Journal of Cardiovascular Magnetic Resonance最新文献

Background: Exercise cardiovascular magnetic resonance (ExCMR) imaging using supine in-scanner ergometer has shown promise in differentiating pathological dilated cardiomyopathy (DCM) from physiological exercise-induced cardiac remodeling. Since 2020, the National Heart Centre Singapore (NHCS) has incorporated ExCMR into its clinical workflow for patients with suspected DCM. This study aims to compare the costs associated with ExCMR versus conventional CMR in the evaluation of DCM.

Methods: A retrospective analysis was conducted on patients referred for conventional CMR between 2016 and 2019, and those referred for ExCMR from 2020 to 2023. Both imaging modalities followed standardized protocols, with ExCMR incorporating additional assessments during peak exercise. Costs were recorded in Singapore dollars (SGD) prior to the application of healthcare subsidies.

Results: The total cost for conventional CMR was SGD 1831.36, while ExCMR was associated with a higher initial cost of SGD 2336.48. However, ExCMR resulted in significantly fewer abnormal imaging findings and a reduced need for follow-up investigations (6.5% (9/139) vs 56.8% (71/125), p<0.001). A decision tree analysis and probabilistic sensitivity analysis (PSA) revealed that diagnosing 1000 suspected DCM patients with ExCMR could result in a cost savings of approximately SGD 182,323 compared to conventional CMR, with a 64% probability of being cost-effective.

Conclusion: These findings indicate that ExCMR offers a physiologically informative approach for diagnosing DCM, with the potential to reduce overdiagnosis of cardiac dilatation in active, healthy adults. Although further research is necessary to assess long-term outcomes, ExCMR appears to be a cost-effective imaging modality for DCM diagnosis, warranting reconsideration of its perceived higher cost.

Background: Cardiac magnetic resonance imaging (CMR) studies contain a wealth of information on a patient's cardiovascular status. The ability to extract this data from free-text reports could serve to automate clinical decision support tools and generate data for retrospective clinical knowledge discovery, and clinical operational purposes. Few studies have examined the automatic extraction of data from free-text CMR reports, and the existing studies that do have key limitations, including small sample size and disease-specific data extraction. Existing studies also fail to extract features associated with the cardiovascular conditions that reflect nuances in natural language, such as uncertainty, severity, subtype, and anatomical locations of the condition. The goal of this study was to build a broad named entity recognition model to automatically extract a broad variety of common CMR findings and their associated attributes from CMR reports.

Methods: We fine-tuned a Large Language Model Meta AI (LLaMA) model trained to identify 34 cardiovascular conditions and their associated attributes, including certainty, severity, location, and subtype of the condition. This model was trained on 1778 MRI reports and tested on 397 reports in an held-out test set and another 428 reports from another site in our hospital system with independent radiology practice and scanners.

Results: Our model shows robust performance in predicting the mention of the 31 cardiovascular conditions (average F1=0.85). It also showed strong performance predicting attributes, including certainty (average F1=0.97) and severity (average F1=0.97). Model performance on the external validation set was generally slightly lower than the internal validation set, but performance was still strong (average F1=0.78 for mention, 0.97 for certainty, and 0.96 for severity).

Conclusion: CMR-LLaMA has strong performance identifying a variety of concept mentions and moderate accuracies in extracting a selection of other associated attributes. NLP models can be used to automate the extraction of data from CMR reports to potentially assist with clinical and research workflow.

Background: First-pass stress-perfusion cardiovascular magnetic resonance (CMR) imaging is the guidelines-recommended non-invasive test for the detection of obstructive coronary artery disease (CAD). Recently developed quantitative perfusion CMR (QP CMR) allows quantification of myocardial blood flow. Moreover, the latest developments established several methods of CAD assessment without the need for a contrast agent, including stress T1 mapping reactivity (∆T1) and oxygenation-sensitive CMR (OS-CMR). These methods might eliminate the need for contrast administration in clinical practice, reducing time, invasiveness, and costs, thereby simplifying the evaluation of patients with suspected obstructive CAD. The ADVOCATE-CMR study aims to validate QP CMR, ∆T1, and OS-CMR imaging against invasive fractional flow reserve (FFR) for the detection of obstructive CAD. The study also aims to head-to-head compare the diagnostic accuracy of these CMR techniques with the conventional visual assessment of stress-perfusion CMR and to correlate them to short- and long-term clinical outcomes.

Study design and methodology: ADVOCATE-CMR is a single-center, observational, prospective, cross-sectional cohort study. The study will enroll 182 symptomatic patients with suspected obstructive CAD scheduled for invasive coronary angiography (ICA). Before ICA, all participants will undergo CMR imaging, including OS-CMR with breathing maneuvers, rest, and adenosine stress T1 mapping and rest and adenosine stress first-pass perfusion. Subsequently, ICA will be performed, including FFR, instantaneous wave-free ratio, resting Pd/Pa, coronary flow reserve, and index of microvascular resistance measurements in all main coronary arteries. A follow-up CMR scan with the same protocol will be performed at 3 months after ICA. Clinical follow-up will be performed at 3, 6 months, 1 and 3 years after ICA.

Conclusion: The ADVOCATE-CMR will be the first study comprehensively evaluating and comparing head-to-head the diagnostic performance of a range of contrast- and non-contrast agent-based CMR imaging methods (including QP CMR, ∆T1, and OS-CMR) for the detection of FFR-defined obstructive CAD. We expect to establish a validated and time-efficient diagnostic workflow available to a wide range of general CMR services. Finally, these improvements may enable CMR to become an effective non-invasive, radiation-free gatekeeper for ICA in patients with suspected obstructive CAD, potentially without the need for a contrast agent.

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: Extracellular volume (ECV) determined by cardiovascular magnetic resonance (CMR) is considered a marker of diffuse myocardial fibrosis and a predictor of mortality. Using personalized computational models, we investigated the relationship between ECV, conduction velocity (CV), and cell radius in aortic stenosis (AS) patients.

Methods: CMR was performed on 12 AS patients (6 males, 6 females) before and three months after surgical aortic valve replacement (AVR). All patients had a QRS duration ≤110ms, and no scar on late gadolinium enhanced (LGE) CMR. Computational biventricular models were developed from each CMR dataset. Using patient-specific ECV and the relative change in cell radius between the time points as inputs, tissue conductivity was adjusted in each model to match the patient's QRS duration. A physiological pattern of ventricular depolarization was mimicked by simultaneously pacing each model from five activation sites. CV was measured during a simulation of apical pacing, using two points positioned at the right ventricular septum of the model.

Results: Left ventricular mass decreased after AVR (62 [58-79] vs 51 [41-60]g/m², p=0.0005) while ECV increased (24.2 [20.6-24.8] vs 28.0 [25.1-29.5] %, p=0.0008). No changes in the patient's QRS duration (89.0 [80.5-99.0] vs 88 [78.5-99.5]ms, p=0.2148) were observed. No changes in the CV obtained from the models (64.3 [61.9-72.8] vs 66.0 [60.0-74.5]cm/s, p=0.5186) were found between the time points, suggesting there was no substantial increase in diffuse fibrosis. ECV was negatively correlated with cell radius (r=-0.5267, p=0.0082), but not correlated with CV obtained from the models (r=-0.2036, p=0.3399).

Conclusion: Increased ECV three months after AVR in patients with no LGE scar and with normal ventricular conduction appears to be a footprint of reverse ventricular remodeling that does not necessarily translate into changes in myocardial CV.

Background: Cardiac diffusion tensor imaging (cDTI) is sensitive to imaging parameters, including the number of unique diffusion encoding directions (ND) and number of repetitions (NR; analogous to number of signal averages). However, there is no clear guidance for optimizing these parameters in the clinical setting.

Methods: Spin echo cDTI data with second-order motion-compensated diffusion encoding gradients were acquired in 10 healthy volunteers on a 3T magnetic resonance imaging scanner with different diffusion encoding schemes in pseudo-randomized order. The data were subsampled to yield 96 acquisition schemes with 6 ≤ ND ≤ 30 and 33 ≤ total number of acquisitions (NA_all) ≤ 180. Stratified bootstrapping with robust fitting was performed to assess the accuracy and precision of each acquisition scheme. This was quantified across a mid-ventricular short-axis slice in terms of root mean squared difference (RMSD), with respect to the full reference dataset, and standard deviation (SD) across bootstrap samples, respectively.

Results: For the same acquisition time, the ND = 30 schemes had on average 48%, 40%, 34%, and 34% lower RMSD and 6.2%, 7.4%, 10%, and 5.6% lower SD in mean diffusivity (MD), fractional anisotropy (FA), helix angle (HA), and absolute sheetlet angle (|E2A|) compared to the ND = 6 schemes. Given a fixed number of high b-value acquisitions, there was a trend toward lower RMSD and SD of MD and FA with increasing numbers of low b-value acquisitions. Higher NA_all with longer acquisition times led to improved accuracy in all metrics, whereby quadrupling NA_all from 40 to 160 volumes led to a 20%, 39%, 11%, and 5.4% reduction in RMSD of MD, FA, HA, and |E2A|, respectively, averaged across six diffusion encoding schemes. Precision was also improved with a corresponding 53%, 50%, 53%, and 36% reduction in SD.

Conclusion: We observed that accuracy and precision were enhanced by (i) prioritizing number of diffusion encoding directions over NR given a fixed acquisition time, (ii) acquiring sufficient low b-value data, and (iii) using longer protocols where feasible. For clinically relevant protocols, our findings support the use of ND = 30 and NA_b50:NA_b500 ≥ 1/3 for better accuracy and precision in cDTI parameters. These findings are intended to help guide protocol optimization for harmonization of cDTI.

Background: Acute aortic syndromes in Marfan syndrome (MFS) often occur before reaching the surgical diameter threshold, highlighting the need for new imaging biomarkers.

Objectives: Aim was to compare cardiovascular magnetic resonance (CMR)-derived aortic three-dimensional (3D) distensibility and displacement in MFS patients with or without a history of aortic root surgery (RR or native) and healthy volunteers.

Methods: The participants underwent 3T CMR of the thoracic aorta using an accelerated non-contrast-enhanced, free breathing, 3D cine balanced steady state free precession sequence, with spatiotemporal resolution: (1.0 mm)³/∼33ms. A deep learning-based algorithm was used to obtain aorta segmentations. Non-rigid registration of these segmentations was subsequently used to calculate 3D distensibility and its separate components: 2-dimensional distensibility, longitudinal strain, and displacement in the ascending (AAo) and descending aorta (DAo).

Results: Forty-seven volunteers, 51 native, and 33 RR MFS patients were included. AAo and DAo distensibility (10^-3*mmHg^-1) were different for healthy volunteers vs native vs RR patients (AAo: 5.1±1.4 vs 3.6±1.4 vs. 1.4±0.7, p<0.001, DAo: 3.2±1.1 vs. 2.5±0.9 vs 2.4±1.0, p=0.001). Sinotubular junction displacement (mm) was significantly higher for healthy volunteers vs native MFS vs RR MFS patients (10.3±1.3 vs 8.7±2.1 vs 5.7±1.6, p<0.001). In native patients, age (β=-0.06 (95% CI:-0.10 to -0.01), p=0.014) and root diameter (β=-0.1 (95% CI: -0.19 to -0.02), p=0.018) were negatively associated with AAo 3D distensibility, independent of male sex, body surface area, and aortic tortuosity index.

Conclusion: Aortic 3D distensibility and displacement, derived from 4-dimensional CMR, were significantly diminished in MFS compared to volunteers and should be investigated longitudinally to assess their potential value in predicting aortic events and guiding therapy.

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.