Inline perfusion mapping provides insights into the disease mechanism in hypertrophic cardiomyopathy

Objective

In patients with hypertrophic cardiomyopathy (HCM), the role of small vessel disease and myocardial perfusion remains incompletely understood and data on absolute myocardial blood flow (MBF, mL/g/min) are scarce. We measured MBF using cardiovascular magnetic resonance fully quantitative perfusion mapping to determine the relationship between perfusion, hypertrophy and late gadolinium enhancement (LGE) in HCM.

Methods

101 patients with HCM with unobstructed epicardial coronary arteries and 30 controls (with matched cardiovascular risk factors) underwent pixel-wise perfusion mapping during adenosine stress and rest. Stress, rest MBF and the myocardial perfusion reserve (MPR, ratio of stress to rest) were calculated globally and segmentally and then associated with segmental wall thickness and LGE.

Results

In HCM, 79% had a perfusion defect on clinical read. Stress MBF and MPR were reduced compared with controls (mean±SD 1.63±0.60 vs 2.30±0.64 mL/g/min, p<0.0001 and 2.21±0.87 vs 2.90±0.90, p=0.0003, respectively). Globally, stress MBF fell with increasing indexed left ventricle mass (R² for the model 0.186, p=0.036) and segmentally with increasing wall thickness and LGE (both p<0.0001). In 21% of patients with HCM, MBF was lower during stress than rest (MPR <1) in at least one myocardial segment, a phenomenon which was predominantly subendocardial. Apparently normal HCM segments (normal wall thickness, no LGE) had reduced stress MBF and MPR compared with controls (mean±SD 1.88±0.81 mL/g/min vs 2.32±0.78 mL/g/min, p<0.0001).

Conclusions

Microvascular dysfunction is common in HCM and associated with hypertrophy and LGE. Perfusion can fall during vasodilator stress and is abnormal even in apparently normal myocardium suggesting it may be an early disease marker.

A Multicenter, Scan-Rescan, Human and Machine Learning CMR Study to Test Generalizability and Precision in Imaging Biomarker Analysis

BACKGROUND

Automated analysis of cardiac structure and function using machine learning (ML) has great potential, but is currently hindered by poor generalizability. Comparison is traditionally against clinicians as a reference, ignoring inherent human inter- and intraobserver error, and ensuring that ML cannot demonstrate superiority. Measuring precision (scan:rescan reproducibility) addresses this. We compared precision of ML and humans using a multicenter, multi-disease, scan:rescan cardiovascular magnetic resonance data set.

METHODS

One hundred ten patients (5 disease categories, 5 institutions, 2 scanner manufacturers, and 2 field strengths) underwent scan:rescan cardiovascular magnetic resonance (96% within one week). After identification of the most precise human technique, left ventricular chamber volumes, mass, and ejection fraction were measured by an expert, a trained junior clinician, and a fully automated convolutional neural network trained on 599 independent multicenter disease cases. Scan:rescan coefficient of variation and 1000 bootstrapped 95% CIs were calculated and compared using mixed linear effects models.

RESULTS

Clinicians can be confident in detecting a 9% change in left ventricular ejection fraction, with greater than half of coefficient of variation attributable to intraobserver variation. Expert, trained junior, and automated scan:rescan precision were similar (for left ventricular ejection fraction, coefficient of variation 6.1 [5.2%-7.1%], P=0.2581; 8.3 [5.6%-10.3%], P=0.3653; 8.8 [6.1%-11.1%], P=0.8620). Automated analysis was 186× faster than humans (0.07 versus 13 minutes).

CONCLUSIONS

Automated ML analysis is faster with similar precision to the most precise human techniques, even when challenged with real-world scan:rescan data. Assessment of multicenter, multi-vendor, multi-field strength scan:rescan data (available at www.thevolumesresource.com) permits a generalizable assessment of ML precision and may facilitate direct translation of ML to clinical practice.

Quantitative Myocardial Perfusion in Fabry Disease

BACKGROUND

Fabry disease (FD) is an X-linked lysosomal storage disease resulting in tissue accumulation of sphingolipids. Key myocardial processes that lead to adverse outcomes in FD include storage, hypertrophy, inflammation, and fibrosis. These are quantifiable by multiparametric cardiovascular magnetic resonance. Recent developments in cardiovascular magnetic resonance perfusion mapping allow rapid in-line perfusion quantification permitting broader clinical application, including the assessment of microvascular dysfunction. We hypothesized that microvascular dysfunction in FD would be associated with storage, fibrosis, and edema.

METHODS

A prospective, observational study of 44 FD patients (49 years, 43% male, 24 [55%] with left ventricular hypertrophy [LVH]) and 27 healthy controls with multiparametric cardiovascular magnetic resonance including vasodilator stress perfusion mapping. Myocardial blood flow (MBF) was measured and its associations with other processes investigated.

RESULTS

Compared with LVH- FD, LVH+ FD had higher left ventricular ejection fraction (73% versus 68%), more late gadolinium enhancement (85% versus 15%), and a lower stress MBF (1.76 versus 2.36 mL/g per minute). The reduction in stress MBF was more pronounced in the subendocardium than subepicardium. LVH- FD had lower stress MBF than controls (2.36 versus 3.00 mL/g per minute; P=0.002). Across all FD, late gadolinium enhancement and low native T1 were independently associated with reduced stress MBF. On a per-segment basis, stress MBF was independently associated with wall thickness, T2, extracellular volume fraction, and late gadolinium enhancement.

CONCLUSIONS

FD patients have reduced perfusion, particularly in the subendocardium with greater reductions with LVH, storage, edema, and scar. Perfusion is reduced even without LVH suggesting it is an early disease marker.

Quantitative cardiac MRI

Cardiac MRI has become an indispensable imaging modality in the investigation of patients with suspected heart disease. It has emerged as the gold standard test for cardiac function, volumes, and mass and allows noninvasive tissue characterization and the assessment of myocardial perfusion. Quantitative MRI already has a key role in the development and incorporation of machine learning in clinical imaging, potentially offering major improvements in both workflow efficiency and diagnostic accuracy. As the clinical applications of a wide range of quantitative cardiac MRI techniques are being explored and validated, we are expanding our capabilities for earlier detection, monitoring, and risk stratification of disease, potentially guiding personalized management decisions in various cardiac disease models. In this article we review established and emerging quantitative techniques, their clinical applications, highlight novel advances, and appraise their clinical diagnostic potential. Level of Evidence: 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2020;51:693-711.

Myocardial native T1 and extracellular volume with healthy ageing and gender

Aims

To determine how native myocardial T1 and extracellular volume (ECV) change with age, both to understand aging and to inform on normal reference ranges.

Methods and results

Ninety-four healthy volunteers with no a history or symptoms of cardiovascular disease or diabetes underwent cardiovascular magnetic resonance at 1.5 T. Mid-ventricular short axis native and post-contrast T1 maps by Shortened MOdified Look-Locker Inversion-recovery (ShMOLLI), MOdified Look-Locker Inversion Recovery (MOLLI) [pre-contrast: 5s(3s)3s, post-contrast: 4s(1s)3s(1s)2s] and saturation recovery single-shot acquisition (SASHA) were acquired and ECV by these three techniques were derived for the mid anteroseptum. Mean age was 50 ± 14 years (range 20-76), male 52%, with no age difference between genders (males 51 ± 14 years; females 49 ± 15 years, P = 0.55). Quoting respectively ShMOLLI, MOLLI, SASHA throughout, mean myocardial T1 was 957 ± 30 ms, 1025 ± 38 ms, 1144 ± 45 ms (P < 0.0001) and ECV 28.4 ± 3.0% [95% confidence interval (CI) 27.8-29.0], 27.3 ± 2.7 (95% CI 26.8-27.9), 24.1 ± 2.9% (95% CI 23.5-24.7) (P < 0.0001), with all values higher in females for all techniques (T1 +18 ms, +35 ms, +51 ms; ECV +2.7%, +2.6%, +3.4%). Native myocardial T1 reduced slightly with age (R2 = 0.042, P = 0.048; R2 = 0.131, P < 0.0001-on average by 8-11 ms/decade-but not for SASHA (R2 = 0.033 and P = 0.083). ECV did not change with age (R2 = 0.003, P = 0.582; R2 = 0.002, P = 0.689; R2 = 0.003, P = 0.615). Heart rate decreased slightly with age (R2 = 0.075, coefficient = -0.273, P = 0.008), but there was no relationship between age and other blood T1 influences (haematocrit, iron, high density lipoprotein-cholesterol).

Conclusion

Gender influences native T1 and ECV with women having a higher native T1 and ECV. Native T1 measured by MOLLI and ShMOLLI was slightly lower with increasing age but not with SASHA and ECV was independent of age for all techniques.

Texture analysis of cardiovascular magnetic resonance cine images differentiates aetiologies of left ventricular hypertrophy

AIM

To investigate whether unenhanced cardiovascular magnetic resonance (CMR) balanced steady state free precession (bSSFP) cine images could be analysed using textural analysis (TA) software to differentiate different aetiologies of disease causing increased myocardial wall thickness (left ventricular hypertrophy [LVH]) and indicate the severity of myocardial tissue abnormality.

MATERIALS AND METHODS

A mid short axis unenhanced cine frame of 216 patients comprising 50 cases of hypertrophic cardiomyopathy (HCM; predominantly Left ventricular outflow tract obstruction [LVOTO] subtype), 52 cases of cardiac amyloid (CA; predominantly AL: light chain subtype), 68 cases of aortic stenosis (AS), 15 hypertensive patients with LVH (HTN+LVH), and 31 healthy volunteers (HV) underwent TA of the CMR cine images (CMRTA) using TexRAD (TexRAD Ltd, Cambridge, UK). Among the HV, 16/31 were scanned twice to form a test-retest reproducibility cohort. CMRTA comprised a filtration-histogram technique to extract and quantify features using six parameters.

RESULTS

Test-retest analysis in the HV showed a medium filter (3 mm) was the most reproducible (intra-class correlation of 0.9 for kurtosis and skewness and 0.8 for mean and SD). Disease cohorts were statistically different (p<0.001) to HV for all parameters. Pairwise comparisons of CMRTA parameters showed kurtosis and skewness was consistently significant in ranking the degree of difference from HV (greatest to least): CA, HCM, LVH+HTN, AS (p<0.001). Similarly, mean, standard deviation, entropy, and mean positive pixel (MPP) were consistent in ranking degree of difference from HV: HCM, CA, AS and HTN+LVH.

CONCLUSION

Radiomic features of bSSFP CMR data sets derived using TA show promise in discriminating between the aetiologies of LVH.

Quantitative myocardial perfusion in coronary artery disease: A perfusion mapping study

BACKGROUND

Cardiac MR stress perfusion remains a qualitative technique in clinical practice due to technical and postprocessing challenges. However, automated inline perfusion mapping now permits myocardial blood flow (MBF, ml/g/min) quantification on-the-fly without user input.

PURPOSE

To investigate the diagnostic performance of this novel technique in detecting occlusive coronary artery disease (CAD) in patients scheduled to undergo coronary angiography.

STUDY TYPE

Prospective, observational.

SUBJECTS

Fifty patients with suspected CAD and 24 healthy volunteers.

FIELD STRENGTH

1.5T. SEQUENCE: "Dual" sequence multislice 2D saturation recovery.

ASSESSMENT

All patients underwent cardiac MR with perfusion mapping and invasive coronary angiography; the healthy volunteers had MR with perfusion mapping alone.

STATISTICAL TESTS

Comparison between numerical variables was performed using an independent t-test. Receiver operator characteristic (ROC) curves were generated for transmyocardial, endocardial stress MBF, and myocardial perfusion reserve (MPR, the stress:rest MBF ratio) to diagnose severe (>70%) stenoses as measured by 3D quantitative coronary angiography (QCA). ROC curves were compared by the method of DeLong et al. RESULTS: Compared with volunteers, patients had lower stress MBF and MPR even in vessels with <50% stenosis (2.00 vs. 3.08 ml/g/min, respectively). As stenosis severity increased (<50%, 50-70%, >70%), MBF and MPR decreased. To diagnose occlusive (>70%) CAD, endocardial and transmyocardial stress MBF were superior to MPR (area under the curve 0.92 [95% CI 0.86-0.97] vs. 0.90 [95% CI 0.84-0.95] and 0.80 [95% CI 0.72-0.87], respectively). An endocardial threshold of 1.31 ml/g/min provided a per-coronary artery sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 90%, 82%, 50%, and 98%, with a per-patient diagnostic performance of 100%, 66%, 57%, and 100%, respectively.

DATA CONCLUSION

Perfusion mapping can diagnose occlusive CAD with high accuracy and, in particular, high sensitivity and NPV make it a potential "rule-out" test.

LEVEL OF EVIDENCE

1 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:756-762.

Pericardial Disease in Cancer Patients

PURPOSE OF REVIEW

To understand the variety of conditions in which the pericardium may be affected in cancer patients.

RECENT FINDINGS

Cancer may affect the pericardium directly (primary cancer; uncommon) or through metastases (commoner). Cancer treatment (chemotherapy and radiotherapy) may affect the pericardium leading to pericarditis and myopericarditis. Pericardial effusions, tamponade and constrictive pericarditis are complications that can also occur. A variety of techniques (predominantly cardiac imaging related) are used to make the diagnosis with the treatment strategy dependent on whether the pericardial disease is due to cancer or as a result of cancer treatment. A variety of pericardial diseases may be caused by cancer and cancer treatment. Determining the aetiology and providing effective treatment can often be challenging.