Journal of Cardiovascular Magnetic Resonance最新文献

Background: Myocardial fibrosis is a common feature in various cardiac diseases. It causes adverse cardiac remodeling and is associated with poor clinical outcomes. Late gadolinium enhancement (LGE) and extracellular volume fraction (ECV) are the standard MRI techniques for detecting focal and diffuse myocardial fibrosis. However, these contrast-enhanced techniques require the administration of gadolinium contrast agents, which is not applicable to patients with gadolinium contraindications. To eliminate the need of contrast agents, we develop and apply an endogenous free-breathing T1ρ dispersion imaging technique (FB-MultiMap) for diagnosing diffuse myocardial fibrosis in a cohort with suspected cardiomyopathies.

Methods: The proposed FB-MultiMap technique, enabling T2, T1ρ and their difference (myocardial fibrosis index, mFI) quantification in a single scan was developed in phantoms and 15 healthy subjects. In the clinical study, 55 patients with suspected cardiomyopathies were imaged using FB-MultiMap, conventional native T1 mapping, LGE, and ECV imaging. The accuracy of the endogenous parameters for predicting increased ECV was evaluated using receiver operating characteristic (ROC) curve analysis. In addition, the correlation of native T1, T1ρ, and mFI with ECV was respectively assessed using Pearson correlation coefficients.

Results: FB-MultiMap showed a good agreement with conventional separate breath-hold mapping techniques in phantoms and healthy subjects. Considering all the patients, T1ρ was more accurate than mFI and native T1 for predicting increased ECV, with area under the curve (AUC) values of 0.91, 0.79 and 0.75, respectively, and showed stronger correlation with ECV (correlation coefficient r: 0.72 vs. 0.52 vs. 0.40). In the subset of 47 patients with normal T2 values, the diagnostic performance of mFI was significantly strengthened (AUC=0.90, r=0.83), outperforming T1ρ and native T1.

Conclusion: The proposed free-breathing T1ρ dispersion imaging technique enabling simultaneous quantification of T2, T1ρ and mFI in a single scan has shown great potential for diagnosing diffuse myocardial fibrosis in patients with complex cardiomyopathies without contrast agents.

Background: Cardiovascular magnetic resonance (CMR) chemical shift encoding (CSE) enables myocardial fat imaging. We sought to develop a deep learning network (FastCSE) to accelerate CSE.

Methods: FastCSE was built on a super-resolution generative adversarial network extended to enhance complex-valued image sharpness. FastCSE enhances each echo image independently before water-fat separation. FastCSE was trained with retrospectively identified cines from 1519 patients (56 ± 16 years; 866 men) referred for clinical 3T CMR. In a prospective study of 16 participants (58 ± 19 years; 7 females) and 5 healthy individuals (32 ± 17 years; 5 females), dual-echo CSE images were collected with 1.5 × 1.5mm², 2.5 × 1.5 mm², and 3.8 × 1.9mm² resolution using generalized autocalibrating partially parallel acquisition (GRAPPA). FastCSE was applied to images collected with resolution of 2.5 × 1.5mm² and 3.8 × 1.9 mm² to restore sharpness. Fat images obtained from two-point Dixon reconstruction were evaluated using a quantitative blur metric and analyzed with 5-way analysis of variance.

Results: FastCSE successfully reconstructed CSE images inline. FastCSE acquisition, with a resolution of 2.5 × 1.5mm² and 3.8 × 1.9 mm², reduced the number of breath-holds without impacting visualization of fat by approximately 1.5-fold and 3-fold compared to GRAPPA acquisition with a resolution of 1.5 × 1.5 mm², from 3.0 ± 0.8 breath-holds to 2.0 ± 0.2 and 1.1 ± 0.4 breath-holds, respectively. FastCSE improved image sharpness and removed ringing artifacts in GRAPPA fat images acquired with a resolution of 2.5 × 1.5 mm² (0.31 ± 0.03 vs. 0.35 ± 0.04, P < 0.001) and 3.8 × 1.9 mm² (0.31 ± 0.03 vs. 0.42 ± 0.06, P < 0.001). Blurring in FastCSE images was similar to blurring in images with 1.5 × 1.5 mm² resolution (0.32 ±0.03 vs. 0.31 ± 0.03, P = 0.78; 0.32 ± 0.03 vs. 0.31 ± 0.03, P = 0.90).

Conclusion: We showed that a deep learning-accelerated CSE technique based on complex-valued resolution enhancement can reduce the number of breath-holds in CSE imaging without impacting the visualization of fat. FastCSE showed similar image sharpness compared to a standardized parallel imaging method.

Purpose: To apply free-running three-dimensional (3D) cine balanced steady state free precession (bSSFP) CMR framework in combination with AI segmentations to quantify time-resolved aortic displacement, diameter and diameter change.

Methods: In this prospective study, we implemented a free-running 3D cine bSSFP sequence with scan time of about 4minutes facilitated by pseudo-spiral Cartesian undersampling and compressed-sensing reconstruction. Automated segmentation of all cardiac timeframes was applied through the use of nnU-Net. Dynamic 3D motion maps were created for three repeated scans per volunteer, leading to the detailed quantification of motion, as well as the measurement and change in diameter of the ascending aorta.

Results: A total of 14 adult healthy volunteers (median age, 28 years (IQR: 26.0-31.3), 6 female) were included. Automated segmentation compared to manual segmentation of the aorta test set showed a Dice score of 0.93 ± 0.02. The median (interquartile range) over all volunteers for the largest maximum and mean ascending aorta (AAo) displacement in the first scan was 13.0 (4.4) mm and 5.6 (2.4) mm, respectively. Peak mean diameter in the AAo was 25.9 (2.2) mm and peak mean diameter change was 1.4 (0.5) mm. The maximum individual variability over the three repeated scans of maximum and mean AAo displacement was 3.9 (1.6) mm and 2.2 (0.8) mm, respectively. The maximum individual variability of mean diameter and diameter change were 1.2 (0.5) mm and 0.9 (0.4) mm.

Conclusion: A free-running 3D cine bSSFP CMR scan with a scan time of four minutes combined with an automated nnU-net segmentation consistently captured the aorta's cardiac motion-related 4D displacement, diameter, and diameter change.

Objectives: Patients with syndromic heritable thoracic aortic diseases (sHTAD) who underwent prophylactic aortic root replacement are at high risk of distal aortic events, but the underlying mechanisms are poorly understood. This prospective, longitudinal study aims to assess the impact of valve-sparing aortic root replacement (VSARR) on aortic fluid dynamics and biomechanics in these patients, and to examine whether they present altered haemodynamics or biomechanics prior to surgery compared to sHTAD patients with no indication for surgery (sHTAD-NSx) and healthy volunteers (HV).

Methods: Sixteen patients with Marfan or Loeys-Dietz syndrome underwent two 4D flow CMR studies before (sHTAD-preSx) and after VSARR (sHTAD-postSx). Two age, sex and BSA matched cohorts of 40 HV and 16 sHTAD-NSx patients with available 4D flow CMR, were selected for comparison. In-plane rotational flow (IRF), systolic flow reversal ratio (SFRR), wall shear stress (WSS), pulse wave velocity (PWV) and aortic strain were analysed in the ascending (AscAo) and descending aorta (DescAo).

Results: All patients with sHTAD presented altered haemodynamics and increased aortic stiffness (p<0.05) compared to HV, both in the AscAo (median PWV 7.4 in sHTAD-NSx; 6.8 in sHTAD-preSx; 4.9m/s in HV) and DescAo (median PWV 9.1 in sHTAD-NSx; 8.1 in sHTAD-preSx; 6.3m/s in HV). Patients awaiting VSARR had markedly reduced in-plane (median IRF -2.2 vs 10.4 cm²/s in HV, p=0.001), but increased through-plane flow rotation (median SFRR 7.8 vs 3.8% in HV, p=0.002), and decreased WSS (0.36 vs 0.47N/m² in HV, p=0.004) in the proximal DescAo. After VSARR, proximal DescAo in-plane rotational flow (p=0.010) and circumferential WSS increased (p=0.011), no longer differing from HV, but through-plane rotational flow, axial WSS and stiffness remained altered. Patients in which aortic tortuosity was reduced after surgery showed greater post-surgical increase in IRF compared to those in which tortuosity increased (median IRF increase 18.1 vs 3.3cm²/s, p=0.047). Most AscAo flow alterations were restored to physiological values after VSARR.

Conclusions: In patients with sHTAD, VSARR partially restores downstream fluid dynamics to physiological levels. However, some flow disturbances and increased stiffness persist in the proximal DescAo. Further longitudinal studies are needed to evaluate whether persistent alterations contribute to post-surgical risk.

Aims: Myocardial inflammation is increasingly detected non-invasively by tissue mapping with cardiovascular magnetic resonance (CMR). Intraindividual agreement with endomyocardial biopsy (EMB) or marker of myocardial injury, high-sensitive troponin (hs-cTnT) in patients with clinically suspected viral myocarditis not understood.

Methods and results: Prospective multicentre study of consecutive patients with clinically suspected myocarditis who underwent blood testing for hs-cTnT, CMR and EMB as a part of diagnostic work-up. EMB was considered positive based on immunohistological criteria in line with the ESC definitions. CMR diagnoses employed tissue mapping using sequence-specific cut-off for native T1 and T2 mapping; active inflammation was defined as T1≥2SD and T2≥2SD above the mean of normal range. Hs-cTnT of greater than 13.9ng/1 was considered significant. A total of 114 patients (age (mean±SD) 54±16, 65% males) were included, of which 79(69%) had positive EMB-criteria, 64(56%) CMR criteria, and a total of 58 (51%) positive troponin. Agreement between EMB and CMR diagnostic criteria was poor (CMR vs. ESC: AUCs: 0.51 (0.39-0.62)). The agreement between the significant hs-cTnT rise and CMR-based diagnosis of myocarditis was good (AUC: 0.84 (0.68-0.92); p<0.001), but poor for EMB (0.50 (0.40-0.61). Hs-cTnT was significantly associated with native T1 and T2, hs-CRP and NT-pro BNP (r=0.37, r=0.35, r=0.30, r=0.25 p<0.001), but not immunohistochemical criteria or viral presence.

Conclusions: In clinically suspected viral myocarditis, all diagnostic approaches reflect the pathophysiological elements of myocardial inflammation, however the differing underlying drivers only partially overlap. The EMB and CMR diagnostic algorithms are neither interchangeable in terms of interpretation of myocardial inflammation nor in their relationship with myocardial injury.

"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 (CMR) in the diagnosis and management of cardiovascular disease. The 2023 digital collection of cases are presented in this manuscript.

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 underwent stress CMR. Dual bolus and dual sequence 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 + 0.60ml/g/min, p<0.001 and 0.20 + 0.48ml/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 + 0.51ml/g/min, p<0.001 and 0.14 + 0.36ml/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 + 0.68ml/g/min (p=0.758) for segmental values and -0.01 + 0.49ml/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.

Conclusions: There is 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. Whilst MPR values did not show statistically significant differences between DB and DS methods.

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: Aortic blood flow characterization by 4D flow MRI 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: 212 volunteers were included, and 191 volunteers completed the full study protocol. All underwent 4D flow MRI 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 [AoA], descending [DAo], suprarenal [SRA], and infrarenal aorta [IRA]. 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 ± 8.2cm/s vs. (F) 38.4 ± 6.9cm/s, p=0.001 and, (M) 55.9 ± 9.9cm/s vs. (F) 46.5 ± 5.5cm/s, p=0.002), 2) normalized vorticity in AoR in the 50-59 years age group ((M) 27539 ± 5042s^-1mL^-1 vs. (F) 30849 ± 7285s^-1mL^-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 as 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.