Myocardial blood flow (MBF) and 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.

Physiological stress may reveal myocardial deformations not evident at rest. Single-beat myocardial tagging in exercise cardiovascular magnetic resonance (Ex-CMR) enables assessment of myocardial deformation at both rest and during exercise. In this study, we developed the myocardial torsional reserve (MTR), a quantitative metric of exercise-induced rotational deformation, and evaluated its potential as a marker of abnormal myocardial mechanics in patients with overt or exercise-induced heart failure with preserved ejection fraction (HFpEF).

Cardiac magnetic resonance (CMR) 4D flow has been introduced as a valuable technique for deriving intracardiac blood flow components and kinetic energy (KE), providing profound mechanistic insights into heart failure patients. This study aimed to explore the prognostic value of CMR 4D flow-derived intracardiac blood flow components and KEs in patients with heart failure with reduced ejection fraction (HFrEF).

Current myocardial arterial spin labeling (ASL) methods are sensitive to noise (background and physiology), which limits the accuracy of myocardial blood flow (MBF) measurement. In this study, we demonstrated a new deep learning-enabled myocardial ASL approach (DeepMASL) and evaluated its accuracy to quantify MBF in a canine model of coronary arterial disease in vivo. The reference method was invasive microsphere measurements.

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.

Cardiac Magnetic Resonance Imaging (CMRI), the gold standard approach for characterizing myocardial infarction (MI), frequently relies on Late Gadolinium Enhancement (LGE) using gadolinium-based contrast agents (GBCA). Whereas novel GBCAs targeting specific molecules have not yet entered clinical practice, chemical exchange saturation transfer (CEST) MRI shows promise for detecting various endogenous molecules. This study explored the potential of natural D-glucose as a biodegradable MRI contrast agent for imaging MI on day 7 by employing glucose-weighted CEST MRI (glucoCEST).

Cardiac magnetic resonance (CMR) is a well-established imaging modality for the assessment of cardiovascular diseases. However, attainable image resolution remains lower than that of X-ray computed tomography (CT) due to long scan times and the need for respiratory motion correction. In this work, we combine a previously proposed motion-corrected model-based deep-learning reconstruction for undersampled 3D whole-heart CMR with data-consistent super-resolution to enable high-resolution 3D whole-heart CMR from significantly shortened scans.

Cardiac Diffusion Tensor Imaging (cDTI) non-invasively provides unique insights into cardiac microstructure. Current protocols require multiple breath-hold repetitions to achieve adequate signal-to-noise ratio, resulting in lengthy scan times. The aim of this study was to develop a cDTI de-noising method that would enable the reduction of repetitions while preserving image quality.

Measurements of cardiac size and function drive clinical decisions. Left ventricle (LV) metrics can be derived from cardiac MR images by delineating the blood pool and myocardium, by either drawing a rounded contour to approximate the compacted myocardial border, or by delineating the papillary muscles and trabeculae (trabecular segmentation). There is no consensus as to which is best, particularly in the emergent AI era. We developed machine-learning (ML) approaches for both and compared them for clinically important metrics (error rate, precision, and prognosis).

To develop a reconstruction framework for 3D real-time cine cardiovascular magnetic resonance (CMR) from highly undersampled data without requiring fully sampled training datasets.

Cardiac magnetic resonance (CMR) is a reference-standard modality for heart diseases, although its clinical application is restricted by prolonged acquisition times. Recently, artificial intelligence (AI), particularly deep learning (DL), has exhibited the potential to accelerate the CMR acquisition through technological advances. Prospective validation of its diagnostic performance across multiple clinical sequences remains underexplored. This research aims to assess the functions of the compressed sensing artificial intelligence (CSAI) algorithm in accelerating CMR acquisition, enhancing image quality, and maintaining diagnostic accuracy versus conventional sensitivity encoding (SENSE) reconstruction.

Phosphorus-31 magnetic resonance spectroscopy (³¹P MRS) is the only non-invasive imaging modality that directly quantifies myocardial energy metabolism in vivo. While extensively studied, its readiness for clinical application in heart failure remains uncertain. This meta-analysis aimed to evaluate the association between myocardial phosphocreatine-to-ATP (PCr/ATP) ratio, measured by ³¹P MRS, and heart failure, as a step toward assessing its translational potential as a clinical biomarker.

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.

To develop an end-to-end 4D flow MRI analysis pipeline for automated hemodynamic analysis with full on-scanner deployment.

Dilated cardiomyopathy (DCM) represents a major cause of heart failure (HF), but current HF prediction models lack validation in DCM cohorts. Late gadolinium enhancement (LGE) predicts mortality in DCM patients. The incremental value of LGE to existing models warrants exploration.

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.

Cardiac cine MRI faces the challenges of prolonged examination times and repeated breathhold (BH). This study evaluated the clinical feasibility of the deep learning (DL)-accelerated cine sequences, which shorten the acquisition time (AT) and achieve comparable image quality (IQ) and function.

In people with kidney failure requiring haemodialysis, sudden cardiac death and arrhythmia are prevalent causes of mortality, driven by left ventricular (LV) hypertrophy and myocardial fibrosis. Native T1, a non-contrast magnetic resonance imaging (MRI) technique, is thought to represent myocardial fibrosis in this population. We hypothesised that MRI measures of cardiac structure and function would associate with mortality.