Journal of Cardiovascular Magnetic Resonance最新文献

Background: Cardiovascular magnetic resonance (CMR) has demonstrated excellent performance in the diagnosis of cardiac amyloidosis (CA). However, misdiagnosis occasionally occurs because the morphological and functional features of CA are non-specific. This study was performed to determine the value of non-contrast CMR T1ρ in the diagnosis of CA.

Methods: This prospective study included 45 patients with CA, 30 patients with hypertrophic cardiomyopathy (HCM), and 10 healthy controls (HCs). All participants underwent cine (whole heart), T1ρ mapping, pre- and post-contrast T1 mapping imaging (three slices), and late gadolinium enhancement using a 3T whole-body MRI system. All participants underwent T1ρ at two spin-locking frequencies: 0Hz and 298Hz. ECV maps were obtained using pre- and post-contrast T1 maps. The myocardial T1ρ dispersion map, termed myocardial dispersion index (MDI), was also calculated. All parameters were measured in the left ventricular myocardial wall. Participants in the HC group were scanned twice on different days to assess the reproducibility of T1ρ measurements.

Results: Excellent reproducibility was observed upon evaluation of the coefficient of variation between two scans (T1ρ [298Hz]: 3.1%; T1ρ [0Hz], 2.5%). The ECV (HC: 27.4 ± 2.8% vs. HCM: 32.6 ± 5.8% vs. CA: 46 ± 8.9%; p < 0.0001), T1ρ [0Hz] (HC: 35.8 ± 1.7 ms vs. HCM: 40.0 ± 4.5 ms vs. CA: 51.4 ± 4.4 ms; p < 0.0001) and T1ρ [298Hz] (HC: 41.9 ± 1.6 ms vs. HCM: 48.8 ± 6.2 ms vs. CA: 54.4 ± 5.2 ms; p < 0.0001) progressively increased from the HC group to the HCM group, and then the CA group. The MDI progressively decreased from the HCM group to the HC group, and then the CA group (HCM: 8.8 ± 2.8 ms vs. HC: 6.1 ± 0.9 ms vs. CA: 3.4 ± 2.1 ms; p < 0.0001). For differential diagnosis, the combination of MDI and T1ρ [298Hz] showed the greatest sensitivity (98.3%) and specificity (95.5%) between CA and HCM, compared with the native T1 and ECV.

Conclusions: The T1ρ and MDI approaches can be used as non-contrast CMR imaging biomarkers to improve the differential diagnosis of patients with CA.

Background: Time-resolved, three-dimensional phase-contrast magnetic resonance imaging (4D flow MRI) plays an important role in assessing cardiovascular diseases. However, the manual or semi-automatic segmentation of aortic vessel boundaries in 4D flow data introduces variability and limits reproducibility of aortic hemodynamics visualization and quantitative flow-related parameter computation. This paper explores the potential of deep learning to improve 4D flow MRI segmentation by developing models for automatic segmentation and analyzes the impact of the training data on the generalization of the model across different sites, scanner vendors, sequences, and pathologies.

Methods: The study population consists of 260 4D flow MRI datasets, including subjects without known aortic pathology, healthy volunteers, and patients with bicuspid aortic valve (BAV) examined at different hospitals. The dataset was split to train segmentation models on subsets with different representations of characteristics such as pathology, gender, age, scanner model, vendor, and field strength. An enhanced 3D U-net convolutional neural network (CNN) architecture with residual units was trained for 2D+t aortic cross-sectional segmentation. The model performance was evaluated using Dice score, Hausdorff distance, and average symmetric surface distance on test data, datasets with characteristics not represented in the training set (model-specific), and an overall evaluation set. Standard diagnostic flow parameters were computed and compared with manual segmentation results using Bland-Altman analysis and interclass correlation.

Results: The representation of technical factors such as scanner vendor and field strength in the training dataset had the strongest influence on the overall segmentation performance. Age had a greater impact than gender. Models solely trained on BAV patients' datasets performed well on datasets of healthy subjects but not vice versa.

Conclusion: This study highlights the importance of considering a heterogeneous dataset for the training of widely applicable automatic CNN segmentations in 4D flow MRI, with a particular focus on the inclusion of different pathologies and technical aspects of data acquisition.

Background: Exertional heatstroke (EHS) is increasingly common in young trained soldiers. However, the prognosis marker in EHS patients remains unclear. To evaluate cardiac MRI feature tracking (CMR-FT) derived left ventricle (LV) strain as a biomarker for return to training (RTT) in trained soldiers with EHS in a prospective CMR cohort.

Methods: Trained soldiers (participants) with EHS underwent cardiac MR cine sequences between June 2020 and August 2023. Two-dimensional (2D) LV strain parameters were derived. At 3 months after index CMR, the participants with persistent cardiac symptoms including chest pain, dyspnea, palpitations, syncope, and recurrent heat-related illness were defined as non-RTT. Multivariable logistic regression analysis is used to develop a predictive RTT model. The performance of different models was compared using the area under curve (AUC).

Results: A total of 80 participants (median age, 21 years; interquartile range (IQR), 20-23 years) and 27 health controls (median age, 21 years; IQR, 20-22 years) were prospectively included. Of the 77 participants, 32 (41.6%) had persistent cardiac symptoms and were not able to RTT at 3 months follow-up after experiencing EHS. The 2D global longitudinal strain (GLS) was significantly impaired in EHS participants compared to the healthy control group (-15.81 ± 1.67% vs -16.93 ± 1.22%, P =.001), which also showed significantly statistical differences between participants with RTT and non-RTT (-14.99 ± 3.54% vs -16.53 ± 1.43%, P <.001). 2D-GLS (≤ -15.00%) (odds ratio, 1.53; 95% confidence interval (CI): 1.08, 2.17; P =.016) was an independent predictor for RTT even after adjusting known risk factors. 2D-GLS provided incremental prognostic value over the clinical model and conventional CMR parameters model (AUCs: 0.72 vs 0.88, P =.013; 0.79 vs 0.88, P =.023; respectively).

Conclusions: Two-dimensional global longitudinal strain (≤ -15.00%) is an incremental prognostic CMR biomarker to predict return to training in exertional heatstroke soldiers.

Background: This study aimed to validate respiratory-resolved 5D flow MRI against real-time 2D phase contrast MRI, assess the impact of number of respiratory states, and measure the impact of respiration on hemodynamics in congenital heart disease (CHD) patients.

Methods: Respiratory-resolved 5D flow MRI derived net and peak flow measurements were compared to real-time 2D phase contrast MRI derived measurements in 10 healthy volunteers. Pulmonary to systemic flow ratios (Qp:Qs) were measured in 19 CHD patients and aortopulmonary collateral burden was measured in 5 Fontan patients. Additionally, the impact of number of respiratory states on measured respiratory-driven net flow changes was investigated in 10 healthy volunteers and 19 CHD patients (shunt physiology, n=11, single ventricle disease (SVD), n=8).

Results: There was good agreement between 5D flow MRI and real-time 2D phase contrast derived net and peak flow. Respiratory driven changes had good correlation (rho=0.64, p<0.001). In healthy volunteers, fewer than four respiratory states reduced measured respiratory driven flow changes in veins (5.2mL/cycle, p<0.001) and arteries (1.7mL/cycle, p=0.05). Respiration drove substantial venous net flow changes in SVD (64% change) and shunt patients (57% change). Respiration had significantly greater impact in SVD patients compared to shunt patients in the right and left pulmonary arteries (46% vs 15%, p=0.003 & 59% vs 20%, p=0.002). Qp:Qs varied by 37±24% over respiration in SVD patients and 12±20% in shunt patients. Aortopulmonary collateral burden varied by 118±84% over respiration in Fontan patients. The smallest collateral burden was measured during active inspiration in all patients and the greatest burden was during active expiration in 4 of 5 patients. Reduced respiratory resolution blunted measured flow changes in the caval veins of shunt and SVD patients (p<0.005).

Conclusions: Respiratory-resolved 5D flow MRI measurements agree with real-time 2D phase contrast. Venous measurements are sensitive to number of respiratory states, whereas arterial measurements are more robust. Respiration has substantial impact on caval vein flow, Qp:Qs, and collateral burden in CHD patients.

Background: Aortic diameter growth in type B aortic dissection (TBAD) is associated with progressive aortic dilation, resulting in increased mortality in patients with both de novo TBAD (dnTBAD) and residual dissection after type A dissection repair (rTAAD). Preemptive thoracic endovascular aortic repair may improve mortality in patients with TBAD, although it is unclear which patients may benefit most from early intervention. In vivo hemodynamic assessment using four-dimensional (4D) flow magnetic resonance imaging (MRI) has been used to characterize TBAD patients with growing aortas. In this longitudinal study, we investigated whether changes over time in 4D flow derived true and false lumen (TL, FL) hemodynamic parameters correlate with aortic growth rate which is a marker of increased risk.

Methods: We retrospectively identified TBAD patients with baseline and follow-up 4D flow MRI at least 120 days apart. Patients with TBAD intervention before baseline or between scans were excluded. 4D flow MRI data analysis included segmentation of the TL and FL, followed by voxel-wise calculation of TL and FL total kinetic energy (KE), maximum velocity (MV), mean forward flow (FF), and mean reverse flow (RF). Changes over time (Δ) were calculated for all hemodynamic parameters. Maximal diameter in the descending aorta was measured from MR angiogram images acquired at the time of 4D flow. Aortic growth rate was defined as the change in diameter divided by baseline diameter and standardized to scan interval.

Results: 32 patients met inclusion criteria (age: 56.9±14.1 years, Female: 13, n=19 rTAAD, n=13 dnTBAD). Mean follow up time was 538 days (range: 135-1689). Baseline aortic diameter did not correlate with growth rate. In the entire cohort, Δ FL MV (rho=0.37, p=.04) and Δ FL RF (rho=0.45, p=0.01) correlated with growth rate. In rTAAD only, Δ FL MV (rho=0.48, p=.04) and Δ FL RF (rho=0.51, p=0.03) correlated with growth rate, while in dnTBAD only, Δ TL KE (rho=0.63, p=.02) and Δ TL MV (rho=0.69, p=.01) correlated with growth rate.

Conclusions: 4D flow derived longitudinal hemodynamic changes correlate with aortic growth rate in TBAD and may provide additional prognostic value for risk stratification. 4D flow MRI could be integrated into existing imaging protocols to allow for identification of TBAD patients who would benefit from preemptive surgical or endovascular intervention.

Background: Patients with diabetes (DM) and heart failure (HF) have worse outcomes than normoglycaemic HF patients. Cardiovascular magnetic resonance (CMR) can identify ischaemic heart disease (IHD) and quantify coronary microvascular dysfunction (CMD) using myocardial perfusion reserve (MPR). We aimed to quantify extent of silent IHD and CMD in patients with DM presenting with HF.

Methods: Prospectively recruited outpatients undergoing assessment into the aetiology of HF underwent inline quantitative perfusion CMR for calculation of stress and rest myocardial blood flow (MBF) and MPR. Exclusions included angina or history of IHD. Patients were followed up (median 3.0 years) for major adverse cardiovascular events (MACE).

Results: Final analysis included 343 patients (176 normoglycaemic, 84 with pre-diabetes and 83 with DM). Prevalence of silent IHD was highest in DM (31%), then pre-diabetes (20%) then normoglycaemia (17%). Stress MBF was lowest in DM (1.53±0.52), then pre-diabetes (1.59±0.54) then normoglycaemia (1.83±0.62). MPR was lowest in DM (2.37±0.85) then pre-diabetes (2.41±0.88) then normoglycaemia (2.61±0.90). During follow up 45 patients experienced at least one MACE. On univariate Cox regression analysis MPR and presence of silent IHD were both associated with MACE. However, after correction for HbA1c, age and left ventricular ejection fraction the associations were no longer significant.

Conclusions: Patients with DM and HF had higher prevalence of silent IHD, more evidence of CMD and worse cardiovascular outcomes than their non-diabetic counterparts. These findings highlight the potential value of CMR for assessment of silent IHD and CMD in patients with DM presenting with HF.

Background: Aortic wall shear stress (WSS) is a known predictor of ascending aortic growth in patients with a bicuspid aortic valve (BAV). The aim of this study was to study regional WSS and changes over time in BAV patients.

Methods: BAV patients and age-matched healthy controls underwent 4D flow CMR. Regional, peak systolic ascending aortic WSS, aortic valve function, aortic stiffness measures and aortic dimensions were assessed. In BAV patients, 4D flow CMR was repeated after three years follow-up and both at baseline and follow-up computed tomography angiography (CTA) was acquired. Aortic growth (volume increase of ≥5%) was measured on CTA. Regional WSS differences within patients' aorta and WSS changes over time were analysed using linear mixed-effect models and were associated with clinical parameters.

Results: Thirty BAV patients (aged 34 years [IQR 25-41]) were included in the follow-up analysis. Additionally, another 16 BAV patients and 32 healthy controls (aged 33 years [IQR 28-48]) were included for other regional analyses. Magnitude, axial, and circumferential WSS increased over time (all p<0.001) irrespective of aortic growth. The percentage of regions exposed to a magnitude WSS >95th percentile of healthy controls increased from 21% (baseline 506/2400 regions) to 31% (follow-up 734/2400 regions) (p<0.001). WSS angle, a measure of helicity near the aortic wall, decreased during follow-up. Magnitude WSS changes over time were associated with systolic blood pressure, peak aortic valve velocity, aortic valve regurgitation fraction, aortic stiffness indexes, and normalized flow displacement (all p<0.05).

Conclusions: An increase of regional WSS over time was observed in BAV patients, irrespective of aortic growth. The increasing WSSs comprising a larger area of the aorta warrants further research to investigate the possible predictive value for aortic dissection.