Native T1 mapping and extracellular volume fraction for differentiation of myocardial diseases from normal CMR controls in routine clinical practice

A comparative study of synthetic and venous hematocrit for calculating cardiovascular magnetic resonance-derived extracellular volume

The extracellular volume (ECV) fraction derived from cardiac magnetic resonance (CMR) can reflect various pathologies. The application of ECVs was limited by the strict requirement that hematocrit (Hct0) should be obtained within 24 hours of CMR scan. The aim of this study was to obtain accurate and convenient ECV calculated from the venous Hct and synthetic Hct in CMR. A total of 839 subjects were retrospectively enrolled. The subjects were divided into derivation cohort for local sex-specific models and validation cohort for assessing the accuracy of different ECVs. In the validation cohort, venous Hcts from 7 days before the scan (Hct1 - 7), outside 7 days (Hct> 7), the closest day (Hctclosest), and Hctsyn were compared with Hct0. The agreement and correlation of the conventional ECV (ECV0) with the corresponding ECVs were analyzed. The factors affecting the accuracy of ECVsyn were assessed. ECV1-7 and ECVclosest had the best correlation and smallest bias with ECV0 (R = 0.959 and 0.951, bias = 0.02% and - 0.03%). When using an absolute 2% error as the standard, the performance of ECV1-7 was the best, with an accuracy of 81.0%, followed by ECVclosest (78.8%), ECV> 7 (77.2%) and ECVsyn (70.7%). Abnormally low and high Hcts and decreased left ventricular ejection fractions were associated with miscalculation of ECVsyn, especially patients with dilated cardiomyopathy. We recommend extending the time interval between a Hct and a CMR scan to 7 days for ECV calculation. The synthetic ECV should be used cautiously, especially for patients with extremely low or high Hcts, decreased cardiac function, and dilated cardiomyopathy.

Cardiac amyloidosis and aortic stenosis: a state-of-the-art review

Cardiac amyloidosis is caused by the extracellular deposition of amyloid fibrils in the heart, involving not only the myocardium but also any cardiovascular structure. Indeed, this progressive infiltrative disease also involves the cardiac valves and, specifically, shows a high prevalence with aortic stenosis. Misfolded protein infiltration in the aortic valve leads to tissue damage resulting in the onset or worsening of valve stenosis. Transthyretin cardiac amyloidosis and aortic stenosis coexist in patients > 65 years in about 4-16% of cases, especially in those undergoing transcatheter aortic valve replacement. Diagnostic workup for cardiac amyloidosis in patients with aortic stenosis is based on a multi-parametric approach considering clinical assessment, electrocardiogram, haematologic tests, basic and advanced echocardiography, cardiac magnetic resonance, and technetium labelled cardiac scintigraphy like technetium-99 m (99mTc)-pyrophosphate, 99mTc-3,3-diphosphono-1,2-propanodicarboxylic acid, and 99mTc-hydroxymethylene diphosphonate. However, a biopsy is the traditional gold standard for diagnosis. The prognosis of patients with coexisting cardiac amyloidosis and aortic stenosis is still under evaluation. The combination of these two pathologies worsens the prognosis. Regarding treatment, mortality is reduced in patients with cardiac amyloidosis and severe aortic stenosis after undergoing transcatheter aortic valve replacement. Further studies are needed to confirm these findings and to understand whether the diagnosis of cardiac amyloidosis could affect therapeutic strategies. The aim of this review is to critically expose the current state-of-art regarding the association of cardiac amyloidosis with aortic stenosis, from pathophysiology to treatment.

Quantitative cardiac MRI parameters for assessment of myocarditis in children and adolescents: a systematic review and meta-analysis

AIM

To evaluate the role of quantitative cardiac magnetic resonance imaging (CMRI) parameters in myocarditis, including acute and chronic myocarditis (AM and CM), for children and adolescents.

MATERIALS AND METHODS

PRISMA principles were followed. PubMed, EMBASE, Web of Science, Cochrane Library, and grey literature were searched. The Newcastle-Ottawa Scale (NOS) and the Agency for Healthcare Research and Quality (AHRQ) checklist were utilised for quality assessment. Quantitative CMRI parameters were extracted and a meta-analysis was performed in comparison with healthy controls. The overall effect size was measured as the weighted mean difference (WMD).

RESULTS

Ten quantitative CMRI parameters of seven studies were analysed. Compared with the control group, the myocarditis group reported longer native T1 relaxation time (WMD=54.00, 95% confidence interval [CI]: 33.21,74.79, p<0.001), longer T2 relaxation time (WMD=2.13, 95% CI: 0.98, 3.28, p<0.001), increased extracellular volume (ECV; WMD=3.13, 95% CI: 1.34,4.91, p=0.001), elevated early gadolinium enhancement (EGE) ratio (WMD=1.47, 95% CI: 0.65,2.28, p<0.001), and increased T2-weighted ratio (WMD=0.43, 95% CI: 0.21,0.64, p<0.001). The AM group had longer native T1 relaxation times (WMD=72.02, 95% CI: 32.78,111.27, p<0.001), increased T2-weighted ratios (WMD=0.52, 95% CI: 0.21,0.84 p=0.001), and impaired left ventricular ejection fractions (LVEF; WMD=-5.84, 95% CI: -9.69, -1.99, p=0.003). Impaired LVEF (WMD=-2.24, 95% CI: -3.32, -1.17, p<0.001) was observed in the CM group.

CONCLUSION

Statistical differences can be observed in some CMRI parameters between patients with myocarditis and healthy controls; however, apart from native T1 mapping, there were no large differences in other parameters between two groups, which may reveal the limited benefit of CMRI in assessing myocarditis in children and adolescents.

Increase in skeletal muscle extracellular volume as an under-recognised change detected at cardiac MRI in hypertrophic cardiomyopathy

AIM

To explore skeletal muscle change and its correlation with the myocardium in hypertrophic cardiomyopathy (HCM) using cardiac magnetic resonance imaging (cMRI) with T1 mapping and late gadolinium enhancement (LGE).

MATERIALS AND METHODS

This retrospective study enrolled 50 HCM patients and 35 healthy controls. The extracellular volume (ECV) of the skeletal muscle and myocardium, the presence and absence of LGE of the myocardium, and cardiac troponin T (cTnT), were assessed. In the HCM group, the elevated ECV_skeletal group was defined as ECV_skeletal >2 standard deviations (SD) above the mean value of the controls. Statistical analyses included Student's t-test, the Mann-Whitney U-test, and linear regression.

RESULTS

ECV_skeletal in the HCM group was higher than in the control group (mean 13.0 versus 10.9%; p<0.001), with 20 (40%) HCM patients having elevated ECV_skeletal (ECV_skeletal ≥13.7%). In the HCM group, ECV_skeletal had a positive linear correlation with global myocardial ECV (r=0.37, p=0.009). In addition, the elevated ECV_skeletal group had a higher cTnT than the non-elevated group (log cTnT, mean 1.55 versus 1.16, p=0.045). Furthermore, segmental myocardial ECV in the elevated ECV_skeletal group was higher than in the non-elevated group, despite the presence or absence of myocardial LGE (median 30.1 versus 27.2%; 26.5 versus 24.6%, both p<0.001) or hypertrophy (median 29.0 versus 26.0%; 26.8 versus 24.8%, both p<0.001).

CONCLUSION

In the HCM patients, ECV_skeletal was higher than in the healthy controls. Furthermore, some ECV_skeletal changes had corresponding changes in the cTnT and myocardium.

Automatic uncertainty-based quality controlled T1 mapping and ECV analysis from native and post-contrast cardiac T1 mapping images using Bayesian vision transformer

Deep learning-based methods for cardiac MR segmentation have achieved state-of-the-art results. However, these methods can generate incorrect segmentation results which can lead to wrong clinical decisions in the downstream tasks. Automatic and accurate analysis of downstream tasks, such as myocardial tissue characterization, is highly dependent on the quality of the segmentation results. Therefore, it is of paramount importance to use quality control methods to detect the failed segmentations before further analysis. In this work, we propose a fully automatic uncertainty-based quality control framework for T1 mapping and extracellular volume (ECV) analysis. The framework consists of three parts. The first one focuses on segmentation of cardiac structures from a native and post-contrast T1 mapping dataset (n=295) using a Bayesian Swin transformer-based U-Net. In the second part, we propose a novel uncertainty-based quality control (QC) to detect inaccurate segmentation results. The QC method utilizes image-level uncertainty features as input to a random forest-based classifier/regressor to determine the quality of the segmentation outputs. The experimental results from four different types of segmentation results show that the proposed QC method achieves a mean area under the ROC curve (AUC) of 0.927 on binary classification and a mean absolute error (MAE) of 0.021 on Dice score regression, significantly outperforming other state-of-the-art uncertainty based QC methods. The performance gap is notably higher in predicting the segmentation quality from poor-performing models which shows the robustness of our method in detecting failed segmentations. After the inaccurate segmentation results are detected and rejected by the QC method, in the third part, T1 mapping and ECV values are computed automatically to characterize the myocardial tissues of healthy and cardiac pathological cases. The native myocardial T1 and ECV values computed from automatic and manual segmentations show an excellent agreement yielding Pearson coefficients of 0.990 and 0.975 (on the combined validation and test sets), respectively. From the results, we observe that the automatically computed myocardial T1 and ECV values have the ability to characterize myocardial tissues of healthy and cardiac diseases like myocardial infarction, amyloidosis, Tako-Tsubo syndrome, dilated cardiomyopathy, and hypertrophic cardiomyopathy.

T1 values and extracellular volume fraction in asymptomatic subjects: variations in left ventricular segments and correlation with cardiovascular risk factors

To evaluate variations in pre-contrast (preT1) and post-contrast (postT1) myocardial T1 values and extracellular volume fraction (ECV) according to left ventricular (LV) segments and to find correlations between them and cardiovascular risk factors. The 233 asymptomatic subjects (210 men, 23 women; aged 54.1 ± 6.0 years) underwent cardiac magnetic resonance imaging with preT1 and postT1 mapping on a 1.5-T scanner. T1 values and ECVs were evaluated according to LV segments, age, sex, and estimated glomerular filtration rate (eGFR). Based on the presence of hypertension (HTN) and diabetes mellitus (DM), subjects were subdivided into the control, HTN, DM, and HTN and DM (HTN-DM) groups. T1 values and ECV showed significant differences between septal and lateral segments at the mid-ventricular and basal levels (p ≤ 0.003). In subgroup analysis, the HTN-DM group showed a significantly higher ECV (0.260 ± 0.023) than the control (0.240 ± 0.021, p = 0.011) and HTN (0.241 ± 0.024, p = 0.041) groups. Overall postT1 and ECV of the LV had significant correlation with eGFR (r = 0.19, p = 0.038 for postT1; r =  − 0.23, p = 0.011 for ECV). Septal segments show higher preT1 and ECV but lower postT1 than lateral segments at the mid-ventricular and basal levels. ECV is significantly affected by HTN, DM, and eGFR, even in asymptomatic subjects.

Segmental strain for scar detection in acute myocardial infarcts and in follow-up exams using non-contrast CMR cine sequences

Background

The purpose of the study was to investigate feasibility of infarct detection in segmental strain derived from non-contrast cardiac magnetic resonance (CMR) cine sequences in patients with acute myocardial infarction (AMI) and in follow-up (FU) exams.

Methods

57 patients with AMI (mean age 61 ± 12 years, CMR 2.8 ± 2 days after infarction) were retrospectively included, FU exams were available in 32 patients (35 ± 14 days after first CMR). 43 patients with normal CMR (54 ± 11 years) served as controls. Dedicated software (Segment CMR, Medviso) was used to calculate global and segmental strain derived from cine sequences. Cine short axis stacks and segmental circumferential strain calculations of every patient and control were presented to two blinded readers in random order, who were advised to identify potentially infarcted segments, blinded to LGE and clinical information.

Results

Impaired global strain was measured in AMI patients compared to controls (global peak circumferential strain [GPCS] p = 0.01; global peak longitudinal strain [GPLS] p = 0.04; global peak radial strain [GPRS] p = 0.01). In both imaging time points, mean segmental peak circumferential strain [SPCS] was impaired in infarcted tissue compared to remote segments (AMI: p = 0.03, FU: p = 0.02). SPCS values in infarcted segments were similar between AMI and FU (p = 0.8). In SPCS calculations, 141 from 189 acutely infarcted segments were accurately detected (74.6%), visual evaluation of correlating cine images detected 43.4% infarcts. In FU, 80% infarcted segments (91/114 segments) were detected in SPCS and 51.8% by visual evaluation of correlating short axis cine images (p = 0.01).

Conclusion

Segmental circumferential strain derived from routinely acquired native cine sequences detects nearly 75% of acute infarcts and 80% of infarcts in subacute follow-up CMR, significantly more than visual evaluation of correlating cine images alone. Acute infarcts may display only subtle impairment of wall motion and no obvious wall thinning, thus SPCS calculation might be helpful for scar detection in patients with acute infarcts, when LGE images are not available.

Quality of science and reporting for radiomics in cardiac magnetic resonance imaging studies: a systematic review

OBJECTIVES

To evaluate the quality of radiomics studies using cardiac magnetic resonance imaging (CMR) according to the radiomics quality score (RQS), Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines, and the standards defined by the Image Biomarker Standardization Initiative (IBSI) and identify areas needing improvement.

MATERIALS AND METHODS

PubMed and Embase were searched to identify radiomics studies using CMR until March 10, 2021. Of the 259 identified articles, 32 relevant original research articles were included. Studies were scored according to the RQS, TRIPOD guidelines, and IBSI standards by two cardiac radiologists.

RESULTS

The mean RQS was 14.3% of the maximum (5.16 out of 36). RQS were low for the demonstration of validation (-60.6%), calibration statistics (1.6%), potential clinical utility (3.1%), and open science (3.1%) items. No study conducted a phantom study or cost-effectiveness analysis. The adherence to TRIPOD guidelines was 55.9%. Studies were deficient in reporting title (3.1%), stating objective in abstract and introduction (6.3% and 9.4%), missing data (0%), discrimination/calibration (3.1%), and how to use the prediction model (3.1%). According to the IBSI standards, non-uniformity correction, image interpolation, grey-level discretization, and signal intensity normalization were performed in two (6.3%), four (12.5%), six (18.8%), and twelve (37.5%) studies, respectively.

CONCLUSION

The quality of radiomics studies using CMR is suboptimal. Improvements are needed in the areas of validation, calibration, clinical utility, and open science. Complete reporting of study objectives, missing data, discrimination/calibration, how to use the prediction model, and preprocessing steps are necessary.

KEY POINTS

• The quality of science in radiomics studies using CMR is currently inadequate. • RQS were low for validation, calibration, clinical utility, and open science; no study conducted a phantom study or cost-effectiveness analysis. • In stating the study objective, missing data, discrimination/calibration, how to use the prediction model, and preprocessing steps, improvements are needed.