Journal of Cardiovascular Magnetic Resonance最新文献

"Cases of SCMR" is a case series on the SCMR website (https://www.scmr.org) for the purpose of education. The cases reflect the clinical presentation and the use of cardiovascular magnetic resonance in the diagnosis and management of cardiovascular disease. The 2023 digital collection of cases is presented in this article.

Background: Quantitative stress cardiac magnetic resonance (CMR) can be performed using the dual-sequence (DS) technique or dual-bolus (DB) method. It is unknown if DS and DB produce similar results for myocardial blood flow (MBF) and myocardial perfusion reserve (MPR). The study objective is to investigate if there are any differences between DB- and DS-derived MBF and MPR.

Methods: Retrospective observational study with 168 patients who underwent stress CMR. DB and DS methods were simultaneously performed on each patient on the same day. Global and segmental stress MBF and rest MBF values were collected.

Results: Using Bland-Altman analysis, segmental and global stress MBF values were higher in DB than DS (0.22 ± 0.60 mL/g/min, p < 0.001 and 0.20 ± 0.48 mL/g/min, p = 0.005, respectively) with strong correlation (r = 0.81, p < 0.001 for segmental and r = 0.82, p < 0.001 for global). In rest MBF, segmental and global DB values were higher than by DS (0.15 ± 0.51 mL/g/min, p < 0.001 and 0.14 ± 0.36 mL/g/min, p = 0.011, respectively) with strong correlation (r = 0.81, p < 0.001 and r = 0.77, p < 0.001). Mean difference between MPR by DB and DS was -0.02 ± 0.68 mL/g/min (p = 0.758) for segmental values and -0.01 ± 0.49 mL/g/min (p = 0.773) for global values. MPR values correlated strongly as well in both segmental and global, both (r = 0.74, p < 0.001) and (r = 0.75, p < 0.001), respectively.

Conclusion: There is a very good correlation between DB- and DS-derived MBF and MPR values. However, there are significant differences between DB- and DS-derived global stress and rest MBF. While MPR values did not show statistically significant differences between DB and DS methods.

Background: Aortic blood flow characterization by four-dimensional (4D) flow cardiovascular magnetic resonance (CMR) is increasingly performed in aneurysm research. A limited number of studies have established normal values that can aid the recognition of abnormal flow at an early stage. This study aims to establish additional sex-specific and age-dependent reference values for flow-related parameters in a large cohort of healthy adults.

Methods: Two hundred and twelve volunteers were included, and 191 volunteers completed the full study protocol. All underwent 4D flow CMR of the entire aorta. Quantitative values for velocity, vorticity, helicity, as well as total, circumferential, and axial wall shear stress (WSS) were determined for the aortic root (AoR), ascending aorta (AAo), aortic arch, descending aorta (DAo), suprarenal aorta, and infrarenal aorta. Vorticity and helicity were indexed for segment volume (mL).

Results: The normal values were estimated per sex and age group, where significant differences between males (M) and females (F) were found only for specific age groups. More specifically, the following variables were significantly different after applying the false discovery rate correction for multiple testing: 1) velocity in the AAo and DAo in the 60-70 years age group (mean ± SD: (M) 47.0 ± 8.2 cm s^-1 vs (F) 38.4 ± 6.9 cm s^-1, p = 0.001 and, (M) 55.9 ± 9.9 cm s^-1 vs (F) 46.5 ± 5.5 cm s^-1, p = 0.002), 2) normalized vorticity in AoR in the 50-59 years age group ((M) 27,539 ± 5042 s^-1 mL^-1 vs (F) 30,849 ± 7285 s^-1 mL^-1, p = 0.002), 3) axial WSS in the Aao in the 18-29 age group ((M) 1098 ± 203 mPa vs (F) 921 ± 121 mPa, p = 0.002). Good to strong negative correlations with age were seen for almost all variables, in different segments, and for both sexes.

Conclusion: This study describes reference values for aortic flow-related parameters acquired by 4D flow MRI. We observed limited differences between males and females. A negative relationship with age was seen for almost all flow-related parameters and segments.

Background: Fully automatic analysis of myocardial perfusion MRI datasets enables rapid and objective reporting of stress/rest studies in patients with suspected ischemic heart disease. Developing deep learning techniques that can analyze multi-center datasets despite limited training data and variations in software (pulse sequence) and hardware (scanner vendor) is an ongoing challenge.

Methods: Datasets from 3 medical centers acquired at 3T (n = 150 subjects; 21,150 first-pass images) were included: an internal dataset (inD; n = 95) and two external datasets (exDs; n = 55) used for evaluating the robustness of the trained deep neural network (DNN) models against differences in pulse sequence (exD-1) and scanner vendor (exD-2). A subset of inD (n = 85) was used for training/validation of a pool of DNNs for segmentation, all using the same spatiotemporal U-Net architecture and hyperparameters but with different parameter initializations. We employed a space-time sliding-patch analysis approach that automatically yields a pixel-wise "uncertainty map" as a byproduct of the segmentation process. In our approach, dubbed Data Adaptive Uncertainty-Guided Space-time (DAUGS) analysis, a given test case is segmented by all members of the DNN pool and the resulting uncertainty maps are leveraged to automatically select the "best" one among the pool of solutions. For comparison, we also trained a DNN using the established approach with the same settings (hyperparameters, data augmentation, etc.).

Results: The proposed DAUGS analysis approach performed similarly to the established approach on the internal dataset (Dice score for the testing subset of inD: 0.896 ± 0.050 vs. 0.890 ± 0.049; p = n.s.) whereas it significantly outperformed on the external datasets (Dice for exD-1: 0.885 ± 0.040 vs. 0.849 ± 0.065, p < 0.005; Dice for exD-2: 0.811 ± 0.070 vs. 0.728 ± 0.149, p < 0.005). Moreover, the number of image series with "failed" segmentation (defined as having myocardial contours that include bloodpool or are noncontiguous in ≥1 segment) was significantly lower for the proposed vs. the established approach (4.3% vs. 17.1%, p < 0.0005).

Conclusions: The proposed DAUGS analysis approach has the potential to improve the robustness of deep learning methods for segmentation of multi-center stress perfusion datasets with variations in the choice of pulse sequence, site location or scanner vendor.

Background: Four-dimensional cardiovascular magnetic resonance flow imaging (4D flow CMR) 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 the reproducibility of aortic hemodynamics visualization and quantitative flow-related parameter computation. This paper explores the potential of deep learning to improve 4D flow CMR 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 CMR 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 three-dimensional U-net convolutional neural network (CNN) architecture with residual units was trained for time-resolved two-dimensional aortic cross-sectional segmentation. 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 CMR, with a particular focus on the inclusion of different pathologies and technical aspects of data acquisition.

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 magnetic resonance imaging system. All participants underwent T1ρ at two spin-locking frequencies: 0 and 298 Hz. Extracellular volume (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 [298 Hz]: 3.1%; T1ρ [0 Hz], 2.5%). The ECV (HC: 27.4 ± 2.8% vs HCM: 32.6 ± 5.8% vs CA: 46 ± 8.9%; p < 0.0001), T1ρ [0 Hz] (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 [298 Hz] (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 [298 Hz] showed the greatest sensitivity (98.3%) and specificity (95.5%) between CA and HCM, compared with the native T1 and ECV.

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

Background: Exertional heatstroke (EHS) is increasingly common in young trained soldiers. However, prognostic markers in EHS patients remain unclear. The objective of this study was to evaluate cardiovascular magnetic resonance (CMR) feature tracking derived left ventricle (LV) strain as a biomarker for return to training (RTT) in trained soldiers with EHS.

Methods: Trained soldiers (participants) with EHS underwent CMR 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 was 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 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 (-15.8 ± 1.7% vs -16.9 ± 1.2%, P = 0.001), which also showed significant statistical differences between participants with RTT and non-RTT (-15.0 ± 3.5% vs -16.5 ± 1.4%, P < 0.001). 2D-GLS (≤ -15.0%) (odds ratio, 1.53; 95% confidence interval: 1.08, 2.17; P = 0.016) 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 = 0.013; 0.79 vs 0.88, P = 0.023; respectively).

Conclusion: Two-dimensional global longitudinal strain (≤ -15.0%) is an incremental prognostic CMR biomarker to predict RTT in soldiers suffering from EHS.

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

Methods: Prospectively recruited outpatients undergoing assessment into the etiology of HF underwent in-line 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 normoglycemic, 84 with pre-diabetes, and 83 with DM). Prevalence of silent IHD was highest in DM 31% ( 26/83), then pre-diabetes 20% (17/84) then normoglycemia 17%, ( 30/176). Stress MBF was lowest in DM (1.53 ± 0.52), then pre-diabetes (1.59 ± 0.54) then normoglycemia (1.83 ± 0.62). MPR was lowest in DM (2.37 ± 0.85) then pre-diabetes (2.41 ± 0.88) then normoglycemia (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.

Conclusion: 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 the 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 four-dimensional (4D) flow cardiovascular magnetic resonance (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 3 years of follow-up and both at baseline and follow-up computed tomography angiography (CTA) were acquired. Aortic growth (volume increase of ≥5%) was measured on CTA. Regional WSS differences within patients' aorta and WSS changes over time were analyzed using linear mixed-effect models and were associated with clinical parameters.

Results: Thirty BAV patients (aged 34 years [interquartile range (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).

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