter may decrease the radiation dose in unenhanced scans without negatively affecting subjective image quality [46]. The tin filter reduces the proportion of low-energy photons and therefore increases the average photon energy.
The most recent step in hardware advances for cCTA is the so-called photon-counting CT. It uses photon-counting detectors that separately register the energy of each photon. These offer a smaller pixel size and do not require the coating of each detection pixel by an optical reflector, which accounts for a 2- to 3-fold higher resolution than conventional CT (approximately 250 µm). In a recent clinical study photon-counting coronary CT led to significantly improved image quality (detectability indexes 2.3-fold to 2.9-fold higher) at a comparable radiation dose in 14 patients who underwent both standard energy-integrating detector dual-layer cCTA and photon-counting CT [47]. These results are promising since the higher resolution allowed for the visualization of smaller coronary vessels and the improved image quality was most evident in the presence of coronary stents and calcifications, which both are associated with impaired diagnostic image quality in standard cCTA. An example of a photon-counting cCTA scan is provided in Figure 2.
Apart from hardware improvements, computational advances with iterative or model-based reconstruction algorithms have improved image quality and reduced radiation dose [48,49,50].
Since invasive FFR is the current reference standard to assess the hemodynamic relevance of coronary stenosis, great effort has been directed into implementing a non-invasive cCTA-derived FFR value that can be interpreted similarly to its invasive equivalent [2,51].
Different techniques have evolved that are either applied in a core lab or on-site. Models based on fluid dynamics are computationally demanding, whereas recent advances in machine learning enabled faster on-site calculation [14,52,53]. The analysis is performed as post-processing using standard clinical cCTA images and does not need additional radiation.
A number of studies show the discriminatory power of CT-FFR for the prediction of hemodynamic relevance to be superior to cCTA alone when compared to invasive FFR or stress CMR, improving diagnostic accuracy and especially specificity [51,54,55,56]. Implemented in the diagnostic work-up of patients with suspected CAD, it was able to significantly lower the rate of invasive angiography showing no obstructive CAD when compared to cCTA alone [57]. This was achieved without a negative impact on clinical outcomes [57]. Of note, machine learning-derived risk scores have been shown to add additional value to the prediction of inducible ischemia [58]. cCTA with CT-FFR has also been shown to have a high agreement with the decision derived from invasive coronary angiography in patients with left main or three-vessel CAD being evaluated for coronary artery bypass surgery and may also be combined with cCTA-derived features of plaque vulnerability providing an even better prognostic stratification [59,60]. Figure 3 gives an imaging example of CT-FFR.
Another example of cCTA exceeding the limits of pure anatomical testing for CAD is CT perfusion (CTP). The anatomical visualization of coronary arteries by cCTA is followed by vasodilator stress and repeated CT imaging to assess perfusion deficits in patients where cCTA alone was not able to exclude hemodynamically relevant stenosis [61]. CTP may also be performed without vasodilator stress, although the diagnostic value is limited [62].CTP has been shown to possess incremental diagnostic value over cCTA alone and was comparable to PET-CT or vasodilator stress CMR with respect to invasive FFR as reference [61,63,64,65,66,67]. An example is given in Figure 4. Limitations include the increased radiation exposure, longer and unpredictable scanning times since CTP only is used when the on-site imaging specialist is not able to exclude hemodynamically relevant stenosis on the first images, and the current lack of large multi-center and multi-vendor studies [61].

4. Advances in CMR

Extensive advances have also been made in the field of CMR introducing a more robust imaging acquisition, new acquisition sequences, and vast post-processing possibilities. The relatively long acquisition times remain a modality-dependent issue but decreased considerably. For example, acquisition time of cine images was reduced by ~80% through the introduction of compressed sensing [68]. In addition, fast and native T1, extracellular volume and T2 mapping-derived parameters have become a promising biomarker for ischemic and non-ischemic cardiomyopathies, potentially reducing the need for time-consuming LGE imaging for myocardial tissue characterization [69,70,71]. Furthermore, developments in machine learning may render CMR scans without time-consuming ECG-triggering and breathing maneuvers possible, although these techniques have not been integrated into clinical routine yet [72,73].
The introduction of myocardial strain measurements boosted the assessment of myocardial function with superior predictive power than ejection fraction [74]. Myocardial strain measures the deformation of the myocardium during the cardiac cycle and has been shown to be an early risk marker in a wide spectrum of cardiac diseases such as of cardiotoxicity in cancer patients or heart failure in asymptomatic patients [75,76,77]. In patients following acute cardiac events such as myocardial infarction, reduced strain-derived parameters were associated with future cardiac events [78]. Strain measurements can be acquired without the use of contrast agents, fast (down to in one heartbeat) and with low inter- and intra-observer variability [79,80].
Improved resolution achieved by combining undersampling and motion correction has also allowed obtaining sub-millimeter isotropic CMR images. In proof-of-concept studies, the derived coronary lumenography visualizes coronaries at a quality that is sufficient to screen for coronary anomalies and even (to some degree) stenoses [81,82].
In stress CMR, the introduction of quantitative rather than qualitative perfusion imaging has decreased reader dependency and enabled a faster and simpler analysis [83]. Furthermore, it allows for the identification of globally reduced blood flow [83]. As an alternative to perfusion CMR, some proof-of-concept studies have shown a promising diagnostic value of medication-free protocols using, e.g., hyperventilation or a dynamic handgrip exercise as a stressor [84,85]. These can be combined with a number of advanced CMR sequences such as myocardial strain or myocardial oxygenation to create a completely medication- and needle-free CMR exam [84,86,87,88]. However, these studies currently lack large multicenter trials and have therefore not been introduced into clinical routine yet.

5. Safety Profile of cCTA vs. CMR

Risks of both cCTA or stress CMR are linked to the technique itself, the application of contrast agent or necessary stress medication in the course of the exam.
CMR is a non-ionizing technique working with a static magnetic field and radiofrequency pulses that may be causative for (reversible) headaches or vertigo [89]. Besides, a careful patient selection and preparation by experienced personnel is necessary to avoid interactions of metallic items (e.g., piercings, medical implants) with the electromagnetic field [90]. cCTA employs ionizing radiation coming along with stochastic health effects and potential damage to cells and genetic material [91]. However, technical advances have led to an impressive decrease in radiation dose. In the PROTECTION VI study including 61 hospitals in 32 countries, the radiation dose decreased by 78% between 2007 and 2017 without an increase in non-interpretable exams [92]. Foldyna et al. recently reported an effective radiation dose of 4.5 mSv for cCTAs in a large multi-center study of 64,317 performed scans in both academic and non-academic sites [7]. This must be seen in context to the average yearly background radiation of approximately ~3.1 mSv and would roughly translate to an excess cancer risk of <1 in 1000 [93,94]. Nevertheless, the large variability of the radiation dose between sites (up to 37-fold) remains problematic and underlines the importance of trained personnel and modern equipment [92].
Acute adverse reactions can occur after both CT and CMR contrast agent application, although slightly more frequently in Iodine-based CT (0.2–0.4%) than Gadolinium-based MRI (0.1–0.18%) contrast agents [7,95,96,97]. Specific adverse effects of Iodine-based CT contrast agents include kidney injury (~2.6%) and disturbance of thyroid function [98]. Gadolinium-based MRI contrast agents have been linked to possible tissue deposition (e.g., the brain) and are in very rare cases causative for nephrogenic systemic fibrosis in patients with severely impaired renal function [99,100]. However, tissue deposition is currently of unknown clinical significance and both entities occur significantly rarer (in most studies to the limits of non-existence) with the use of modern macrocyclic contrast agents as opposed to previously used linear contrast agents [100,101,102,103,104,105].
Apart from contrast agents, both cCTA and stress CMR usually require specific medication. For cCTA, beta blockers and glyceryl trinitrate (GTN) are used to achieve adequate heart rate control, if necessary, and to dilate coronary arteries prior to the exam [97]. These may lead to blood pressure drops, which is why there should be no application of GTN if systolic blood pressure is <90 mmHg [97]. Generally, both medications are well tolerated in outpatients as well as in inpatients with rare occurrences of vasovagal symptoms (~0.3%) or cardiac arrhythmias (~0.1%) [7,97]. In contrast, caution is advised in non-stable patients and patients with aortic stenosis.
For CMR, vasodilator stress CMR with regadenoson, dipyridamole, or adenosine is the current standard for patients with suspected CAD. Of those, regadenoson is best tolerated due to its high specificity to the A2A receptor [8]. Common adverse events include a paroxysmal AV block (<0.3%), hypotension (<0.2%), angina (<0.1%) and bronchospasms (0.5–0.8%) [8]. Patients with a history of COPD and especially asthma are at higher risk for bronchospasm, which is why the use of regadenoson should be considered in them [8]. Although the incidence of non-fatal adverse events is higher in patients with pre-existing cardiac conditions, serious adverse events still remain low [106]. As an alternative to perfusion CMR using vasodilator stress, dobutamine stress CMR analyzing ischemia-induced wall motion abnormalities can be conducted. However, higher rates of adverse events (i.e., non-sustained ventricular arrhythmias in 0.4%, atrial fibrillation in 1.6%), patient discomfort (i.e., severe chest pain or dyspnea in 4.0%) and longer exam times have limited its usage in clinical routine [107]. Nevertheless, it may be advantageous in advanced CAD cases with previous coronary artery bypass graft surgery, large myocardial scars with unknown viability, or total coronary artery occlusion in which stress perfusion is often inconclusive.
In summary, both cCTA and vasodilator stress CMR exhibit excellent safety profiles.

6. Discussion

Over the last decade, cCTA has evolved from an imaging modality, which was mainly used to exclude significant CAD in patients at a rather low risk, to a valuable method for risk stratification and therapeutic decision making resulting in a significant improvement in patient outcome. Image quality has improved, radiation exposure has decreased, and anatomical information can be complemented by information on the hemodynamic relevance of stenoses. Consequently, it is recommended as a first-line imaging modality in patients with stable chest pain in the current guidelines [3,4,108]. Hence, it may seem like the undisputable imaging modality in suspected and proven CAD patients.
However, several factors limit the use of cCTA and the most relevant are summarized in Table 1. First, the risk of Iodine contrast-induced nephropathy has to be taken into account in patients with impaired renal function, a common comorbidity in cardiac patients [98]. This and the associated radiation exposure make cCTA unappealing for frequent follow-up examinations. Furthermore, patients with heart rhythm disorders (e.g., premature contractions, atrial fibrillation) still reveal robust CMR stress perfusion imaging, whereas cCTA image quality might be impaired and possibly necessary acquisition repetitions come at the cost of increased radiation and contrast agent exposures.
Second, although assessment of coronary stents has improved, assessment of in-stent stenosis and their functional assessment with CT-FFR is hampered and in some cases even not possible. In addition, the evaluation of patients with severe and extensive coronary calcifications, coronary artery occlusion (with collateral circulation) or previous coronary artery bypass graft surgery can be challenging.
In CT-FFR negative stenoses, additional factors such as symptom burden, current medication and comorbidities must be considered to decide whether further testing might be necessary. Here, the cCTAs and CT-FFRs blind spot concerning the presence of the coronary microvascular disease has to be mentioned [109]. CTP might be possible in those patients but has only recently been evaluated in a proof-of-concept study [110].
CAD evaluation by stress CMR is an important imaging modality in the diagnosis and risk stratification and, thus, is represented in current guidelines [2,3,108].
Of note, the concept of inducible ischemia is not yet fully understood and is the subject of current discussions [111,112]. The ISCHEMIA trial even questioned the prognostic impact of invasive revascularization at all [112]. However, considerable limitations, such as a significant number of revascularizations (25.7%) in the conservative arm and the inclusion of patients with no or mild ischemia (14.1%) or functional tests with low levels of evidence (25.0%) limit the validity of that conclusion [112]. Interestingly, studies comparing myocardial perfusion defects with myocardial oxygenation defects have shown those not to be identical in patients with CAD [113,114]. Nevertheless, patients without ischemia in stress CMR have a good prognosis demonstrating its value for risk stratification and therapeutic management [29,30].
In patients with myocardial scarring, the viability of ischemic regions and therefore the potential benefit of revascularization is important. In these patients, the diagnostic assessment goes beyond stenosis evaluation alone and CMR can provide information on both, ischemia and viability, in a single examination. Its ability to characterize myocardial tissue allows for differential diagnostics in patients with MINOCA as CMR can identify various etiologies such as myocarditis, tako-tsubo syndrome, and thromboembolic myocardial infarction. The visualization/quantification of arrhythmogenic substrate of scar and exact calculation of left-ventricular ejection fraction with CMR is also useful in clinical practice to decide for or against an implantable cardioverter-defibrillator in patients with ischemic and non-ischemic heart disease [108].
Regarding the choice for the appropriate imaging modality aside from the individual patient site-specific conditions should be taken into consideration. Especially, the diagnostic quality of cCTA depends on the scanner model. On a global scale, ≤64 slice CT detectors are still widely used and the possibilities for CTP or CT-FFR are limited in most areas due to technical and reimbursement restrictions [7]. As mentioned above, radiation dose also varies widely from site to site. On the contrary, the impact of the latest available scanner is less relevant in vasodilator stress CMR. On a global scale, non-imaging stress tests still play an important role especially in emerging or developing countries due to the low costs [115].
Apart from hardware, local staff expertise may also be different as not all sites offer cCTA and CMR. Reimbursement is different in every country and influences waiting times, local expertise, and referral choice [116]. The total costs of both modalities are not easy to compare since they depend on available hardware, software and staff in comparison to the occupancy rate of the scanner. Overall, stress CMR is more expensive than cCTA, but the possible recompensation is also higher. However, adjusted to a benefit in quality of life, CMR seems to be more cost-effective than cCTA [117].
The strength of cCTA evidently lies in the identification of additional at-risk patients, in which preventive approaches (e.g., lifestyle changes, medication) are beneficial. Here, the fact that >50% of all myocardial infarctions are caused by non-obstructive plaques has to be emphasized, so the initiation of preventive treatment is key in influencing patient outcome [17,118]. However, the benefit of cCTA diminishes without therapeutic consequences. In a patient already on optimal ASA and statin medication (e.g., for peripheral artery disease), no therapeutic benefit of early CAD detection can be achieved, if there is no need for revascularization. As comorbidities and cardiovascular risk factors become increasingly common in advanced age, an age-specific approach to the PROMISE trial showed anatomic testing to provide better prognostic discrimination for patients < 65 years, whereas functional testing offered better prognostic discrimination in patients > 65 years.
The potential to detect early stages of CAD makes cCTA the diagnostic modality of choice for a large proportion of patients with suspected CAD, taking into account that 75% of patients in the SCOT-HEART trial had no obstructive CAD, despite a high prevalence of cardiovascular risk factors [18].
Although this review focuses on CAD patients, cardiac and pulmonary comorbidities also play an important role in the selection of the appropriate modality. While heavy smokers suffering from dyspnea may benefit from a combined assessment of the coronary arteries and the lungs, younger patients with a differential diagnosis of myocarditis or cardiomyopathy may benefit from myocardial tissue characterization offered by a CMR.
In general, for CAD evaluation, cCTA is favorable in patients that are younger, have few comorbidities, no known cardiac disease and are currently not taking preventive medication, whereas stress CMR is more appropriate in older patients, in those with known CAD, already taking ASA/statin and in need of regular follow-ups.
Of note, both cCTA and stress CMR can be used as a downstream diagnostic testing method in patients with an inconclusive test result of the primarily chosen modality.
Thus, it is important to view cCTA and stress CMR as mostly complementary diagnostic modalities, each possessing strengths and limitations but both providing valuable and accurate diagnostic information to guide physicians’ treatment decisions. Physicians should strive for an individual approach in each patient with regard to patient-related factors and patient wishes considering each modality’s strengths and limitations.

Author Contributions

Conceptualization, L.D.W. and F.A.; literature research, L.D.W.; writing—original draft preparation, L.D.W.; writing—review and editing, L.D.W., F.A., N.F. and D.L.; visualization, L.D.W., F.A. and D.L.; All authors have read and agreed to the published version of the manuscript.


L.D.W. was supported by the Rotation Grand (D.10021788) of the DZHK (German Centre for Cardiovascular Research).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Conflicts of Interest

All authors declare no conflicts of interest.


ASA Acetylsalicylic acid
CADCoronary artery disease
cCTACoronary computed tomography angiography
CMRCardiac magnetic resonance imaging
CTComputed tomography
CTP CT perfusion
DECTDual-energy computed tomography
FFRFractional Flow Reserve
GTNGlyceryl Trinitrate
LADLeft anterior descending artery
LGELate Gadolinium Enhancement
MACEMajor adverse cardiac event
MINOCAMyocardial infarction with nonobstructive coronary arteries
NICENational Institute for Health and Care Excellence


  1. Roth, G.A.; Mensah, G.A.; Johnson, C.O.; Addolorato, G.; Ammirati, E.; Baddour, L.M.; Barengo, N.C.; Beaton, A.Z.; Benjamin, E.J.; Benziger, C.P.; et al. Global burden of cardiovascular diseases and risk factors, 1990–2019: Update from the GBD 2019 study. J. Am. Coll. Cardiol. 2020, 76, 2982–3021. [Google Scholar] [CrossRef] [PubMed]
  2. Knuuti, J.; Wijns, W.; Saraste, A.; Capodanno, D.; Barbato, E.; Funck-Brentano, C.; Prescott, E.; Storey, R.F.; Deaton, C.; Cuisset, T.; et al. 2019 Esc Guidelines for the Diagnosis and Management of Chronic Coronary Syndromes. Eur. Heart J. 2020, 41, 407–477. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Writing Committee Members; Gulati, M.; Levy, P.D.; Mukherjee, D.; Amsterdam, E.; Bhatt, D.L.; Birtcher, K.K.; Blankstein, R.; Boyd, J.; Bullock-Palmer, R.P.; et al. 2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR guideline for the evaluation and diagnosis of chest pain: A report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. J. Am. Coll. Cardiol. 2021, 78, e187–e285. [Google Scholar] [PubMed]
  4. National Institute for Health and Clinical Excellence (NICE). Chest Pain of Recent Onset: Assessment and Diagnosis of Recent Onset Chest Pain or Discomfort of Suspected Cardiac Origin (Update); NICE: London, UK, 2016. [Google Scholar]
  5. Baessato, F.; Guglielmo, M.; Muscogiuri, G.; Baggiano, A.; Fusini, L.; Scafuri, S.; Babbaro, M.; Mollace, R.; Collevecchio, A.; Guaricci, A.I.; et al. Stress CMR in Known or Suspected CAD: Diagnostic and Prognostic Role. BioMed. Res. Int. 2021, 2021, 6678029. [Google Scholar] [CrossRef]
  6. Reeves, R.A.; Halpern, E.J.; Rao, V.M. Cardiac Imaging Trends from 2010 to 2019 in the Medicare Population. Radiol. Cardiothorac. Imaging 2021, 3, e210156. [Google Scholar] [CrossRef]
  7. Foldyna, B.; Uhlig, J.; Gohmann, R.; Lücke, C.; Mayrhofer, T.; Lehmkuhl, L.; Natale, L.; Vliegenthart, R.; Lotz, J.; Salgado, R.; et al. Quality and safety of coronary computed tomography angiography at academic and non-academic sites: Insights from a large European registry (ESCR MR/CT Registry). Eur. Radiol. 2022, 32, 5246–5255. [Google Scholar] [CrossRef]
  8. Menadas, J.V.M.; Gonzalez, M.P.G.; Lopez-Lereu, M.P.; Ortega, L.H.; Gonzalez, A.M.M. Safety and tolerability of regadenoson in comparison with adenosine stress cardiovascular magnetic resonance: Data from a multicentre prospective registry. Int. J. Cardiovasc. Imaging 2021, 38, 195–209. [Google Scholar] [CrossRef]
  9. Knuuti, J.; Ballo, H.; Juarez-Orozco, L.E.; Saraste, A.; Kolh, P.; Rutjes, A.W.S.; Jüni, P.; Windecker, S.; Bax, J.J.; Wijns, W. The performance of non-invasive tests to rule-in and rule-out significant coronary artery stenosis in patients with stable angina: A meta-analysis focused on post-test disease probability. Eur. Heart J. 2018, 39, 3322–3330. [Google Scholar] [CrossRef]
  10. Mancini, G.J.; Leipsic, J.; Budoff, M.J.; Hague, C.J.; Min, J.K.; Stevens, S.R.; Reynolds, H.R.; O’Brien, S.M.; Shaw, L.J.; Manjunath, C.N.; et al. CT Angiography Followed by Invasive Angiography in Patients With Moderate or Severe Ischemia-Insights From the ISCHEMIA Trial. JACC Cardiovasc. Imaging 2021, 14, 1384–1393. [Google Scholar] [CrossRef]
  11. Vanhecke, T.E.; Madder, R.; Weber, J.E.; Bielak, L.F.; Peyser, P.A.; Chinnaiyan, K.M. Development and Validation of a Predictive Screening Tool for Uninterpretable Coronary CT Angiography Results. Circ. Cardiovasc. Imaging 2011, 4, 490–497. [Google Scholar] [CrossRef]
  12. Virmani, R.; Burke, A.P.; Farb, A.; Kolodgie, F.D. Pathology of the vulnerable plaque. J. Am. Coll. Cardiol. 2006, 47, C13–C18. [Google Scholar] [CrossRef] [Green Version]
  13. A Ferraro, R.; Van Rosendael, A.R.; Lu, Y.; Andreini, D.; Al-Mallah, M.H.; Cademartiri, F.; Chinnaiyan, K.; Chow, B.J.W.; Conte, E.; Cury, R.C.; et al. Non-obstructive high-risk plaques increase the risk of future culprit lesions comparable to obstructive plaques without high-risk features: The ICONIC study. Eur. Heart J. Cardiovasc. Imaging 2020, 21, 973–980. [Google Scholar] [CrossRef]
  14. von Knebel Doeberitz, P.L.; De Cecco, C.N.; Schoepf, U.J.; Albrecht, M.H.; van Assen, M.; De Santis, D.; Gaskins, J.; Martin, S.; Bauer, M.J.; Ebersberger, U.; et al. Impact of Coronary Computerized Tomography Angiography-Derived Plaque Quantification and Machine-Learning Computerized Tomography Fractional Flow Reserve on Adverse Cardiac Outcome. Am. J. Cardiol. 2019, 124, 1340–1348. [Google Scholar] [CrossRef]
  15. Williams, M.C.; Moss, A.J.; Dweck, M.; Adamson, P.D.; Alam, S.; Hunter, A.; Shah, A.S.; Pawade, T.; Weir-McCall, J.R.; Roditi, G.; et al. Coronary Artery Plaque Characteristics Associated With Adverse Outcomes in the SCOT-HEART Study. J. Am. Coll. Cardiol. 2019, 73, 291–301. [Google Scholar] [CrossRef]
  16. Chow, B.J.; Small, G.; Yam, Y.; Chen, L.; McPherson, R.; Achenbach, S.; Al-Mallah, M.; Berman, D.S.; Budoff, M.J.; Cademartiri, F. Prognostic and Therapeutic Implications of Statin and Aspirin Therapy in Individuals with Nonobstructive Coronary Artery Disease: Results from the Confirm (Coronary Ct Angiography Evaluation for Clinical Outcomes: An International Multicenter Registry) Registry. Arter. Thromb. Vasc. Biol. 2015, 35, 981–989. [Google Scholar]
  17. Hoffmann, U.; Ferencik, M.; Udelson, J.E.; Picard, M.H.; Truong, Q.A.; Patel, M.R.; Huang, M.; Pencina, M.; Mark, D.B.; Heitner, J.F. Prognostic Value of Noninvasive Cardiovascular Testing in Patients with Stable Chest Pain: Insights from the Promise Trial (Prospective Multicenter Imaging Study for Evaluation of Chest Pain). Circulation 2017, 135, 2320–2332. [Google Scholar] [CrossRef]
  18. Scot-Heart investigators. Ct Coronary Angiography in Patients with Suspected Angina Due to Coronary Heart Disease (Scot-Heart): An Open-Label, Parallel-Group, Multicentre Trial. Lancet 2015, 385, 2383–2391. [Google Scholar] [CrossRef] [Green Version]
  19. Williams, M.C.; Hunter, A.; Shah, A.S.V.; Assi, V.; Lewis, S.; Smith, J.; Berry, C.; Boon, N.A.; Clark, E.; Flather, M.; et al. Use of Coronary Computed Tomographic Angiography to Guide Management of Patients With Coronary Disease. J. Am. Coll. Cardiol. 2016, 67, 1759–1768. [Google Scholar] [CrossRef] [Green Version]
  20. Douglas, P.S.; Hoffmann, U.; Patel, M.R.; Mark, D.B.; Al-Khalidi, H.R.; Cavanaugh, B.; Cole, J.; Dolor, R.J.; Fordyce, C.B.; Huang, M.; et al. Outcomes of Anatomical versus Functional Testing for Coronary Artery Disease. New Engl. J. Med. 2015, 372, 1291–1300. [Google Scholar] [CrossRef] [Green Version]
  21. Øvrehus, K.A.; Diederichsen, A.; Grove, E.L.; Steffensen, F.H.; Mortensen, M.B.; Jensen, J.M.; Mickley, H.; Nielsen, L.H.; Busk, M.; Sand, N.P.R.; et al. Reduction of Myocardial Infarction and All-Cause Mortality Associated to Statins in Patients Without Obstructive CAD. JACC Cardiovasc. Imaging 2021, 14, 2400–2410. [Google Scholar] [CrossRef]
  22. Mortensen, M.B.; Steffensen, F.H.; Bøtker, H.E.; Jensen, J.M.; Sand, N.P.R.; Kragholm, K.H.; Kanstrup, H.; Sørensen, H.T.; Leipsic, J.; Blaha, M.J.; et al. CAD Severity on Cardiac CTA Identifies Patients With Most Benefit of Treating LDL-Cholesterol to ACC/AHA and ESC/EAS Targets. JACC Cardiovasc. Imaging 2020, 13, 1961–1972. [Google Scholar] [CrossRef] [PubMed]
  23. Jørgensen, M.E.; Andersson, C.; Nørgaard, B.; Abdulla, J.; Shreibati, J.B.; Torp-Pedersen, C.; Gislason, G.; Shaw, R.E.; Hlatky, M. Functional Testing or Coronary Computed Tomography Angiography in Patients With Stable Coronary Artery Disease. J. Am. Coll. Cardiol. 2017, 69, 1761–1770. [Google Scholar] [CrossRef] [PubMed]
  24. Oikonomou, E.K.; Desai, M.Y.; Marwan, M.; Kotanidis, C.P.; Antonopoulos, A.S.; Schottlander, D.; Channon, K.M.; Neubauer, S.; Achenbach, S.; Antoniades, C. Perivascular Fat Attenuation Index Stratifies Cardiac Risk Associated With High-Risk Plaques in the CRISP-CT Study. J. Am. Coll. Cardiol. 2020, 76, 755–757. [Google Scholar] [CrossRef] [PubMed]
  25. Oikonomou, E.; Marwan, M.; Desai, M.Y.; Mancio, J.; Alashi, A.; Centeno, E.H.; Thomas, S.; Herdman, L.; Kotanidis, C.; Thomas, K.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, 929–939. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  26. The DISCHARGE Trial Group; Maurovich-Horvat, P.; Bosserdt, M.; Kofoed, K.F.; Rieckmann, N.; Benedek, T.; Donnelly, P.; Rodriguez-Palomares, J.; Erglis, A.; Štěchovský, C.; et al. CT or Invasive Coronary Angiography in Stable Chest Pain. New Engl. J. Med. 2022, 386, 1591–1602. [Google Scholar] [CrossRef]
  27. Stillman, A.E.; Oudkerk, M.; Bluemke, D.A.; de Boer, M.J.; Bremerich, J.; Garcia, E.V.; Gutberlet, M.; van der Harst, P.; Hundley, W.G.; Jerosch-Herold, M.; et al. Imaging the myocardial ischemic cascade. Int. J. Cardiovasc. Imaging 2018, 34, 1249–1263. [Google Scholar] [CrossRef]
  28. Belardinelli, L.; Shryock, J.C.; Snowdy, S.; Zhang, Y.; Monopoli, A.; Lozza, G.; Ongini, E.; A Olsson, R.; Dennis, D.M. The A2A adenosine receptor mediates coronary vasodilation. J. Pharmacol. Exp. Ther. 1998, 284, 1066–1073. [Google Scholar]
  29. Nagel, E.; Greenwood, J.P.; McCann, G.P.; Bettencourt, N.; Shah, A.M.; Hussain, S.T.; Perera, D.; Plein, S.; Bucciarelli-Ducci, C.; Paul, M.; et al. Magnetic Resonance Perfusion or Fractional Flow Reserve in Coronary Disease. New Engl. J. Med. 2019, 380, 2418–2428. [Google Scholar] [CrossRef]
  30. Greenwood, J.P.; Ripley, D.P.; Berry, C.; McCann, G.P.; Plein, S.; Bucciarelli-Ducci, C.; Dall, E.; Prasad, A.; Bijsterveld, P.; Foley, J.R.; et al. Effect of Care Guided by Cardiovascular Magnetic Resonance, Myocardial Perfusion Scintigraphy, or Nice Guidelines on Subsequent Unnecessary Angiography Rates: The Ce-Marc 2 Randomized Clinical Trial. JAMA 2016, 316, 1051–1060. [Google Scholar] [CrossRef] [Green Version]
  31. Kwong, R.Y.; Ge, Y.; Steel, K.; Bingham, S.; Abdullah, S.; Fujikura, K.; Wang, W.; Pandya, A.; Chen, Y.-Y.; Mikolich, J.R.; et al. Cardiac Magnetic Resonance Stress Perfusion Imaging for Evaluation of Patients With Chest Pain. J. Am. Coll. Cardiol. 2019, 74, 1741–1755. [Google Scholar] [CrossRef]
  32. Lipinski, M.J.; McVey, C.M.; Berger, J.S.; Kramer, C.M.; Salerno, M. Prognostic Value of Stress Cardiac Magnetic Resonance Imaging in Patients With Known or Suspected Coronary Artery Disease: A Systematic Review and Meta-Analysis. J. Am. Coll. Cardiol. 2013, 62, 826–838. [Google Scholar] [CrossRef] [Green Version]
  33. Gu, H.; Bing, R.; Chin, C.; Fang, L.; White, A.C.; Everett, R.; Spath, N.; Park, E.; Chambers, J.B.; Newby, D.E.; et al. First-phase ejection fraction by cardiovascular magnetic resonance predicts outcomes in aortic stenosis. J. Cardiovasc. Magn. Reson. 2021, 23, 73. [Google Scholar] [CrossRef]
  34. Soto-Iglesias, D.; Penela, D.; Jáuregui, B.; Acosta, J.; Fernández-Armenta, J.; Linhart, M.; Zucchelli, G.; Syrovnev, V.; Zaraket, F.; Terés, C.; et al. Cardiac Magnetic Resonance-Guided Ventricular Tachycardia Substrate Ablation. JACC Clin. Electrophysiol. 2020, 6, 436–447. [Google Scholar] [CrossRef]
  35. Bisbal, F.; Guiu, E.; Cabanas-Grandío, P.; Berruezo, A.; Prat-Gonzalez, S.; Vidal, B.; Garrido, C.; Andreu, D.; Fernandez-Armenta, J.; Tolosana, J.M.; et al. CMR-Guided Approach to Localize and Ablate Gaps in Repeat AF Ablation Procedure. JACC Cardiovasc. Imaging 2014, 7, 653–663. [Google Scholar] [CrossRef] [Green Version]
  36. Pegg, T.J.; Selvanayagam, J.B.; Jennifer, J.; Francis, J.M.; Karamitsos, T.D.; Dall, E.; Smith, K.L.; Taggart, D.P.; Neubauer, S. Prediction of Global Left Ventricular Functional Recovery in Patients with Heart Failure Undergoing Surgical Revascularisation, Based on Late Gadolinium Enhancement Cardiovascular Magnetic Resonance. J. Cardiovasc. Magn. Reson. 2010, 12, 56. [Google Scholar] [CrossRef] [Green Version]
  37. Klug, G.; Mayr, A.; Schenk, S.; Esterhammer, R.; Schocke, M.; Nocker, M.; Jaschke, W.; Pachinger, O.; Metzler, B. Prognostic value at 5 years of microvascular obstruction after acute myocardial infarction assessed by cardiovascular magnetic resonance. J. Cardiovasc. Magn. Reson. 2012, 14, 46. [Google Scholar] [CrossRef] [Green Version]
  38. Wu, K.C.; Weiss, R.G.; Thiemann, D.R.; Kitagawa, K.; Schmidt, A.; Dalal, D.; Lai, S.; Bluemke, D.A.; Gerstenblith, G.; Marbán, E.; et al. Late Gadolinium Enhancement by Cardiovascular Magnetic Resonance Heralds an Adverse Prognosis in Nonischemic Cardiomyopathy. J. Am. Coll. Cardiol. 2008, 51, 2414–2421. [Google Scholar] [CrossRef] [Green Version]
  39. Solomon, S.D.; Anavekar, N.; Skali, H.; McMurray, J.J.; Swedberg, K.; Yusuf, S.; Granger, C.B.; Michelson, E.L.; Wang, D.; Pocock, S.; et al. Influence of Ejection Fraction on Cardiovascular Outcomes in a Broad Spectrum of Heart Failure Patients. Circulation 2005, 112, 3738–3744. [Google Scholar] [CrossRef] [Green Version]
  40. Scott, P.A.; Rosengarten, J.A.; Murday, D.C.; Peebles, C.R.; Harden, S.P.; Curzen, N.P.; Morgan, J.M. Left Ventricular Scar Burden Specifies the Potential for Ventricular Arrhythmogenesis: An LGE-CMR Study. J. Cardiovasc. Electrophysiol. 2012, 24, 430–436. [Google Scholar] [CrossRef]
  41. Reynolds, H.R.; Maehara, A.; Kwong, R.Y.; Sedlak, T.; Saw, J.; Smilowitz, N.R.; Mahmud, E.; Wei, J.; Marzo, K.; Matsumura, M.; et al. Coronary Optical Coherence Tomography and Cardiac Magnetic Resonance Imaging to Determine Underlying Causes of Myocardial Infarction With Nonobstructive Coronary Arteries in Women. Circulation 2021, 143, 624–640. [Google Scholar] [CrossRef]
  42. Ferreira, V.M.; Schulz-Menger, J.; Holmvang, G.; Kramer, C.M.; Carbone, I.; Sechtem, U.; Kindermann, I.; Gutberlet, M.; Cooper, L.T.; Liu, P.; et al. Cardiovascular Magnetic Resonance in Nonischemic Myocardial Inflammation: Expert Recommendations. J. Am. Coll. Cardiol. 2018, 72, 3158–3176. [Google Scholar] [CrossRef] [PubMed]
  43. Patel, A.R.; Kramer, C.M. Role of Cardiac Magnetic Resonance in the Diagnosis and Prognosis of Nonischemic Cardiomyopathy. JACC Cardiovasc. Imaging 2017, 10, 1180–1193. [Google Scholar] [CrossRef] [PubMed]
  44. Tsurusaki, M.; Sofue, K.; Hori, M.; Sasaki, K.; Ishii, K.; Murakami, T.; Kudo, M. Dual-Energy Computed Tomography of the Liver: Uses in Clinical Practices and Applications. Diagnostics 2021, 11, 161. [Google Scholar] [CrossRef] [PubMed]
  45. Siemens Healthcare GmbH. Somatom Force Product Brochure; Siemens Healthcare GmbH: Erlangen, Germany, 2020. [Google Scholar]
  46. Vingiani, V.; Abadia, A.F.; Schoepf, U.J.; Fischer, A.M.; Varga-Szemes, A.; Sahbaee, P.; Allmendinger, T.; Tesche, C.; Griffith, L.P.; Marano, R.; et al. Low-Kv Coronary Artery Calcium Scoring with Tin Filtration Using a Kv-Independent Reconstruction Algorithm. J. Cardiovasc. Comput. Tomogr. 2020, 14, 246–250. [Google Scholar] [CrossRef]
  47. Si-Mohamed, S.A.; Boccalini, S.; Lacombe, H.; Diaw, A.; Varasteh, M.; Rodesch, P.-A.; Dessouky, R.; Villien, M.; Tatard-Leitman, V.; Bochaton, T.; et al. Coronary CT Angiography with Photon-counting CT: First-In-Human Results. Radiology 2022, 303, 303–313. [Google Scholar] [CrossRef]
  48. André, F.; Fortner, P.; Vembar, M.; Mueller, D.; Stiller, W.; Buss, S.J.; Kauczor, H.-U.; Katus, H.A.; Korosoglou, G. Improved image quality with simultaneously reduced radiation exposure: Knowledge-based iterative model reconstruction algorithms for coronary CT angiography in a clinical setting. J. Cardiovasc. Comput. Tomogr. 2017, 11, 213–220. [Google Scholar] [CrossRef]
  49. Andreini, D.; Lin, F.Y.; Rizvi, A.; Cho, I.; Heo, R.; Pontone, G.; Bartorelli, A.L.; Mushtaq, S.; Villines, T.C.; Carrascosa, P.; et al. Diagnostic Performance of a Novel Coronary CT Angiography Algorithm: Prospective Multicenter Validation of an Intracycle CT Motion Correction Algorithm for Diagnostic Accuracy. Am. J. Roentgenol. 2018, 210, 1208–1215. [Google Scholar] [CrossRef] [Green Version]
  50. Deseive, S.; Chen, M.Y.; Korosoglou, G.; Leipsic, J.; Martuscelli, E.; Carrascosa, P.; Mirsadraee, S.; White, C.; Hadamitzky, M.; Martinoff, S.; et al. Prospective Randomized Trial on Radiation Dose Estimates of Ct Angiography Applying Iterative Image Reconstruction: The Protection V Study. JACC Cardiovasc. Imaging 2015, 8, 888–896. [Google Scholar] [CrossRef]
  51. Lossnitzer, D.; Klenantz, S.; Andre, F.; Goerich, J.; Schoepf, U.J.; Pazzo, K.L.; Sommer, A.; Brado, M.; Gückel, F.; Sokiranski, R.; et al. Stable patients with suspected myocardial ischemia: Comparison of machine-learning computed tomography-based fractional flow reserve and stress perfusion cardiovascular magnetic resonance imaging to detect myocardial ischemia. BMC Cardiovasc. Disord. 2022, 22, 34. [Google Scholar] [CrossRef]
  52. Coenen, A.; Kim, Y.H.; Kruk, M.; Tesche, C.; De Geer, J.; Kurata, A.; Lubbers, M.L.; Daemen, J.; Itu, L.; Rapaka, S.; et al. Diagnostic Accuracy of a Machine-Learning Approach to Coronary Computed Tomographic Angiography-Based Fractional Flow Reserve: Result from the Machine Consortium. Circ Cardiovasc. Imaging 2018, 11, e007217. [Google Scholar] [CrossRef] [Green Version]
  53. Tesche, C.; Gray, H.N. Machine Learning and Deep Neural Networks Applications in Coronary Flow Assessment: The Case of Computed Tomography Fractional Flow Reserve. J. Thorac. Imaging 2020, 35, S66–S71. [Google Scholar] [CrossRef]
  54. Koo, B.-K.; Erglis, A.; Doh, J.-H.; Daniels, D.V.; Jegere, S.; Kim, H.-S.; Dunning, A.; DeFrance, T.; Lansky, A.; Leipsic, J.; 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, 1989–1997. [Google Scholar] [CrossRef]
  55. Norgaard, B.L.; Leipsic, J.; Gaur, S.; Seneviratne, S.; Ko, B.S.; Ito, H.; Jensen, J.M.; Mauri, L.; De Bruyne, B.; Bezerra, 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, 1145–1155. [Google Scholar] [CrossRef] [Green Version]
  56. Nakazato, R.; Park, H.B.; Berman, D.S.; Gransar, H.; Koo, B.K.; Erglis, A.; Lin, F.Y.; Dunning, A.M.; Budoff, M.J.; Malpeso, J.; et al. Noninvasive Fractional Flow Reserve Derived from Computed Tomography Angiography for Coronary Lesions of Intermediate Stenosis Severity: Results from the Defacto Study. Circ Cardiovasc. Imaging 2013, 6, 881–889. [Google Scholar] [CrossRef] [Green Version]
  57. Douglas, P.S.; Pontone, G.; Hlatky, M.A.; Patel, M.R.; Norgaard, B.L.; Byrne, R.A.; Curzen, N.; Purcell, I.; Gutberlet, M.; Rioufol, G.; et al. Clinical Outcomes of Fractional Flow Reserve by Computed Tomographic Angiography-Guided Diagnostic Strategies Vs. Usual Care in Patients with Suspected Coronary Artery Disease: The Prospective Longitudinal Trial of Ffr(Ct): Outcome and Resource Impacts Study. Eur. Heart J. 2015, 36, 3359–3367. [Google Scholar] [PubMed] [Green Version]
  58. Dey, D.; Gaur, S.; Ovrehus, K.A.; Slomka, P.J.; Betancur, J.; Goeller, M.; Hell, M.M.; Gransar, H.; Berman, D.S.; Achenbach, S.; et al. Integrated prediction of lesion-specific ischaemia from quantitative coronary CT angiography using machine learning: A multicentre study. Eur. Radiol. 2018, 28, 2655–2664. [Google Scholar] [CrossRef] [PubMed]
  59. Collet, C.; Onuma, Y.; Andreini, D.; Sonck, J.; Pompilio, G.; Mushtaq, S.; La Meir, M.; Miyazaki, Y.; De Mey, J.; Gaemperli, O.; et al. Coronary computed tomography angiography for heart team decision-making in multivessel coronary artery disease. Eur. Heart J. 2018, 39, 3689–3698. [Google Scholar] [CrossRef]
  60. Lee, J.M.; Choi, K.H.; Koo, B.-K.; Park, J.; Kim, J.; Hwang, D.; Rhee, T.-M.; Kim, H.Y.; Jung, H.W.; Kim, K.-J.; et al. Prognostic Implications of Plaque Characteristics and Stenosis Severity in Patients With Coronary Artery Disease. J. Am. Coll. Cardiol. 2019, 73, 2413–2424. [Google Scholar] [CrossRef]
  61. Balla, S.; Nieman, K. From Inception to 2020: A Review of Dynamic Myocardial CT Perfusion Imaging. Curr. Cardiovasc. Imaging Rep. 2021, 14, 1. [Google Scholar] [CrossRef]
  62. Baumann, S.; Rutsch, M.; Becher, T.; Kryeziu, P.; Haubenreisser, H.; Vogler, N.; Schoenike, A.C.; Borggrefe, M.; Schoenberg, O.S.; Akin, I.; et al. Clinical Impact of Rest Dual–energy Computed Tomography Myocardial Perfusion in Patients with Coronary Artery Disease. Vivo 2017, 31, 1153–1157. [Google Scholar] [CrossRef] [Green Version]
  63. Andreini, D.; Mushtaq, S.; Pontone, G.; Conte, E.; Collet, C.; Sonck, J.; D’Errico, A.; Di Odoardo, L.A.F.; Guglielmo, M.; Baggiano, A.; et al. CT Perfusion Versus Coronary CT Angiography in Patients With Suspected In-Stent Restenosis or CAD Progression. JACC Cardiovasc. Imaging 2020, 13, 732–742. [Google Scholar] [CrossRef] [PubMed]
  64. Takx, R.A.; Blomberg, B.A.; El Aidi, H.; Habets, J.; de Jong, P.A.; Nagel, E.; Hoffmann, U.; Leiner, T. Diagnostic Accuracy of Stress Myocardial Perfusion Imaging Compared to Invasive Coronary Angiography With Fractional Flow Reserve Meta-Analysis. Circ. Cardiovasc. Imaging 2015, 8, e002666. [Google Scholar] [CrossRef] [PubMed]
  65. Lu, M.; Wang, S.; Sirajuddin, A.; Arai, A.E.; Zhao, S. Dynamic stress computed tomography myocardial perfusion for detecting myocardial ischemia: A systematic review and meta-analysis. Int. J. Cardiol. 2018, 258, 325–331. [Google Scholar] [CrossRef]
  66. van Assen, M.; De Cecco, C.N.; Eid, M.; von Knebel Doeberitz, P.; Scarabello, M.; Lavra, F.; Bauer, M.J.; Mastrodicasa, D.; Duguay, T.M.; Zaki, B.; et al. Prognostic value of CT myocardial perfusion imaging and CT-derived fractional flow reserve for major adverse cardiac events in patients with coronary artery disease. J. Cardiovasc. Comput. Tomogr. 2019, 13, 26–33. [Google Scholar] [CrossRef]
  67. Rossi, A.; Wragg, A.; Klotz, E.; Pirro, F.; Moon, J.C.; Nieman, K.; Pugliese, F. Dynamic Computed Tomography Myocardial Perfusion Imaging: Comparison of Clinical Analysis Methods for the Detection of Vessel-Specific Ischemia. Circ. Cardiovasc. Imaging 2017, 10, e005505. [Google Scholar] [CrossRef] [Green Version]
  68. Kido, T.; Kido, T.; Nakamura, M.; Watanabe, K.; Schmidt, M.; Forman, C.; Mochizuki, T. Compressed sensing real-time cine cardiovascular magnetic resonance: Accurate assessment of left ventricular function in a single-breath-hold. J. Cardiovasc. Magn. Reson. 2016, 18, 50. [Google Scholar] [CrossRef] [Green Version]
  69. Hirschberg, K.; Braun, S.M.; Paul, O.; Ochs, M.; Riffel, J.; Andre, F.; Salatzki, J.; Lebel, J.; Luu, J.; Hillier, E.; et al. The diagnostic accuracy of truncated cardiovascular MR protocols for detecting non-ischemic cardiomyopathies. Int. J. Cardiovasc. Imaging 2021, 38, 841–852. [Google Scholar] [CrossRef]
  70. Haaf, P.; Garg, P.; Messroghli, D.R.; Broadbent, D.A.; Greenwood, J.P.; Plein, S. Cardiac T1 Mapping and Extracellular Volume (ECV) in clinical practice: A comprehensive review. J. Cardiovasc. Magn. Reson. 2016, 18, 89. [Google Scholar] [CrossRef] [Green Version]
  71. Karur, G.R.; Hanneman, K. Cardiac MRI T1, T2, and T2* mapping in clinical practice. Adv. Clin. Radiol. 2019, 1, 27–41. [Google Scholar] [CrossRef]
  72. Mao, X.; Lee, H.L.; Hu, Z.; Cao, T.; Han, F.; Ma, S.; Serry, F.M.; Fan, Z.; Xie, Y.; Li, D.; et al. Simultaneous Multi-Slice Cardiac Mr Multitasking for Motion-Resolved, Non-Ecg, Free-Breathing T1-T2 Mapping. Front. Cardiovasc. Med. 2022, 9, 833257. [Google Scholar] [CrossRef]
  73. Morales, M.A.; Assana, S.; Cai, X.; Chow, K.; Haji-Valizadeh, H.; Sai, E.; Tsao, C.; Matos, J.; Rodriguez, J.; Berg, S.; et al. An inline deep learning based free-breathing ECG-free cine for exercise cardiovascular magnetic resonance. J. Cardiovasc. Magn. Reson. 2022, 24, 47. [Google Scholar] [CrossRef] [PubMed]
  74. Amzulescu, M.S.; De Craene, M.; Langet, H.; Pasquet, A.; Vancraeynest, D.; Pouleur, A.C.; Vanoverschelde, J.L.; Gerber, B.L. Myocardial Strain Imaging: Review of General Principles, Validation, and Sources of Discrepancies. Eur. Heart J. Cardiovasc. Imaging 2019, 20, 605–619. [Google Scholar] [CrossRef]
  75. Romano, S.; Judd, R.M.; Kim, R.J.; Kim, H.W.; Klem, I.; Heitner, J.F.; Shah, D.J.; Jue, J.; White, B.E.; Indorkar, R.; et al. Feature-Tracking Global Longitudinal Strain Predicts Death in a Multicenter Population of Patients With Ischemic and Nonischemic Dilated Cardiomyopathy Incremental to Ejection Fraction and Late Gadolinium Enhancement. JACC Cardiovasc. Imaging 2018, 11, 1419–1429. [Google Scholar] [CrossRef]
  76. Giusca, S.; Korosoglou, G.; Montenbruck, M.; Geršak, B.; Schwarz, A.K.; Esch, S.; Kelle, S.; Wülfing, P.; Dent, S.; Lenihan, D.; et al. Multiparametric Early Detection and Prediction of Cardiotoxicity Using Myocardial Strain, T1 and T2 Mapping, and Biochemical Markers: A Longitudinal Cardiac Resonance Imaging Study During 2 Years of Follow-Up. Circ. Cardiovasc. Imaging 2021, 14, e012459. [Google Scholar] [CrossRef]
  77. Korosoglou, G.; Giusca, S.; Montenbruck, M.; Patel, A.R.; Lapinskas, T.; Götze, C.; Zieschang, V.; Al-Tabatabaee, S.; Pieske, B.; Florian, A.; et al. Fast Strain-Encoded Cardiac Magnetic Resonance for Diagnostic Classification and Risk Stratification of Heart Failure Patients. JACC Cardiovasc. Imaging 2021, 14, 1177–1188. [Google Scholar] [CrossRef]
  78. Grove, G.L.; Pedersen, S.; Olsen, F.J.; Skaarup, K.G.; Jørgensen, P.G.; Shah, A.M.; Biering-Sørensen, T. Layer-specific global longitudinal strain obtained by speckle tracking echocardiography for predicting heart failure and cardiovascular death following STEMI treated with primary PCI. Int. J. Cardiovasc. Imaging 2021, 37, 2207–2215. [Google Scholar] [CrossRef]
  79. Giusca, S.; Korosoglou, G.; Zieschang, V.; Stoiber, L.; Schnackenburg, B.; Stehning, C.; Gebker, R.; Pieske, B.; Schuster, A.; Backhaus, S.; et al. Reproducibility study on myocardial strain assessment using fast-SENC cardiac magnetic resonance imaging. Sci. Rep. 2018, 8, 14100. [Google Scholar] [CrossRef] [Green Version]
  80. Korosoglou, G.; Youssef, A.A.; Bilchick, K.C.; Ibrahim el, S.; Lardo, A.C.; Lai, S.; Osman, N.F. Real-Time Fast Strain-Encoded Magnetic Resonance Imaging to Evaluate Regional Myocardial Function at 3.0 Tesla: Comparison to Conventional Tagging. J. Magn. Reson. Imaging 2008, 27, 1012–1018. [Google Scholar] [CrossRef]
  81. Nakamura, S.; Ishida, M.; Nakata, K.; Ichikawa, Y.; Takase, S.; Takafuji, M.; Ito, H.; Nakamori, S.; Kurita, T.; Dohi, K.; et al. Long-term prognostic value of whole-heart coronary magnetic resonance angiography. J. Cardiovasc. Magn. Reson. 2021, 23, 56. [Google Scholar] [CrossRef]
  82. Bustin, A.; Rashid, I.; Cruz, G.; Hajhosseiny, R.; Correia, T.; Neji, R.; Rajani, R.; Ismail, T.F.; Botnar, R.M.; Prieto, C. 3D whole-heart isotropic sub-millimeter resolution coronary magnetic resonance angiography with non-rigid motion-compensated PROST. J. Cardiovasc. Magn. Reson. 2020, 22, 24. [Google Scholar] [CrossRef]
  83. Sharrack, N.; Chiribiri, A.; Schwitter, J.; Plein, S. How to do quantitative myocardial perfusion cardiovascular magnetic resonance. Eur. Heart J. Cardiovasc. Imaging 2021, 23, 315–318. [Google Scholar] [CrossRef]
  84. Ochs, A.; Nippes, M.; Salatzki, J.; Weberling, L.D.; Riffel, J.; Müller-Hennessen, M.; Giannitsis, E.; Osman, N.; Stehning, C.; André, F.; et al. Dynamic Handgrip Exercise: Feasibility and Physiologic Stress Response of a Potential Needle-Free Cardiac Magnetic Resonance Stress Test. Front. Cardiovasc. Med. 2021, 8, 755759. [Google Scholar] [CrossRef] [PubMed]
  85. Ochs, M.M.; Kajzar, I.; Salatzki, J.; Ochs, A.T.; Riffel, J.; Osman, N.; Katus, H.A.; Friedrich, M.G. Hyperventilation/Breath-Hold Maneuver to Detect Myocardial Ischemia by Strain-Encoded Cmr: Diagnostic Accuracy of a Needle-Free Stress Protocol. JACC Cardiovasc. Imaging 2021, 14, 1932–1944. [Google Scholar] [CrossRef] [PubMed]
  86. Siry, D.; Riffel, J.H.; Salatzki, J.; Andre, F.; Ochs, M.; Weberling, L.D.; Giannitsis, E.; Katus, H.A.; Friedrich, M.G. Hypverventilation Strain Cmr Imaging in Patients with Acute Chest Pain. Sci. Rep. 2022, 12, 13584. [Google Scholar] [CrossRef] [PubMed]
  87. Weberling, L.D.; Friedrich, M.G. Oxygenation-Sensitive Cardiac Magnetic Resonance Imaging; Radiologie: Berlin/Heidelberg, Germany, 2022. [Google Scholar]
  88. Fischer, K.; Yamaji, K.; Luescher, S.; Ueki, Y.; Jung, B.; von Tengg-Kobligk, H.; Windecker, S.; Friedrich, M.G.; Eberle, B.; Guensch, D.P. Feasibility of cardiovascular magnetic resonance to detect oxygenation deficits in patients with multi-vessel coronary artery disease triggered by breathing maneuvers. J. Cardiovasc. Magn. Reson. 2018, 20, 31. [Google Scholar] [CrossRef] [Green Version]
  89. Weintraub, M.I.; Khoury, A.; Cole, S.P. Biologic Effects of 3 Tesla (T) Mr Imaging Comparing Traditional 1.5 T and 0.6 T in 1023 Consecutive Outpatients. J. Neuroimaging 2007, 17, 241–245. [Google Scholar] [CrossRef]
  90. Ocazionez, D.; Dicks, D.L.; Favinger, J.L.; Shroff, G.S.; Damani, S.; Kicska, G.A.; Reddy, G.P. Magnetic Resonance Imaging Safety in Cardiothoracic Imaging. J. Thorac. Imaging 2014, 29, 262–269. [Google Scholar] [CrossRef]
  91. Sáfrány, G.; Lumniczky, K.; Manti, L. New Discoveries in Radiation Science. Cancers 2021, 13, 1034. [Google Scholar] [CrossRef]
  92. Stocker, T.J.; Deseive, S.; Leipsic, J.; Hadamitzky, M.; Chen, M.Y.; Rubinshtein, R.; Heckner, M.; Bax, J.J.; Fang, X.-M.; Grove, E.L.; et al. Reduction in radiation exposure in cardiovascular computed tomography imaging: Results from the PROspective multicenter registry on radiaTion dose Estimates of cardiac CT angIOgraphy iN daily practice in 2017 (PROTECTION VI). Eur. Heart J. 2018, 39, 3715–3723. [Google Scholar] [CrossRef] [Green Version]
  93. Lin, E.C. Radiation Risk From Medical Imaging. Mayo Clin. Proc. 2010, 85, 1142–1146. [Google Scholar] [CrossRef] [Green Version]
  94. Cao, C.-F.; Ma, K.-L.; Shan, H.; Liu, T.-F.; Zhao, S.-Q.; Wan, Y.; Zhang, J.; Wang, H.-Q. CT Scans and Cancer Risks: A Systematic Review and Dose-response Meta-analysis. BMC Cancer 2022, 22, 1238. [Google Scholar] [CrossRef]
  95. Aran, S.; Shaqdan, K.; Abujudeh, H. Adverse allergic reactions to linear ionic gadolinium-based contrast agents: Experience with 194, 400 injections. Clin. Radiol. 2015, 70, 466–475. [Google Scholar] [CrossRef]
  96. Fakhran, S.; Alhilali, L.; Kale, H.; Kanal, E. Assessment of Rates of Acute Adverse Reactions to Gadobenate Dimeglumine: Review of More Than 130,000 Administrations in 7.5 Years. AJR Am. J. Roentgenol. 2015, 204, 703–706. [Google Scholar] [CrossRef]
  97. Andre, F.; Fortner, P.; Emami, M.; Seitz, S.; Brado, M.; Gückel, F.; Sokiranski, R.; Sommer, A.; Frey, N.; Görich, J.; et al. Factors influencing the safety of outpatient coronary CT angiography: A clinical registry study. BMJ Open 2022, 12, e058304. [Google Scholar] [CrossRef]
  98. Nijssen, E.C.; Rennenberg, R.J.; Nelemans, P.J.; Essers, B.A.; Janssen, M.M.; Vermeeren, M.A.; Ommen, V.V.; Wildberger, J.E. Prophylactic Hydration to Protect Renal Function from Intravascular Iodinated Contrast Material in Patients at High Risk of Contrast-Induced Nephropathy (Amacing): A Prospective, Randomised, Phase 3, Controlled, Open-Label, Non-Inferiority Trial. Lancet 2017, 389, 1312–1322. [Google Scholar] [CrossRef]
  99. McDonald, R.J.; McDonald, J.S.; Kallmes, D.F.; Jentoft, M.E.; Murray, D.L.; Thielen, K.R.; Williamson, E.E.; Eckel, L.J. Intracranial Gadolinium Deposition after Contrast-enhanced MR Imaging. Radiology 2015, 275, 772–782. [Google Scholar] [CrossRef] [Green Version]
  100. Lange, S.; Mędrzycka-Dąbrowska, W.; Zorena, K.; Dąbrowski, S.; Ślęzak, D.; Malecka-Dubiela, A.; Rutkowski, P. Nephrogenic Systemic Fibrosis as a Complication after Gadolinium-Containing Contrast Agents: A Rapid Review. Int. J. Environ. Res. Public Health 2021, 18, 3000. [Google Scholar] [CrossRef]
  101. Chen, J.W. Does Brain Gadolinium Deposition Have Clinical Consequence? Lessons from Animal Studies. Radiology 2021, 301, 417–419. [Google Scholar] [CrossRef]
  102. Radbruch, A.; Haase, R.; Kieslich, P.J.; Weberling, L.D.; Kickingereder, P.; Wick, W.; Schlemmer, H.-P.; Bendszus, M. No Signal Intensity Increase in the Dentate Nucleus on Unenhanced T1-weighted MR Images after More than 20 Serial Injections of Macrocyclic Gadolinium-based Contrast Agents. Radiology 2017, 282, 699–707. [Google Scholar] [CrossRef] [Green Version]
  103. Radbruch, A.; Weberling, L.D.; Kieslich, P.J.; Eidel, O.; Burth, S.; Kickingereder, P.; Heiland, S.; Wick, W.; Schlemmer, H.P.; Bendszus, M. Gadolinium Retention in the Dentate Nucleus and Globus Pallidus Is Dependent on the Class of Contrast Agent. Radiology 2015, 275, 783–791. [Google Scholar] [CrossRef]
  104. Weberling, L.D.; Kieslich, P.J.; Kickingereder, P.V.; Wick, W.; Bendszus, M.; Schlemmer, H.-P.; Radbruch, A. Increased Signal Intensity in the Dentate Nucleus on Unenhanced T1-Weighted Images After Gadobenate Dimeglumine Administration. Investig. Radiol. 2015, 50, 743–748. [Google Scholar] [CrossRef] [PubMed]
  105. Woolen, S.A.; Shankar, P.R.; Gagnier, J.J.; MacEachern, M.P.; Singer, L.; Davenport, M.S. Risk of Nephrogenic Systemic Fibrosis in Patients with Stage 4 or 5 Chronic Kidney Disease Receiving a Group Ii Gadolinium-Based Contrast Agent: A Systematic Review and Meta-Analysis. JAMA Intern. Med. 2020, 180, 223–230. [Google Scholar] [CrossRef] [PubMed]
  106. Karamitsos, T.D.; Arnold, J.R.; Pegg, T.J.; Cheng, A.S.H.; Van Gaal, W.J.; Francis, J.M.; Banning, A.P.; Neubauer, S.; Selvanayagam, J.B. Tolerance and safety of adenosine stress perfusion cardiovascular magnetic resonance imaging in patients with severe coronary artery disease. Int. J. Cardiovasc. Imaging 2008, 25, 277–283. [Google Scholar] [CrossRef] [PubMed]
  107. Wahl, A.; Paetsch, I.; Gollesch, A.; Roethemeyer, S.; Foell, D.; Gebker, R.; Langreck, H.; Klein, C.; Fleck, E.; Nagel, E. Safety and feasibility of high-dose dobutamine-atropine stress cardiovascular magnetic resonance for diagnosis of myocardial ischaemia: Experience in 1000 consecutive cases. Eur. Heart J. 2004, 25, 1230–1236. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  108. McDonagh, T.A.; Metra, M.; Adamo, M.; Gardner, R.S.; Baumbach, A.; Böhm, M.; Burri, H.; Butler, J.; Čelutkienė, J.; Chioncel, O.; et al. 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure. Eur. Heart J. 2021, 42, 3599–3726. [Google Scholar] [CrossRef]
  109. Mileva, N.; Nagumo, S.; Mizukami, T.; Sonck, J.; Berry, C.; Gallinoro, E.; Monizzi, G.; Candreva, A.; Munhoz, D.; Vassilev, D.; et al. Prevalence of Coronary Microvascular Disease and Coronary Vasospasm in Patients With Nonobstructive Coronary Artery Disease: Systematic Review and Meta-Analysis. J. Am. Heart Assoc. 2022, 11, e023207. [Google Scholar] [CrossRef]
  110. Michallek, F.; Nakamura, S.; Ota, H.; Ogawa, R.; Shizuka, T.; Nakashima, H.; Wang, Y.-N.; Ito, T.; Sakuma, H.; Dewey, M.; et al. Fractal analysis of 4D dynamic myocardial stress-CT perfusion imaging differentiates micro- and macrovascular ischemia in a multi-center proof-of-concept study. Sci. Rep. 2022, 12, 5085. [Google Scholar] [CrossRef]
  111. Perera, D.; Clayton, T.; O’Kane, P.D.; Greenwood, J.P.; Weerackody, R.; Ryan, M.; Morgan, H.P.; Dodd, M.; Evans, R.; Canter, R.; et al. Percutaneous Revascularization for Ischemic Left Ventricular Dysfunction. N. Engl. J. Med. 2022, 387, 1351–1360. [Google Scholar] [CrossRef]
  112. Maron, D.J.; Hochman, J.S.; Reynolds, H.R.; Bangalore, S.; O’Brien, S.M.; Boden, W.E.; Chaitman, B.R.; Senior, R.; López-Sendón, J.; Alexander, K.P.; et al. Initial Invasive or Conservative Strategy for Stable Coronary Disease. N. Engl. J. Med. 2020, 382, 1395–1407. [Google Scholar] [CrossRef]
  113. Arnold, J.R.; Karamitsos, T.D.; Bhamra-Ariza, P.; Francis, J.M.; Searle, N.; Robson, M.D.; Howells, R.K.; Choudhury, R.P.; Rimoldi, O.E.; Camici, P.G.; et al. Myocardial Oxygenation in Coronary Artery Disease: Insights from Blood Oxygen Level-Dependent Magnetic Resonance Imaging at 3 Tesla. J. Am. Coll. Cardiol. 2012, 59, 1954–1964. [Google Scholar] [CrossRef] [Green Version]
  114. Karamitsos, T.D.; Leccisotti, L.; Arnold, J.R.; Recio-Mayoral, A.; Bhamra-Ariza, P.; Howells, R.K.; Searle, N.; Robson, M.D.; Rimoldi, O.E.; Camici, P.G.; et al. Relationship between Regional Myocardial Oxygenation and Perfusion in Patients with Coronary Artery Disease: Insights from Cardiovascular Magnetic Resonance and Positron Emission Tomography. Circ. Cardiovasc. Imaging 2010, 3, 32–40. [Google Scholar] [CrossRef] [Green Version]
  115. Hertz, J.T.; Fu, T.; Vissoci, J.R.; Rocha, T.A.H.; Carvalho, E.; Flanagan, B.; De Andrade, L.; Limkakeng, A.T.; Staton, C.A. The distribution of cardiac diagnostic testing for acute coronary syndrome in the Brazilian healthcare system: A national geospatial evaluation of health access. PLoS ONE 2019, 14, e0210502. [Google Scholar] [CrossRef]
  116. Petersen, S.E.; Friebel, R.; Ferrari, V.; Han, Y.; Aung, N.; Kenawy, A.; Albert, T.S.E.; Naci, H. Recent Trends and Potential Drivers of Non-invasive Cardiovascular Imaging Use in the United States of America and England. Front. Cardiovasc. Med. 2021, 7, 617771. [Google Scholar] [CrossRef]
  117. Pandya, A.; Yu, Y.-J.; Ge, Y.; Nagel, E.; Kwong, R.Y.; Abu Bakar, R.; Grizzard, J.D.; Merkler, A.E.; Ntusi, N.; Petersen, S.E.; et al. Evidence-based cardiovascular magnetic resonance cost-effectiveness calculator for the detection of significant coronary artery disease. J. Cardiovasc. Magn. Reson. 2022, 24, 1. [Google Scholar] [CrossRef]
  118. Stone, G.W.; Maehara, A.; Lansky, A.J.; de Bruyne, B.; Cristea, E.; Mintz, G.S.; Mehran, R.; McPherson, J.; Farhat, N.; Marso, S.P.; et al. A Prospective Natural-History Study of Coronary Atherosclerosis. N. Engl. J. Med. 2011, 364, 226–235. [Google Scholar] [CrossRef]
Figure 1. Imaging example of a stress CMR. A 58-year-old female patient with atypical angina symptoms and low pretest probability was referred for a stress CMR. The stress perfusion imaging revealed an extensive perfusion deficit (red arrows) of the anterior and anteroseptal wall on the basal (A), mid-ventricular (B) and apical (C) slice. An invasive coronary angiography showed a subtotal stenosis of the proximal left anterior descending artery (LAD) and the diagonal branch.
Figure 1. Imaging example of a stress CMR. A 58-year-old female patient with atypical angina symptoms and low pretest probability was referred for a stress CMR. The stress perfusion imaging revealed an extensive perfusion deficit (red arrows) of the anterior and anteroseptal wall on the basal (A), mid-ventricular (B) and apical (C) slice. An invasive coronary angiography showed a subtotal stenosis of the proximal left anterior descending artery (LAD) and the diagonal branch.
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Figure 2. ECG-gated ultra-high-resolution cCTA of the heart using photon-counting. Images show the heart of a 79-year-old male patient referred for preprocedural planning of a transcatheter aortic valve replacement. Despite extensive calcifications (Agatston score of 3388) and two coronary stents in the LAD, a visualization of the coronary arteries was possible. (A): unenhanced coronary calcium scoring CT, axial reconstructions with a slice thickness of 2.0 mm using the Br36 kernel. (B): ultra-high-resolution cCTA, axial reconstructions with a slice thickness of 0.4 mm and an increment of 0.2 mm using the kernel Bv56 and moderate iteration Q3. (C): Curved multi-planar reconstruction of the LAD. An in-stent stenosis can be ruled out. (D): Invasive coronary angiography of the LAD confirming the CT findings. Courtesy of Christopher L. Schlett, University of Freiburg, Germany.
Figure 2. ECG-gated ultra-high-resolution cCTA of the heart using photon-counting. Images show the heart of a 79-year-old male patient referred for preprocedural planning of a transcatheter aortic valve replacement. Despite extensive calcifications (Agatston score of 3388) and two coronary stents in the LAD, a visualization of the coronary arteries was possible. (A): unenhanced coronary calcium scoring CT, axial reconstructions with a slice thickness of 2.0 mm using the Br36 kernel. (B): ultra-high-resolution cCTA, axial reconstructions with a slice thickness of 0.4 mm and an increment of 0.2 mm using the kernel Bv56 and moderate iteration Q3. (C): Curved multi-planar reconstruction of the LAD. An in-stent stenosis can be ruled out. (D): Invasive coronary angiography of the LAD confirming the CT findings. Courtesy of Christopher L. Schlett, University of Freiburg, Germany.
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Figure 3. Imaging of a cCTA with non-invasive CT-FFR measurements. 65-year-old male patient referred for suspected CAD. Three-dimensional reconstruction of the coronary tree with color-coded FFR calculations. These show a hemodynamically relevant stenosis in the distal right coronary artery ((B), FFR 0.56), whereas stenosis in the distal LAD (A) is not hemodynamically relevant (FFR 0.85). Courtesy of Sebastian J. Buss, MVZ-DRZ Heidelberg, Germany.
Figure 3. Imaging of a cCTA with non-invasive CT-FFR measurements. 65-year-old male patient referred for suspected CAD. Three-dimensional reconstruction of the coronary tree with color-coded FFR calculations. These show a hemodynamically relevant stenosis in the distal right coronary artery ((B), FFR 0.56), whereas stenosis in the distal LAD (A) is not hemodynamically relevant (FFR 0.85). Courtesy of Sebastian J. Buss, MVZ-DRZ Heidelberg, Germany.
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Figure 4. Imaging example of a CTP. A 78-year-old female patient with atypical angina and intermediate pretest probability. (A) shows the multiplanar reconstructions of the basal short axis with a DECT at 90 kV and 150 kV. (B) shows the perfused blood volume which is reduced in two segments (red arrows).
Figure 4. Imaging example of a CTP. A 78-year-old female patient with atypical angina and intermediate pretest probability. (A) shows the multiplanar reconstructions of the basal short axis with a DECT at 90 kV and 150 kV. (B) shows the perfused blood volume which is reduced in two segments (red arrows).
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Table 1. Considerable patient factors to select either cCTA or stress CMR as primary diagnostic modality. The combination of the different factors should be taken into consideration to attain the best individual approach.
Table 1. Considerable patient factors to select either cCTA or stress CMR as primary diagnostic modality. The combination of the different factors should be taken into consideration to attain the best individual approach.
Favor cCTAFavor Stress CMR
Previous coronary stentNoYes
Preventive medical therapyNoneASA, statin
Patient ageMiddle-agedAdvanced,
Young with known coronary anomalies
Pretest probability of CADLow/IntermediateHigh
Regular follow-up neededNoYes
Previous diagnostic work-upInconclusive stress testcCTA with non-diagnostic image quality or stenosis of indetermined hemodynamic significance
Metallic implantsNon-removable metallic implants without or with unknown MR safety,
Non-removable metallic implants which may impair CMR image quality severely
Severe claustrophobia
Hyperthyroidism, moderately/ severely impaired kidney function, high heart rate
Potential differential diagnosis other than CADPulmonary or aortic pathologyMyocarditis, pericarditis, thrombembolism–MINOCA assessment
Viability assessment requiredNoYes
Assessment of myocardial edema, function, scar tissue or fibrosis requiredNoYes
Severe or extensive coronary calcifications expected or provenNoYes
AllergiesAllergy to Gadolinium-based contrast agentsAllergy to Iodine-based contrast agents
Modern CT scanners available (≥128 slice detectors)YesNo
Additional factors to be consideredLocal expertise
Timely availability (if necessary)
Patient preference
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Weberling, L.D.; Lossnitzer, D.; Frey, N.; André, F. Coronary Computed Tomography vs. Cardiac Magnetic Resonance Imaging in the Evaluation of Coronary Artery Disease. Diagnostics 2023, 13, 125.

AMA Style

Weberling LD, Lossnitzer D, Frey N, André F. Coronary Computed Tomography vs. Cardiac Magnetic Resonance Imaging in the Evaluation of Coronary Artery Disease. Diagnostics. 2023; 13(1):125.

Chicago/Turabian Style

Weberling, Lukas D., Dirk Lossnitzer, Norbert Frey, and Florian André. 2023. "Coronary Computed Tomography vs. Cardiac Magnetic Resonance Imaging in the Evaluation of Coronary Artery Disease" Diagnostics 13, no. 1: 125.

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