arXiv subjects

C. A. T. van den Berg

Publications and source records attributed to C. A. T. van den Berg.

6 recordsLinked to original sources

arXiv2026

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

physics.med-ph↗

arXiv2026

Fast undersampled dynamic MRI reconstruction using explicit representation learning with Gaussian splatting

Motivation: Quickly obtaining high-quality MRI from accelerated acquisitions is important to mitigate motion artifacts, maintain patient comfort, and improve clinical efficiency. Goals: To obtain high-quality dynamic MRI using efficient, personalized models. Approach: We propose a novel explicit representation learning approach using Gaussian splatting. Multiple Gaussian primitives are trained to represent the underlying tissue. We extend the Gaussian splatting framework to model anatomical motion, enabling learning an efficient, explicit representation of dynamic MRI. Results: Gaussian splats can be trained in 60s with 0.5ms/dynamic inference time. High-quality cardiac MRI is obtained at R=16. We show that the properties of the Gaussians directly encode physiological properties.

physics.med-ph↗

arXiv2024

Data-driven discovery of mechanical models directly from MRI spectral data

Finding interpretable biomechanical models can provide insight into the functionality of organs with regard to physiology and disease. However, identifying broadly applicable dynamical models for in vivo tissue remains challenging. In this proof of concept study we propose a reconstruction framework for data-driven discovery of dynamical models from experimentally obtained undersampled MRI spectral data. The method makes use of the previously developed spectro-dynamic framework which allows for reconstruction of displacement fields at high spatial and temporal resolution required for model identification. The proposed framework combines this method with data-driven discovery of interpretable models using Sparse Identification of Non-linear Dynamics (SINDy). The design of the reconstruction algorithm is such that a symbiotic relation between the reconstruction of the displacement fields and the model identification is created. Our method does not rely on periodicity of the motion. It is successfully validated using spectral data of a dynamic phantom gathered on a clinical MRI scanner. The dynamic phantom is programmed to perform motion adhering to 5 different (non-linear) ordinary differential equations. The proposed framework performed better than a 2-step approach where the displacement fields were first reconstructed from the undersampled data without any information on the model, followed by data-driven discovery of the model using the reconstructed displacement fields. This study serves as a first step in the direction of data-driven discovery of in vivo models.

physics.med-ph↗

arXiv2022

Free-breathing motion compensated 4D (3D+respiration) T2-weighted turbo spin-echo MRI for body imaging

Purpose: To develop and evaluate a free-breathing respiratory motion compensated 4D (3D+respiration) $T_2$-weighted turbo spin echo sequence with application to radiology and MR-guided radiotherapy. Methods: k-space data are continuously acquired using a rewound Cartesian acquisition with spiral profile ordering (rCASPR) to provide matching contrast to the conventional linear phase encode ordering and to sort data into multiple respiratory phases. Low-resolution respiratory-correlated 4D images were reconstructed with compressed sensing and used to estimate non-rigid deformation vector fields, which were subsequently used for a motion compensated image reconstruction. rCASPR sampling was compared to linear and CASPR sampling in terms of point-spread-function (PSF) and image contrast with in silico, phantom and in vivo experiments. Reconstruction parameters for low-resolution 4D-MRI (spatial resolution and temporal regularization) were determined using a grid search. The proposed motion compensated rCASPR was evaluated in eight healthy volunteers and compared to free-breathing scans with linear sampling. Image quality was compared based on visual inspection and quantitatively by means of the gradient entropy. Results: rCASPR provided a superior PSF (similar in ky and narrower in kz) and showed no considerable differences in images contrast compared to linear sampling. The optimal 4D-MRI reconstruction parameters were spatial resolution=$4.5 mm^3$ and $λ_t=10^{-4}$. The groupwise average gradient entropy was 22.31 for linear, 22.20 for rCASPR, 22.14 for soft-gated rCASPR and 22.02 for motion compensated rCASPR. Conclusion: The proposed motion compensated rCASPR enables high quality free-breathing T2-TSE with minimal changes in image contrast and scan time. The proposed method therefore enables direct transfer of clinically used 3D TSE sequences to free-breathing.

physics.med-ph↗

arXiv2020

Technical feasibility of Magnetic Resonance Fingerprinting on a 1.5T MRI-Linac

Hybrid MRI-linac (MRL) systems enable daily multiparametric quantitative MRI to assess tumor response to radiotherapy. Magnetic Resonance Fingerprinting (MRF) may provide time efficient means of rapid multiparametric quantitative MRI. The accuracy of MRF, however, relies on adequate control over system imperfections, such as eddy currents and B1+, which are different and not as well established on MRL systems compared to diagnostic systems. In this study we investigate the technical feasibility of gradient spoiled 2D MRF on a 1.5T MRL. We show with phantom experiments that the MRL generates reliable MRF signals that are temporally stable during the day and have good agreement with spin-echo reference measurements. Subsequent in-vivo MRF scans in healthy volunteers and a patient with a colorectal liver metastasis showed good image quality, where the quantitative values of selected organs corresponded with the values reported in literature. Therefore we conclude that gradient spoiled 2D MRF is feasible on a 1.5T MRL with similar performance as on a diagnostic system. The precision and accuracy of the parametric maps are sufficient for further investigation of the clinical utility of MRF for online quantitatively MRI-guided radiotherapy.

physics.med-ph↗

arXiv2015

Enhancement of magnetic resonance imaging with metasurfaces

Magnetic resonance imaging (MRI) is the cornerstone technique for diagnostic medicine, biology, and neuroscience. This imaging method is highly innovative, noninvasive and its impact continues to grow. It can be used for measuring changes in the brain after enhanced neural activity, detecting early cancerous cells in tissue, as well as for imaging nanoscale biological structures, and controlling fluid dynamics, and it can be beneficial for cardiovascular imaging. The MRI performance is characterized by a signal-to-noise ratio, however the spatial resolution and image contrast depend strongly on the scanner design. Here, we reveal how to exploit effectively the unique properties of metasurfaces for the substantial improvement of MRI efficiency. We employ a metasurface created by an array of wires placed inside the MRI scanner under an object, and demonstrate a giant enhancement of the magnetic field by means of subwavelength near-field manipulation with the metasurface, thus strongly increasing the scanner sensitivity, signal-to-noise ratio, and image resolution. We demonstrate experimentally this effect for a commercially available MRI scanner and a biological tissue sample. Our results are corroborated by measured and simulated characteristics of the metasurface resonator, and our approach can enhance dramatically functionalities of widely available low-field MRI devices.

physics.med-ph↗