Cardiovascular diseases (CVDs) are the leading cause of death globally, taking an estimated 17.9 million lives each year according to the World Health Organization.1 The prevalence and mortality of CVD in China is continuously rising, with an estimated 330 million patients suffering from CVDs.2 The increased burden of CVD has become the most important public health issue in China, leading to continuous exploration into the assessment and prevention of CVDs, furthering our understanding of these diseases.
Computed tomography angiography (CTA) and magnetic resonance angiography (MRA) are the commonly used examination methods for CVD.3 Cardiothoracic computed tomography (CT) has undergone rapid maturation over the last decade and is now of proven clinical utility in the diagnosis and management of coronary artery disease (CAD) and pulmonary thromboembolism. The next decade will undoubtedly witness further advances in hardware and advanced analytics that will potentially increase the role of cardiovascular CT and magnetic resonance in clinical cardiovascular practice.4 The overall development trend of cardiovascular imaging has progressed from anatomy to function and tissue characterization, from macro (general) to micro (molecular), from minimally invasive to noninvasive, from slightly harmful to harmless. It aims at providing comprehensive information that integrates anatomic, functional, and histologic assessments of CVD.5 Cardiovascular imaging is gradually moving from the backburner to the forefront, from the supporting to the leading, playing an increasingly important role in guiding the diagnosis, prognosis, evaluation, and risk stratification of CVD. Therefore, this review focuses on the development of cardiovascular imaging over the past 2 decades in China.
Advance in Techniques
Multislice helical CT was first used in clinical practice in 1998, and ever since CT hardware and software technology has progressed greatly. CT scanners used for cardiac imaging have evolved from electron-beam CT, capable of detecting coronary calcification, to multidetector CT scanners (from 4 to 320 detectors, dual-source CT) capable of performing contrast-enhanced coronary computed tomography angiography (CCTA).6 Since the advent of the electron-beam CT, there has been a dramatic increase in its application for coronary calcium screening. However, a lack of data in the current literature with regard to patient selection, referral bias, sample size limitations, and decision guidance has led to no recommendation of widespread use of this technique by the American College of Cardiology (ACC)/American Heart Association (AHA) Expert Consensus Document.7 Chinese radiologists have further validated the screening efficacy of electron-beam CT in patients who are asymptomatic but with risk factors for developing myocardial ischemia, through the use of single-photon emission computed tomography (SPECT). He et al’s8 study prospectively enrolled 411 asymptomatic subjects with a total coronary artery calcium score (CACS) assessed on electron-beam CT. This study confirmed the value of electron-beam CT in identifying a population at high risk of having myocardial ischemia in patients with known or suspected CAD. This study supported the role of electron-beam CT as the initial screening tool for identifying individuals at various stages of CAD development for whom therapeutic decision-making may differ considerably. Subsequently, technical improvement for image quality in the detection of CAD with luminal stenosis ≥50% was proposed by Lu et al9 using baseline heart rate–adjusted electrocardiogram triggering for electron-beam CCTA scan.
Over the first decade of the multidetector CCTA’s rapid development, it has become an accurate and effective tool for cardiovascular imaging with many clinical applications and an ever-growing list of appropriate indications. New well-defined and streamlined protocols for imaging patients using CCTA emphasizes both optimization of image quality and minimization of patient’s exposure to radiation dose.10–12 In an attempt to improve the safety at dose optimization and generalization of CCTA in CAD patients, a direct head-to-head comparison in the same patient population conducted by Chinese researchers10 shows that the addition of iterative reconstruction techniques allows further reduction of radiation doses by 52%, without penalties in image quality or, more importantly, in diagnostic accuracy compared with invasive coronary angiography as a reference standard. This study has further validated the safety of CCTA since concern about radiation doses associated with noninvasive CCTA has risen with each new generation of multidetector-row CT systems, reaching a peak with the launch of the first-generation 64-slice scanners in 2004.
In 2005, dual-source CT was introduced to the world. One of the many “new-generation” CT technologies that has improved image quality of cardiovascular imaging to new levels, it provides temporal resolution equivalent to a quarter of the rotation time, independent of the patient’s heart rate. Zhu et al13 have exploited the optimization of contrast media injection protocols in relation to the patient’s weight. Such dual-source CT techniques have been proved to be an effective and accurate tool for patients with CAD.14 However, data on the use of CTA in asymptomatic patients was lacking, particularly in high-risk asymptomatic patients such as those with diabetes. Xu et al15 expanded the scope of noninvasive dual-source CTA to assess asymptomatic CAD in community-dwelling Chinese adults with early diabetes or prediabetes, with the study being nested in with studies on CCTA evaluation of asymptomatic CAD in patients with advanced diabetic status.16 Evidence from this study showing a markedly increased risk of significant coronary stenosis on CCTA in patients with early diabetes without a clinical manifestation of myocardial ischemia has contradicted the American Diabetes Association consensus guidelines, which recommends CAD screening only for diabetic individuals with cardiovascular symptoms.17
Anatomic and Morphologic Assessment With CCTA
In 2013, the European Society of Cardiology and NICE guidelines proposed CCTA as a first-line tool and an alternative to stress imaging techniques for the assessment of patients with suspected stable CAD and low-to-intermediate pretest probability of CAD.18,19 In 2019, CCTA was recommended for patients with chronic coronary syndromes at a higher level (I/B) by the European Society of Cardiology guidelines.20 In this context, numerous morphologic image makers have been investigated over the past few decades to identify the significance of coronary stenosis and patients with a high risk of rupturing (vulnerable plaque), with the hope of being able to predict events.21 A morphologic index characterized on CCTA, ratio of lesion length (LL) to the fourth power of minimal lumen diameter (MLD) (LL/MLD4) ratio, was analyzed to determine that, with a flow fractional ratio (FFR) <0.80 being regarded as functionally significant, it proved to be the only independent predictor of hemodynamically significant stenosis.22 The anatomic information of plaques revealed by CCTA and CACS have been found to improve risk prediction over the traditional routine risk factors that were reported by Chinese radiologists.23,24 In a retrospective large cohort study comprising 4425 patients with a median follow-up period of 1081 days, it was found that the location, stenosis severity, and the composition of the plaque shown by CCTA is superior to CACS for the prediction of adverse outcomes.25
For over a decade, characteristics of plaque burden and high-risk plaques have been analyzed noninvasively on the basis of CCTA, aiding in the diagnosis, prognosis, and treatment.26 The capability to characterize coronary atherosclerosis using CCTA has led to an abundance of large-scale clinical outcome data that directly relates plaque morphology and characteristics to adverse CAD outcomes.27 Recently, a large sample of retrospective cohort study (n=705) was conducted to assess the long-term prognosis of patients with high-risk plaques on CCTA, and was the first to demonstrate that the presence of high-risk plaque features was a significant independent predictor of a long-term (5-y) all-cause mortality and cardiovascular mortality.28 Researchers from China have filled the knowledge gaps by providing data on the long-term prognosis of patients with high-risk plaques on CCTA as only the prognostic power of the anatomic features with a short-term follow-up of 25 and 37 months had been studied.29,30
Functional and Hemodynamic Evaluation With Computed Tomography–derived Fractional Flow Reserve (CT-FFR)
With the continuous progress of imaging and postprocessing technology, functional imaging technologies have been increasingly used for clinical assessments and decision-making. Among these technologies, transluminal attenuation gradient and CT-FFR have been frequently reported,4,31 and China has made a series of remarkable contributions to this rapid development of cardiac CT techniques.
Currently, CT-FFR from HeartFlow (Heartflow Inc., Redwood City, CA) dominates the field of 3-dimensional computational fluid dynamics (CFD) in cardiovascular imaging because of its established evidence and approval by the Food and Drug Administration and the National Institute for Health and Care Excellence in the United States and United Kingdom, respectively. It should be noted, however, that CT-FFR based on other 3-dimensional CFD (Siemens, Erlangen, Germany),32 1-dimensional CFD (Toshiba Medical Systems Corp, Tokyo, Japan),33 machine learning (ML) algorithms (Siemens, Erlangen, Germany)32 and deep learning (DL) algorithms (Keya Medical Technology, Shenzhen, China)34 had begun to emerge before 2019. The key for China is to break through this technology bottleneck in estimating lesion-specific ischemia using our own indigenous technology.35 In 2019, Tang and colleagues independently developed and validated the high diagnostic performance of a novel on-site algorithm based on 3-dimensional CFD modeling with transluminal attenuation gradient–based boundary conditions coupled to the whole coronary model for CT-FFR calculation. The time to generate CT-FFR data has been shortened rapidly to about 11 minutes, which might be promising for its utility in the China-based population35 (Fig. 1). Impact of image quality and calcification on the performance of CT-FFR was also demonstrated in both this study and another study by the same team.36,37 Meanwhile, CT-FFR shows promise in guiding treatment decisions and in the risk stratification of patients with suspected CAD or de novo 3-vessel coronary lesions.38,39
Furthermore, the use of CT-FFR in CAD involved a validation study that indicated its ability to predict proximal atherosclerotic plaque formation in patients with left anterior descending artery myocardial bridging.40 This study remains vital as the hemodynamic factors were proven to be key in plaque initiation, and myocardial bridging exists as a natural pump for assessing hemodynamic value in plaque initiation, as the heart squeezes to pump blood, the muscle exerts pressure across the bridge and constricts the artery, conversely, the heart dilates and the artery distends.41
Structural and Physiological Analysis With Computed Tomography Perfusion (CTP)
Dynamic stress CT myocardial perfusion was first studied in the late 1970s using thee electron-beam CT. With the development of related equipment, this technology has gradually matured. For patients who need direct measurement of the myocardial perfusion level or patients whose images have poor quality, transluminal attenuation gradient or CT-FFR cannot yield accurate results. Hence, in patients with severe calcification of the coronary artery or in patients with suspected CAD and negative invasive angiography results, dynamic stress CTP) imaging should be the first choice. Previous studies that combined CCTA and CTP or hyperemic myocardial blood flow (MBF) were able to simultaneously assess the structural and physiological functions of coronary artery lesions.42 The field of CTP has been quiescent of late, especially when compared with the field of CFD coronary modeling. However, recent evidence from China has demonstrated that stress myocardial blood flow ratio, defined as the ratio of hyperemic MBF in a coronary artery with stenosis to hyperemic MBF in a normal coronary artery, could be a novel and accurate method to identify flow-limiting coronary stenosis against the reference standard of invasive FFR ≤0.80. Moreover, the combination of stenosis ≥50% by CCTA and stress myocardial blood flow ratio resulted in an area under the curve of 0.91, which was significantly higher compared with hyperemic MBF.43 This demonstrates how Chinese scholars keep digging into the potential of CTP even during a relative slump period.
CT-FFR and CTP are increasingly used to guide treatment decisions and evaluate the validity of stenosis-specific interventions. Both technologies are based on CT imaging, merging both approaches into closely related technologies, allowing anatomic and functional evaluation within 1 modality31 (Fig. 1). However, they focus on different levels of the ischemic cascade and are based on different physiological principles. It remains unclear whether these technologies will be competing or complementary techniques, or which will be superior. Pontone et al44 reported a comparative diagnostic performance between CT-FFR and CTP in determining ischemic-specific lesions with FFR as the reference standard. However, Li et al45 showed that MBF derived from dynamic CTP outperformed CT-FFR for identifying lesions causing ischemia in their study. Though stress dynamic CTP is implemented in minor medical centers in clinical practice in China due to the unavailable adenosine or adenosine triphosphate, or faulty emergency care and first aid, a larger population study comparing the performance of stress dynamic CTP and CT-FFR would be persuasive.
Chronic Total Occlusion (CTO) and In-stent Restenosis (ISR) on CCTA
CTO of a coronary artery is found in up to 15% to 25% of patients undergoing invasive angiography and is associated with adverse prognosis.46 CCTA is not only used to predict outcomes but is also a useful imaging tool that improves the chances of CTO recanalization by an experienced operator. Regardless of physicians’ experience and competence, the anatomic characteristics of a CTO are probably the most important determinants of procedural success47 and prognosis.48 In China, Zhang and colleagues made some efforts across several studies on CTO with CCTA. First, Li et al49 proposed a novel anatomic feature, the presence of linear intrathrombus enhancement, and proved it to be the strongest associated factor in predicting a successful percutaneous coronary intervention (PCI) when compared with other established predictors. The findings indicate that a linear intrathrombus enhancement serving as a potential conduit for wire crossing may contribute to a successful PCI. Subsequently, CCTA has been proven to be reliable in the evaluation of retrograde collateral flow distal to CTO lesions according to a previous study,50 and then the presence of well-developed distal collaterals as revealed by CCTA in patients with CTO lesions was reported to correlate with the lower frequency and extent of downstream myocardial infarction.51 The association of this noninvasive finding with clinically salvageable downstream myocardium has been explored extensively, in which morphologic predictors of failed antegrade PCI on the basis of preprocedure CCTA and coronary angiography imaging may be identified. It might assist in determining which patients with CTO lesions would benefit from an early retrograde PCI strategy.52 The current studies take CCTA one step forward from a prediction tool to a clinically practical tool in the interventional cardiologist’s toolbox.
PCI is the most common revascularization procedure performed in patients with CAD worldwide; ISR after stent placement is possible and is clinically relevant. In patients with coronary artery stents, it is often difficult to evaluate the lumen with CCTA because of blooming artifacts, image-based subtraction techniques53 as well as virtual monochromatic imaging from dual-energy acquisition54 were explored to improve diagnostic accuracy for the evaluation of ISR. A few studies have investigated the potential value of CTP to additionally increase CCTA diagnostic power in stented patients.30 Since it is generally difficult to evaluate stent patency with CCTA alone owing to technical limitations in imaging stents, using anatomic and morphologic indexes, like the changes in coronary opacification normalized to the aorta across coronary stents, it was possible to innovatively demonstrate ISR severity in obstructive ISR in stents <3 mm in diameter.55 CCTA-derived noncalcified plaque volume, lesion length, and remodeling index before stent implantation portend incremental predictive value for ISR.56 Recently, Tang et al57 validated the feasibility of functional ISR using CT-FFR in patients with stents using invasive FFR as the reference standard, and explored the prognostic value of CT-FFR in the prediction of adverse outcomes and its subsequent clinical implications. This is the first substantial attempt to determine CT-FFR’s prognostic value in predicting adverse cardiovascular events in patients with prior stent implantation,58 offering not only anatomic but even functional evaluation of ISR and its impact on prognosis with CCTA noninvasively.
Pulmonary Thromboembolism and CTA
Modern multidetector contrast-enhanced pulmonary CTA has widely replaced pulmonary angiography and ventilation-perfusion (V/Q) scanning as the dominant imaging modality for suspected pulmonary thromboembolism, particularly acute pulmonary embolism (APE). Current cutting-edge advances in CT technology, such as dual-energy pulmonary CTA, faster image acquisition times, and fine tuning of contrast bolus administration, have further refined pulmonary CTA through improved image quality and diagnostic accuracy of APE, providing added value in prognosis and patient management.59
Zhang and his research group have been involved with the functional assessment of APE on dual-energy CT, contributing most of the evidence that explains the vast potential of this novel technique. Over a decade ago, the feasibility and incremental value of dual-energy CT in the diagnosis of APE was demonstrated in an animal model with pathologic proof of APE as the reference standard. Dual-energy CT equipped with blood flow imaging simultaneously provided morphologic and functional data, offering complementary information that maximized the accuracy in detecting PE.60 Lung V/Q mismatch on dual-energy CT in a 19-year-old patient with APE was first reported in Circulation, expanding the scope of xenon-enhanced dual-energy CT from only mapping the xenon distribution in patients with emphysema or asthma and in assessing the collateral ventilation of bronchus atresia in a static or dynamic scan.61 Dual-energy computed tomography pulmonary angiography (CTPA) combined with renal CT venography in 1 CT scan also was applied for patients with nephrotic syndrome to demonstrate that APE was more common than renal vein thrombosis and was most frequently found in patients with membranous nephropathy (Fig. 2). It might give clues to clinicians that early PE in patients with nephrotic syndrome who are in distress should be ruled out, particularly in patients with membranous nephropathy.62 In addition to APE, the diagnostic performance of CTPA for chronic thromboembolic pulmonary hypertension (CTEPH) remains controversial. Wang et al63 compared prospectively the performance of SPECT, V/Q planar scintigraphy, and CTPA in identifying CTEPH using digital subtraction pulmonary arteriography as the reference standard in a total of 229 participants suspected of having CTEPH. As a result, both V/Q scanning and CTPA showed good efficacy for diagnosing CTEPH at the patient level, while V/Q scanning was more sensitive and less specific than CTPA for detecting vascular obstructions in segmental pulmonary arteries.
CARDIAC MAGNETIC RESONANCE (CMR)
CMR was introduced into clinical practice in China in the 1980s and now has become an important imaging tool for the diagnosis of multiple CVDs, such as CAD, cardiomyopathy, and cardiac amyloidosis, and especially for visualizing and quantifying cardiovascular morphology, function, myocardial perfusion, and myocardial tissue characterization. With the continuous innovation in hardware and postprocessing technology such as quantitative imaging, magnetic resonance perfusion, MRA, late gadolinium enhancement (LGE), T1/T2 mapping, extracellular volume (ECV), and feature tracking, research on CMR in China has made immense contributions to the development of cardiovascular imaging.
Among the various noninvasive multi-imaging modalities, CMR allows for an accurate assessment of CAD and clarifies 3 key issues regarding the management of patients: the existence of myocardial ischemia, viability of the myocardium, and the establishment of risk stratification.64
According to current guidelines in the United States and Europe,18,65 stress perfusion CMR is recommended as a first-line test in patients with a probability of intermediate CAD and an uninterpretable electrocardiogram at rest, with a class I indication (Level of Evidence: B) in Europe or a class IIb indication (Level of Evidence: B) in the United States. In addition to accurately identifying obstructive CAD,8,66 its usefulness in identifying standard-of-care-guided revascularization and adverse prognosis was verified.67 A domestic meta-analysis conducted by Li et al68 to further evaluate the accuracy of stress perfusion CMR to diagnose CAD with FFR as the reference standard reported that the pooled sensitivity and specificity were 0.90 and 0.87, respectively, at the patient level in 650 patients. Zhou et al69 further confirmed that myocardial perfusion reserve index derived from stress CMR perfusion, a robust semiquantitative imaging marker diagnosing microvascular angina with an accuracy of 92%,70 could independently predict major adverse cardiac events in patients with ischemic symptoms but without obstructive CAD over 5 years’ follow-up at 3 different imaging centers in Hong Kong. Therefore, the study called for more clinical attention to coronary microvascular disease and the potential role of myocardial perfusion reserve index for the assessment of patients.
Besides the assessment of myocardial blood supply, CMR can serve as a multifunctional modality to evaluate coronary artery stenosis intuitively. Coronary MRA has contributed significantly to our current understanding of CAD pathophysiology71 by providing insights into coronary artery distensibility in response to stress, plaque characteristics, and plaque inflammation without contrast medium and radiation exposure.72 Its usefulness as a noninvasive research method to assess CAD in different groups of patients has been demonstrated in both single and multiple centers,73 thereby appearing as a promising imaging method for the screening of CAD. With the significant technical limitations of coronary MRA including reduced spatial resolution, long acquisition time, and low signal-to-noise ratio, He et al74 also attempted to apply self-navigated whole-heart coronary 3T MRA, a technique developed to address these limitations, in identifying significant luminal narrowing in 32 patients with CAD with invasive coronary angiography as the reference standard with a sensitivity and specificity of 0.78 and 0.75, respectively. Thus, all these studies conclude that noncontrast coronary MRA is an attractive option for CAD assessment, albeit relatively underdeveloped compared with CCTA.
Hypertrophic Cardiomyopathy (HCM)/Dilated Cardiomyopathy (DCM)
Myocardial fibrosis is a pathologic finding present in HCM and DCM that can result in malignant ventricular arrhythmias and sudden cardiac death (SCD).75
CMR has become an important component of the current diagnostic method for both cardiomyopathies. LGE detects nonischemic LGE in ∼30% of patients, which correlates with replacement fibrosis on histology,76 providing incremental value in addition to left ventricular (LV) ejection fraction for predicting all-cause mortality and SCD events. Therefore, it has the potential to guide therapy decisions such as the selection of patients for implantable cardioverter-defibrillators.76 Weng et al77 performed a meta-analysis to identify the predictive value of LGE for SCD in patients with HCM, in a sample of 2993 patients, 1658 (55%) of whom were LGE positive. They reported the risk of SCD to be significantly associated with not only with the presence of LGE, but most importantly, with the extent of LGE. Extensive LGE may thus be potentially considered a novel risk marker to help identify high-risk patients. However, LGE performs poorly in the context of subtle, diffuse fibrosis, and technical mistakes in myocardial nulling can lead to nondiagnostic LGE images.78 Hence, the value of native myocardial T1 mapping and ECV fraction, a robust magnetic resonance imaging (MRI) technique that allows measurement of absolute T1 values of the myocardium, was recently used to evaluate myocardial fibrosis in patients with nonobstructive HCM and no LGE by Xu et al79 and as a result, prolonged myocardial T1 and elevated ECV in HCM suggests diffuse myocardial fibrosis, even in the absence of regionally apparent LGE and hemodynamic obstruction. In 659 patients with DCM, the prognostic value of T1 mapping and the ECV was evaluated during a mean follow-up of 66 months. T1 mapping and the ECV had prognostic value in patients with DCM, and were particularly important in patients with DCM without LGE.80 Moreover, the capabilities of new diffuse weighted CMR and strain imaging technologies were validated by several domestic studies in demonstrating the extent and severity of myocardial fibrosis and prognostic value, where diffusion-weighted CMR displayed a high sensitivity in characterizing the extent of diffuse myocardial fibrosis in patients with HCM.81 Generally, these studies by Chinese researchers have pointed out that using a combination of T1 mapping, ECV fraction, LGE, and other new techniques (diffuse weighted CMR and strain imaging) could provide optimal risk stratification for patients either with HCM or DCM, with great benefit in improving the subsequent treatment and prognosis of patients.
Cardiac involvement with amyloidosis is common, and is one of the main determinants of prognosis; hence, early identification and risk stratification are vital to provide timely clinical intervention.82 CMR can perform myocardial tissue characterization in addition to conventional volume and function evaluation using novel techniques. LGE of the LV myocardium was long deemed to be the standard reference to detect cardiac amyloid deposition, while native T1 and ECV were elevated in patients with immunoglobulin light-chain amyloidosis, suggesting that T1 mapping can be used to detect myocardial involvement at an early stage.83 First-pass perfusion imaging using CMR for demonstrating the high prevalence of LV regional myocardial microvascular function in patients with cardiac amyloidosis was clarified, also suggesting that CMR is capable of detecting early cardiac involvement of amyloidosis.84 Native T1, LGE, and determination of ECV were additionally useful in providing prognostic information in patients with cardiac amyloidosis.85 Furthermore, analysis of myocardial strain also offers new insight into the disease’s mechanisms through the use of heart deformation analysis and feature tracking, providing a new dimension to investigate cardiac amyloidosis.86 According to the study from Wan et al,87 myocardial strains and transmural LGE were independently associated with all-cause mortality in patients with light-chain amyloidosis and myocardial strains, enabling reliable differentiation of transmural myocardial amyloid from nontransmural cardiac amyloidosis, which were also significantly associated with the extent of myocardial LGE. This research confirmed that abnormalities from myocardial strains indicate the extent of cardiac amyloid infiltration and independent prognostic information for all-cause mortality in patients with AL amyloidosis.
Disease tracking is pivotal after therapy in patients with amyloidosis. Currently, consensus guidelines are based on the free light chain, cardiac biomarkers, and ejection fraction in assessing hematologic and cardiac responses to therapy,88 but none of these variables directly reflects the myocardial amyloid burden. CMR with T1 mapping is sensitive to amyloid deposition and myocyte response to infiltration, making it an attractive tool for noninvasive monitoring of disease progression or response.89 Myocardial strain and myocardial perfusion are also used to track cardiovascular responses in patients with dialysis-related amyloidosis.90 To comprehensively clarify the myocardial amyloid burden after therapy, Li et al91 recently applied a multiparametric CMR protocol to follow the cardiac change after chemotherapy in 43 patients with light-chain amyloidosis. LV longitudinal strain and right ventricular ejection fraction were found to be more sensitive in monitoring ventricular function, and myocardial T2 was increased even in patients with a superior hematologic response, suggesting that serum free light-chain clearance may not predict cardiac response. A noncontrast CMR protocol with cine and T2 mapping at a 1-year follow-up was also recommended for therapy monitoring. These findings suggest that the change of myocardial amyloid burden gives a direct assessment of cardiac efficacy that should be paid more attention (Fig. 3).
ARTIFICIAL INTELLIGENCE (AI) AND CARDIOVASCULAR IMAGING
AI technologies are showing their potential to change how cardiovascular imaging is used and interpreted through improving quality control, test selection, quantification, reporting, diagnostics, ease of use, and workflow.92 Two distinct approaches have been reported in the application of AI to cardiovascular imaging. Classic machine learning methods have been used with a multitude of clinical and/or precomputed image features to predict diagnostic or prognostic outcomes from large datasets.92 More advanced AI methods, such as DL methods, directly interrogate images for image segmentation or outcome prediction tasks. DL is particularly suited for large and complex datasets with many features. Segmentation is the process of content extraction from an input medical image, volume, or sequence of images or volumes, with cardiovascular image segmentation being a currently developing field. Tao et al93 developed a DL-based method for fully automated quantification of LV function from short-axis cine MRIs and evaluated its performance in a multivendor and multicenter setting. Zhang et al94 proposed a DL framework on nonenhanced cardiac cine MRI to confirm the presence, position, transmurality, and size of chronic myocardial infarction, which is particularly helpful in patients with CAD and coexisting renal impairment that cannot undergo LGE imaging for myocardial infarction evaluation. Overall, AI has contributed to the ability to further prognosticate cardiovascular outcomes with incorporation of anatomic and functional information from CT-based imaging in addition to MRI95 and continues to grow vigorously, reminding us to seize the opportunity presented by a much discussed topic worldwide.
CARDIOVASCULAR IMAGING: TOMORROW
Technical Advances in Cardiovascular Imaging
In the future, some promising technical advances in both cardiovascular CT and MRI might have potential for the development of cardiovascular imaging that deserves more attention. In cardiovascular CT, 4-dimensional CTA can depict cardiovascular anatomy and dynamics, and track cardiac chambers and valves.96 Photon-counting CT has emerged as a promising new technique, which will allow for reduced radiation exposure, increased spatial resolution, correction of beam-hardening artifacts, and alternative contrast agent protocols while creating opportunities for quantitative imaging, ready to dramatically change CT.97 In cardiovascular MRI, 4-dimensional flow MRI offers the opportunity to derive advanced hemodynamic measures, such as vorticity and helicity, wall shear stress, pressure gradients, viscous energy loss, turbulent kinetic energy, and pulse wave velocity, initially focused on the heart and large vessels, now it can be applied to other vascular territory throughout the human circulatory system.98 Dark-blood LGE method is a promising new tool for noninvasive assessment of myocardial fibrosis. Future work should focus on the implementation in dark-blood LGE approach with the recent introduction of compressed sensing and AI-based CMR reconstruction techniques.99
Prospective Randomized Clinical Trials in Cardiovascular Imaging
Large-scale, prospective, randomized, clinical trials in cardiovascular imaging has a strong global demand with class IA recommendation. Data provided from these studies may help to reconcile differing ACC/AHA and European Society of Cardiology guideline recommendations, and enable more informed choices by patients, physicians, insurers, and policymakers. For example, the rationale for the PROMISE trial100 was to be the first large-scale prospective clinical trial to provide randomized data that compared the effect of choosing functional testing versus anatomic testing by CCTA on health outcomes, safety, cost, and management of patients with suspected obstructive CAD.101 In many ways, this study was what cardiologists had been wanting for a long time. Given that CCTA has been practiced for >20 years and invasive functional testing for >40 years, physicians may not have been equally familiar with noninvasive CT-derived functional methods (such as CT-FFR). With this background, CT-FFR (Heartflow Inc.) is also being evaluated in a randomized controlled trial in the United Kingdom, being compared with NICE-guided standard of care for patients with stable chest pain (FORECAST study) and will be followed by a larger international randomized controlled trial evaluating CT-FFR against traditional testing algorithms in the outpatient setting, and a planned comparison with traditional interventional strategies among those referred for invasive angiography.2 Furthermore, the effect of test results based on CT or MRI upon patient outcome is determined by how physicians use the test information. It will be also very interesting to determine how myocardial ischemia or other abnormalities established by the tests influence the prognosis.102 In general, in the future, prospective randomized trials should focus on dedicated, well-defined subgroups based on specific information such as clinical characteristics, biomarker levels, and imaging modalities.
Translational Imaging in CVD
The past few decades have seen an explosion in the development and use of methods for cardiovascular imaging such as hemodynamic adjudication, shear stress analysis using computational flow dynamics, more accurate and robust plaque characterization with CCTA, and advanced quantification of CT or MRI data through AI and radiomics. The confluence of innovative imaging technologies, affordable computing power, and economies of scale have ushered in a new era of “translational” imaging that permits us to peer into the genetics-driven pathophysiology that have allowed researchers and clinicians to gain insight into its role in the etiology of many diseases.103 Moreover, these modalities offer a diverse range of spatial resolutions and tissue penetration depths such that clinicians and researchers must tailor the imaging modality used to match their biological question. Last, we believe that ongoing technological progress in these imaging modalities, coupled with the ability of global clinical centers and groups to champion modalities for unique applications, and the increased availability and reduced cost of equipment, will drive discovery and research as much as the underlying clinical need and biological questions being asked.104
Multimodality and Multidisciplinary Cardiovascular Imaging
This is already happening in terms of technological evolution, wherein information about structure, function, physiology, and pathology is being effectively integrated, creating combined modalities such as SPECT and CT, positron emission tomography and CT or MRI, and CT plus FFR. Integration will evolve to become the core of patient-centric imaging decision-making and would be still required of future multimodality imagers.105 Some experts emphasize the necessity of multimodality imaging and multimodality imagers to become more rigorous, involving the ability to perform imaging on the basis of outcomes, to avoid the costly quagmire of multiple tests, and, more importantly, to add value to patient care decisions. It indicates that the tremendous technological revolution in the last 2 decades has brought an increased demand for dedicated cardiac imagers who are cross-trained in multiple modalities. Thus, fellowship training programs for cardiovascular multimodality imagers might be imperative, for different imaging modalities have yielded new insights about cardiovascular pathology and disease progression, guided and optimized patient care, and ultimately improved clinical outcomes.
Currently, the most concentrated area of cardiac multidisciplinary imaging belongs to cross-border collaboration between medicine and engineering, inseparable from the innovation and development of interdisciplinary talents focusing on intelligent analytics, intelligent decision-making, and precision medicine formed on top of health care big data.106 Chemists and biologists have also shown interest in visualizing pathologic processes at a cellular level, and in targeting therapy combined with CMR.107 More diversified and deeper cooperation in cardiovascular imaging by cardiologists, pharmacists, radiologists, and technicians to comprehensively and systematically solve problems is expected in the future.
Cardiovascular imaging with CT and MRI continues to undergo development amid an increasing number of clinical applications that likely have the potential to change how clinicians manage CVD prevention, diagnosis, and treatment, leaving China both a great opportunity and a challenge. There has been expanding incorporation of these advances into large-scale prospective clinical trials and into statements and guidelines involving coronary, structural heart, myocardial, and pulmonary artery disease. For practicing and encouraging cardiothoracic radiologists in China or in other countries, a knowledge base of past and present progress in cardiovascular imaging remains essential, helping the field grow and stay robust. We look forward to new discoveries in the next iteration of this collection.
The authors appreciate Professor U. Joseph Schoepf and Mr. Callum E. Gill from the Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina (25 Courtenay Drive, Charleston, SC 29425), for their editorial help and excellent comments for this document.
1. World Health Organization. Health topics/cardiovascular diseases dashboard. Available at: https://www.who.int/health-topics/cardiovascular-diseases#tab=tab_1
. Accessed June 11, 2022.
2. The Writing Committee of the Report on Cardiovascular Health and Diseases in China. Report on Cardiovascular Health and Diseases in China 2019: an updated summary. Chin Circ J. 2020;35:833–854.
3. Gulsin GS, McVeigh N, Leipsic JA, et al. Cardiovascular CT and MRI in 2020: review of key articles. Radiology. 2021;301:263–277.
4. Nicol ED, Norgaard BL, Blanke P, et al. The future of cardiovascular computed tomography: advanced analytics and clinical insights. JACC Cardiovasc Imaging. 2019;12:1058–1072.
5. Raman SV, Zareba KM. Coronary artery disease testing: past, present, and future. JACC Cardiovasc Imaging. 2017;10:1359–1360.
6. Min JK, Narula J. MDCT for cardiovascular evaluation: are there 2 “One-Stop Shops”? JACC Cardiovasc Imaging. 2016;9:907–909.
7. O’Rourke RA, Brundage BH, Froelicher VF, et al. American College of Cardiology/American Heart Association Expert Consensus Document on electron-beam computed tomography for the diagnosis and prognosis of coronary artery disease. J Am Coll Cardiol. 2000;36:326–340.
8. He ZX, Hedrick TD, Pratt CM, et al. Severity of coronary artery calcification by electron beam computed tomography predicts silent myocardial ischemia. Circulation. 2000;101:244–251.
9. Lu B, Zhuang N, Mao SS, et al. Baseline heart rate-adjusted electrocardiographic triggering for coronary artery electron-beam CT angiography. Radiology. 2004;233:590–595.
10. Yin WH, Lu B, Li N, et al. Iterative reconstruction to preserve image quality and diagnostic accuracy at reduced radiation dose in coronary CT angiography: an intraindividual comparison. JACC Cardiovasc Imaging. 2013;6:1239–1249.
11. Zhang LJ, Wang Y, Schoepf UJ, et al. Image quality, radiation dose, and diagnostic accuracy of prospectively ECG-triggered high-pitch coronary CT angiography at 70 kVp in a clinical setting: comparison with invasive coronary angiography. Eur Radiol. 2016;26:797–806.
12. Zhang LJ, Qi L, Wang J, et al. Feasibility of prospectively ECG-triggered high-pitch coronary CT angiography with 30 mL iodinated contrast agent at 70 kVp: initial experience. Eur Radiol. 2014;24:1537–1546.
13. Zhu X, Zhu Y, Xu H, et al. Dual-source CT coronary angiography involving injection protocol with iodine load tailored to patient body weight and body mass index: estimation of optimal contrast material dose. Acta Radiol. 2013;54:149–155.
14. Flohr TG, Schoepf UJ, Ohnesorge BM. Chasing the heart new developments for cardiac CT. J Thorac Imaging. 2007;22:4–16.
15. Xu Y, Bi YF, Li M, et al. Significant coronary stenosis in asymptomatic Chinese with different glycemic status. Diabetes Care. 2013;36:1687–1694.
16. Sassa S, Shimada K, Yoshida K, et al. Comparison of 64-slice multi-detector computed tomography coronary angiography between asymptomatic, type 2 diabetes mellitus and impaired glucose tolerance patients. J Cardiol. 2008;52:133–139.
17. American Diabetes Association. Consensus development conference on the diagnosis of coronary heart disease in people with diabetes: 10-11 February 1998, Miami, Florida. Diabetes Care. 1998;21:1551–1559.
18. Montalescot G, Sechtem U, Achenbach S, et al. ESC guidelines on the management of stable coronary artery disease: the Task Force on the management of stable coronary artery disease of the European Society of Cardiology. Eur Heart J. 2013;34:2949–3003.
19. Chest pain of recent onset: assessment and diagnosis (Clinical Guideline 95). NICEGuidelines 2016. Available at: https://www.nice.org.uk/guidance/cg95
. Accessed August 14, 2018.
20. Knuuti J, Wijns W, Saraste A, et al. 2019 ESC guidelines for the diagnosis and management of chronic coronary syndromes. Eur Heart J. 2020;41:407–477.
21. Motoyama S, Ito H, Sarai M, et al. Plaque characterization by coronary computed tomography angiography and the likelihood of acute coronary events in mid-term follow-up. J Am Coll Cardiol. 2015;66:337–346.
22. Li MH, Zhang JY, Pan JW, et al. Coronary stenosis: Morphologic index characterized by using CT angiography correlates with fractional flow reserve and is associated with hemodynamic status. Radiology. 2013;269:713–721.
23. Sarwar A, Shaw LJ, Shapiro MD, et al. Diagnostic and prognostic value of absence of coronary artery calcification. JACC Cardiovasc Imaging. 2009;2:675–688.
24. Min JK, Dunning A, Lin FY, et al. Age- and sex-related differences in all-cause mortality risk based on coronary computed tomography angiography findings. J Am Coll Cardiol. 2011;58:849–860.
25. Hou ZH, Lu B, Gao Y, et al. Prognostic value of coronary CT angiography and calcium score for major adverse cardiac events in outpatients. JACC Cardiovasc Imaging. 2012;5:990–999.
26. Versteylen MO, Kietselaer BL, Dagnelie PC, et al. Additive value of semiautomated quantification of coronary artery disease using cardiac computed tomographic angiography to predict future acute coronary syndrome. J Am Coll Cardiol. 2013;61:2296–2305.
27. Abdelrahman KM, Chen MY, Dey AK, et al. Coronary computed tomography angiography from clinical uses to emerging technologies: JACC state-of-the-art review. J Am Coll Cardiol. 2020;76:1226–1243.
28. Yang Y, Shah JP, Zeng HJ, et al. Prevalence and prognosis of high-risk plaque on coronary CT angiography in hospitalized patients. JACC Cardiovasc Imaging. 2020;13:522–523.
29. Ferencik M, Mayrhofer T, Bittner DO, 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;3:144–152.
30. Andreini D, Magnoni M, Conte E, et al. Coronary plaque features on CTA can identify patients at increased risk of cardiovascular events. JACC Cardiovasc Imaging. 2020;13:1704–1717.
31. Schoepf UJ, van Assen M. FFR-CT and CT myocardial perfusion imaging: friends or foes? JACC Cardiovasc Imaging. 2019;12:2472–2474.
32. Tesche C, De Cecco CN, Albrecht MH, et al. Coronary CT angiography-derived fractional flow reserve. Radiology. 2017;285:17–33.
33. Ko BS, Cameron JD, Munnur RK, et al. Noninvasive CT-derived FFR based on structural and fluid analysis: a comparison with invasive FFR for detection of functionally significant stenosis. JACC Cardiovasc Imaging. 2017;10:663–673.
34. Wang ZQ, Zhou YJ, Zhao YX, et al. Diagnostic accuracy of a deep learning approach to calculate FFR from coronary CT angiography. J Geriatr Cardiol. 2019;16:42–48.
35. Tang CX, Liu CY, Lu MJ, et al. CT FFR for ischemia-specific CAD with a new computational fluid dynamics algorithm: a Chinese multicenter study. JACC Cardiovasc Imaging. 2020;13:980–990.
36. Jiang MD, Zhang XL, Liu H, et al. The effect of coronary calcification on diagnostic performance of machine learning-based CT-FFR: a Chinese multicenter study. Eur Radiol. 2021;31:1482–1493.
37. Xu PP, Li JH, Zhou F, et al. The influence of image quality on diagnostic performance of a machine learning-based fractional flow reserve derived from coronary CT angiography. Eur Radiol. 2020;30:2525–2534.
38. Qiao HY, Tang CX, Schoepf UJ, et al. Impact of machine learning-based coronary computed tomography angiography fractional flow reserve on treatment decisions and clinical outcomes in patients with suspected coronary artery disease. Eur Radiol. 2020;30:5841–5851.
39. Qiao HY, Li JH, Schoepf UJ, et al. Prognostic implication of CT-FFR based functional SYNTAX score in patients with de novo three-vessel disease. Eur Heart J Cardiovasc Imaging. 2020;22:1434–1442.
40. Zhou F, Tang CX, Schoepf UJ, et al. Machine learning using CT-FFR predicts proximal atherosclerotic plaque formation associated with lad myocardial bridging. JACC Cardiovasc Imaging. 2019;12:1591–1593.
41. Tarantini G, Migliore F, Cademartiri F, et al. Left anterior descending artery myocardial bridging: a clinical approach. J Am Coll Cardiol. 2016;68:2887–2899.
42. Wong DT, Ko BS, Cameron JD, et al. Comparison of diagnostic accuracy of combined assessment using adenosine stress computed tomography perfusion+computed tomography angiography with transluminal attenuation gradient+computed tomography angiography against invasive fractional flow reserve. J Am Coll Cardiol. 2014;63:1904–1912.
43. Yang JJ, Dou GH, He B, et al. Stress myocardial blood flow ratio by dynamic CT perfusion identifies hemodynamically significant CAD. JACC Cardiovasc Imaging. 2020;13:966–976.
44. Pontone G, Baggiano A, Andreini D, et al. Stress computed tomography perfusion versus fractional flow reserve CT derived in suspected coronary artery disease: the PERFECTION Study. JACC Cardiovasc Imaging. 2019;12:1487–1497.
45. Li YH, Yu MM, Dai X, et al. Detection of hemodynamically significant coronary stenosis: CT myocardial perfusion versus machine learning CT fractional flow reserve. Radiology. 2019;293:305–314.
46. Rubinshtein R, Danenberg H. Preprocedural coronary CT angiography effect on the likelihood to restore flow in chronic total occlusion. JACC Cardiovasc Imaging. 2021;14:2005–2007.
47. Brilakis ES, Mashayekhi K, Tsuchikane E, et al. Guiding principles for chronic total occlusion percutaneous coronary intervention. Circulation. 2019;140:420–433.
48. Opolski MP, Gransar H, Lu Y, et al. Prognostic value of chronic total occlusions detected on coronary computed tomographic angiography. Heart. 2019;105:196–203.
49. Li MH, Zhang JY, Pan JW, et al. Coronary total occlusion lesions: linear intrathrombus enhancement at CT predicts better outcome of percutaneous coronary intervention. Radiology. 2013;266:443–451.
50. Li M, Zhang J, Pan J, Lu Z. Obstructive coronary artery disease: reverse attenuation gradient sign at CT indicates distal retrograde flow—a useful sign for differentiating chronic total occlusion from subtotal occlusion. Radiology. 2013;266:766–772.
51. Zhang JY, Li YH, Li MH, et al. Collateral vessel opacification with CT in patients with coronary total occlusion and its relationship with downstream myocardial infarction. Radiology. 2014;271:703–710.
52. Luo C, Huang MP, Li JL, et al. Predictors of interventional success of antegrade PCI for CTO. JACC Cardiovasc Imaging. 2015;8:804–813.
53. Amanuma M, Kondo T, Sano T, et al. Assessment of coronary in-stent restenosis: value of subtraction coronary computed tomography angiography. Int J Cardiovasc Imaging. 2016;32:661–670.
54. Hickethier T, Wenning J, Bratke G, et al. Evaluation of soft-plaque stenoses in coronary artery stents using conventional and monoenergetic images: first in-vitro experience and comparison of two different dual-energy techniques. Quant Imaging Med Surg. 2020;10:612–623.
55. Gao Y, Lu B, Hou ZH, et al. Coronary in-stent restenosis: assessment with corrected coronary opacification difference across coronary stents measured with CT angiography. Radiology. 2015;275:403–412.
56. Tesche C, De Cecco CN, Vliegenthart R, et al. Coronary CT angiography-derived quantitative markers for predicting in-stent restenosis. J Cardiovasc Comput Tomogr. 2016;10:377–383.
57. Tang CX, Guo BJ, Schoepf JU, et al. Feasibility and prognostic role of machine learning-based FFRCT
in patients with stent implantation. Eur Radiol. 2021;31:6592–6604.
58. Matteucci A, Massaro G, Mamas MA, et al. Expanding the role of fractional flow reserve derived from computed tomography (FFR CT
) for the non-invasive imaging of patients with coronary stents: rise of the machines? Eur Radiol. 2021;31:6589–6591.
59. Danad I, Fayad ZA, Willemink MJ, et al. New applications of cardiac computed tomography: dual-energy, spectral, and molecular CT imaging. JACC Cardiovasc Imaging. 2015;8:710–723.
60. Zhang LJ, Zhao YE, Wu SH, et al. Pulmonary embolism detection with dual-energy CT: experimental study of dual-source CT in rabbits. Radiology. 2009;252:61–70.
61. Zhang LJ, Zhou CS, Lu GM. Dual energy computed tomography demonstrated lung ventilation/perfusion mismatch in a 19-year-old patient with pulmonary embolism. Circulation. 2012;126:2441–2443.
62. Zhang LJ, Zhang ZL, Li SJ, et al. Pulmonary embolism and renal vein thrombosis in patients with nephrotic syndrome: prospective evaluation of prevalence and risk factors with CT. Radiology. 2014;273:897–906.
63. Wang M, Wu DY, Ma RZ, et al. Comparison of V/Q SPECT and CT angiography for the diagnosis of chronic thromboembolic pulmonary hypertension. Radiology. 2020;296:420–429.
64. Lozano R, Naghavi M, Foreman K, et al. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2012;380:2095–2128.
65. Windecker S, Kolh P, Alfonso F, et al. 2014 ESC/EACTS Guidelines on myocardial revascularization: the Task Force on Myocardial Revascularization of the ESC and the EACTS developed with the special contribution of the EAPCI. Eur Heart J. 2014;35:2541–2619.
66. Rønnow Sand NP, Nissen L, Winther S, et al. Prediction of coronary revascularization in stable angina: comparison of FFR(CT) with CMR stress perfusion imaging. JACC Cardiovasc Imaging. 2020;13:994–1004.
67. Ebersberger U, Makowski MR, Schoepf UJ, et al. Magnetic resonance myocardial perfusion imaging at 3.0 Tesla for the identification of myocardial ischaemia: comparison with coronary catheter angiography and fractional flow reserve mea- surements. Eur Heart J Cardiovasc Imaging. 2013;14:1174–1180.
68. Li M, Zhou T, Yang LF, et al. Diagnostic accuracy of myocardial magnetic resonance perfusion to diagnose ischemic stenosis with fractional flow reserve as reference: systematic review and meta-analysis. JACC Cardiovasc Imaging. 2014;7:1098–1105.
69. Zhou W, Lee JCY, Leung ST, et al. Long-term prognosis of patients with coronary microvascular disease using stress perfusion cardiac magnetic resonance. JACC Cardiovasc Imaging. 2021;14:602–611.
70. Liu A, Wijesurendra RS, Liu JM, et al. Diagnosis of microvascular angina using cardiac magnetic resonance. J Am Coll Cardiol. 2018;71:969–979.
71. Nguyen PK, Meyer C, Engvall J, et al. Noninvasive assessment of coronary vasodilation using cardio- vascular magnetic resonance in patients at high risk for coronary artery disease. J Cardiovasc Magn Reson. 2008;10:28.
72. Botnar RM, Stuber M, Kissinger KV, et al. Noninvasive coronary vessel wall and plaque imaging with magnetic resonance imaging. Circulation. 2000;102:2582–2587.
73. Liu W, Wu S, Wang Z, et al. Relationship between coronary hyper-intensive plaques identified by cardiovascular magnetic resonance and clinical severity of acute coronary syndrome. J Cardiovasc Magn Reson. 2021;23:12.
74. He Y, Pang J, Dai Q, et al. Diagnostic performance of self-navigated whole-heart contrast-enhanced coronary 3-T MR angiography. Radiology. 2016;281:401–408.
75. Elming MB, Hammer-Hansen S, Voges I, et al. Myocardial fibrosis and the effect of primary prophylactic defibrillator implantation in patients with non-ischemic systolic heart failure-DANISH-MRI. Am Heart J. 2020;221:165–176.
76. Halliday BP, Gulati A, Ali A, et al. Association between midwall late gadolinium enhancement and sudden cardiac death in patients with dilated cardiomyopathy and mild and moderate left ventricular systolic dysfunction. Circulation. 2017;135:2106–2115.
77. Weng Z, Yao J, Chan RH, et al. Prognostic value of LGE-CMR in HCM: A meta-analysis. JACC Cardiovasc Imaging. 2016;9:1392–1402.
78. Axelsson Raja A, Farhad H, Valente AM, et al. Prevalence and progression of late gadolinium enhancement in children and adolescents with hypertrophic cardiomyopathy. Circulation. 2018;138:782–792.
79. Xu J, Zhuang B, Sirajuddin A, et al. MRI T1 mapping in hypertrophic cardiomyopathy: evaluation in patients without late gadolinium enhancement and hemodynamic obstruction. Radiology. 2020;294:275–286.
80. Li S, Zhou D, Sirajuddin A, et al. T1 mapping and extracellular volume fraction in dilated cardiomyopathy: a prognosis study. JACC Cardiovasc Imaging. 2021;15:578–590.
81. Yang F, Wang L, Wang J, et al. Prognostic value of fast semi-automated left atrial long-axis strain analysis in hypertrophic cardiomyopathy. J Cardiovasc Magn Reson. 2021;23:36.
82. Falk RH. Diagnosis and management of the cardiac amyloidoses. Circulation. 2005;112:2047–2060.
83. Lin L, Li X, Feng J, et al. The prognostic value of T1 mapping and late gadolinium enhancement cardiovascular magnetic resonance imaging in patients with light chain amyloidosis. J Cardiovasc Magn Reson. 2018;20:2.
84. Li R, Yang ZG, Wen LY, et al. Regional myocardial microvascular dysfunction in cardiac amyloid light-chain amyloidosis: assessment with 3T cardiovascular magnetic resonance. J Cardiovasc Magn Reson. 2016;18:16.
85. Martinez-Naharro A, Kotecha T, Norrington K, et al. Native T1 and extracellular volume in transthyretin amyloidosis. JACC Cardiovasc Imaging. 2019;12:810–819.
86. Tang CX, Petersen SE, Sanghvi MM, et al. Cardiovascular magnetic resonance imaging for amyloidosis: the state-of-the-art. Trends Cardiovasc Med. 2019;29:83–94.
87. Wan K, Sun JY, Yang D, et al. Left ventricular myocardial deformation on cine MR images: relationship to severity of disease and prognosis in light-chain amyloidosis. Radiology. 2018;288:73–80.
88. Palladini G, Dispenzieri A, Gertz MA, et al. New criteria for response to treatment in immunoglobulin light chain amyloidosis based on free light chain measurement and cardiac biomarkers: impact on survival outcomes. J Clin Oncol. 2012;30:4541–4549.
89. Hur DJ, Dicks DL, Huber S, et al. Serial native T1 mapping to monitor cardiac response to treatment in light-chain amyloidosis. Circ Cardiovasc Imaging. 2016;9:e004770; pii.
90. Buchanan C, Mohammed A, Cox E, et al. Intradialytic cardiac magnetic resonance imaging to assess cardiovascular responses in a short-term trial of hemodiafiltration and hemodialysis. J Am Soc Nephrol. 2017;28:1269–1277.
91. Li X, Huang S, Miao H, et al. Multiparameter cardiac magnetic resonance to monitor therapeutic effect after chemotherapy in light chain amyloidosis patients. JACC Cardiovasc Imaging. 2021;14:1485–1487.
92. Dey D, Slomka PJ, Leeson P, et al. Artificial intelligence
in cardiovascular imaging: JACC state-of-the-art review. J Am Coll Cardiol. 2019;73:1317–1335.
93. Tao Q, Yan W, Wang Y, et al. Deep learning-based method for fully automatic quantification of left ventricle function from cine MR images: a multivendor, multicenter study. Radiology. 2019;290:81–88.
94. Zhang N, Yang G, Gao Z, et al. Deep Learning for diagnosis of chronic myocardial infarction on nonenhanced cardiac cine MRI. Radiology. 2019;291:606–617.
95. Motwani M, Dey D, Berman DS, et al. Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicenter prospective registry analysis. Eur Heart J. 2017;38:500–507.
96. Ortuño JE, Vegas-Sánchez-Ferrero G, Gómez-Valverde JJ, et al. Automatic estimation of aortic and mitral valve displacements in dynamic CTA with 4D graph-cuts. Med Image Anal. 2020;65:101748.
97. Pourmorteza A. Photon-counting CT: scouting for quantitative imaging biomarkers. Radiology. 2021;298:153–154.
98. Soulat G, McCarthy P, Markl M. 4D flow with MRI. Annu Rev Biomed Eng. 2020;22:103–126.
99. Bustin A, Fuin N, Botnar RM, et al. From compressed-sensing to artificial intelligence
-based cardiac MRI reconstruction. Front Cardiovasc Med. 2020;7:17.
100. Douglas PS, Hoffmann U, Patel MR, et al. Outcomes of anatomical versus functional testing for coronary artery disease. N Engl J Med. 2015;372:1291–1300.
101. Hoffmann U, Douglas PS. The PROMISE trial: an inside perspective. J Cardiovasc Comput Tomogr. 2015;9:252–254.
102. Fihn SD, Gardin JM, Abrams J, et al. 2012 ACCF/AHA/ACP/ AATS/PCNA/SCAI/STS guideline for the diagnosis and management of patients with stable ischemic heart disease: executive summary. J Am Coll Cardiol. 2012;60:2564–2603.
103. González A, Schelbert EB, Díez J, et al. Myocardial interstitial fibrosis in heart failure: biological and translational perspectives. J Am Coll Cardiol. 2018;71:1696–1706.
104. Farber G, Boczar KE, Wiefels CC, et al. The future of cardiac molecular imaging. Semin Nucl Med. 2020;50:367–385.
105. Chandrashekhar Y, Dilsizian V, Kramer CM, et al. Implementing multimodality imaging in the future. JACC Cardiovasc Imaging. 2016;9:91–98.
106. Hendel RC. Appropriate use of cardiac imaging: a multidisciplinary mandate. Am J Med. 2021;134:e350.
107. Li YL, Chen BY, Yang XY, et al. S100a8/a9 signaling causes mitochondrial dysfunction and cardiomyocyte death in response to ischemic/reperfusion injury. Circulation. 2019;140:751–764.