Explore the vast range of titles available.

This document is an update to the 2013 publication of the Society for Cardiovascular Magnetic Resonance (SCMR) Board of Trustees Task Force on Standardized Protocols. Concurrent with this publication, 3 additional task forces will publish documents that should be referred to in conjunction with the present document. The first is a document on the Clinical Indications for CMR, an update of the 2004 document. The second task force will be updating the document on Reporting published by that SCMR Task Force in 2010. The 3rd task force will be updating the 2013 document on Post-Processing. All protocols relative to congenital heart disease are covered in a separate document. The section on general principles and techniques has been expanded as more of the techniques common to CMR have been standardized. A section on imaging in patients with devices has been added as this is increasingly seen in day-to-day clinical practice. The authors hope that this document continues to standardize and simplify the patient-based approach to clinical CMR. It will be updated at regular intervals as the field of CMR advances.

In these patients, optimization of acquisition parameters can significantly improve image quality. [...]new LGE techniques, including dark blood delayed enhancement, have been described and appear to have additive clinical utility. [...]it is timely to present an update on how we perform LGE at our center [2]. [...]breatheld segmented IR-GRE and/or FIDDLE images are acquired in the left ventricular (LV) short and long axis views (2-, 3-, and 4- chamber views). [...]when attempting to visualize infarction or scar, there is substantial patient to patient variation in the clearance of contrast, and using a fixed wait time before acquiring LGE images can result in missing the optimal imaging window in individuals with rapid contrast clearance.

With mounting data on its accuracy and prognostic value, cardiovascular magnetic resonance (CMR) is becoming an increasingly important diagnostic tool with growing utility in clinical routine. Given its versatility and wide range of quantitative parameters, however, agreement on specific standards for the interpretation and post-processing of CMR studies is required to ensure consistent quality and reproducibility of CMR reports. This document addresses this need by providing consensus recommendations developed by the Task Force for Post Processing of the Society for Cardiovascular MR (SCMR). The aim of the task force is to recommend requirements and standards for image interpretation and post processing enabling qualitative and quantitative evaluation of CMR images. Furthermore, pitfalls of CMR image analysis are discussed where appropriate.

Background Routine cine cardiovascular magnetic resonance (CMR) allows for the measurement of left atrial (LA) volumes. Normal reference values for LA volumes have been published based on a group of European individuals without known cardiovascular disease (CVD) but not on one of similar United States (US) based volunteers. Furthermore, the association between grades of LA dilatation by CMR and outcomes has not been established. We aimed to assess the relationship between grades of LA dilatation measured on CMR based on US volunteers without known CVD and all-cause mortality in a large, multicenter cohort of patients referred for a clinically indicated CMR scan. Method We identified 85 healthy US subjects to determine normal reference LA volumes using the biplane area-length method and indexed for body surface area (LAVi). Clinical CMR reports of patients with LA volume measures ( n  = 11,613) were obtained. Data analysis was performed on a cloud-based system for consecutive CMR exams performed at three geographically distinct US medical centers from August 2008 through August 2017. We identified 10,890 eligible cases. We categorized patients into 4 groups based on LAVi partitions derived from US normal reference values: Normal (21–52 ml/m 2 ), Mild (52–62 ml/m 2 ), Moderate (63–73 ml/m 2 ) and Severe (> 73 ml/m 2 ). Mortality data were ascertained for the patient group using electronic health records and social security death index. Cox proportional hazard risk models were used to derive hazard ratios for measuring association of LA enlargement and all-cause mortality. Results The distribution of LAVi from healthy subjects without known CVD was 36.3 ± 7.8 mL/m 2 . In clinical patients, enlarged LA was associated with older age, atrial fibrillation, hypertension, heart failure, inpatient status and biventricular dilatation. The median follow-up duration was 48.9 (IQR 32.1–71.2) months. On univariate analyses, mild [Hazard Ratio (HR) 1.35 (95% Confidence Interval [CI] 1.11 to 1.65], moderate [HR 1.51 (95% CI 1.22 to 1.88)] and severe LA enlargement [HR 2.14 (95% CI 1.81 to 2.53)] were significant predictors of death. After adjustment for significant covariates, moderate [HR 1.45 (95% CI 1.1 to 1.89)] and severe LA enlargement [HR 1.64 (95% CI 1.29 to 2.08)] remained independent predictors of death. Conclusion LAVi determined on routine cine-CMR is independently associated with all-cause mortality in patients undergoing a clinically indicated CMR.

Background Phase contrast (PC) cardiovascular magnetic resonance (CMR) is widely employed for flow quantification, but analysis typically requires time consuming manual segmentation which can require human correction. Advances in machine learning have markedly improved automated processing, but have yet to be applied to PC-CMR. This study tested a novel machine learning model for fully automated analysis of PC-CMR aortic flow. Methods A machine learning model was designed to track aortic valve borders based on neural network approaches. The model was trained in a derivation cohort encompassing 150 patients who underwent clinical PC-CMR then compared to manual and commercially-available automated segmentation in a prospective validation cohort. Further validation testing was performed in an external cohort acquired from a different site/CMR vendor. Results Among 190 coronary artery disease patients prospectively undergoing CMR on commercial scanners (84% 1.5T, 16% 3T), machine learning segmentation was uniformly successful, requiring no human intervention: Segmentation time was < 0.01 min/case (1.2 min for entire dataset); manual segmentation required 3.96 ± 0.36 min/case (12.5 h for entire dataset). Correlations between machine learning and manual segmentation-derived flow approached unity ( r  = 0.99, p  < 0.001). Machine learning yielded smaller absolute differences with manual segmentation than did commercial automation (1.85 ± 1.80 vs. 3.33 ± 3.18 mL, p  < 0.01): Nearly all (98%) of cases differed by ≤5 mL between machine learning and manual methods. Among patients without advanced mitral regurgitation, machine learning correlated well ( r  = 0.63, p  < 0.001) and yielded small differences with cine-CMR stroke volume (∆ 1.3 ± 17.7 mL, p  = 0.36). Among advanced mitral regurgitation patients, machine learning yielded lower stroke volume than did volumetric cine-CMR (∆ 12.6 ± 20.9 mL, p  = 0.005), further supporting validity of this method. Among the external validation cohort ( n  = 80) acquired using a different CMR vendor, the algorithm yielded equivalently small differences (∆ 1.39 ± 1.77 mL, p  = 0.4) and high correlations ( r  = 0.99, p  < 0.001) with manual segmentation, including similar results in 20 patients with bicuspid or stenotic aortic valve pathology (∆ 1.71 ± 2.25 mL, p  = 0.25). Conclusion Fully automated machine learning PC-CMR segmentation performs robustly for aortic flow quantification - yielding rapid segmentation, small differences with manual segmentation, and identification of differential forward/left ventricular volumetric stroke volume in context of concomitant mitral regurgitation. Findings support use of machine learning for analysis of large scale CMR datasets.

Acute myocardial infarct (AMI) size depicted by late gadolinium enhancement cardiovascular magnetic resonance (CMR) is increasingly used as an efficacy endpoint in randomized trials comparing AMI therapies. Infarct size is quantified using manual planimetry (MANUAL), visual scoring (VISUAL), or automated techniques using signal-intensity thresholding (AUTO). Although AUTO is considered the most reproducible, prior studies did not account for the subjective determination of endocardial/epicardial borders, which all methods require. For MANUAL and VISUAL, prior studies did not address how to treat intermediate signal intensities due to partial volume. To assess sources of variability, AMI size was measured in 30 patients and 12 controls by 3 core-laboratories using 8 methods, each separated by more than 2 months time (n = 720 evaluations). The methods were: (1,2) AUTOSegment, AUTOFWHM (using Segment software or the full-width-at-half-maximum algorithm, respectively); (3,4) AUTO-UCSegment, AUTO-UCFWHM (user correction for endocardial border pixels, no-reflow, etc.); (5) MANUAL; (6) MANUAL-ISI (adjustment for intermediate signal-intensities); (7) VISUAL; (8) VISUAL-ISI. Mean infarct size varied between 16.8% and 27.2% of LV mass depending on method. Even automated techniques with no user interaction for infarct borders resulted in significant within-patient variability given the need to subjectively trace endocardial/epicardial contours. The coefficient-of-variation (CV) was 10.6% and 14.6% for AUTOSegment and AUTOFWHM, respectively. For manual and visual categories, reproducibility was improved when intermediate signal-intensities were considered (MANUAL-ISI vs MANUAL: CV = 8.3% vs 14.4%; p = 0.03; VISUAL-ISI vs VISUAL: CV = 8.4% vs 10.9%; p = 0.01). For AUTO-UCSegment, MANUAL-ISI, and VISUAL-ISI (best technique in each category) within-patient variability due to the quantification method was less than 10% of total variability, and the required sample sizes for detecting a 5% absolute difference in infarct size were 62, 63, and 62 patients, respectively. Among CMR core-laboratories, an important source of variability in infarct size quantification is the subjective delineation of endocardial/epicardial borders. When intermediate signal intensities are considered in manual planimetry and visual scoring, reproducibility and impact on sample size are similar to automated techniques.

Myocardial infarcts are routinely detected by nuclear imaging techniques such as single photon emission computed tomography (SPECT) myocardial perfusion imaging. A newly developed technique for infarct detection based on contrast-enhanced cardiovascular magnetic resonance (CMR) has higher spatial resolution than SPECT. We postulated that this technique would detect infarcts missed by SPECT. We did contrast-enhanced CMR and SPECT examinations in 91 patients with suspected or known coronary artery disease. All CMR and SPECT images were scored, using a 14-segment model, for the presence, location, and spatial extent of infarction. To compare each imaging modality to a gold standard, we also acquired contrast-enhanced CMR and SPECT images in 12 dogs with, and three dogs without, myocardial infarction as defined by histochemical staining. In animals, contrast-enhanced CMR and SPECT detected all segments with nearly transmural infarction (>75% transmural extent of the left-ventricular wall). CMR also identified 100 of the 109 segments (92%) with subendocardial infarction (<50% transmural extent of the left-ventricular wall), whereas SPECT identified only 31 (28%). SPECT and CMR showed high specificity for the detection of infarction (97% and 98%, respectively). In patients, all segments with nearly transmural infarction, as defined by contrast-enhanced CMR, were detected by SPECT. However, of the 181 segments with subendocardial infarction, 85 (47%) were not detected by SPECT. On a per patient basis, six (13%) individuals with subendocardial infarcts visible by CMR had no evidence of infarction by SPECT. SPECT and CMR detect transmural myocardial infarcts at similar rates. However, CMR systematically detects subendocardial infarcts that are missed by SPECT.

The Society for Cardiovascular Magnetic Resonance (SCMR) recommendations for training and competency of cardiovascular magnetic resonance (CMR) technologists document will define the knowledge, experiences and skills required for a technologist to be competent in CMR imaging. By providing a framework for CMR training and competency the overarching goal is to promote the performance of high-quality CMR and to foster the increased adoption of CMR into clinical care.

In patients with coronary artery disease and left ventricular dysfunction, the distinction between reversible and irreversible myocardial injury is important. The identification of viable myocardium is useful in predicting which patients will have increased left ventricular ejection fractions 1 – 7 and improved survival 8 – 11 after revascularization. Noninvasive methods for assessing myocardial viability include positron-emission tomography, single-photon-emission computed tomography, and dobutamine echocardiography. These techniques have proven clinical utility, but each has limitations that may reduce its diagnostic accuracy. For example, they interpret myocardial viability as an all-or-none phenomenon within a myocardial region, since none can assess the transmural extent of viability of . . .

Background Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) is widely used to identify cardiac neoplasms, for which diagnosis is predicated on enhancement stemming from lesion vascularity: Impact of contrast-enhancement pattern on clinical outcomes is unknown. The objective of this study was to determine whether cardiac metastasis (C MET ) enhancement pattern on LGE-CMR impacts prognosis, with focus on heterogeneous lesion enhancement as a marker of tumor avascularity. Methods Advanced (stage IV) systemic cancer patients with and without C MET matched (1:1) by cancer etiology underwent a standardized CMR protocol. C MET was identified via established LGE-CMR criteria based on lesion enhancement; enhancement pattern was further classified as heterogeneous (enhancing and non-enhancing components) or diffuse and assessed via quantitative (contrast-to-noise ratio (CNR); signal-to-noise ratio (SNR)) analyses. Embolic events and mortality were tested in relation to lesion location and contrast-enhancement pattern. Results 224 patients were studied, including 112 patients with C MET and unaffected (C MET -) controls matched for systemic cancer etiology/stage. C MET enhancement pattern varied (53% heterogeneous, 47% diffuse). Quantitative analyses were consistent with lesion classification; CNR was higher and SNR lower in heterogeneously enhancing C MET (p < 0.001)—paralleled by larger size based on linear dimensions (p < 0.05). Contrast-enhancement pattern did not vary based on lesion location (p = NS). Embolic events were similar between patients with diffuse and heterogeneous lesions (p = NS) but varied by location: Patients with right-sided lesions had threefold more pulmonary emboli (20% vs. 6%, p = 0.02); those with left-sided lesions had lower rates equivalent to controls (4% vs. 5%, p = 1.00). Mortality was higher among patients with C MET (hazard ratio [HR] = 1.64 [CI 1.17–2.29], p = 0.004) compared to controls, but varied by contrast-enhancement pattern: Diffusely enhancing C MET had equivalent mortality to controls (p = 0.21) whereas prognosis was worse with heterogeneous C MET (p = 0.005) and more strongly predicted by heterogeneous enhancement (HR = 1.97 [CI 1.23–3.15], p = 0.005) than lesion size (HR = 1.11 per 10 cm [CI 0.53–2.33], p = 0.79). Conclusions Contrast-enhancement pattern and location of C MET on CMR impacts prognosis. Embolic events vary by C MET location, with likelihood of PE greatest with right-sided lesions. Heterogeneous enhancement—a marker of tumor avascularity on LGE-CMR—is a novel marker of increased mortality risk.