arXiv subjects

Max H. C. van Riel

Publications and source records attributed to Max H. C. van Riel.

4 recordsLinked to original sources

arXiv2026

An in vivo validation dataset for dynamic volumetric MRI

Dynamic volumetric MRI provides valuable information on in vivo motion and biomechanics, with applications spanning cardiac, musculoskeletal, or pulmonary imaging, amongst others. Developing reconstruction methods for time-resolved volumetric MRI is challenging due to the inherently slow acquisition process of MRI, which makes it an active area of research. However, in vivo validation of these methods remains challenging due to the lack of publicly available datasets with fully sampled ground-truth images. Here, we present a publicly available in vivo dataset designed to facilitate the development and validation of dynamic volumetric MRI reconstruction algorithms. Controlled and repeatable deformations of the muscles in the thigh were induced using a pneumatic pressure cuff, enabling the acquisition of both undersampled dynamic data and fully sampled validation images. The dataset comprises multichannel undersampled k-space data from nine healthy volunteers across four different dynamic deformations, with fully sampled validation data for one deformation. Additionally, an anatomical reference scan and muscle segmentation masks are provided for each subject. To illustrate a possible image reconstruction and validation approach, a binning-based reconstruction was performed on the undersampled data from six dynamic repetitions. The resulting images were consistent with the corresponding fully sampled validation images. This dataset offers possibilities for validating and advancing time-resolved volumetric MRI reconstruction methods.

physics.med-ph↗

arXiv2025

Quasi-static in vivo elastography from internal displacement information only

As disease often alters the structural properties of soft tissue, noninvasive elastography techniques have emerged to quantitatively assess in vivo mechanical properties. Magnetic Resonance Elastography (MRE) based on dynamic deformations is the standard technique for imaging mechanical properties, but the viscoelastic nature of soft tissue makes the results dependent on the actuation frequency, which can be limiting. In this proof-of-principle study we propose a noise robust framework for reconstructing relative stiffness properties from quasi-static in vivo displacement fields captured on a physiological time scale. The acquisition is performed using a pneumatic pressure cuff to induce tissue deformation in a controlled manner. The reconstruction does not require boundary information which is generally hard to access in vivo nor spatial derivatives of displacement fields that are known to amplify noise. The validity of our framework is corroborated with in silico experiments on a numerical phantom. In vivo experiments on the thigh of a volunteer demonstrate the repeatability of the method. As an application, the quantitative change in muscle stiffness during isometric knee flexion is investigated which yielded physiologically meaningful results.

physics.med-ph↗

arXiv2023

Time-Resolved Reconstruction of Motion, Force, and Stiffness using Spectro-Dynamic MRI

Measuring the dynamics and mechanical properties of muscles and joints is important to understand the (patho)physiology of muscles. However, acquiring dynamic time-resolved MRI data is challenging. We have previously developed Spectro-Dynamic MRI which allows the characterization of dynamical systems at a high spatial and temporal resolution directly from k-space data. This work presents an extended Spectro-Dynamic MRI framework that reconstructs 1) time-resolved MR images, 2) time-resolved motion fields, 3) dynamical parameters, and 4) an activation force, at a temporal resolution of 11 ms. An iterative algorithm solves a minimization problem containing four terms: a motion model relating the motion to the fully-sampled k-space data, a dynamical model describing the expected type of dynamics, a data consistency term describing the undersampling pattern, and finally a regularization term for the activation force. We acquired MRI data using a dynamic motion phantom programmed to move like an actively driven linear elastic system, from which all dynamic variables could be accurately reconstructed, regardless of the sampling pattern. The proposed method performed better than a two-step approach, where time-resolved images were first reconstructed from the undersampled data without any information about the motion, followed by a motion estimation step.

physics.med-ph↗

arXiv2020

Optimization of MR Fingerprinting for Free-Breathing Quantitative Abdominal Imaging

In this work, we propose a free-breathing magnetic resonance fingerprinting method that can be used to obtain $B_1^+$-robust quantitative maps of the abdomen in a clinically acceptable time. A three-dimensional MR fingerprinting sequence with a radial stack-of-stars trajectory was implemented for quantitative abdominal imaging. The k-space acquisition ordering was adjusted to improve motion-robustness. The flip angle pattern was optimized using the Cramér-Rao Lower Bound, and the encoding efficiency of sequences with 300, 600, 900, and 1800 flip angles was evaluated. To validate the sequence, a movable multicompartment phantom was developed. Reference multiparametric maps were acquired under stationary conditions using a previously validated MRF method. Periodic motion of the phantom was used to investigate the motion-robustness of the proposed sequence. The best performing sequence length (600 flip angles) was used to image the abdomen during a free-breathing volunteer scan. When using a series of 600 or more flip angles, the estimated $T_1$ values in the stationary phantom showed good agreement with the reference scan. Phantom experiments revealed that motion-related artefacts can appear in the quantitative maps, and confirmed that a motion-robust k-space ordering is essential in preventing these artefacts. The in vivo scan demonstrated that the proposed sequence can produce clean parameter maps while the subject breathes freely. Using this sequence, it is possible to generate $B_1^+$-robust quantitative maps of proton density, $T_1$, and $B_1^+$ under free-breathing conditions at a clinically usable resolution within 5 minutes.

physics.med-ph↗