Motion-compensated compressed sensing for dynamic imaging

Cardiac magnetic resonance (CMR) images play a growing role in the diagnostic imaging of cardiovascular diseases. Full coverage of the left ventricle (LV), from base to apex, is a basic criterion for CMR image quality and necessary for accurate measurement of cardiac volume and functional assessment. Incomplete coverage of the LV is identified through visual inspection, which is time-consuming and usually done retrospectively in the assessment of large imaging cohorts. This paper proposes a novel automatic method for determining LV coverage from CMR images by using Fisher-discriminative three-dimensional (FD3D) convolutional neural networks (CNNs). In contrast to our previous method employing 2D CNNs, this approach utilizes spatial contextual information in CMR volumes, extracts more representative high-level features and enhances the discriminative capacity of the baseline 2D CNN learning framework, thus achieving superior detection accuracy. A two-stage framework is proposed to identify missing basal and apical slices in measurements of CMR volume. First, the FD3D CNN extracts high-level features from the CMR stacks. These image representations are then used to detect the missing basal and apical slices. Compared to the traditional 3D CNN strategy, the proposed FD3D CNN minimizes within-class scatter and maximizes between-class scatter. We performed extensive experiments to validate the proposed method on more than 5,000 independent volumetric CMR scans from the UK Biobank study, achieving low error rates for missing basal/apical slice detection (4.9%/4.6%). The proposed method can also be adopted for assessing LV coverage for other types of CMR image data.

To develop a cardiac computed tomographic (CT) method with which to determine extracellular volume (ECV) fraction, with cardiac magnetic resonance (MR) imaging as the reference standard. Study participants provided written informed consent to participate in this institutional review board-approved study. ECV was measured in healthy subjects and patients with heart failure by using cardiac CT and cardiac MR imaging. Paired Student t test, linear regression analysis, and Pearson correlation analysis were used to determine the relationship between cardiac CT and MR imaging ECV values and clinical parameters. Twenty-four subjects were studied. There was good correlation between myocardial ECV measured at cardiac MR imaging and that measured at cardiac CT (r = 0.82, P < .001). As expected, ECV was higher in patients with heart failure than in healthy control subjects for both cardiac CT and cardiac MR imaging (P = .03, respectively). For both cardiac MR imaging and cardiac CT, ECV was positively associated with end diastolic and end systolic volume and inversely related to ejection fraction (P < .05 for all). Mean radiation dose was 1.98 mSv ± 0.16 (standard deviation) for each cardiac CT acquisition. ECV at cardiac CT and that at cardiac MR imaging showed good correlation, suggesting the potential for myocardial tissue characterization with cardiac CT.

Cardiac computed tomography (CT) produces high-quality anatomical images of the cardiac valves and associated structures. Cardiac magnetic resonance imaging (MRI) provides images of valve morphology, and allows quantitative evaluation of valvular dysfunction and determination of the impact of valvular lesions on cardiovascular structures. Recent studies have demonstrated that cardiac CT and MRI are important adjuncts to echocardiography for the evaluation of aortic and mitral valvular heart diseases (VHDs). Radiologists should be aware of the technical aspects of cardiac CT and MRI that allow comprehensive assessment of aortic and mitral VHDs, as well as the typical imaging features of common and important aortic and mitral VHDs on cardiac CT and MRI.

Non-contrast Cine Cardiac Magnetic Resonance image radiomics features and machine learning algorithms for myocardial infarction detection

Pulmonary hypertension (PH) is a fatal pulmonary vascular disease. The standard diagnosis of PH heavily relies on an invasive technique, i.e., right heart catheterization, which leads to a delay in diagnosis and serious consequences. Noninvasive approaches are crucial for detecting PH as early as possible; however, it remains a challenge, especially in detecting mild PH patients. To address this issue, we present a new fully automated framework, hereinafter referred to as PHNet, for noninvasively detecting PH patients, especially improving the detection accuracy of mild PH patients, based on cine cardiac magnetic resonance (CMR) images. The PHNet framework employs a hybrid strategy of adaptive triplet and binary cross-entropy losses (HSATBCL) to enhance discriminative feature learning for classifying PH and non-PH. Triplet pairs in HSATBCL are created using a semi-hard negative mining strategy which maintains the stability of the training process. Experiments show that the detection error rate of PHNet for mild PH is reduced by 24.5% on average compared to state-of-the-art PH detection models. The hybrid strategy can effectively improve the model's ability to detect PH, making PHNet achieve an average area under the curve (AUC) of 0.964, an accuracy of 0.912, and an F1-score of 0.884 in the internal validation dataset. In the external testing dataset, PHNet achieves an average AUC value of 0.828. Thus, PHNet has great potential for noninvasively detecting PH based on cine CMR images in clinical practice. Future research could explore more clinical information and refine feature extraction to further enhance the network performance.

The high morbidity and mortality of heart disease present a significant threat to human health. The development of methods for the quick and accurate diagnosis of heart diseases, enabling their effective treatment, has become a key issue of concern. Right ventricular (RV) segmentation from cine cardiac magnetic resonance (CMR) images plays a significant role in evaluating cardiac function for clinical diagnosis and prognosis. However, due to the complex structure of the RV, traditional segmentation methods are ineffective for RV segmentation. In this paper, we propose a new deep atlas network to improve the learning efficiency and segmentation accuracy of a deep learning network by integrating multi-atlas. First, a dense multi-scale U-net (DMU-net) is presented to acquire transformation parameters from atlas images to target images. The transformation parameters map the atlas image labels to the target image labels. Second, using a spatial transformation layer, the atlas images are deformed based on these parameters. Finally, the network is optimized by backpropagation with two loss functions where the mean squared error function (MSE) is used to measure the similarity of the input images and transformed images. Further, the Dice metric (DM) is used to quantify the overlap between the predicted contours and the ground truth. In our experiments, 15 datasets are used in testing, and 20 cine CMR images are selected as atlas. The mean values and standard deviations for the DM and Hausdorff distance are 0.871 and 4.67mm, 0.104 and 2.528mm, respectively. The correlation coefficients of endo-diastolic volume, endo-systolic volume, ejection fraction, and stroke volume are 0.984, 0.926, 0.980, and 0.991, respectively, and the mean differences between all of the mentioned parameters are 3.2, -1.7, 0.02, and 4.9, respectively. Most of these differences are within the allowable range of 95%, indicating that the results are acceptable and show good consistency. The segmentation results obtained in this method are compared with those obtained by other methods that provide satisfactory performance. The other methods provide better segmentation effects at the base, but either no segmentation or the wrong segmentation at the top, which demonstrate that the deep atlas network can improve top-area segmentation accuracy. Our results indicate that the proposed method can achieve better segmentation results than the previous methods, with both high relevance and consistency, and has the potential for clinical application.

In diagnosing heart diseases, the estimation of cardiac performance indices requires accurate segmentation of the left ventricle (LV) wall from cine cardiac magnetic resonance (CMR) images. MR imaging is noninvasive and generates clear images; however, it is impractical to manually process the huge number of images generated to calculate the performance indices. In this dissertation, we introduce a novel, fast, robust, bi-directional coupled parametric deformable models that are capable of segmenting the LV wall borders using first- and second-order visual appearance features. These features are embedded in a new stochastic external force that preserves the topology of the LV wall to track the evolution of the parametric deformable models control points. We tested the proposed segmentation approach on 15 data sets in 6 infarction patients using the Dice similarity coefficient (DSC) and the average distance (AD) between the ground truth and automated segmentation contours. Our approach achieves a mean DSC value of 0.926±0.022 and mean AD value of 2.16±0.60 mm compared to two other level set methods that achieve mean DSC values of 0.904±0.033 and 0.885±0.02; and mean AD values of 2.86±1.35 mm and 5.72±4.70 mm, respectively. Also, a novel framework for assessing both 3D functional strain and wall thickening from 4D cine cardiac magnetic resonance imaging (CCMR) is introduced. The introduced approach is primarily based on using geometrical features to track the LV wall during the cardiac cycle. The 4D tracking approach consists of the following two main steps: (i) Initially, the surface points on the LV wall are tracked by solving a 3D Laplace equation between two subsequent LV surfaces; and (ii) Secondly, the locations of the tracked LV surface points are iteratively adjusted through an energy minimization cost function using a generalized Gauss-Markov random field (GGMRF) image model in order to remove inconsistencies and preserve the anatomy of the heart wall during the tracking process. Then the circumferential strains are straight forward calculated from the location of the tracked LV surface points. In addition, myocardial wall thickening is estimated by co-allocation of the corresponding points, or matches between the endocardium and epicardium surfaces of the LV wall using the solution of the 3D laplace equation. Experimental results on in vivo data confirm the accuracy and robustness of our method. Moreover, the comparison results demonstrate that our approach outperforms 2D wall thickening estimation approaches.

ACR Clinical Statement on Noninvasive Cardiac Imaging

Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Spanish Ministry of Science, Innovation and Universities; Instituto de Salud Carlos III Introduction In Marfan syndrome (MFS) patients reduced longitudinal strain of the ascending aorta (AAo) as measured by applying feature-tracking on cine cardiac magnetic resonance (CMR) images predicts aortic root dilation and aortic events during the follow-up. Speckle-tracking is well established for cardiac deformation assessment but proximal aorta applications are challenging due to limited wall thickness and substantial cardiac motion. Moreover, echocardiography is widely used in the clinical assessment aortic diseases. Purpose We aimed to test a speckle-tracking tool for root longitudinal strain analysis in terms of comparison with CMR-derived AAo longitudinal strain and reproducibility and as predictor of dilation in MFS patients. Methods Thirty-five MFS patients diagnosed by original GHENT criteria, with maximum aortic root diameter of 45 mm and free from previous aortic dissection or cardiac/aortic surgery and non-severe aortic regurgitation were consecutive enrolled and followed-up. CMR and echocardiography were performed less than 2 months apart. Baseline and final aortic root diameter were measured on CMR images. To quantify aortic root cyclic elongation by echocardiography, two regions of interests were manually created covering both walls in a parasternal long-axis view and tracked along the cardiac cycle (Figure 1). Longitudinal strain was computed as the average of maximum increase in relative distance of several sub-regions covering both walls. CMR-derived AAo longitudinal strain was available in 29 patients. Intra-observer reproducibility was tested in 15 patients via intraclass correlation coefficient (ICC) for single-rater absolute agreement. Results Aortic root longitudinal strain by echocardiography was mildly related to CMR-derived AAo longitudinal strain (R = 0.27) and was larger compared to CMR-derived values (16.2 ± 6.0 vs 11.3 ± 4.3). Reproducibility was high, with ICC of 0.811, R = 0.802, p &amp;lt; 0.001. After a mean follow up of 76 ± 13 months, aortic root diameter grew in 20 patients with a rate of 0.29± 0.24 mm/year. Overall mean growth-rate was 0.87 ± 0.33 mm/year. In multivariable analysis corrected for age and baseline aortic root diameter, baseline longitudinal strain by echocardiography was independently and inversely related to progressive dilation (p = 0.033). Conclusions The measurement of aortic root longitudinal strain by speckle-tracking echocardiography is feasible. Aortic root longitudinal strain is an independent predictor of progressive dilation in MFS patients. This may permit the improvement of risk-stratification in aortic diseases in large scale studies. Abstract Figure 1

Detecting abnormalities in two-dimensional (2D) and three-dimensional (3D) medical structures is among the most interesting and challenging research areas in the medical imaging field. Obtaining the desired accurate automated quantification of abnormalities in medical structures is still very challenging. This is due to a large and constantly growing number of different objects of interest and associated abnormalities, large variations of their appearances and shapes in images, different medical imaging modalities, and associated changes of signal homogeneity and noise for each object. The main objective of this dissertation is to address these problems and to provide proper mathematical models and techniques that are capable of analyzing low and high resolution medical data and providing an accurate, automated analysis of the abnormalities in medical structures in terms of their area/volume, shape, and associated abnormal functionality. This dissertation presents different preliminary mathematical models and techniques that are applied in three case studies: (i) detecting abnormal tissue in the left ventricle (LV) wall of the heart from delayed contrast-enhanced cardiac magnetic resonance images (MRI), (ii) detecting local cardiac diseases based on estimating the functional strain metric from cardiac cine MRI, and (iii) identifying the abnormalities in the corpus callosum (CC) brain structure—the largest fiber bundle that connects the two hemispheres in the brain—for subjects that suffer from developmental brain disorders. For detecting the abnormal tissue in the heart, a graph-cut mathematical optimization model with a cost function that accounts for the object’s visual appearance and shape is used to segment the the inner cavity. The model is further integrated with a geometric model (i.e., a fast marching level set model) to segment the outer border of the myocardial wall (the LV). Then the abnormal tissue in the myocardium wall (also called dead tissue, pathological tissue, or infarct area) is identified based on a joint Markov-Gibbs random field (MGRF) model of the image and its region (segmentation) map that accounts for the pixel intensities and the spatial interactions between the pixels. Experiments with real in-vivo data and comparative results with ground truth (identified by a radiologist) and other approaches showed that the proposed framework can accurately detect the pathological tissue and can provide useful metrics for radiologists and clinicians. To estimate the strain from cardiac cine MRI, a novel method based on tracking the LV wall geometry is proposed. To achieve this goal, a partial differential equation (PDE) method is applied to track the LV

PERIOPERATIVE myocardial complications after noncardiac surgery affect more than 1 million operations each year and are leading causes of morbidity and mortality, especially among patients undergoing vascular surgery. Myocardial complications such as perioperative myocardial infarction (MI) are common and are the most likely cause for perioperative death in all surgical populations.1In general surgery, the risk for perioperative MI is 0.8% in men older than 50 yr2and varies with the cardiovascular status, comorbidities, and the extent of the procedure, reaching more than 20% among patients undergoing vascular surgery.3As patients become older and sicker and procedures become more aggressive and extensive, physicians must find novel approaches to evaluate and prepare cardiac patients for noncardiac procedures and reduce perioperative myocardial events.The American Heart Association–American College of Cardiology guidelines for cardiac risk evaluation use the patient's history, physical examination, and functional capacity, and taking into account the expected surgery, one may recommend further assessment with noninvasive testing or coronary angiography.4The purpose of this review is to provide an overview of available imaging tools that could potentially be used for perioperative evaluation of cardiac patients before noncardiac surgery, in accord with recent guidelines and with focus on the recent progress made with cardiac computed tomography (CT).Traditionally, preoperative evaluation has relied on the patient's history, physical examination, and functional capacity. After preoperative evaluation, the physician should determine the clinical predictors as major, intermediate, or minor4and evaluate the functional capacity of the patient. New York Heart Association class II heart failure, which equals 4 metabolic equivalents,4has also been found to be an important predictor of perioperative cardiac complications after major noncardiac surgery. The cutoff value of 4 or more metabolic equivalents determines an adequate cardiac functional capacity and reserve and predicts perioperative cardiac events in patients treated with high-risk noncardiac surgery.5Finally, the extent and risk associated with the procedure should be categorized as major, intermediate, or minor. The cardiac risk is the combined incidence of cardiac death and nonfatal MI and is greater than 5% for high-risk procedures, whereas intermediate-risk procedures have less than 5% cardiac risk (1–5%), and low-risk procedures have less than 1% cardiac risk.4Indeed, based on these criteria, the flowchart of Eagle et al. 4suggests when a noninvasive test or coronary angiography may be appropriate to evaluate cardiac function, reserve, or ability to withstand surgical stress. Pertinent to the utility of evaluating cardiac structure and function, new imaging techniques for evaluation of the heart have emerged in the past couple of decades. These techniques may provide more comprehensive information regarding the structure and function of the heart and coronary arteries, and evaluate adequately patients who have mechanical restrictions to perform exercise-induced stress. Such developments in cardiac imaging may eventually provide an opportunity to revise and refine the steps involving the evaluation of cardiac patients.6,7This approach should be evaluated cautiously and should take into consideration recent recommendations and guidelines. For example, the current American Heart Association–American College of Cardiology guidelines recommend the use of perioperative β-blocker therapy as an alternative approach to decrease cardiovascular risk.8β-Blockers initiated at least 1 week before major vascular surgery and continued for 30 days postoperatively reduced significantly the perioperative incidence of nonfatal MI and death from cardiac causes in high-risk patients.9In addition to beta blockers, statins may reduce perioperative mortality in patients undergoing major vascular surgery and may have an additive effect to β-blockers.10,11The strategy of preoperative coronary artery revascularization before elective major vascular surgery to reduce perioperative cardiac morbidity and mortality was investigated in the Coronary Artery Revascularization Prophylaxis trial, a multicenter study involving patients with vascular disease and significant coronary artery disease (CAD), but no unstable angina. Cardiac revascularization did not result in increased survival, either perioperatively or in long-term follow-up, in patients who needed elective vascular surgery.12However, although the Coronary Artery Revascularization Prophylaxis study is a cornerstone trial in the perioperative care of cardiac patients for noncardiac surgery, it had some limitations. In particular, only 9% of the patients scheduled to undergo vascular operations were eligible for the study. The main reasons for exclusion were insufficient cardiac risk, an urgent vascular surgery, previous revascularization without ischemia, severe coexisting illnesses, left main coronary artery stenosis of at least 50%, left ventricular ejection fraction less than 20%, and severe aortic stenosis. The outcome of these patients has not been adequately evaluated.Indeed, taking into consideration the patient's perioperative cardiac risk according to clinical predictors, functional capacity, and the extent of the future surgery, the anesthesiologist must decide whether further cardiac assessment or perioperative medical management are indicated. Patients whose functional capacity is difficult to establish, who underwent previous coronary revascularization, who have unstable or changed cardiac status, or who have severe comorbidities may need further evaluation. Several imaging techniques are available for evaluation of cardiac patients, including cardiac CT, coronary angiography, stress echocardiography, cardiac magnetic resonance imaging (CMRI), and myocardial nuclear studies.Computed tomography is widely available in large medical centers and is routinely used in clinical practice. The basic principle of CT is that a fan-shaped, thin x-ray beam passes through the body at many angles to allow for cross-sectional images. After collimation of the beam (i.e. , achieving a definitive slice thickness using a collimator) to reduce scatter, the photons are recorded on a corresponding detector array, and the transmission data are digitized. A “filtered back projection” reconstruction algorithm, which takes into account the attenuation of the x-ray beam along its path, allows for reconstruction of the grayscale values of each picture element (pixel) with reference to the value for water and air, to depict cross-sectional images. Reconstruction algorithms and multirow detectors applied in current scanners enable three-dimensional volumetric imaging and multiple high-quality reconstructions of various volumes of interest.13Current clinical scanners used for cardiovascular imaging employ either a rotating x-ray source with a circular, stationary detector array (e.g. , helical CT) or electromagnetic deflection of an electron beam to replace mechanical motion (electron beam computed tomography [EBCT]).14Multidetector computed tomography (MDCT) is a helical CT with a large array of detectors, which allow it to acquire a large number of slices simultaneously (4–256) and greatly increase its resolution. However, to obtain quantitative measurements of tissue opacity within a specific cardiac phase (e.g. , for measurement of perfusion), the scanning time should generally be less than 100 ms.15Sufficiently high temporal resolution (the time required to acquire the data for one image) is currently offered only by the EBCT (50 ms/image) and the novel Dual-Source CT (SOMATOM® Defi-nition; Siemens Medical Systems, Forchheim, Germany) (83 ms/image), a recently released model of MDCT that has two x-ray tubes (rather than one) positioned at 90° to each other, thereby doubling temporal resolution.16However, this technique remains to be validated for such measurements.Electron beam computed tomography was developed in the 1980s and hailed as an ultimate cardiac scanner. It allows almost simultaneous data acquisition from up to eight parallel slices (7–8 mm thick) by rapid sweeping of an electron beam along target rings in as little as 50 ms per scan. Because it does not involve moving parts (and therefore decreases the need for cooling), the speed of acquisition (temporal resolution) with EBCT is faster than with MDCT (table 1) and usually does not require slowing the heart rate pharmacologically. The high speed of EBCT is offset by moderate image quality and relatively restricted power for acquisition of a large number of images, which decreased its popularity for comprehensive cardiac studies. Consequently, its availability is limited, resulting in declining use of this technology.Multidetector computed tomography is a relatively ubiquitous and newer scanner that has temporal resolution of 330–400 ms/image, which enables many studies of cardiac anatomy and global function (e.g. , ejection fraction and cardiac output). Spatial resolution can be achieved with 64-slice scanners using isotropic voxels (consistent three-dimensional image quality in any reconstruction plane) of 0.4 × 0.4 × 0.4 mm. The technique involves continuous rotation of the x-ray tube and detectors and simultaneous translation of the patient through the gantry opening, and can acquire multiple simultaneous sections of variable widths using prospective electrocardiographic triggering.17Alternatively, retrospective electrocardiographic gating enables reconstruction of images at any desired time in the cardiac cycle. Data can be used from multiple slices to reconstruct other imaging planes. The images are of best quality when the resting heart rate (HR) is less than 70 beats/min. At faster heart rates, motion artifacts may become more prominent, and therefore HR may need to be pharmacologically decreased before scanning. The temporal resolution of MDCT determines the overall scan time. As gantry rotation speeds increase, the minimum slice thickness decreases, with submillimeter sections throughout the heart acquired during a single breath hold. The overall CT scan time is approximately 12 s, and the mean total time for the examination is less than 13 min with 64-slice technology.The improvement in MDCT technology enabled the assessment of significant luminal stenosis and identification of nonstenotic atherosclerotic plaques. Virtual noninvasive angiography, with three-dimensional reconstruction of coronary anatomy from cardiac CT images, can provide information about coronary luminal obstruction, calcium scoring, and composition of the plaque.18Furthermore, it has the added benefit of offering fine details of the examined vessels (fig. 1).Excellent sensitivity and specificity were found for evaluation of proximal, middle and distal left anterior descending, first diagonal, proximal and distal circumflex, obtuse marginal and proximal mid and distal right coronary artery.19A meta-analysis of diagnostic performance of MDCT compared with invasive coronary angiography showed a sensitivity of 85% and a specificity of 95% for identification of CAD. On average, 87% of segments had diagnostic image quality, with a significant increase from 78% with 4-slice systems to 96% with the more recent 16-slice systems.6Multidetector computed tomography could provide a clinically useful tool in the workup of symptomatic patients before angiography.20A patient with chronic chest pain indicative of CAD could undergo CT angiography, and if low calcium score and no circumferential calcifications (or other test results indicative of ischemia) are found, invasive angiography may not be indicated, because MDCT has excellent negative predictive value to rule out CAD. By this approach, patients with primarily microvessel disease (which may cause angina and abnormal stress test results) may be identified and not required to go through unnecessary fluoroscopic angiography, and aggressive medical therapy would be indicated before surgery.20MDCT also has high diagnostic accuracy in detecting bypass graft stenosis and occlusions in symptomatic patients after coronary artery bypass grafting, which might potentially reduce dramatically the number of unnecessary invasive angiographies performed in these patients.21However, although the MDCT technique has been extensively evaluated for its accuracy for detection of CAD compared with standard coronary angiography,6,19,20,22it has not been applied for the routine evaluation of cardiac patients for noncardiac surgery and, to date, is not a recommended technique for perioperative risk stratification.The main technical limitations of cardiac CT for evaluation of the coronary arteries include the difficulty in handling cardiac motion, arrhythmia, severe calcifications, vessel size less than 1.5 mm, breathing, the presence of stents, and poor enhancement.22Lesions with extensive calcified components and implanted coronary stents compromise the accuracy of MDCT coronary angiography by causing artifacts.23Stent type and diameter influence evaluability of in-stent restenosis by MDCT, but in evaluable stents, sensitivity is still 86% and specificity is 98%.24When HR is reduced below 70 beats/min, image quality is improved, especially in terms of the visualization of the right coronary and left circumflex arteries, which are both significantly prone to motion artifacts (particularly at higher HR) because of their close proximity to the atrium, which is reactivated during the early diastolic phase.25For evaluation of coronary artery calcium volume with MDCT, thin-slice retrospective spiral electrocardiographic–gated scanning is desirable.26Most studies on coronary calcification have been performed using EBCT, which is still considered the “gold standard” (table 1). To image the coronary arteries with EBCT, 30–40 axial images are obtained with 3-mm slice thickness, using single-slice prospective electrocardiographic triggering in the craniocaudal direction along the full length of the heart. Rapid image acquisition at 100 ms allows accurate measurement of calcium deposits in the coronary arteries.27Quantification of coronary artery calcifications was found to be independently associated with cardiac events in a 3-yr follow-up of thousands of initially asymptomatic patients.28Noninvasive characterization and quantification of atherosclerotic plaque burden may also have important implications for the prevention of CAD progression and its complications.18For the past several years, CMRI has been considered the reference standard for assessment of left ventricular (LV) functions. However, cardiac CT is playing an increasingly important role in this evaluation. Both CT and CMRI surpass two-dimensional imaging techniques, such as standard two-dimensional echocardiography, for cardiac quantification because of their ability to generate contiguous short axis cine images, allowing for three-dimensional measurements without the use of geometric assumptions.29Postprocessing tools allow fast and semiautomatic determination of LV function parameters from MDCT data in analogy to known CMRI evaluation approaches.30Studies have demonstrated excellent correlation between cardiac CT and CMRI for LV ejection fraction, end-diastolic volume, end-systolic volume, stroke volume, and myocardial mass.30Furthermore, global and regional LV functions agree well with echocardiography, with correlation coefficients ranging from 0.91 to 0.97 for MDCT and from 0.93 to 0.98 for CMRI.7,30,31Although MDCT is not considered to be the first-line modality for assessment of LV function, it can provide a combined assessment of cardiac morphology and function without the need for additional radiation exposure in patients undergoing MDCT coronary angiography. The acquisition of images is performed according to the R–R interval. For MDCT, the image data are gated with the electrocardiogram to allow reconstruction at various times throughout the cardiac cycle. CT allows quantitative analysis of regional and global systolic function in normal and pathologic conditions by using short axis slices from the base to the apex of the heart. End-diastole and end-systole are defined as maximal and minimal LV volume, and LV ejection fraction is the difference between them.30Diastolic function can also be assessed from the rate of change of the LV volume during diastole.13Also, because temporal resolution for electrocardiographic-gated cardiac CT scans (down to 165 ms) is poorer in comparison to cine CMRI (30–40 ms), CT imaging may miss peak ejection rate or peak filling rate. However, advances in MDCT imaging that will improve temporal resolution may correct this problem.16Regional wall motion abnormalities, myocardial thinning, ventricular aneurysm, and mural thrombi in the infarcted area can all be detected by cardiac CT. Dual-phase contrast CT can detect acute MI characterized by an initial filling defect and late enhancement at the site of the damaged myocardium. Late enhancement may have the potential to distinguish viable from nonviable myocardium, and has significant prognostic value for the recovery of the myocardial wall motion and thickness after ischemia and in response to therapeutic revascularization.32In patients with previous MI, MDCT permits accurate, noninvasive assessment of coronary artery stenosis, LV function, and perfusion, assessed from a single data set.33If the myocardium supplied by the stenotic or occluded vessel is still viable, medical treatment or revascularization can be considered to reduce the risk of perioperative ischemia in the affected myocardium.Experimental studies in animals and humans demonstrated that cardiac CT could be used to assess microvascular function, although this technique is not used clinically, partially because of high radiation exposure. Evaluation of myocardial perfusion at rest and after infusion of vasodilators imposing cardiac challenge can reveal the presence of otherwise undetectable limited myocardial flow reserve, which might have a significant value for detection of borderline or very early alterations in cardiac microvascular function, as well as detecting increases in microvascular permeability that may reflect endothelial dysfunction or ischemic changes.34The use of CT scanning is limited by the need for contrast media and radiation exposure (table 2). The risks associated with contrast media administration include extravasation at the contrast injection site, allergic contrast reaction, and a decline in function, as well as and vascular In patients with administration of contrast media is media has been reduced and is than that used during angiography potential of alternative contrast such as might further decrease the various radiation are with MDCT, and scanning may allow a decrease in radiation of cardiac function and volumes may from to potentially any based on gated that data several cardiac limitations include the need to image quality in patients and the need for and physicians for acquisition and of cardiac CT data of the limitations of MDCT is the need to decrease heart rate at time of which may be in some It further the need for CT technology to acquisition time a greater volume of developed scanners such as the Dual-Source MDCT may this to temporal the number of that can be from axial CT images is limited only by gantry rotation speed and patient heart decreased ejection fraction in is associated with decreased overall postoperatively and with increased incidence of heart failure, but no has been found with main associated with cardiac events is the presence of wall motion has a low predictive is appropriate in patients who American Heart Association–American College of Cardiology clinical guidelines and who would require if no surgery were as well as in with aortic sensitivity of can be by the heart either or pharmacologically. stress test with administration of can provide information about cardiac function, ventricular and function, and a is in to 85% of maximal HR or or as wall motion abnormalities, whereas a in ejection fraction in response to administration is a of more severe CAD. has excellent negative predictive values and sensitivity but moderate specificity and is significantly and long-term cardiac risk using initial cardiac risk assessment and noninvasive testing with may the perioperative and long-term treatment of patients undergoing major vascular comparison with other noninvasive a diagnostic new of may improve cardiac risk compared with standard in patients undergoing noncardiac surgery whose functional capacity be evaluated by stress stress has a relatively low and can assess cardiac structure and function, wall thickness, aortic and the presence of However, has sensitivity for detection of single vessel disease and is to distinguish between microvascular and CAD (table because wall motion can be found in progress has been made during the past in which is used to evaluate a of heart and including CAD. CMRI is based on the of which and to an magnetic CMRI a causes deflection of the from the direction of the main magnetic The is during the of the back their in the magnetic are and data are used for image many image are for assessment of global ventricular and function, ejection fraction, stroke volume, and detection of and acute and chronic LV can be and using analysis of may find use in the clinical magnetic resonance imaging has higher temporal resolution but resolution (the ability to distinguish between two on the image) than cardiac resolution of 1.5 × 1.5 may have an CMRI for detection of because its overall accuracy in the detection of coronary artery stenosis is higher (table 2). The axial image of MDCT allows more vascular as compared with CMRI that may artifacts to sensitivity and specificity of CMRI for detection of coronary artery stenosis are and whereas of coronary segments are is noninvasive and high-quality images of the heart (table 2). However, CMRI examination be applied in patients who have a or and the presence of and stents can CMRI images and to is the of the test than an during which the patient must be CMRI is an important noninvasive tool and does not the use of potential contrast are needed to evaluate its use as a perioperative tool and its correlation with perioperative cardiac use of myocardial computed tomography and tomography has in the past two because of their in useful information about myocardial perfusion and function based on administration of an unstable with has been used for many for preoperative cardiac evaluation. the coronary response associated with testing not the HR and has been used in patients scheduled to undergo vascular surgery. However, has low and negative predictive and was not significantly associated with the incidence of perioperative MI, ischemia, or other the other the of this test was when used in patients undergoing aortic surgery whose risk could not have been on the of clinical value of imaging for preoperative cardiac assessment in high-risk has been demonstrated in patients before and vascular of myocardial with is important in patients with LV function to and the potential of revascularization can be These studies showed excellent correlation of with cardiac both perioperatively within 30 days and at long-term has high sensitivity in coronary disease but limited sensitivity for detecting significant disease and CAD (table 2). It also little functional data other than myocardial perfusion and has relatively low resolution that the quality of scanners have a maximal of approximately is required for before the performance of myocardial perfusion because the coronary effect of or is by with can be used to functional recovery of viable myocardium after coronary artery a is from a a in and with an results in of both the and the with to in the of electromagnetic radiation of two which in from each and are detected by a of radiation detectors at The difference in time the photons each radiation detector is used to detect the source of the of radiation detectors are in the gantry with million of per the imaging is useful in noninvasive quantification of myocardial flow and coronary flow reserve, and is also of detecting early disease in high-risk asymptomatic and the progression or of is to especially in patients and in undergoing remains a significant its high and limited availability currently its use as a technology of coronary angiography is well and involves injection of contrast media into the coronary artery by imaging in multiple planes. is by comparison of diameter at a stenotic site with that of a normal reference can evaluate coronary stenosis, LV function, and (table coronary angiography is an invasive technique and has a major rate of including mortality in of it may involve for the patient and is associated with other such as vascular complications for diagnostic and for complications and complications the risk of coronary angiography with that of MDCT, some risks are common to both procedures, such as an allergic contrast reaction, and exposure to whereas are to contrast media injection causes severe allergic in of risks of a of for MDCT angiography and for coronary angiography. However, the and risks and a overall risk of mortality from coronary that for MDCT angiography use of a power in a vascular with cardiac CT an additional risk of which in of the relatively significant number of negative invasive angiographies performed each the risks to this procedure by using noninvasive can greatly to the morbidity and mortality of coronary angiography. the number of negative angiographies might to significant because the of cardiac may be as as times that of cardiac evaluation of patients with or known cardiac disease for noncardiac surgery has been in the of of and The preoperative evaluation on assessment of clinical predictors, functional capacity, and the extent of the surgical The first-line to reduce perioperative morbidity and mortality include medical therapy with with HR and However, the strategy may also include of noninvasive imaging techniques that provide comprehensive and assessment of cardiac structure and In particular, in patients with restricted physical in assessment of the functional capacity is obtained using imaging with evaluation of the heart may potentially to treatment Cardiac CT is can provide coronary angiography for plaque assessment and scoring, and can assess LV function, and myocardial in a fast and relatively CT can in out significant CAD and determination of the need for invasive angiography. cardiac CT might potentially be the tool for overall evaluation of cardiac patients for noncardiac surgery. at this is no that preoperative cardiac CT scanning is to reduce perioperative studies are needed to assess the role of cardiac CT in the evaluation of patients at risk for cardiovascular disease before noncardiac surgery.

Magnetic Resonance Imaging (MRI) has evolved as a clinical standard-of-care imaging modality for cardiac morphology, function assessment, and guidance of cardiac interventions. All these applications rely on accurate extraction of the myocardial tissue and blood pool from the imaging data. Here we propose a framework for left ventricle (LV) segmentation from cardiac cine MRI. First, we segment the LV blood pool using iterative graph cuts, and subsequently use this information to segment the myocardium. We formulate the segmentation procedure as an energy minimization problem in a graph subject to the shape prior obtained by label propagation from an average atlas using affine registration. The proposed framework has been validated on 30 patient cardiac cine MRI datasets available through the STACOM LV segmentation challenge and yielded fast, robust, and accurate segmentation results.

Over the last 15 years, cardiovascular magnetic resonance (CMR) imaging has progressively evolved to become an indispensable tool in cardiology. It is a non-invasive technique that enables objective and functional assessment of myocardial tissue. Recent innovations in magnetic resonance imaging scanner technology and parallel imaging techniques have facilitated the generation of T1 and T2 parametric mapping to explore tissue characteristics. The emergence of strain imaging has enabled cardiologists to evaluate cardiac function beyond conventional metrics. Significant progress in computer processing capabilities and cloud infrastructure has supported the growth of artificial intelligence in CMR imaging. In this review article, we describe recent advances in T1/T2 mapping, myocardial strain, and artificial intelligence in CMR imaging.

This paper presents a novel edge following technique for image segmentation designed to segment the left ventricle in cardiac magnetic resonance (MR) images. This is a required step to determine the volume of left ventricle in a cardiac MR image, which is an essential tool for cardiac diagnosis. The traditional method for extracting them from cardiac MR images is by human delineation. This method is accuracy but time consuming. So a new ventricular segmentation technique is proposed in order to reduce the analysis time with similar accuracy level compared to doctorspsila opinions. Our proposed technique can detect ventricle edges in MR images using the information from the vector image model and the edge map. We also compare the proposed segmentation technique with the active contour model (ACM) and the gradient vector flow (GVF) by using the opinions of two skilled doctors as the ground truth. The experimental results show that our technique is able to provide more accurate segmentation results than the classical contour models and visually close to the manual segmentation by the experts. The results evaluated using a numerical measure by mean of the probability of error in image segmentation also confirm the visual evaluation.

Cardiac cine magnetic resonance imaging (MRI) is a widely used technique for the noninvasive assessment of cardiac functions. Deep neural networks have achieved considerable progress in overcoming various challenges in cine MRI analysis. However, deep learning models cannot be used for classification because limited cine MRI data are available. To overcome this problem, features from cine image settings are derived by handcrafting and addition of other clinical features to the classical machine learning approach for ensuring the model fits the MRI device settings and image parameters required in the analysis. In this study, a novel method was proposed for classifying heart disease (cardiomyopathy patient groups) using only segmented output maps. In the encoder–decoder network, the fully convolutional EfficientNetB5-UNet was modified to perform the semantic segmentation of the MRI image slice. A two-dimensional thickness algorithm was used to combine the segmentation outputs for the 2D representation of images of the end-diastole (ED) and end-systole (ES) cardiac volumes. The thickness images were subsequently used for classification by using a few-shot model with an adaptive subspace classifier. Model performance was verified by applying the model to the 2017 MICCAI Medical Image Computing and Computer-Assisted Intervention dataset. High segmentation performance was achieved as follows: the average Dice coefficients of segmentation were 96.24% (ED) and 89.92% (ES) for the left ventricle (LV); the values for the right ventricle (RV) were 92.90% (ED) and 86.92% (ES). The values for myocardium were 88.90% (ED) and 90.48% (ES). An accuracy score of 92% was achieved in the classification of various cardiomyopathy groups without clinical features. A novel rapid analysis approach was proposed for heart disease diagnosis, especially for cardiomyopathy conditions using cine MRI based on segmented output maps.