The developed delayed-phase dynamic contrast-enhanced MRI model for myocardial tissue assessment shows potential to enhance lesion contrast, quantify clinically relevant physiologic parameters, and support comprehensive evaluation of myocardial injury in heart disease.

The spectral quality of magnetic resonance spectroscopic imaging (MRSI) can be affected by strong magnetic field inhomogeneities, posing a challenge for 3D-MRSI’s widespread clinical use with standard scanner-equipped 2nd-order shim coils. To overcome this, we designed an empirical unified shim–RF head coil (32-ch RF receive and 51-ch shim) for 3D-MRSI improvement. We compared its shimming performance and 3D-MRSI brain coverages against the standard scanner shim (2nd-order spherical harmonic (SH) shim coils) and integrated parallel reception, excitation, and shimming (iPRES) 32-ch AC/DC head coil. We also simulated a theoretical 3rd-, 4th-, and 5th-order SH shim as a benchmark to assess the UNIfied shim–RF coil (UNIC) improvements. In this preliminary study, the whole-brain coverage was simulated by using B₀ field maps of twenty-four healthy human subjects (n = 24). Our results demonstrated that UNIC substantially improves brain field homogeneity, reducing whole-brain frequency standard deviations by 27% compared to the standard 2nd-order scanner shim and 17% compared to the iPRES shim. Moreover, UNIC enhances whole-brain coverage of 3D-MRSI by up to 34% compared to the standard 2nd-order scanner shim and up to 13% compared to the iPRES shim. UNIC markedly increases coverage in the prefrontal cortex by 147% and 47% and in the medial temporal lobe and temporal pole by 29% and 13%, respectively, at voxel resolutions of 1.4 cc and 0.09 cc for 3D-MRSI. Furthermore, UNIC effectively reduces variations in shim quality and brain coverage among different subjects compared to scanner shim and iPRES shim. Anticipated advancements in higher-order shimming (beyond 6th order) are expected via optimized designs using dimensionality reduction methods.

${\mathrm{B}}_0$ field inhomogeneity is a long-lasting issue for Cardiac MRI (CMR) in high-field (3T and above) scanners. The inhomogeneous ${\mathrm{B}}_0$ fields can lead to corrupted image quality, prolonged scan time, and false diagnosis. ${\mathrm{B}}_0$ shimming is the most straightforward way to improve the ${\mathrm{B}}_0$ homogeneity. However, today’s standard cardiac shimming protocol requires manual selection of a shim volume, which often falsely includes regions with large ${\mathrm{B}}_0$ deviation (e.g., liver, fat, and chest wall). The flawed shim field compromises the reliability of high-field CMR protocols, which significantly reduces the scan efficiency and hinders its wider clinical adoption. This study aims to develop a dual-channel deep learning model that can reliably contour the cardiac region for ${\mathrm{B}}_0$ shim without human interaction and under variable imaging protocols. By utilizing both the magnitude and phase information, the model achieved a high segmentation accuracy in the ${\mathrm{B}}_0$ field maps compared to the conventional single-channel methods (Dice score: 2D-mag = 0.866, 3D-mag = 0.907, and 3D-mag-phase = 0.938, all p < 0.05). Furthermore, it shows better generalizability against the common variations in MRI imaging parameters and enables significantly improved ${\mathrm{B}}_0$ shim compared to the standard method (SD(${\mathrm{B}}_0$Shim): Proposed = 15 ± 11% vs. Standard = 6 ± 12%, p < 0.05). The proposed autonomous model can boost the reliability of cardiac shimming at 3T and serve as the foundation for more reliable and efficient high-field CMR imaging in clinical routines.

Relentless mechanical work of the heart is powered by continuous oxygen consumption. How the heart uses oxygen is a defining feature of its health. Invasive studies have established that impaired oxygen consumption by the myocardium predicts contractile dysfunction and adverse outcomes. Despite its importance, noninvasive quantification of myocardial oxygen use remains limited. Magnetic resonance imaging (MRI) signal is known to be sensitive to blood oxygenation and has the potential to quantify myocardial oxygen consumption noninvasively, without exogenous contrast agents and free of ionizing radiation. However, its clinical translation has been impeded by the need for complex biophysical calibration, vulnerability to imaging artifacts and consistent vital motions, and the requirement of lengthy acquisition times. Here, we introduce a rapid, self-calibrated cardiac MRI framework that overcomes these barriers through high-resolution, motion-resolved coronary sinus oximetry, which can quantify myocardial oxygen extraction of the whole heart within 3 minutes. We optimized the imaging parameters via numerical simulations and validated them against invasive coronary sinus catheterization in a porcine model. We combined the method with clinical MRI sequences and demonstrated the feasibility of quantifying myocardial oxygen consumption and myocardial oxygen efficiency in patients with and without heart failure secondary to myocardial infarction in a single institution. This needle-free approach establishes a practical framework for noninvasive characterization of myocardial oxygen metabolism. It holds the potential to facilitate early disease detection, inform personalized therapeutic strategies, and guide the development of cardiometabolic therapies aimed at addressing the ongoing heart failure epidemic.