Fast Prospective Motion Correction for MRI Based on Electromagnetic Induction Coils with Low Footprint and In-place Calibration.

Birth of the Coil: another Milestone towards a fully reproducible low-field MRI scanner for head-imaging

This work presents an open-source, optimized solenoid head coil tailored for the 50 mT open-source scanner (OSII ONE v2.1), set the basis for a fully reliable and reproducible component for the open-source OSII ONE MRI scanner.

Prospective motion correction with data reacquisition based on fat navigators for T1-weighted brain MRI at 3T.

Intra‐MRI Head Motion Tracking and Correction: A Quantitative In Vivo Evaluation Framework

Despite numerous magnetic resonance imaging (MRI) head motion mitigation strategies, the lack of rigorous evaluation limits their optimization and clinical adoption. We propose an in vivo framework combining a visual instruction system for reproducible head motion with reference standard interpose displacement estimation to assess intra‐MRI tracking accuracy and precision. Its utility is demonstrated by comparing a markerless optical system (MOS) and a fat‐signal navigator (FatNav). Six participants underwent 3T T1‐weighted brain MRI with a FatNav module, performing visually guided 2° and 4° head rotations around the X‐ and Z‐axes using MOS feedback. T1‐weighted images were acquired at seven distinct head poses. MOS and FatNav motion estimates were compared against rigid registration of the T1‐weighted images, which served as the reference standard. MOS‐ and FatNav‐corrected images for the three successive head rotations were also compared using the structural similarity index measure (SSIM), peak signal‐to‐noise ratio (PSNR), and a focus measure. FatNav accuracy was inferior for translations (p < 0.001) and 2°–4° rotations but improved to match MOS for subtle pitch+ and yaw+, even surpassing it for subtle yaw−. Meanwhile, MOS precision was higher for yaw+ than yaw− (p < 0.001) but inferior to FatNav for pitch+ (p = 0.041). MOS better restored T1‐weighted image fidelity, yielding higher SSIM, PSNR, and focus (p < 0.01). Notably, the framework detected a subtle improvement in FatNav performance with neck masking, an effect uncaptured by conventional image quality metrics. In conclusion, while image quality metrics suggested superior overall correction with MOS, our framework provided a more detailed characterization of in vivo performance differences.

Fast ungated five-dimensional cardiac MRI on a 1.5 T MR-linac for MRI-guided radiotherapy

Background: Stereotactic arrhythmia radio-ablation (STAR) for patients with ventricular tachycardia is currently limited by complex cardiorespiratory motion. Current 5D-MRI motion models require long acquisition and reconstruction times, limiting clinical viability. Objective: To develop a fast, ungated 5D-MRI reconstruction method for personalized motion characterization to support MRI-guided STAR treatments. Methods: We propose a fast, ungated 5D-MRI reconstruction method based on the CMR-MOTUS framework. The method uses a 3D Cartesian acquisition with a joint optimization framework to reconstruct a motion-corrected reference image and low-rank deformation vector fields (DVFs). By exploiting the low rank structure, we explicitly disentangle respiratory and cardiac motion during optimization. Then, the DVFs are used for 5D-MRI reconstruction with a retrospectively adjustable number of motion states. Validation was performed using digital and physical cardiorespiratory phantoms. Furthermore, the approach was evaluated using 10 healthy volunteers, comparing motion consistency with 2D cine MRI. Results: Validation of 5D CMR-MOTUS using digital and physical phantoms demonstrated accurate 5D-MRI reconstruction. In the physical phantom, 5D CMR-MOTUS achieved a left-ventricle DICE of 0.96 +/- 0.01. In the volunteer cohort, the 5D-MRI scans showed strong motion to 2D cine MRI, with a cardiac motion error of 0.1 +/- 0.9 mm and a respiratory motion error of 0.2 +/- 2.9 mm. Crucially, 5D-MRI data were acquired in 1 minute and reconstructed in 6 minutes. Conclusions: The proposed 5D-MRI method enables rapid, high-quality, and personalized motion characterization, demonstrating potential for integration into MRI-guided STAR treatments. Data Availability: The 3D k-space data and 5D reconstructions for the ten volunteers are publicly available at https://doi.org/10.5281/zenodo.21278894

Dual-Echo bSSFP for Rapid T2-Sensitive Quantitative Contrast Imaging at Ultra-Low Field.

OBJECTIVE Rapid characterization of transverse-relaxation-sensitive MRI contrast is important for evaluating tissue-dependent signal behavior, but remains challenging in ultra-low-field (ULF) MRI because of limited signal-to-noise ratio (SNR) and acquisition-efficiency constraints. This study aims to develop a rapid, high-SNR, sequence-specific T2-sensitive quantitative contrast imaging method for ULF MRI. METHODS A balanced dual-echo steady-state (bDESS) sequence was developed to acquire two echoes at predefined echo times within each repetition of a balanced steady-state free precession acquisition. A logarithmic-ratio operator, based on a mono-exponential attenuation approximation, was used to derive a sequence-specific T2-sensitive contrast index, termed T2SDI, from the two echo magnitudes. The proposed method was implemented on a custom-built 6.5 mT MRI system and evaluated using numerical simulations, CuSO₄ phantom experiments, and in vivo brain imaging. RESULTS Numerical simulations showed that T2SDI exhibited a monotonic and approximately linear dependence on T2 under controlled field-inhomogeneity conditions. Phantom experiments confirmed that bDESS-derived T2SDI increased with CPMG-measured reference T2 and showed higher SNR than dual-echo SPGR. The method was further demonstrated in vivo by generating T2SDI maps of the human brain. CONCLUSION This study presents a sequence-specific, index-based method for rapid T2-sensitive quantitative contrast imaging in ULF MRI. SIGNIFICANCE The proposed dual-echo bSSFP/bDESS method provides a high-SNR and time-efficient strategy for sequence-specific T2-sensitive contrast characterization at ultra-low field.

Digital Twin Modeling of MR Gradient Coils for Accurate Gradient Safety Evaluation of Implantable Medical Devices.

Even with identical coil sizes and gradient strengths, differences in coil design can cause variations in field distribution, affecting gradient safety assessment of magnetic resonance imaging (MRI) for implanted devices. This study proposed a digital twin approach for gradient coil design to enable refined evaluation of gradient-induced risks. Based on the target field method, additional specific field constraints (SFC) were applied to all magnetic field components outside the region of linearity (ROL). Magnetic field distributions of three clinical axial coils were measured using a custom probe and a tailored field acquisition strategy. The measured data were partially incorporated as design constraints, with some data used to validate the model. The resulting digital twin model was applied to predict gradient-induced voltages (GIVs) along a simulated deep brain stimulation (DBS) lead path, with experimental measurements used for comparison. The results indicated that incorporating SFC reduced the discrepancy between the simulated and measured fields by over 84.5% compared with conventional designs. The linear regression R2 between predicted and measured GIVs for the X-, Y- and Z-axis gradient coils was 0.985, 0.939, and 0.973, respectively, with maximum prediction errors below 0.18 V. The proposed MR gradient digital twin method provides a helpful framework for constructing clinically relevant gradient testing environments and supports future studies on refined gradient-related safety evaluation.