Simultaneous multislice steady-state free precession myocardial perfusion with full left ventricular coverage and high resolution at 1.5 T.

PURPOSE: To implement and evaluate a simultaneous multi-slice balanced SSFP (SMS-bSSFP) perfusion sequence and compressed sensing reconstruction for cardiac MR perfusion imaging with full left ventricular (LV) coverage (nine slices/heartbeat) and high spatial resolution (1.4 × 1.4 mm2 ) at 1.5T. METHODS: A preliminary study was performed to evaluate the performance of blipped controlled aliasing in parallel imaging (CAIPI) and RF-CAIPI with gradient-controlled local Larmor adjustment (GC-LOLA) in the presence of fat. A nine-slice SMS-bSSFP sequence using RF-CAIPI with GC-LOLA with high spatial resolution (1.4 × 1.4 mm2 ) and a conventional three-slice sequence with conventional spatial resolution (1.9 × 1.9 mm2 ) were then acquired in 10 patients under rest conditions. Qualitative assessment was performed to assess image quality and perceived signal-to-noise ratio (SNR) on a 4-point scale (0: poor image quality/low SNR; 3: excellent image quality/high SNR), and the number of myocardial segments with diagnostic image quality was recorded. Quantitative measurements of myocardial sharpness and upslope index were performed. RESULTS: Fat signal leakage was significantly higher for blipped CAIPI than for RF-CAIPI with GC-LOLA (7.9% vs. 1.2%, p = 0.010). All 10 SMS-bSSFP perfusion datasets resulted in 16/16 diagnostic myocardial segments. There were no significant differences between the SMS and conventional acquisitions in terms of image quality (2.6 ± 0.6 vs. 2.7 ± 0.2, p = 0.8) or perceived SNR (2.8 ± 0.3 vs. 2.7 ± 0.3, p = 0.3). Inter-reader variability was good for both image quality (ICC = 0.84) and perceived SNR (ICC = 0.70). Myocardial sharpness was improved using the SMS sequence compared to the conventional sequence (0.37 ± 0.08 vs 0.32 ± 0.05, p < 0.001). There was no significant difference between measurements of upslope index for the SMS and conventional sequences (0.11 ± 0.04 vs. 0.11 ± 0.03, p = 0.84). CONCLUSION: SMS-bSSFP with multiband factor 3 and compressed sensing reconstruction enables cardiac MR perfusion imaging with three-fold increased spatial coverage and improved myocardial sharpness compared to a conventional sequence, without compromising perceived SNR, image quality, upslope index or number of diagnostic segments.

CardiSort: a convolutional neural network for cross vendor automated sorting of cardiac MR images.

OBJECTIVES: To develop an image-based automatic deep learning method to classify cardiac MR images by sequence type and imaging plane for improved clinical post-processing efficiency. METHODS: Multivendor cardiac MRI studies were retrospectively collected from 4 centres and 3 vendors. A two-head convolutional neural network ('CardiSort') was trained to classify 35 sequences by imaging sequence (n = 17) and plane (n = 10). Single vendor training (SVT) on single-centre images (n = 234 patients) and multivendor training (MVT) with multicentre images (n = 434 patients, 3 centres) were performed. Model accuracy and F1 scores on a hold-out test set were calculated, with ground truth labels by an expert radiologist. External validation of MVT (MVTexternal) was performed on data from 3 previously unseen magnet systems from 2 vendors (n = 80 patients). RESULTS: Model sequence/plane/overall accuracy and F1-scores were 85.2%/93.2%/81.8% and 0.82 for SVT and 96.1%/97.9%/94.3% and 0.94 MVT on the hold-out test set. MVTexternal yielded sequence/plane/combined accuracy and F1-scores of 92.7%/93.0%/86.6% and 0.86. There was high accuracy for common sequences and conventional cardiac planes. Poor accuracy was observed for underrepresented classes and sequences where there was greater variability in acquisition parameters across centres, such as perfusion imaging. CONCLUSIONS: A deep learning network was developed on multivendor data to classify MRI studies into component sequences and planes, with external validation. With refinement, it has potential to improve workflow by enabling automated sequence selection, an important first step in completely automated post-processing pipelines. KEY POINTS: • Deep learning can be applied for consistent and efficient classification of cardiac MR image types. • A multicentre, multivendor study using a deep learning algorithm (CardiSort) showed high classification accuracy on a hold-out test set with good generalisation to images from previously unseen magnet systems. • CardiSort has potential to improve clinical workflows, as a vital first step in developing fully automated post-processing pipelines.

Deep-Learning-Based Preprocessing for Quantitative Myocardial Perfusion MRI.

BACKGROUND: Quantitative myocardial perfusion cardiac MRI can provide a fast and robust assessment of myocardial perfusion status for the noninvasive diagnosis of myocardial ischemia while being more objective than visual assessment. However, it currently has limited use in clinical practice due to the challenging postprocessing required, particularly the segmentation. PURPOSE: To evaluate the efficacy of an automated deep learning (DL) pipeline for image processing prior to quantitative analysis. STUDY TYPE: Retrospective. POPULATION: In all, 175 (350 MRI scans; 1050 image series) clinical patients under both rest and stress conditions (135/10/30 training/validation/test). FIELD STRENGTH/SEQUENCE: 3.0T/2D multislice saturation recovery T1 -weighted gradient echo sequence. ASSESSMENT: Accuracy was assessed, as compared to the manual operator, through the mean square error of the distance between landmarks and the Dice similarity coefficient of the segmentation and bounding box detection. Quantitative perfusion maps obtained using the automated DL-based processing were compared to the results obtained with the manually processed images. STATISTICAL TESTS: Bland-Altman plots and intraclass correlation coefficient (ICC) were used to assess the myocardial blood flow (MBF) obtained using the automated DL pipeline, as compared to values obtained by a manual operator. RESULTS: The mean (SD) error in the detection of the time of peak signal enhancement in the left ventricle was 1.49 (1.4) timeframes. The mean (SD) Dice similarity coefficients for the bounding box and myocardial segmentation were 0.93 (0.03) and 0.80 (0.06), respectively. The mean (SD) error in the RV insertion point was 2.8 (1.8) mm. The Bland-Altman plots showed a bias of 2.6% of the mean MBF between the automated and manually processed MBF values on a per-myocardial segment basis. The ICC was 0.89, 95% confidence interval = [0.87, 0.90]. DATA CONCLUSION: We showed high accuracy, compared to manual processing, for the DL-based processing of myocardial perfusion data leading to quantitative values that are similar to those achieved with manual processing. LEVEL OF EVIDENCE: 3 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2020;51:1689-1696.

Importance of operator training and rest perfusion on the diagnostic accuracy of stress perfusion cardiovascular magnetic resonance.

BACKGROUND: Clinical evaluation of stress perfusion cardiovascular magnetic resonance (CMR) is currently based on visual assessment and has shown high diagnostic accuracy in previous clinical trials, when performed by expert readers or core laboratories. However, these results may not be generalizable to clinical practice, particularly when less experienced readers are concerned. Other factors, such as the level of training, the extent of ischemia, and image quality could affect the diagnostic accuracy. Moreover, the role of rest images has not been clarified. The aim of this study was to assess the diagnostic accuracy of visual assessment for operators with different levels of training and the additional value of rest perfusion imaging, and to compare visual assessment and automated quantitative analysis in the assessment of coronary artery disease (CAD). METHODS: We evaluated 53 patients with known or suspected CAD referred for stress-perfusion CMR. Nine operators (equally divided in 3 levels of competency) blindly reviewed each case twice with a 2-week interval, in a randomised order, with and without rest images. Semi-automated Fermi deconvolution was used for quantitative analysis and estimation of myocardial perfusion reserve as the ratio of stress to rest perfusion estimates. RESULTS: Level-3 operators correctly identified significant CAD in 83.6% of the cases. This percentage dropped to 65.7% for Level-2 operators and to 55.7% for Level-1 operators (p < 0.001). Quantitative analysis correctly identified CAD in 86.3% of the cases and was non-inferior to expert readers (p = 0.56). When rest images were available, a significantly higher level of confidence was reported (p = 0.022), but no significant differences in diagnostic accuracy were measured (p = 0.34). CONCLUSIONS: Our study demonstrates that the level of training is the main determinant of the diagnostic accuracy in the identification of CAD. Level-3 operators performed at levels comparable with the results from clinical trials. Rest images did not significantly improve diagnostic accuracy, but contributed to higher confidence in the results. Automated quantitative analysis performed similarly to level-3 operators. This is of increasing relevance as recent technical advances in image reconstruction and analysis techniques are likely to permit the clinical translation of robust and fully automated quantitative analysis into routine clinical practice.

3D myocardial T1 mapping using saturation recovery.

PURPOSE: To propose a 3D quantitative high-resolution T1 mapping technique, called 3D SASHA (saturation-recovery single-shot acquisition), which combines a saturation recovery pulse with 1D-navigator-based-respiratory motion compensation to acquire the whole volume of the heart in free breathing. The sequence was tested and validated both in a T1 phantom and in healthy subjects. MATERIALS AND METHODS: The 3D SASHA method was implemented on a 1.5T scanner. A diaphragmatic navigator was used to allow free-breathing acquisition and the images were acquired with a resolution of 1.4 × 1.4 × 8 mm3 . For assessment of accuracy and precision the sequence was compared with the reference gold-standard inversion-recovery spin echo (IRSE) pulse sequence in a T1 phantom, while for the in vivo studies (10 healthy volunteers) 3D SASHA was compared with the clinically used 2D MOLLI (3-3-5) and 2D SASHA protocols. RESULTS: There was good agreement between the T1 values measured in a T1 phantom with 3D SASHA and the reference IRSE pulse sequences (1111.6 ± 31 msec vs. 1123.6 ± 8 msec, P = 0.9947). Mean and standard deviation of the myocardial T1 values in healthy subjects measured with 2D MOLLI, 2D SASHA, and 3D SASHA sequences were 881 ± 40 msec, 1181.3 ± 32 msec, and 1153.6 ± 28 msec respectively. CONCLUSION: The proposed 3D SASHA sequence allows for high-resolution free-breathing whole-heart T1 -mapping with T1 values in good agreement with the 2D SASHA and improved precision. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2017;46:218-227.

Microvascular ischemia in hypertrophic cardiomyopathy: new insights from high-resolution combined quantification of perfusion and late gadolinium enhancement.

BACKGROUND: Microvascular ischemia is one of the hallmarks of hypertrophic cardiomyopathy (HCM) and has been associated with poor outcome. However, myocardial fibrosis, seen on cardiovascular magnetic resonance (CMR) as late gadolinium enhancement (LGE), can be responsible for rest perfusion defects in up to 30% of patients with HCM, potentially leading to an overestimation of the ischemic burden. We investigated the effect of left ventricle (LV) scar on the total LV ischemic burden using novel high-resolution perfusion analysis techniques in conjunction with LGE quantification. METHODS: 30 patients with HCM and unobstructed epicardial coronary arteries underwent CMR with Fermi constrained quantitative perfusion analysis on segmental and high-resolution data. The latter were corrected for the presence of fibrosis on a pixel-by-pixel basis. RESULTS: High-resolution quantification proved more sensitive for the detection of microvascular ischemia in comparison to segmental analysis. Areas of LGE were associated with significant reduction of myocardial perfusion reserve (MPR) leading to an overestimation of the total ischemic burden on non-corrected perfusion maps. Using a threshold MPR of 1.5, the presence of LGE caused an overestimation of the ischemic burden of 28%. The ischemic burden was more severe in patients with fibrosis, also after correction of the perfusion maps, in keeping with more severe disease in this subgroup. CONCLUSIONS: LGE is an important confounder in the assessment of the ischemic burden in patients with HCM. High-resolution quantitative analysis with LGE correction enables the independent evaluation of microvascular ischemia and fibrosis and should be used when evaluating patients with HCM.

Myocardial Injury on CMR in Patients With COVID-19 and Suspected Cardiac Involvement.

BACKGROUND: Myocardial injury in patients with COVID-19 and suspected cardiac involvement is not well understood. OBJECTIVES: The purpose of this study was to characterize myocardial injury in a multicenter cohort of patients with COVID-19 and suspected cardiac involvement referred for cardiac magnetic resonance (CMR). METHODS: This retrospective study consisted of 1,047 patients from 18 international sites with polymerase chain reaction-confirmed COVID-19 infection who underwent CMR. Myocardial injury was characterized as acute myocarditis, nonacute/nonischemic, acute ischemic, and nonacute/ischemic patterns on CMR. RESULTS: In this cohort, 20.9% of patients had nonischemic injury patterns (acute myocarditis: 7.9%; nonacute/nonischemic: 13.0%), and 6.7% of patients had ischemic injury patterns (acute ischemic: 1.9%; nonacute/ischemic: 4.8%). In a univariate analysis, variables associated with acute myocarditis patterns included chest discomfort (OR: 2.00; 95% CI: 1.17-3.40, P = 0.01), abnormal electrocardiogram (ECG) (OR: 1.90; 95% CI: 1.12-3.23; P = 0.02), natriuretic peptide elevation (OR: 2.99; 95% CI: 1.60-5.58; P = 0.0006), and troponin elevation (OR: 4.21; 95% CI: 2.41-7.36; P < 0.0001). Variables associated with acute ischemic patterns included chest discomfort (OR: 3.14; 95% CI: 1.04-9.49; P = 0.04), abnormal ECG (OR: 4.06; 95% CI: 1.10-14.92; P = 0.04), known coronary disease (OR: 33.30; 95% CI: 4.04-274.53; P = 0.001), hospitalization (OR: 4.98; 95% CI: 1.55-16.05; P = 0.007), natriuretic peptide elevation (OR: 4.19; 95% CI: 1.30-13.51; P = 0.02), and troponin elevation (OR: 25.27; 95% CI: 5.55-115.03; P < 0.0001). In a multivariate analysis, troponin elevation was strongly associated with acute myocarditis patterns (OR: 4.98; 95% CI: 1.76-14.05; P = 0.003). CONCLUSIONS: In this multicenter study of patients with COVID-19 with clinical suspicion for cardiac involvement referred for CMR, nonischemic and ischemic patterns were frequent when cardiac symptoms, ECG abnormalities, and cardiac biomarker elevations were present.

2D high resolution vs. 3D whole heart myocardial perfusion cardiovascular magnetic resonance.

AIMS: Developments in myocardial perfusion cardiovascular magnetic resonance (CMR) allow improvements in spatial resolution and/or myocardial coverage. Whole heart coverage may provide the most accurate assessment of myocardial ischaemic burden, while high spatial resolution is expected to improve detection of subendocardial ischaemia. The objective of this study was to compare myocardial ischaemic burden as depicted by 2D high resolution and 3D whole heart stress myocardial perfusion in patients with coronary artery disease. METHODS AND RESULTS: Thirty-eight patients [age 61 ± 8 (21% female)] underwent 2D high resolution (spatial resolution 1.2 mm2) and 3D whole heart (in-plane spatial resolution 2.3 mm2) stress CMR at 3-T in randomized order. Myocardial ischaemic burden (%) was visually quantified as perfusion defect at peak stress perfusion subtracted from subendocardial myocardial scar and expressed as a percentage of the myocardium. Median myocardial ischaemic burden was significantly higher with 2D high resolution compared with 3D whole heart [16.1 (2.0-30.6) vs. 13.4 (5.2-23.2), P = 0.004]. There was excellent agreement between myocardial ischaemic burden (intraclass correlation coefficient 0.81; P < 0.0001), with mean ratio difference between 2D high resolution vs. 3D whole heart 1.28 ± 0.67 (95% limits of agreement -0.03 to 2.59). When using a 10% threshold for a dichotomous result for presence or absence of significant ischaemia, there was moderate agreement between the methods (κ = 0.58, P < 0.0001). CONCLUSION: 2D high resolution and 3D whole heart myocardial perfusion stress CMR are comparable for detection of ischaemia. 2D high resolution gives higher values for myocardial ischaemic burden compared with 3D whole heart, suggesting that 2D high resolution is more sensitive for detection of ischaemia.

Simultaneous multi-slice steady-state free precession myocardial perfusion with iterative reconstruction and integrated motion compensation.

PURPOSE: Simultaneous multi-slice (SMS) balanced steady-state free precession (bSSFP) acquisition and iterative reconstruction can provide high spatial resolution and coverage for cardiac magnetic resonance (CMR) perfusion. However, respiratory motion remains a challenge for iterative reconstruction techniques employing temporal regularisation. The aim of this study is to evaluate an iterative reconstruction with integrated motion compensation for SMS-bSSFP first-pass myocardial stress perfusion in the presence of respiratory motion. METHODS: Thirty-one patients with suspected coronary artery disease were prospectively recruited and imaged at 1.5 T. A SMS-bSSFP prototype myocardial perfusion sequence was acquired at stress in all patients. All datasets were reconstructed using an iterative reconstruction with temporal regularisation, once with and once without motion compensation (MC and NMC, respectively). Three readers scored each dataset in terms of: image quality (1:poor; 4:excellent), motion/blurring (1:severe motion/blurring; 3:no motion/blurring), and diagnostic confidence (1:poor confidence; 3:high confidence). Quantitative assessment of sharpness was performed. The number of uncorrupted first-pass dynamics was measured on the NMC datasets to classify patients into 'suboptimal breath-hold (BH)' and 'good BH' groups. RESULTS: Compared across all cases, MC performed better than NMC in terms of image quality (3.5 ± 0.5 vs. 3.0 ± 0.8, P = 0.002), motion/blurring (2.9 ± 0.1 vs. 2.2 ± 0.8, P < 0.001), diagnostic confidence (2.9 ± 0.1 vs. 2.3 ± 0.7, P < 0.001) and sharpness index (0.34 ± 0.05 vs. 0.31 ± 0.06, P < 0.001). Fourteen patients with a suboptimal BH were identified. For the suboptimal BH group, MC performed better than NMC in terms of image quality (3.8 ± 0.4 vs. 2.6 ± 0.8, P < 0.001), motion/blurring (3.0 ± 0.1 vs. 1.6 ± 0.7, P < 0.001), diagnostic confidence (3.0 ± 0.1 vs. 1.9 ± 0.7, P < 0.001) and sharpness index (0.34 ± 0.05 vs. 0.30 ± 0.06, P = 0.004). For the good BH group, sharpness index was higher for MC than NMC (0.34 ± 0.06 vs 0.31 ± 0.07, P = 0.03), while there were no significant differences observed for the other three metrics assessed (P > 0.11). There were no significant differences between suboptimal BH MC and good BH MC for any of the reported metrics (P > 0.06). CONCLUSIONS: Integrated motion compensation significantly reduces motion/blurring and improves image quality, diagnostic confidence and sharpness index of SMS-bSSFP perfusion with iterative reconstruction in the presence of motion.

Young athletes: Preventing sudden death by adopting a modern screening approach? A critical review and the opening of a debate.

Preventing sudden cardiac death (SCD) in athletes is a primary duty of sports cardiologists. Current recommendations for detecting high-risk cardiovascular conditions (hr-CVCs) are history and physical examination (H&P)-based. We discuss the effectiveness of H&P-based screening versus more-modern and accurate methods. In this position paper, we review current authoritative statements and suggest a novel alternative: screening MRI (s-MRI), supported by evidence from a preliminary population-based study (completed in 2018), and a prospective, controlled study in military recruits (in development). We present: 1. Literature-Based Comparisons (for diagnosing hr-CVCs): Two recent studies using traditional methods to identify hr-CVCs in >3,000 young athletes are compared with our s-MRI-based study of 5,169 adolescents. 2. Critical Review of Previous Results: The reported incidence of SCD in athletes is presently based on retrospective, observational, and incomplete studies. H&P's screening value seems minimal for structural heart disease, versus echocardiography (which improves diagnosis for high-risk cardiomyopathies) and s-MRI (which also identifies high-risk coronary artery anomalies). Electrocardiography is valuable in screening for potentially high-risk electrophysiological anomalies. 3. Proposed Project : We propose a prospective, controlled study (2 comparable large cohorts: one historical, one prospective) to compare: (1) diagnostic accuracy and resulting mortality-prevention performance of traditional screening methods versus questionnaire/electrocardiography/s-MRI, during 2-month periods of intense, structured exercise (in military recruits, in advanced state of preparation); (2) global costs and cost/efficiency between these two methods. This study should contribute significantly toward a comprehensive understanding of the incidence and causes of exercise-related mortality (including establishing a definition of hr-CVCs) while aiming to reduce mortality.

Hierarchical Bayesian myocardial perfusion quantification.

Myocardial blood flow can be quantified from dynamic contrast-enhanced magnetic resonance (MR) images through the fitting of tracer-kinetic models to the observed imaging data. The use of multi-compartment exchange models is desirable as they are physiologically motivated and resolve directly for both blood flow and microvascular function. However, the parameter estimates obtained with such models can be unreliable. This is due to the complexity of the models relative to the observed data which is limited by the low signal-to-noise ratio, the temporal resolution, the length of the acquisitions and other complex imaging artefacts. In this work, a Bayesian inference scheme is proposed which allows the reliable estimation of the parameters of the two-compartment exchange model from myocardial perfusion MR data. The Bayesian scheme allows the incorporation of prior knowledge on the physiological ranges of the model parameters and facilitates the use of the additional information that neighbouring voxels are likely to have similar kinetic parameter values. Hierarchical priors are used to avoid making a priori assumptions on the health of the patients. We provide both a theoretical introduction to Bayesian inference for tracer-kinetic modelling and specific implementation details for this application. This approach is validated in both in silico and in vivo settings. In silico, there was a significant reduction in mean-squared error with the ground-truth parameters using Bayesian inference as compared to using the standard non-linear least squares fitting. When applied to patient data the Bayesian inference scheme returns parameter values that are in-line with those previously reported in the literature, as well as giving parameter maps that match the independant clinical diagnosis of those patients.

Feasibility of free-breathing quantitative myocardial perfusion using multi-echo Dixon magnetic resonance imaging.

Dynamic contrast-enhanced quantitative first-pass perfusion using magnetic resonance imaging enables non-invasive objective assessment of myocardial ischemia without ionizing radiation. However, quantification of perfusion is challenging due to the non-linearity between the magnetic resonance signal intensity and contrast agent concentration. Furthermore, respiratory motion during data acquisition precludes quantification of perfusion. While motion correction techniques have been proposed, they have been hampered by the challenge of accounting for dramatic contrast changes during the bolus and long execution times. In this work we investigate the use of a novel free-breathing multi-echo Dixon technique for quantitative myocardial perfusion. The Dixon fat images, unaffected by the dynamic contrast-enhancement, are used to efficiently estimate rigid-body respiratory motion and the computed transformations are applied to the corresponding diagnostic water images. This is followed by a second non-linear correction step using the Dixon water images to remove residual motion. The proposed Dixon motion correction technique was compared to the state-of-the-art technique (spatiotemporal based registration). We demonstrate that the proposed method performs comparably to the state-of-the-art but is significantly faster to execute. Furthermore, the proposed technique can be used to correct for the decay of signal due to T2* effects to improve quantification and additionally, yields fat-free diagnostic images.

Robust Non-Rigid Motion Compensation of Free-Breathing Myocardial Perfusion MRI Data.

Kinetic parameter values, such as myocardial perfusion, can be quantified from dynamic contrast-enhanced magnetic resonance imaging data using tracer-kinetic modeling. However, respiratory motion affects the accuracy of this process. Motion compensation of the image series is difficult due to the rapid local signal enhancement caused by the passing of the gadolinium-based contrast agent. This contrast enhancement invalidates the assumptions of the (global) cost functions traditionally used in intensity-based registrations. The algorithms are unable to distinguish whether the differences in signal intensity between frames are caused by the spatial motion artifacts or the local contrast enhancement. In order to address this problem, a fully automated motion compensation scheme is proposed, which consists of two stages. The first of which uses robust principal component analysis (PCA) to separate the local signal enhancement from the baseline signal, before a refinement stage which uses the traditional PCA to construct a synthetic reference series that is free from motion but preserves the signal enhancement. Validation is performed on 18 subjects acquired in free-breathing and 5 clinical subjects acquired with a breath-hold. The validation assesses the visual quality, the temporal smoothness of tissue curves, and the clinically relevant quantitative perfusion values. The expert observers score the visual quality increased by a mean of 1.58/5 after motion compensation and improvement over the previously published methods. The proposed motion compensation scheme also leads to the improved quantitative performance of motion compensated free-breathing image series [30% reduction in the coefficient of variation across quantitative perfusion maps and 53% reduction in temporal variations (p < 0.001)].

Prognostic Value of Quantitative Stress Perfusion Cardiac Magnetic Resonance.

OBJECTIVES: This study sought to evaluate the prognostic usefulness of visual and quantitative perfusion cardiac magnetic resonance (CMR) ischemic burden in an unselected group of patients and to assess the validity of consensus-based ischemic burden thresholds extrapolated from nuclear studies. BACKGROUND: There are limited data on the prognostic value of assessing myocardial ischemic burden by CMR, and there are none using quantitative perfusion analysis. METHODS: Patients with suspected coronary artery disease referred for adenosine-stress perfusion CMR were included (n = 395; 70% male; age 58 ± 13 years). The primary endpoint was a composite of cardiovascular death, nonfatal myocardial infarction, aborted sudden death, and revascularization after 90 days. Perfusion scans were assessed visually and with quantitative analysis. Cross-validated Cox regression analysis and net reclassification improvement were used to assess the incremental prognostic value of visual or quantitative perfusion analysis over a baseline clinical model, initially as continuous covariates, then using accepted thresholds of ≥2 segments or ≥10% myocardium. RESULTS: After a median 460 days (interquartile range: 190 to 869 days) follow-up, 52 patients reached the primary endpoint. At 2 years, the addition of ischemic burden was found to increase prognostic value over a baseline model of age, sex, and late gadolinium enhancement (baseline model area under the curve [AUC]: 0.75; visual AUC: 0.84; quantitative AUC: 0.85). Dichotomized quantitative ischemic burden performed better than visual assessment (net reclassification improvement 0.043 vs. 0.003 against baseline model). CONCLUSIONS: This study was the first to address the prognostic benefit of quantitative analysis of perfusion CMR and to support the use of consensus-based ischemic burden thresholds by perfusion CMR for prognostic evaluation of patients with suspected coronary artery disease. Quantitative analysis provided incremental prognostic value to visual assessment and established risk factors, potentially representing an important step forward in the translation of quantitative CMR perfusion analysis to the clinical setting.

Perfusion dyssynchrony analysis.

AIMS: We sought to describe perfusion dyssynchrony analysis specifically to exploit the high temporal resolution of stress perfusion CMR. This novel approach detects differences in the temporal distribution of the wash-in of contrast agent across the left ventricular wall. METHODS AND RESULTS: Ninety-eight patients with suspected coronary artery disease (CAD) were retrospectively identified. All patients had undergone perfusion CMR at 3T and invasive angiography with fractional flow reserve (FFR) of lesions visually judged >50% stenosis. Stress images were analysed using four different perfusion dyssynchrony indices: the variance and coefficient of variation of the time to maximum signal upslope (V-TTMU and C-TTMU) and the variance and coefficient of variation of the time to peak myocardial signal enhancement (V-TTP and C-TTP). Patients were classified according to the number of vessels with haemodynamically significant CAD indicated by FFR <0.8. All indices of perfusion dyssynchrony were capable of identifying the presence of significant CAD. C-TTP >10% identified CAD with sensitivity 0.889, specificity 0.857 (P < 0.0001). All indices correlated with the number of diseased vessels. C-TTP >12% identified multi-vessel disease with sensitivity 0.806, specificity 0.657 (P < 0.0001). C-TTP was also the dyssynchrony index with the best inter- and intra-observer reproducibility. Perfusion dyssynchrony indices showed weak correlation with other invasive and non-invasive measurements of the severity of ischaemia, including FFR, visual ischaemic burden, and MPR. CONCLUSION: These findings suggest that perfusion dyssynchrony analysis is a robust novel approach to the analysis of first-pass perfusion and has the potential to add complementary information to aid assessment of CAD.