Cardiac MR: From Theory to Practice

Arterial Stiffness: A Strong Determinant of Abnormal Cardiac Magnetic Resonance Imaging in an Untreated Hypertensive Population

Objective

Hypertension significantly impacts cardiovascular health, leading to arterial stiffness and myocardial dysfunction. Pulse wave velocity (PWV) is a recognized measure of arterial stiffness, while cardiac magnetic resonance imaging (MRI) is the gold standard for assessing myocardial structure and function. The aim of the present study is to investigate the relationship between arterial stiffness, ambulatory blood pressure monitoring (ABPM), and cardiac MRI findings in untreated hypertensive individuals.

Methods

This cross-sectional study included 22 untreated hypertensive participants referred to the Hypertension ABPM Center of Excellence at Aristotle University of Thessaloniki. Participants underwent carotid-femoral PWV measurement and 24-hour ABPM. Cardiac function and structure were evaluated through cardiac MRI. Statistical analyses included Mann-Whitney and Kruskal-Wallis tests, with logistic regression for associations between c-f PWV and cardiac abnormalities. A significance threshold of p<0.05 was applied.

Results

The study population had increased office and 24-hour ABPM values. Cardiac MRI revealed systolic LV dysfunction in 31.8% and diastolic LV dysfunction in 63.6% of participants. Myocardial fibrosis was present in 50% of the participants. Elevated PWV was significantly associated with LV systolic dysfunction (p=0.003), LV diastolic dysfunction (p=0.002), myocardial stiffness (p<0.001), and myocardial fibrosis (p = 0.004). Additionally, aortic valve velocity was significantly associated with increased arterial stiffness (p=0.006). Post-hoc analysis of fibrosis showed significant differences (p=0.007 for minimal vs no fibrosis; p=0.011 for severe vs no fibrosis).

Conclusion

The study confirms a significant correlation between increased arterial stiffness, systolic ABPM-derived systolic blood pressure, and cardiac MRI dysfunction in untreated hypertensive individuals. These findings highlight the importance of arterial stiffness evaluation as a diagnostic tool for early detection of myocardial dysfunction, allowing for timely intervention and targeted treatment strategies to mitigate heart damage.

HALSR-Net: Improving CNN Segmentation of Cardiac Left Ventricle MRI with Hybrid Attention and Latent Space Reconstruction

Accurate cardiac MRI segmentation is vital for detailed cardiac analysis, yet the manual process is labor-intensive and prone to variability. Despite advancements in MRI technology, there remains a significant need for automated methods that can reliably and efficiently segment cardiac structures. This paper introduces HALSR-Net, a novel multi-level segmentation architecture designed to improve the accuracy and reproducibility of cardiac segmentation from Cine-MRI acquisitions, focusing on the left ventricle (LV). The methodology consists of two main phases: first, the extraction of the region of interest (ROI) using a regression model that accurately predicts the location of a bounding box around the LV; second, the semantic segmentation step based on HALSR-Net architecture. This architecture incorporates a Hybrid Attention Pooling Module (HAPM) that merges attention and pooling mechanisms to enhance feature extraction and capture contextual information. Additionally, a reconstruction module leverages latent space features to further improve segmentation accuracy. Experiments conducted on an in-house clinical dataset and two public datasets (ACDC and LVQuan19) demonstrate that HALSR-Net outperforms state-of-the-art architectures, achieving up to 98% accuracy and F1-score for the segmentation of the LV cavity and myocardium. The proposed approach effectively addresses the limitations of existing methods, offering a more accurate and robust solution for cardiac MRI segmentation, thereby likely to improve cardiac function analysis and patient care.

The state-of-the-art in cardiac MRI reconstruction: Results of the CMRxRecon challenge in MICCAI 2023

Cardiac magnetic resonance imaging (MRI) provides detailed and quantitative evaluation of the heart's structure, function, and tissue characteristics with high-resolution spatial-temporal imaging. However, its slow imaging speed and motion artifacts are notable limitations. Undersampling reconstruction, especially data-driven algorithms, has emerged as a promising solution to accelerate scans and enhance imaging performance using highly under-sampled data. Nevertheless, the scarcity of publicly available cardiac k-space datasets and evaluation platform hinder the development of data-driven reconstruction algorithms. To address this issue, we organized the Cardiac MRI Reconstruction Challenge (CMRxRecon) in 2023, in collaboration with the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). CMRxRecon presented an extensive k-space dataset comprising cine and mapping raw data, accompanied by detailed annotations of cardiac anatomical structures. With overwhelming participation, the challenge attracted more than 285 teams and over 600 participants. Among them, 22 teams successfully submitted Docker containers for the testing phase, with 7 teams submitted for both cine and mapping tasks. All teams use deep learning based approaches, indicating that deep learning has predominately become a promising solution for the problem. The first-place winner of both tasks utilizes the E2E-VarNet architecture as backbones. In contrast, U-Net is still the most popular backbone for both multi-coil and single-coil reconstructions. This paper provides a comprehensive overview of the challenge design, presents a summary of the submitted results, reviews the employed methods, and offers an in-depth discussion that aims to inspire future advancements in cardiac MRI reconstruction models. The summary emphasizes the effective strategies observed in Cardiac MRI reconstruction, including backbone architecture, loss function, pre-processing techniques, physical modeling, and model complexity, thereby providing valuable insights for further developments in this field.

How low can we go? The effect of acquisition duration on cardiac volume and function measurements in free-running cardiac and respiratory motion-resolved five-dimensional whole-heart cine magnetic resonance imaging at 1.5T

BACKGROUND

Cardiovascular magnetic resonance (CMR) is the gold standard for assessing cardiac volumes and function using two-dimensional (2D) breath-held cine imaging. This technique, however, requires a reliable electrocardiogram (ECG) signal, repetitive breath-holds, and the time-consuming and proficiency-demanding planning of cardiac views. Recently, a free-running framework has been developed for cardiac and respiratory motion-resolved five-dimensional (5D) whole-heart imaging without the need for an ECG signal, repetitive breath-holds, and meticulous plan scanning. In this study, we investigate the impact of acquisition time on cardiac volumetric and functional measurements, when using free-running imaging, compared to reference standard 2D cine imaging.

METHODS

Sixteen healthy adult volunteers underwent CMR at 1.5T, including standard 2D breath-held cine imaging and free-running imaging using acquisition durations ranging from 1 to 6 min in randomized order. All datasets were anonymized and analyzed for left-ventricular end-systolic volume (ESV) and end-diastolic volume (EDV), as well as ejection fraction (EF). In a subset of data, intra- and inter-observer agreement was assessed. In addition, image quality and observer confidence were scored using a 4-point Likert scale. Finally, acquisition efficiency was reported for both imaging techniques, which was defined as the time required for data sampling divided by the total scan time.

RESULTS

No significant differences in left-ventricular EDV and ESV were found between free-running imaging for 1, 2, 3, 5, and 6 min and standard 2D breath-held cine imaging. Biases in EDV ranged from -2.4 to -7.4 mL, while biases in ESV ranged from -3.8 to 2.1 mL. No significant differences in EF were found between free-running imaging of any acquisition duration and standard 2D breath-held cine imaging. Biases in EF ranged from -2.8% to 0.94%. Both image quality and observer confidence in free-running imaging improved when the acquisition duration increased. However, they were always lower than standard 2D breath-held cine imaging. Acquisition efficiency improved from 13% for standard 2D cine imaging to 50% or higher for free-running imaging.

CONCLUSION

Free-running CMR with an acquisition duration as short as 1min can provide left-ventricular cardiac volumes and EF comparable to standard 2D breath-held cine imaging, albeit at the expense of both image quality and observer confidence.

Improved ECG-gated cardiac cine imaging with variable initial value based tiny golden-angle radial trajectory

BACKGROUND

Breath-held electrocardiogram-gated cardiac cine imaging (ECG-CINE), as the gold standard for assessing cardiac function in magnetic resonance imaging (MRI), is prone to motion artifacts. Conventional golden-angle (CGA) sampling has emerged as a promising technique for mitigating motion effects in real-time cardiac cine imaging. However, in ECG-CINE, the irregular re-binning of radial k-space profiles based on CGA can exacerbate k-space non-uniformity, resulting in severe streaking artifacts. The recently introduced segmented golden-angle ratio (SGA) scheme aims to solve this problem; nevertheless, it sacrifices the desired motion insensitivity.

PURPOSE

The study aims to develop a more efficient k-space sampling scheme for ECG-CINE that guarantees both improved motion insensitivity and optimized k-space coverage.

METHOD

Theoretically, to enhance motion insensitivity, it is essential that the single-frame radial k-space profiles acquired within each heartbeat (HB) span as close to a full 360-degree range as possible. Meanwhile, to ensure uniform data coverage, the sequentially acquired k-space profiles need to be evenly distributed both within each HB and across multiple HBs. In this study, we propose a Variable Initial value-based tiny Golden-Angle radial trajectory (VIGA) to achieve these two goals. Specifically, VIGA is a two-step approach: First, the tiny golden-angle ratio is applied to the k-space profiles within each HB to maintain motion insensitivity and k-space uniformity as in CGA. Second, a golden ratio of the golden angle used within each HB is applied to the initial k-space profiles across adjacent HBs to optimize coverage further. We validated the proposed VIGA method through numerical simulations, phantom experiments, and prospective and retrospective in vivo cardiac cine experiments.

RESULTS

Numerical simulations revealed that the k-space uniformity of CGA is highly dependent on the number of spokes per HB, whereas VIGA and SGA maintained nearly optimal k-space coverage regardless of this parameter. Both phantom and prospective studies demonstrated that VIGA outperforms CGA when the number of spokes per HB is suboptimal, and surpasses SGA in conditions with residual respiratory motion. The standard deviation of gradient scores indicates statistical significance between CGA and VIGA under free-breathing conditions (p = 0.039) and between SGA and VIGA under all conditions tested (Free-breathing, 200 spokes/HB: p = 0.028; Breath-holding, 200 spokes/HB: p = 0.008; Free-breathing, 200 spokes/HB: p = 0.013; Breath-holding, 200 spokes/HB: p = 0.011). Retrospective results demonstrated that doctor ratings for SGA were lower than those for VIGA, and the ratings for systole images using VIGA were significantly higher than those using CGA (2.55 ± 0.45 vs. 3.29 ± 0.52; p = 0.04).

CONCLUSION

A novel and efficient k-space sampling scheme, named VIGA, was proposed to improve k-space uniformity and motion insensitivity. VIGA facilitates robust image quality in both prospective and retrospective cardiac cine imaging, demonstrating its potential as a clinically viable alternative to CGA and SGA.

Absence of cardiac impairment in patients after severe acute respiratory syndrome coronavirus type 2 infection: A long-term follow-up study

BACKGROUND

Concerns exist that long-term cardiac alterations occur after severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) infection, particularly in patients who were hospitalized in the acute phase or who remain symptomatic. This study investigates potential long-term functional and morphological alterations after SARS-CoV-2 infection.

METHODS

The authors of this study investigated patients after SARS-CoV-2 infection by using a mobile 1.5T clinical magnetic resonance scanner for cardiac alterations. Cardiac function and dimensions were assessed using a highly efficient cardiac magnetic resonance protocol, which included cine sequences, global longitudinal and circumferential strain assessed by fast-Strain-ENCoded imaging, and T1 and T2 mapping. We assessed symptoms through a questionnaire. Patients were compared with a control group matched for age, gender, body mass index, and body surface area.

RESULTS

Median follow-up time was 395 (192-408) days. The final population included 183 participants (age 48.4 ± 14.3 years, 48.1% male (88/183)). During the acute phase of SARS-CoV-2 infection, 27 patients were hospital-admitted. Forty-two patients reported persistent symptoms (shortness of breath, chest pain, palpitations, or leg edema), and 63 reported impaired exercise tolerance. Left ventricular (LV) functional and morphological parameters were within the normal range. T1- and T2-relaxation times were also within the normal range, indicating that the presence of myocardial edema or fibrosis was unlikely. Persistently symptomatic patients showed a slightly reduced indexed LV stroke volume. Functional parameters remained normal in patients who were hospitalized for SARS-CoV-2, persistently symptomatic, or with ongoing impaired exercise tolerance.

CONCLUSION

Irrespective of ongoing symptoms or severity of prior illness, patients who have recovered from SARS-CoV-2 infection demonstrate normal functional and morphological cardiac parameters. Long-term cardiac changes due to SARS-CoV-2 infection appear to be rare.

CRNN-Refined Spatiotemporal Transformer for Dynamic MRI reconstruction

Magnetic Resonance Imaging (MRI) plays a pivotal role in modern clinical practice, providing detailed anatomical visualization with exceptional spatial resolution and soft tissue contrast. Dynamic MRI, aiming to capture both spatial and temporal characteristics, faces challenges related to prolonged acquisition times and susceptibility to motion artifacts. Balancing spatial and temporal resolutions becomes crucial in real-world clinical scenarios. In the realm of dynamic MRI reconstruction, while Convolutional Recurrent Neural Networks (CRNNs) struggle with long-range dependencies, CRNNs require extensive iterations, impacting efficiency. Transformers, known for their effectiveness in high-dimensional imaging, are underexplored in dynamic MRI reconstruction. Additionally, prevailing algorithms fall short of achieving superior results in demanding generative reconstructions at high acceleration rates. This research proposes a novel approach for dynamic MRI reconstruction, named CRNN-Refined Spatiotemporal Transformer Network (CST-Net). The spatiotemporal Transformer initiates reconstruction, modeling temporal and spatial correlations, followed by refinement using the CRNN. This integration mitigates inaccuracies caused by damaged frames and reduces CRNN iterations, enhancing computational efficiency without compromising reconstruction quality. Our study compares the performance of the proposed CST-Net at 6 × and 12 × undersampling rates, showcasing its superiority over existing algorithms. Particularly, in challenging 25× generative reconstructions, the CST-Net outperforms current methods. The comparison includes experiments under both radial and Cartesian undersampling patterns. In conclusion, CST-Net successfully addresses the limitations inherent in existing generative reconstruction algorithms, thereby paving the way for further exploration and optimization of Transformer-based approaches in dynamic MRI reconstruction. Code and Datasets can be available: https://github.com/XWangBin/CST-Net.

Evaluation of myocarditis with a free-breathing three-dimensional isotropic whole-heart joint T1 and T2 mapping sequence

BACKGROUND

The diagnosis of myocarditis by cardiovascular magnetic resonance (CMR) requires the use of T2 and T1 weighted imaging, ideally incorporating parametric mapping. Current two-dimensional (2D) mapping sequences are acquired sequentially and involve multiple breath-holds resulting in prolonged scan times and anisotropic image resolution. We developed an isotropic free-breathing three-dimensional (3D) whole-heart sequence that allows simultaneous T1 and T2 mapping and validated it in patients with suspected myocarditis.

METHODS

Eighteen healthy volunteers and 28 patients with suspected myocarditis underwent conventional 2D T1 and T2 mapping with whole-heart coverage and 3D joint T1/T2 mapping on a 1.5T scanner. Acquisition time, image quality, and diagnostic performance were compared. Qualitative analysis was performed using a 4-point Likert scale. Bland-Altman plots were used to assess the quantitative agreement between 2D and 3D sequences.

RESULTS

The 3D T1/T2 sequence was acquired in 8 min 26 s under free breathing, whereas 2D T1 and T2 sequences were acquired with breath-holds in 11 min 44 s (p = 0.0001). All 2D images were diagnostic. For 3D images, 89% (25/28) of T1 and 96% (27/28) of T2 images were diagnostic with no significant difference in the proportion of diagnostic images for the 3D and 2D T1 (p = 0.2482) and T2 maps (p = 1.0000). Systematic bias in T1 was noted with biases of 102, 115, and 152 ms for basal-apical segments, with a larger bias for higher T1 values. Good agreement between T2 values for 3D and 2D techniques was found (bias of 1.8, 3.9, and 3.6 ms for basal-apical segments). The sensitivity and specificity of the 3D sequence for diagnosing acute myocarditis were 74% (95% confidence interval [CI] 49%-91%) and 83% (36%-100%), respectively, with a c-statistic (95% CI) of 0.85 (0.79-0.91) and no statistically significant difference between the 2D and 3D sequences for the detection of acute myocarditis for T1 (p = 0.2207) or T2 (p = 1.0000).

CONCLUSION

Free-breathing whole-heart 3D joint T1/T2 mapping was comparable to 2D mapping sequences with respect to diagnostic performance, but with the added advantages of free breathing and shorter scan times. Further work is required to address the bias noted at high T1 values, but this did not significantly impact diagnostic accuracy.

Similarity-driven motion-resolved reconstruction for ferumoxytol-enhanced whole-heart MRI in congenital heart disease

A similarity-driven multi-dimensional binning algorithm (SIMBA) reconstruction of free-running cardiac magnetic resonance imaging data was previously proposed. While very efficient and fast, the original SIMBA focused only on the reconstruction of a single motion-consistent cluster, discarding the remaining data acquired. However, the redundant data clustered by similarity may be exploited to further improve image quality. In this work, we propose a novel compressed sensing (CS) reconstruction that performs an effective regularization over the clustering dimension, thanks to the integration of inter-cluster motion compensation (XD-MC-SIMBA). This reconstruction was applied to free-running ferumoxytol-enhanced datasets from 24 patients with congenital heart disease, and compared to the original SIMBA, the same XD-MC-SIMBA reconstruction but without motion compensation (XD-SIMBA), and a 5D motion-resolved CS reconstruction using the free-running framework (FRF). The resulting images were compared in terms of lung-liver and blood-myocardium sharpness, blood-myocardium contrast ratio, and visible length and sharpness of the coronary arteries. Moreover, an automated image quality score (IQS) was assigned using a pretrained deep neural network. The lung-liver sharpness and blood-myocardium sharpness were significantly higher in XD-MC-SIMBA and FRF. Consistent with these findings, the IQS analysis revealed that image quality for XD-MC-SIMBA was improved in 18 of 24 cases, compared to SIMBA. We successfully tested the hypothesis that multiple motion-consistent SIMBA clusters can be exploited to improve the quality of ferumoxytol-enhanced cardiac MRI when inter-cluster motion-compensation is integrated as part of a CS reconstruction.

Invasive and Non-invasive Assessment of Non-culprit Coronary Lesions in Patients with ST-segment Elevation Myocardial Infarction

The angiographic evidence of coronary multivessel disease (MVD) increases significantly the risk of recurrent ischemic events in patients with ST-segment elevation myocardial infarction (STEMI). Recent evidence suggests that a complete revascularization strategy should be considered the standard of care in these patients and performed for significant non-culprit lesions (NCLs) after careful assessment of the individual risk-benefit ratio. However, the optimal timing and the modality for the assessment of NCLs is not fully standardized. This brief review aims to summarise the management of MVD in patients with STEMI and to provide an overview of the principal techniques used to guide revascularisation in this high-risk clinical setting.

AcquisitionFocus: Joint Optimization of Acquisition Orientation and Cardiac Volume Reconstruction Using Deep Learning

In cardiac cine imaging, acquiring high-quality data is challenging and time-consuming due to the artifacts generated by the heart's continuous movement. Volumetric, fully isotropic data acquisition with high temporal resolution is, to date, intractable due to MR physics constraints. To assess whole-heart movement under minimal acquisition time, we propose a deep learning model that reconstructs the volumetric shape of multiple cardiac chambers from a limited number of input slices while simultaneously optimizing the slice acquisition orientation for this task. We mimic the current clinical protocols for cardiac imaging and compare the shape reconstruction quality of standard clinical views and optimized views. In our experiments, we show that the jointly trained model achieves accurate high-resolution multi-chamber shape reconstruction with errors of <13 mm HD95 and Dice scores of >80%, indicating its effectiveness in both simulated cardiac cine MRI and clinical cardiac MRI with a wide range of pathological shape variations.

ESR Essentials: ten steps to cardiac MR-practice recommendations by ESCR

Cardiovascular MR imaging has become an indispensable noninvasive tool in diagnosing and monitoring a broad range of cardiovascular diseases. Key to its clinical success and efficiency are appropriate clinical indication triage, technical expertise, patient safety, standardized preparation and execution, quality assurance, efficient post-processing, structured reporting, and communication and clinical integration of findings. Technological advancements are driving faster, more accessible, and cost-effective approaches. This ESR Essentials article presents a ten-step guide for implementing a cardiovascular MR program, covering indication assessments, optimized imaging, post-processing, and detailed reporting. Future goals include streamlined protocols, improved tissue characterization, and automation for greater standardization and efficiency.

CLINICAL RELEVANCE STATEMENT

The growing clinical role of cardiovascular MR in risk assessment, diagnosis, and treatment planning highlights the necessity for radiologists to achieve expertise in this modality, advancing precision medicine and healthcare efficiency.

KEY POINTS

• Cardiovascular MR is essential in diagnosing and monitoring many acute and chronic cardiovascular pathologies. • Features such as technical expertise, quality assurance, patient safety, and optimized tailored imaging protocols, among others, are essential for a successful cardiovascular MR program. • Ongoing technological advances will push rapid multi-parametric cardiovascular MR, thus improving accessibility, patient comfort, and cost-effectiveness.

KEY POINTS

• Cardiovascular MR is essential in diagnosing and monitoring a wide array of cardiovascular pathologies (Level of Evidence: High). • A successful cardiovascular MR program depends on standardization (Level of Evidence: Low). • Future developments will increase the efficiency and accessibility of cardiovascular MR (Level of Evidence: Low).

Intra-bin correction and inter-bin compensation of respiratory motion in free-running five-dimensional whole-heart magnetic resonance imaging

BACKGROUND

Free-running cardiac and respiratory motion-resolved whole-heart five-dimensional (5D) cardiovascular magnetic resonance (CMR) can reduce scan planning and provide a means of evaluating respiratory-driven changes in clinical parameters of interest. However, respiratory-resolved imaging can be limited by user-defined parameters which create trade-offs between residual artifact and motion blur. In this work, we develop and validate strategies for both correction of intra-bin and compensation of inter-bin respiratory motion to improve the quality of 5D CMR.

METHODS

Each component of the reconstruction framework was systematically validated and compared to the previously established 5D approach using simulated free-running data (N = 50) and a cohort of 32 patients with congenital heart disease. The impact of intra-bin respiratory motion correction was evaluated in terms of image sharpness while inter-bin respiratory motion compensation was evaluated in terms of reconstruction error, compression of respiratory motion, and image sharpness. The full reconstruction framework (intra-acquisition correction and inter-acquisition compensation of respiratory motion [IIMC] 5D) was evaluated in terms of image sharpness and scoring of image quality by expert reviewers.

RESULTS

Intra-bin motion correction provides significantly (p < 0.001) sharper images for both simulated and patient data. Inter-bin motion compensation results in significant (p < 0.001) lower reconstruction error, lower motion compression, and higher sharpness in both simulated (10/11) and patient (9/11) data. The combined framework resulted in significantly (p < 0.001) sharper IIMC 5D reconstructions (End-expiration (End-Exp): 0.45 ± 0.09, End-inspiration (End-Ins): 0.46 ± 0.10) relative to the previously established 5D implementation (End-Exp: 0.43 ± 0.08, End-Ins: 0.39 ± 0.09). Similarly, image scoring by three expert reviewers was significantly (p < 0.001) higher using IIMC 5D (End-Exp: 3.39 ± 0.44, End-Ins: 3.32 ± 0.45) relative to 5D images (End-Exp: 3.02 ± 0.54, End-Ins: 2.45 ± 0.52).

CONCLUSION

The proposed IIMC reconstruction significantly improves the quality of 5D whole-heart MRI. This may be exploited for higher resolution or abbreviated scanning. Further investigation of the diagnostic impact of this framework and comparison to gold standards is needed to understand its full clinical utility, including exploration of respiratory-driven changes in physiological measurements of interest.

Latin-American guidelines of recommendations at discharge from an acute coronary syndrome

The diagnostic criteria, treatments at the time of admission, and drugs used in patients with acute coronary syndrome are well defined in countless guidelines. However, there is uncertainty about the measures to recommend during patient discharge planning. This document brings together the most recent evidence and the standardized and optimal treatment for patients at the time of discharge from hospitalization for an acute coronary syndrome, for comprehensive and safe care in the patient's transition between care from the acute event to the outpatient care, with the aim of optimizing the recovery of viable myocardium, guaranteeing the most appropriate secondary prevention, reducing the risk of a new coronary event and mortality, as well as the adequate reintegration of patients into daily life.

Submillimeter lung MRI at 0.55 T using balanced steady-state free precession with half-radial dual-echo readout (bSTAR)

PURPOSE

To demonstrate the feasibility of high-resolution morphologic lung MRI at 0.55 T using a free-breathing balanced steady-state free precession half-radial dual-echo imaging technique (bSTAR).

METHODS

Self-gated free-breathing bSTAR (TE1 /TE2 /TR of 0.13/1.93/2.14 ms) lung imaging in five healthy volunteers and a patient with granulomatous lung disease was performed using a 0.55 T MR-scanner. A wobbling Archimedean spiral pole (WASP) trajectory was used to ensure a homogenous coverage of k-space over multiple breathing cycles. WASP uses short-duration interleaves randomly tilted by a small polar angle and rotated by a golden angle about the polar axis. Data were acquired continuously over 12:50 min. Respiratory-resolved images were reconstructed off-line using compressed sensing and retrospective self-gating. Reconstructions were performed with a nominal resolution of 0.9 mm and a reduced isotropic resolution of 1.75 mm corresponding to shorter simulated scan times of 8:34 and 4:17 min, respectively. Analysis of apparent SNR was performed in all volunteers and reconstruction settings.

RESULTS

The technique provided artifact-free morphologic lung images in all subjects. The short TR of bSTAR in conjunction with a field strength of 0.55 T resulted in a complete mitigation of off-resonance artifacts in the chest. Mean SNR values in healthy lung parenchyma for the 12:50 min scan were 3.6 ± 0.8 and 24.9 ± 6.2 for 0.9 mm and 1.75 mm reconstructions, respectively.

CONCLUSION

This study demonstrates the feasibility of morphologic lung MRI with a submillimeter isotropic spatial resolution in human subjects with bSTAR at 0.55 T.

Phenotyping heart failure by cardiac magnetic resonance imaging of cardiac macro- and microscopic structure: state of the art review

Heart failure demographics have evolved in past decades with the development of improved diagnostics, therapies, and prevention. Cardiac magnetic resonance (CMR) has developed in a similar timeframe to become the gold-standard non-invasive imaging modality for characterizing diseases causing heart failure. CMR techniques to assess cardiac morphology and function have progressed since their first use in the 1980s. Increasingly efficient acquisition protocols generate high spatial and temporal resolution images in less time. This has enabled new methods of characterizing cardiac systolic and diastolic function such as strain analysis, exercise real-time cine imaging and four-dimensional flow. A key strength of CMR is its ability to non-invasively interrogate the myocardial tissue composition. Gadolinium contrast agents revolutionized non-invasive cardiac imaging with the late gadolinium enhancement technique. Further advances enabled quantitative parametric mapping to increase sensitivity at detecting diffuse pathology. Novel methods such as diffusion tensor imaging and artificial intelligence-enhanced image generation are on the horizon. Magnetic resonance spectroscopy (MRS) provides a window into the molecular environment of the myocardium. Phosphorus (31P) spectroscopy can inform the status of cardiac energetics in health and disease. Proton (1H) spectroscopy complements this by measuring creatine and intramyocardial lipids. Hyperpolarized carbon (13C) spectroscopy is a novel method that could further our understanding of dynamic cardiac metabolism. CMR of other organs such as the lungs may add further depth into phenotypes of heart failure. The vast capabilities of CMR should be deployed and interpreted in context of current heart failure challenges.

Automatic image-based tracking of gadolinium-filled balloon wedge catheters for MRI-guided cardiac catheterization using deep learning

Introduction

Magnetic Resonance Imaging (MRI) is a promising alternative to standard x-ray fluoroscopy for the guidance of cardiac catheterization procedures as it enables soft tissue visualization, avoids ionizing radiation and provides improved hemodynamic data. MRI-guided cardiac catheterization procedures currently require frequent manual tracking of the imaging plane during navigation to follow the tip of a gadolinium-filled balloon wedge catheter, which unnecessarily prolongs and complicates the procedures. Therefore, real-time automatic image-based detection of the catheter balloon has the potential to improve catheter visualization and navigation through automatic slice tracking.

Methods

In this study, an automatic, parameter-free, deep-learning-based post-processing pipeline was developed for real-time detection of the catheter balloon. A U-Net architecture with a ResNet-34 encoder was trained on semi-artificial images for the segmentation of the catheter balloon. Post-processing steps were implemented to guarantee a unique estimate of the catheter tip coordinates. This approach was evaluated retrospectively in 7 patients (6M and 1F, age = 7 ± 5 year) who underwent an MRI-guided right heart catheterization procedure with all images acquired in an orientation unseen during training.

Results

The overall accuracy, specificity and sensitivity of the proposed catheter tracking strategy over all 7 patients were 98.4 ± 2.0%, 99.9 ± 0.2% and 95.4 ± 5.5%, respectively. The computation time of the deep-learning-based segmentation step was ∼10 ms/image, indicating its compatibility with real-time constraints.

Conclusion

Deep-learning-based catheter balloon tracking is feasible, accurate, parameter-free, and compatible with real-time conditions. Online integration of the technique and its evaluation in a larger patient cohort are now warranted to determine its benefit during MRI-guided cardiac catheterization.

Moving toward Precision Medicine in Acute Coronary Syndromes: A Multimodal Assessment of Non-Culprit Lesions

Patients with acute coronary syndrome and multivessel disease experience several recurrent adverse events that lead to poor outcomes. Given the complexity of treating these patients, and the extremely high risk of long-term adverse events, the assessment of non-culprit lesions becomes crucial. Recently, two trials have shown a possible clinical benefit into treat non-culprit lesions using a fraction flow reserve (FFR)-guided approach, compared to culprit-lesion-only PCI. However, the most recent FLOW Evaluation to Guide Revascularization in Multivessel ST-elevation Myocardial Infarction (FLOWER-MI) trial did not show a benefit of the use of FFR-guided PCI compared to an angiography-guided approach. Otherwise, intracoronary imaging using optical coherence tomography (OCT), intravascular ultrasound (IVUS), or near-infrared spectroscopy (NIRS) could provide both quantitative and qualitative assessments of non-culprit lesions. Different studies have shown how the characterization of coronary lesions with intracoronary imaging could lead to clinical benefits in these peculiar group of patients. Moreover, non-invasive evaluations of NCLs have begun to take ground in this context, but more insights through adequately powered and designed studies are needed. The aim of this review is to outline the available techniques, both invasive and non-invasive, for the assessment of multivessel disease in patients with STEMI, and to provide a systematic guidance on the assessment and approach to these patients.

Generalizable Deep Learning Method for Suppressing Unseen and Multiple MRI Artifacts Using Meta-learning

Magnetic Resonance (MR) images suffer from various types of artifacts due to motion, spatial resolution, and under-sampling. Conventional deep learning methods deal with removing a specific type of artifact, leading to separately trained models for each artifact type that lack the shared knowledge generalizable across artifacts. Moreover, training a model for each type and amount of artifact is a tedious process that consumes more training time and storage of models. On the other hand, the shared knowledge learned by jointly training the model on multiple artifacts might be inadequate to generalize under deviations in the types and amounts of artifacts. Model-agnostic meta-learning (MAML), a nested bi-level optimization framework is a promising technique to learn common knowledge across artifacts in the outer level of optimization, and artifact-specific restoration in the inner level. We propose curriculum-MAML (CMAML), a learning process that integrates MAML with curriculum learning to impart the knowledge of variable artifact complexity to adaptively learn restoration of multiple artifacts during training. Comparative studies against Stochastic Gradient Descent and MAML, using two cardiac datasets reveal that CMAML exhibits (i) better generalization with improved PSNR for 83% of unseen types and amounts of artifacts and improved SSIM in all cases, and (ii) better artifact suppression in 4 out of 5 cases of composite artifacts (scans with multiple artifacts).Clinical relevance- Our results show that CMAML has the potential to minimize the number of artifact-specific models; which is essential to deploy deep learning models for clinical use. Furthermore, we have also taken another practical scenario of an image affected by multiple artifacts and show that our method performs better in 80% of cases.

Pulsed ventricular tachycardia: a case study

Ventricular tachycardia (VT) is an arrhythmia that originates from the ventricles of the heart and presents as a wide and prolonged QRS complex on the electrocardiograph of greater than 120 milliseconds, with a heart rate of over 100 beats per minute. VT can occur as a pulsed or pulseless rhythm. Pulseless VT occurs when the ventricles cannot effectively pump blood out of the heart, therefore resulting in no cardiac output. Pulsed VT can manifest with the patient presenting asymptomatically, or with symptoms of reduced cardiac output resulting from poor ventricular filling. There is the potential for the patient to quickly become haemodynamically unstable if not treated. This article discusses a case of pulsed VT, diagnosed and treated out of hours in an acute hospital.

Cardiovascular magnetic resonance findings in Danon disease: a case series of a family

Background

Cardiac involvement constitutes the primary cause of mortality in patients with Danon disease (DD). This study aimed to explore the cardiac magnetic resonance (CMR) features and progressions of DD cardiomyopathies in a family with long-term follow-up.

Methods

Seven patients (five females and two males), belonging to the same family and afflicted with DD, were enrolled in this study between 2017 and 2022. The cardiac structure, function, strain, tissue characteristics on CMR and their evolutions during follow-up were analyzed.

Results

Three young female patients (3/7, 42.86%) exhibited normal cardiac morphology. Four patients (4/7, 57.14%) displayed left ventricle hypertrophy (LVH), and mostly with septal thickening (3/4, 75%). A single male case (1/7, 14.3%) showed decreased LV ejection fraction (LVEF). Nonetheless, the global LV strain of the four adult patients decreased in different degree. The global strain of adolescent male patients was decreased compared to the age-appropriate female patients. Five patients (5/7, 71.43%) exhibited late gadolinium enhancement (LGE), with proportion ranging from 31.6% to 59.7% (median value 42.7%). The most common LGE location was the LV free wall (5/5, 100%), followed by right ventricle insertion points (4/5, 80%) and intraventricular septum (2/5, 40%). Segmental radial strain (rs = -0.586), circumferential strain (r = 0.589), and longitudinal strain (r = 0.514) were all moderately correlated with the LGE proportions of corresponding segments (P < 0.001). T2 hyperintense and perfusion defect foci were identified, overlapping with the LGE areas. During follow-up, both the young male patients exhibited notable deterioration of their cardiac symptoms and CMR. The LVEF and strain decreased, and the extent of LGE increased year by year. One patient underwent T1 mapping examination. The native T1 value was sensitively elevated even in regions without LGE.

Conclusions

Left ventricular hypertrophy, LGE with sparing or relatively less involved IVS, and LV dysfunction are prominent CMR features of Danon cardiomyopathy. Strain and T1 mapping may have advantages in detecting early-stage dysfunction and myocardial abnormalities in DD patients, respectively. Multi-parametric CMR can serve as an optimal instrument for detecting DD cardiomyopathies.

Hypertensive Heart Disease-The Imaging Perspective

Hypertensive heart disease (HHD) develops in response to the chronic exposure of the left ventricle and left atrium to elevated systemic blood pressure. Left ventricular structural changes include hypertrophy and interstitial fibrosis that in turn lead to functional changes including diastolic dysfunction and impaired left atrial and LV mechanical function. Ultimately, these changes can lead to heart failure with a preserved (HFpEF) or reduced (HFrEF) ejection fraction. This review will outline the clinical evaluation of a patient with hypertension and/or suspected HHD, with a particular emphasis on the role and recent advances of multimodality imaging in both diagnosis and differential diagnosis.

Deep Learning-Based Reconstruction for Cardiac MRI: A Review

Cardiac magnetic resonance (CMR) is an essential clinical tool for the assessment of cardiovascular disease. Deep learning (DL) has recently revolutionized the field through image reconstruction techniques that allow unprecedented data undersampling rates. These fast acquisitions have the potential to considerably impact the diagnosis and treatment of cardiovascular disease. Herein, we provide a comprehensive review of DL-based reconstruction methods for CMR. We place special emphasis on state-of-the-art unrolled networks, which are heavily based on a conventional image reconstruction framework. We review the main DL-based methods and connect them to the relevant conventional reconstruction theory. Next, we review several methods developed to tackle specific challenges that arise from the characteristics of CMR data. Then, we focus on DL-based methods developed for specific CMR applications, including flow imaging, late gadolinium enhancement, and quantitative tissue characterization. Finally, we discuss the pitfalls and future outlook of DL-based reconstructions in CMR, focusing on the robustness, interpretability, clinical deployment, and potential for new methods.

Generative myocardial motion tracking via latent space exploration with biomechanics-informed prior

Myocardial motion and deformation are rich descriptors that characterize cardiac function. Image registration, as the most commonly used technique for myocardial motion tracking, is an ill-posed inverse problem which often requires prior assumptions on the solution space. In contrast to most existing approaches which impose explicit generic regularization such as smoothness, in this work we propose a novel method that can implicitly learn an application-specific biomechanics-informed prior and embed it into a neural network-parameterized transformation model. Particularly, the proposed method leverages a variational autoencoder-based generative model to learn a manifold for biomechanically plausible deformations. The motion tracking then can be performed via traversing the learnt manifold to search for the optimal transformations while considering the sequence information. The proposed method is validated on three public cardiac cine MRI datasets with comprehensive evaluations. The results demonstrate that the proposed method can outperform other approaches, yielding higher motion tracking accuracy with reasonable volume preservation and better generalizability to varying data distributions. It also enables better estimates of myocardial strains, which indicates the potential of the method in characterizing spatiotemporal signatures for understanding cardiovascular diseases.

Virtual reality in cardiac interventions-New tools or new toys?

Improvementsin medical imaging and a steady increase in computing power are leading to new possibilities in the field of cardiovascular interventions. Interventions can be planned in advance in greater detail, even to the point of simulating procedures. Nevertheless, all techniques are at an early stage of development. It is of utmost importance that tools, especially if they can be used as decision support are intensively validated and their accuracy is demonstrated. In our commentary, we summarize current techniques for impprovements in planning and guiding of procedures, but also critically discuss the downsides of these techniques. Following the work of Kenichi and colleagues, we also discuss necessary steps in advancing new tools and techniques, particularly as they are used in routine clinical practice. We also discuss the role of artificial intelligence, which could play a crucial role in this context in the future.