Journal of Cardiovascular Magnetic Resonance最新文献

Background: Cardiovascular magnetic resonance (CMR) has a growing role in the diagnosis and management of cardiac disease. However, there is little recent data on the availability of CMR physicians (readers) in the United States (US).

Objective: To demonstrate the geographic proximity and accessibility of patients to CMR services and CMR physicians across the US.

Methods: Using Medicare Part B data in 2022, we analyzed the number and characteristics of CMR readers, their geographical location, and the volume of CMR scans between 2013 and 2022. CMR procedure types were identified using healthcare common procedure coding system (HCPCS) codes 75557, 75559, 75561, and 75563.

Results: Among Medicare beneficiaries in 2022, there were 48,622 CMR scans, up from 17,944 in 2013 (170.9% increase). The lowest scans and reader density were in West Virginia (125.8 procedures and 2.2 readers per million beneficiaries, respectively) and the highest in the District of Columbia (4566.5 procedures and 52.9 readers per million beneficiaries, respectively). No CMR scans were billed in Puerto Rico. Among states and territories that billed for CMR, 50.8 million U.S. citizens were located more than 50 miles from CMR readers and 18.1 million were located more than 100 miles away. Out of 991 readers, 51.9% were radiologists and 48.1% were cardiologists. The median number of scans interpreted by cardiologists was higher than radiologists across all graduation year intervals, and male and female readers interpreted a similar median number of scans. The relative proportion of female readers increased markedly when assessing physicians who graduated after 2010.

Conclusion: This study highlights significant geographic disparities and barriers to accessing CMR in the US.

Background: Secondary pulmonary lymphangiectasia (PL) is a recognised complication of hypoplastic left heart syndrome (HLHS) with an intact or restrictive atrial septum, associated with poor postnatal outcomes. Fetal MRI has been increasingly used to assess pulmonary abnormalities in HLHS, but the prognostic significance of subtle PL-like changes remains unclear. In this study, we evaluate the relationship between fetal MRI lung findings, echocardiographic markers of pulmonary venous obstruction, and postnatal outcomes.

Methods: A retrospective analysis of all fetuses with HLHS who underwent fetal MRI between July 2019 and December 2022 was performed. MRI images were reviewed for features of PL and categorised as "normal," "suspicious," or "diagnostic" of PL. Pulmonary venous Doppler velocity-time integral (VTI) ratios from the most recent fetal echocardiogram were then compared to MRI findings. Postnatal outcomes, including early ventilation, need for intervention, and survival at 28 days and 1 year, were assessed.

Results: Of 20 fetuses with HLHS who underwent MRI, 6/20 (30%) showed features suspicious or diagnostic of PL (5 "suspicious" and 1 "diagnostic"), and 6/20 (30%) showed some evidence of pulmonary venous obstruction (PVO) on echo. While echo markers of PVO were significantly associated with some degree of PL on MRI (p=0.006), neither PL nor PVO predicted the need for early support/intervention or survival in fetuses who underwent active postnatal management.

Conclusion: Fetuses with HLHS may exhibit a spectrum of lung changes on fetal MRI related to pulmonary venous obstruction. Whilst technical factors may also play a role, a degree of caution is advisable when interpreting more subtle forms of PL in fetal life, particularly in the absence of echocardiographic markers of severe atrial restriction. Larger, multi-centre prospective studies are needed to refine diagnostic criteria for PL in HLHS and better understand its prognostic significance in terms of both early and long-term outcome.

Background: Myocardial blood flow (MBF) and myocardial perfusion reserve (MPR) can be quantified using vasodilator stress cardiovascular magnetic resonance (CMR). Exercise stress CMR (Ex-CMR) offers a more physiological assessment of cardiac functional reserve. While visual interpretation of Ex-CMR perfusion has been successfully applied, the feasibility of quantitative Ex-CMR perfusion remains unproven. We aimed to assess the feasibility of quantitative Ex-CMR perfusion imaging for characterizing exercise-induced perfusion responses and to perform a pilot study comparing MBF and MPR among patients with hypertrophic cardiomyopathy (HCM), heart failure with preserved ejection fraction (HFpEF), and non-cardiac dyspnea (NCD).

Methods: In this prospective study, patients with HCM, HFpEF, or NCD underwent Ex-CMR at 3T using a supine ergometer. Exercise was performed outside the scanner bore, followed by stress perfusion imaging 45-60 s post-exercise and rest perfusion 5-7 min later. A dual-sequence protocol with inline pixel-wise quantification was used to calculate MBF and MPR. Image quality and feasibility were visually assessed. Group comparisons were performed using analysis of variance and t-tests; linear regression was used to explore clinical associations.

Results: Of 108 patients enrolled, 9 were excluded due to obstructive coronary artery disease or reduced ejection fraction. Quantitative Ex-CMR was successful (at least one analyzable paired rest and post-exercise slice) in 90% (10/99) of cases. Most frequent quality issues were inadequate gating or arrhythmias and slice misalignment. The final cohort included 89 patients: 34 HCM, 34 HFpEF, and 21 NCD. Patients with HCM showed significantly lower MBF and MPR than HFpEF and NCD (MBF: 1.03 ± 0.27 vs 1.25 ± 0.40 and 1.13 ± 0.25 mL/min/g; MPR: 1.27 ± 0.21 vs 1.41 ± 0.29 and 1.44 ± 0.22; all p < 0.05). Peak exercise heart rate was the strongest independent predictor of MBF (β = 0.009, p < 0.001) and MPR (β = 0.004, p = 0.022).

Conclusion: Ex-CMR quantitative MBF and MPR assessment is feasible in most patients after image quality control. While the increase in MBF was limited during low-to-moderate exercise intensity in this pilot study, Ex-CMR revealed distinct perfusion response patterns among studied cohorts.

Background: Radiomics has been proven to be an important method for the quantitative assessment of atherosclerotic plaques. Therefore, we aimed to evaluate a radiomics approach based on 7.0T high-resolution vessel wall imaging (HR-VWI) to identify culprit middle cerebral artery (MCA) plaques associated with subcortical infarctions.

Methods: One hundred patients with MCA plaques were prospectively enrolled. Among these patients, 145 plaques (74 culprit plaques and 71 non-culprit plaques) were included. A traditional model was constructed by recording the conventional radiological plaque characteristics of HR-VWI. Radiomics features from HR-VWI images were utilized to construct a radiomics model. A combined model was built using both conventional radiological and radiomics features. Receiver operating characteristic (ROC) curves and area under curve (AUC) were used to compare the performance of these models.

Results: Plaque surface irregularity and superior wall location of MCA plaques were independently associated with subcortical infarctions. The traditional model had AUCs of 0.744 and 0.700 in the training and test sets, respectively. The radiomics and the combined model showed improved AUCs: 0.860 and 0.896 in the training sets and 0.795 and 0.833 in the test sets, respectively. The radiomics model was superior to the traditional model (p = 0.042) in the training set. The combined model outperformed the traditional model (training p < 0.001, test p = 0.048).

Conclusion: The radiomics approach based on 7.0T HR-VWI can accurately identify culprit plaques associated with subcortical infarctions, potentially better than conventional HR-VWI features.

Background: Stress perfusion cardiovascular magnetic resonance (CMR) in the presence of atrial fibrillation (AF) has long been challenging due to electrocardiogram (ECG) mis-triggering. However, non-invasive ischemia imaging is important due to an increased risk of myocardial infarction in patients with AF, which has been attributed to underlying microvascular dysfunction. Myocardial blood flow (MBF) in patients with AF is poorly understood, and few studies have attempted to quantify this through non-invasive imaging.

Methods: Patients were recruited for stress perfusion CMR using a research sequence at 3-Tesla. Image acquisition occurred during both vasodilator-induced hyperemia and at rest. Stress and rest MBF maps were automatically generated. Analysis of perfusion maps included assessment of myocardial perfusion reserve (MPR) and endocardial-to-epicardial MBF ratios.

Results: Around 442 patients were analyzed; 63 of whom had a history of AF and were in AF during the scan. Both MBF during hyperemia (stress MBF) and MPR were reduced in patients with AF compared to those in sinus rhythm (median stress MBF 1.85 [1.52-2.24] vs. 2.35 [1.98-2.77] mL/min/g, p<0.001; median MPR 1.95 [1.62-2.19] vs. 2.37 [2.05-2.80], p<0.001). No significant difference was seen between the two groups at rest (p=0.451). When considering co-factors affecting MBF, multivariate linear regression analysis identified the presence of AF as a significant independent contributor to stress MBF and MPR values. Both endocardial and epicardial stress MBF and MPR were reduced in AF compared with sinus rhythm (both p<0.001) and endocardial/epicardial ratios were similar between the groups.

Conclusion: Automated quantitative MBF assessment can be performed in patients with AF. At hyperemia, MBF is reduced in AF compared to sinus rhythm.

Background: Whole-heart coronary magnetic resonance angiography (CMRA) enables noninvasive and accurate detection of coronary artery stenosis. Nevertheless, the visual interpretation of CMRA is constrained by the observer's experience, necessitating substantial training. The purposes of this study were to develop a deep learning (DL) algorithm using a deep convolutional neural network to accurately detect significant coronary artery stenosis in CMRA and to investigate the effectiveness of this DL algorithm as a tool for assisting in accurate detection of coronary artery stenosis.

Methods: Nine hundred and fifty-one coronary segments from 75 patients who underwent both CMRA and invasive coronary angiography (ICA) were studied. Significant stenosis was defined as a reduction in luminal diameter of >50% on quantitative ICA. A DL algorithm was proposed to classify CMRA segments into those with and without significant stenosis. A four-fold cross-validation method was used to train and test the DL algorithm. An observer study was then conducted using 40 segments with stenosis and 40 segments without stenosis. Three radiology experts and three radiology trainees independently rated the likelihood of the presence of stenosis in each coronary segment with a continuous scale from 0 to 1, first without the support of the DL algorithm, then using the DL algorithm.

Results: Significant stenosis was observed in 84 (8.8%) of the 951 coronary segments. Using the DL algorithm trained by the four-fold cross-validation method, the area under the receiver operating characteristic curve (AUC) for the detection of segments with significant coronary artery stenosis was 0.890, with 83.3% sensitivity, 83.6% specificity, and 83.6% accuracy. In the observer study, the average AUC of trainees was significantly improved using the DL algorithm (0.898) compared to that without the algorithm (0.821, p < 0.001). The average AUC of experts tended to be higher with the DL algorithm (0.897), but not significantly different from that without the algorithm (0.879, p = 0.082).

Conclusion: We developed a DL algorithm offering high diagnostic accuracy for detecting significant coronary artery stenosis on CMRA. Our proposed DL algorithm appears to be an effective tool for assisting inexperienced observers to accurately detect coronary artery stenosis in whole-heart CMRA.

Background: Quantitative myocardial mapping is critical for tissue characterization in non-ischemic cardiomyopathy (NICM). However, conventional techniques require separate breath-hold acquisitions, prolonging scan time and impairing co-registration. This study aimed to assess the feasibility and diagnostic performance of a novel free-breathing multimap (FBmultimap) sequence enabling simultaneous T1, T2, and T1ρ mapping in a single acquisition.

Methods: Onehundred-nine participants were prospectively enrolled, including 48 with hypertrophic cardiomyopathy (HCM), 28 with dilated cardiomyopathy (DCM), and 33 healthy controls. All underwent cardiac MRI with both FBmultimap and conventional mapping sequences (modified Look-Locker inversion recovery (MOLLI) T1, T2-prepared balanced steady-state free precession (bSSFP), and T1ρ-prepared bSSFP). Image quality was assessed using subjective (four-point Likert scale) and objective (edge sharpness) methods. Myocardial relaxation times were analyzed in the following two subgroups: (1) HCM and DCM vs. controls, and (2) late gadolinium enhancement (LGE)-positive and LGE-negative patients vs. controls. Combined diagnostic indices (T1 + T1ρ) were derived using logistic regression. Diagnostic performance was evaluated using receiver operating characteristic analysis across the following six models: FBmultimap (T1 + T1ρ), FBmultimap T1, FBmultimap T1ρ, conventional (T1 + T1ρ), MOLLI T1, and T1ρ-prepared bSSFP, with area under the curve (AUC) calculated.

Results: FBmultimap significantly reduced total scan time for T1 + T2 + T1ρ mapping to 66±6 s, compared with 195±10 s using conventional methods (p<0.001), while maintaining comparable image quality (all p>0.05). T1 and T1ρ values measured by FBmultimap were significantly elevated in HCM and DCM groups compared to controls, regardless of LGE status (all p<0.05), whereas T2 values showed no significant differences. FBmultimap (T1 + T1ρ) achieved higher AUCs for distinguishing LGE-positive (0.904) and LGE-negative (0.859) patients from controls than FBmultimap T1 (0.877 and 0.829), FBmultimap T1ρ (0.608 and 0.764), MOLLI T1 (0.770 and 0.671), T1ρ-prepared bSSFP (0.734 and 0.778), and the conventional (T1 + T1ρ) model (0.801 and 0.819).

Conclusion: FBmultimap enables rapid, co-registered, free-breathing mapping of myocardial T1, T2, and T1ρ with high reproducibility and improved diagnostic performance over conventional single-parameter methods. It holds promise as a clinically applicable tool for myocardial fibrosis detection, risk stratification, and longitudinal monitoring in patients with HCM and DCM.

Background: Women and men have been found to display differences in their cardiovascular anatomy and physiology, including differences in their cellular composition. While studies have shown cellular and molecular changes across sexes, few have performed sex-based studies of myocardial microstructure for healthy subjects. The purpose of this study was to quantify the myocardial microstructure in large healthy cohorts across sexes using in-vivo cardiac diffusion tensor imaging (cDTI) based on a second-order motion-compensated (M2) single-shot spin-echo sequence performed on a commercial ultra-high-performance gradient system.

Methods: In this single-center and cross-sectional study, free-breathing cDTI with a M2 spin-echo diffusion-weighted imaging scheme was evaluated in 103 healthy adult subjects (mean age 33.0 years, 52 women) scanned using an MR system with maximum gradient strength of 200mT/m. The diffusion tensor model was fit to obtain cDTI parameters, including mean diffusivity (MD), fractional anisotropy (FA), and helix angle transmurality (HAT).

Results: Women and men did not show significantly different distributions of cDTI parameters (MD, FA, and HAT). Healthy subjects scanned with cDTI protocols performed on an MR system with ultra-high performance gradients have an average of 1.51±0.08 µm²/ms for MD, 0.30±0.02 for FA, and -0.77±0.09°/% for HAT. Furthermore, women were reported to have an average MD 1.52±0.08 µm²/ms, FA 0.30±0.02, HAT -0.76±0.09°/%. Men presented an average of MD 1.50±0.08 µm²/ms, FA 0.30±0.02, and HAT -0.77±0.09°/% (p>0.05 for all cDTI parameters between sexes).

Conclusion: This is the first and largest single-center study to investigate cDTI in a large cohort (N>100) of healthy subjects performed with an ultra-high-performance gradient MR system. No significant difference was discovered in MD, FA, and HAT between men and women, suggesting biological sex does not impact myocardial microstructure in healthy subjects. Future work using ultra-high-performance systems should focus on the evaluation of microstructural changes in patients with cardiovascular disease.

Background: Image reconstruction from highly undersampled four-dimensional (4D) flow magnetic resonance imaging (MRI) data can be very time-consuming and may result in significant underestimation of velocities depending on regularization, thereby limiting the applicability of the method. The objective of the present work was to develop a generalizable self-supervised deep learning-based framework for fast and accurate reconstruction of highly undersampled 4D flow MRI and to demonstrate the utility of the framework for aortic and cerebrovascular applications.

Methods: The proposed deep-learning-based framework, called FlowMRI-Net, employs physics-driven unrolled optimization using a complex-valued convolutional recurrent neural network and is trained in a self-supervised manner. The generalizability of the framework is evaluated using aortic and cerebrovascular 4D flow MRI acquisitions acquired on systems from two different vendors for various undersampling factors (R = 8, 16, 24) and compared to compressed sensing locally low rank (CS-LLR) reconstructions. Evaluation includes an ablation study and a qualitative and quantitative analysis of image and velocity magnitudes.

Results: FlowMRI-Net outperforms CS-LLR for aortic 4D flow MRI reconstruction, resulting in significantly lower vectorial normalized root mean square error and mean directional errors for velocities in the thoracic aorta. Furthermore, the feasibility of FlowMRI-Net's generalizability is demonstrated for cerebrovascular 4D flow MRI reconstruction. Reconstruction times ranged from 3 to 7 min on commodity central processing unit/graphical processing unit hardware.

Conclusion: FlowMRI-Net enables fast and accurate reconstruction of highly undersampled aortic and cerebrovascular 4D flow MRI, with possible applications to other vascular territories.

Background: Cardiac cine imaging is routinely used in patient with suspected or known cardiac dysfunction. Water and fat (W/F) separated cardiovascular magnetic resonance (CMR) will be helpful to distinguish adipose tissue, blood, and myocardium. Inclusion of a multi-echo acquisition in the conventional balanced steady-state free precession (bSSFP) cine sequence can introduce artifacts and reduce temporal resolution. Spiral MRI is known for its signal-to-noise ratio (SNR) efficiency and has the potential to improve temporal efficiency for W/F separated cine imaging. The present work implements a spoiled gradient echo sequence (SPGR) with spiral trajectory to obtain W/F separated cine images simultaneously.

Methods: Three different sequences were performed for comparison, a Cartesian 2-TE bSSFP sequence, a Cartesian 3-TE bSSFP sequence, and the proposed spiral SPGR sequence. Five volunteers were recruited for the scans on a 1.5T scanner with spatial resolution 1.7×1.7×8.0mm³ over a 400×400mm² FOV. In addition to qualitative comparisons, a quantitative measurement is performed in terms of the contrast-to-noise ratio (CNR).

Results: The proposed method to obtain W/F separated cine images provides better temporal efficiency and fewer artifacts compared to conventional Cartesian bSSFP sequences. The 2-TE bSSFP features the highest artifact level, including susceptibility artifacts and fat/water swaps. The proposed method reduces scan time by approximately 50% with similar spatial and temporal resolution with lower specific absorption rate (SAR). The contrast between the blood pool and myocardium is higher when using the spiral readout (p≤0.05). The results suggest that the presented sequence has potential to facilitate simultaneous imaging for water and fat components in a cine scan while shortening exam time and lowering SAR.