Gerrit Arends | Searcharxiv

arXiv2026

Bilateral breast gradient insert prototype for strong diffusion encoding at 3T

Purpose: Diffusion MRI has shown promise for breast cancer screening, lesion characterization,and treatment response monitoring without contrast agents, but further translation is constraint by the gradient performance of conventional systems. The aim of this work is to develop a single-axis high performance bilateral plug-and-play breast gradient insert to enable strong-gradient diffusion MRI. Methods: An in-house breast gradient insert and bed-tabletop was constructed entirely from commercially available materials, providing a cost-effective solution compatible with existing MRI systems. Its wiring pattern was optimized for torque and force balancing, power dissipation, and target field performance. Evaluation included gradient field characterization, peripheral nerve stimulation simulation verification, and temperature and eddy current assessment. The setup was used for imaging of a diffusion phantom based on soy lecithin across a range of b-values. Results: Gradient efficiency reached 2.8 mT/m/A, enabling local strengths up to 1850 mT/m (660 A). No peripheral nerve stimulation was observed during tests on five healthy volunteers. Eddy currents were successfully characterized employed in standard correction methods. Imaging showed the feasibility of $b = 10 000 s/mm^2$ acquisitions at TE = 78 ms versus 161 ms with scanner gradients. Conclusion: This work demonstrates a dedicated bilateral breast gradient insert for safe and feasible strong-gradient breast diffusion MRI, and represents a first step toward dedicated hardware for breast cancer detection and characterization without contrast agents.

physics.med-ph↗

arXiv2025

Implicit neural representations for accurate estimation of the standard model of white matter

Diffusion magnetic resonance imaging (dMRI) enables non-invasive investigation of tissue microstructure. The Standard Model (SM) of white matter aims to disentangle dMRI signal contributions from intra- and extra-axonal water compartments. However, due to the model its high-dimensional nature, accurately estimating its parameters poses a complex problem and remains an active field of research, in which different (machine learning) strategies have been proposed. This work introduces an estimation framework based on implicit neural representations (INRs), which incorporate spatial regularization through the sinusoidal encoding of the input coordinates. The INR method is evaluated on both synthetic and in vivo datasets and compared to existing methods. Results demonstrate superior accuracy of the INR method in estimating SM parameters, particularly in low signal-to-noise conditions. Additionally, spatial upsampling of the INR can represent the underlying dataset anatomically plausibly in a continuous way. The INR is self-supervised, eliminating the need for labeled training data. It achieves fast inference, is robust to noise, supports joint estimation of SM kernel parameters and the fiber orientation distribution function with spherical harmonics orders up to at least 8, and accommodates gradient non-uniformity corrections. The combination of these properties positions INRs as a potentially important tool for analyzing and interpreting diffusion MRI data.

eess.IV↗