arXiv subjects

Klaas P. Pruessmann

Publications and source records attributed to Klaas P. Pruessmann.

7 recordsLinked to original sources

arXiv2026

Effects of Sequence Timing on the Spatio-Temporal Properties of 3D BOLD fMRI: A Formal Framework and Analysis

3D sequences provide an alternative to 2D or multiband sequences for BOLD fMRI. The impact of acquisition time differences between slices is well understood for 2D or multiband sequences. For 3D sequences, k-space is partitioned into multiple segments and the final image depends on samples taken over an extended period of time. Any precise timing information is lost during reconstruction. A theoretical description of how sequence timing impacts 3D BOLD fMRI data properties is lacking in the literature. We present a formal framework that models sequence timing effects and draw connections to existing literature. The framework rests on the statistical description of a spin system that allows incorporation of BOLD signal changes and is completed by a general description of spatial encoding and image reconstruction. Using this formulation, we define three key images: the actual image reconstructed from a segmented 3D acquisition, an optimal reference image unaffected by sequence timing, and an error image representing their difference. Two sets of operators are defined that describe the generation of these images and allow us to analyse the effects of sequence timing independent of the object. All operators take the form of spatio-temporal filters. They mix signal content spatially and based on its temporal waveform. High-frequency BOLD signal content is affected more strongly compared to low-frequency content. Simulations demonstrate that while the total variance of 3D image time series can be reduced, spatio-temporal fidelity is increasingly lost. Furthermore, we hypothesize that the heightened sensitivity of 3D sequences to physiological noise (compared to 2D) is caused by sequence timing damping thermal noise more strongly than physiological fluctuations. The impact of motion and system instabilities on image time series needs to be studied further.

eess.IV↗

arXiv2026

A GPU-enhanced workflow for non-Fourier SENSE reconstruction

Purpose: Image reconstruction in challenging scenarios requires accurate characterisations of coil sensitivity profiles, local off-resonances (B0) and effective encoding fields. Reconstruction methods utilising all of this information rely on signal models that are not compatible with the classical Fourier/k-space interpretation of the coil data. Hence, the FFT and related techniques are no more applicable, rendering image reconstruction computationally demanding. Methods: This article contains a workflow for accurate sensitivity and B0 mapping as well as other required processing steps. An implementation of non-Fourier SENSE reconstruction is provide that is well suited for execution on a GPU using the FFT. Important practical aspects like stopping criteria and sources of image artifacts are analyzed and documented. Results: Highly performant image reconstruction could be demonstrated on a 2D and 3D spiral dataset. These datasets contain trajectories featuring readout durations up to 71.5ms and undersampling factors up to R = 7. Running the reconstruction on a GPU greatly boosts reconstruction speed. Stopping the reconstruction at the right moment is crucial for image quality. All methods included in this article are available in a public code repository. Conclusion: The provided implementation of non-Fourier SENSE reconstruction is highly performant. When it is executed on GPU, runtimes reach a duration feasible in practice. The presented workflow ensures robust and accurate computation of coil sensitive profiles and off-resonance maps.

eess.IV↗

arXiv2025

Servo navigation and phase equalization enhanced by run-time stabilization (PEERS) for 3D EPI time series

Purpose: To enhance time-resolved segmented imaging by synergy of run-time stabilization and retrospective, data-driven phase correction. Methods: A segmented 3D EPI sequence for fMRI time series is equipped with servo navigation based on short orbital navigators and a linear perturbation model, enabling run-time correction for rigid-body motion as well as bulk phase and frequency fluctuation. Complementary retrospective phase correction is based on the repetitive structure of the time series and serves to address residual phase and frequency offsets. The combined approach is termed phase equalization enhanced by run-time stabilization (PEERS). Results: The proposed strategy is evaluated in a phantom and in-vivo. Servo navigation is found to diminish motion confound in raw data and maintain k-space consistency over time series. In turn, retrospective phase equalization is found to eliminate shot-wise phase and frequency offsets relative to the navigator, which are attributed to eddy-currents and vibrations from phase encoding. Retrospective phase equalization reduces the precision requirements for run-time frequency control, supporting the use of short navigators. Relative to conventional volume realignment, PEERS achieved tSNR improvements up to $30\%$ for small motion and in the order of $10\%$ when volunteers tried to hold still. Retrospective phase equalization is found to clearly outperform phase correction based solely on navigator-based frequency estimates. Conclusion: Servo navigation achieves high-precision run-time motion correction for 3D EPI fMRI. Coarse frequency tracking based on short navigators is supplemented by precise retrospective frequency and phase correction. Fully automatic and self-calibrated, PEERS offers effective plug-and-play motion and phase correction for 3D fMRI.

eess.IV↗

arXiv2025

Magnetic Resonance Particle Tracking

Granular materials such as gravel, cereals, or pellets, are ubiquitous in nature, daily life, and industry. While sharing some characteristics with gases, liquids, and solids, granular matter exhibits a wealth of phenomena that defy these analogies and are yet to be fully understood. Advancing granular physics requires experimental observation at the level of individual particles and over a wide range of dynamics. Specifically, it calls for the ability to track large numbers of particles simultaneously, in three dimensions (3D), and with high spatial and temporal resolution. Here, we introduce magnetic resonance particle tracking (MRPT) and show it to achieve such recording with resolution on the scale of micrometers and milliseconds. Enabled by MRPT, we report the direct study of granular glassy dynamics in 3D. Tracking a vibrated granular system over six temporal orders of magnitude revealed dynamical heterogeneities, two-step relaxation, and structural memory closely akin to 3D glass formation in supercooled liquids and colloids. These findings illustrate broad prospective utility of MRPT in advancing the exploration, theory, and numerical models of granular matter.

cond-mat.soft↗

arXiv2022

Temperature distribution in a gas-solid fixed bed probed by rapid magnetic resonance imaging

Controlling the temperature distribution inside catalytic fixed bed reactors is crucial for yield optimization and process stability. Yet, in situ temperature measurements with spatial and temporal resolution are still challenging. In this work, we perform temperature measurements in a cylindrical fixed bed reactor by combining the capabilities of real-time magnetic resonance imaging (MRI) with the temperature-dependent proton resonance frequency (PRF) shift of water. Three-dimensional (3D) temperature maps are acquired while heating the bed from room temperature to 60~$^{\circ}$C using hot air. The obtained results show a clear temperature gradient along the axial and radial dimensions and agree with optical temperature probe measurements with an average error of $\pm$ 1.5~$^{\circ}$C. We believe that the MR thermometry methodology presented here opens new perspectives for the fundamental study of mass and heat transfer in gas-solid fixed beds and in the future might be extended to the study of reactive gas-solid systems.

cond-mat.soft↗

arXiv2018

MR image reconstruction using deep density priors

Algorithms for Magnetic Resonance (MR) image reconstruction from undersampled measurements exploit prior information to compensate for missing k-space data. Deep learning (DL) provides a powerful framework for extracting such information from existing image datasets, through learning, and then using it for reconstruction. Leveraging this, recent methods employed DL to learn mappings from undersampled to fully sampled images using paired datasets, including undersampled and corresponding fully sampled images, integrating prior knowledge implicitly. In this article, we propose an alternative approach that learns the probability distribution of fully sampled MR images using unsupervised DL, specifically Variational Autoencoders (VAE), and use this as an explicit prior term in reconstruction, completely decoupling the encoding operation from the prior. The resulting reconstruction algorithm enjoys a powerful image prior to compensate for missing k-space data without requiring paired datasets for training nor being prone to associated sensitivities, such as deviations in undersampling patterns used in training and test time or coil settings. We evaluated the proposed method with T1 weighted images from a publicly available dataset, multi-coil complex images acquired from healthy volunteers (N=8) and images with white matter lesions. The proposed algorithm, using the VAE prior, produced visually high quality reconstructions and achieved low RMSE values, outperforming most of the alternative methods on the same dataset. On multi-coil complex data, the algorithm yielded accurate magnitude and phase reconstruction results. In the experiments on images with white matter lesions, the method faithfully reconstructed the lesions. Keywords: Reconstruction, MRI, prior probability, machine learning, deep learning, unsupervised learning, density estimation

cs.CV↗

arXiv2009

Bandwidth, expansion, treewidth, separators, and universality for bounded degree graphs

We establish relations between the bandwidth and the treewidth of bounded degree graphs G, and relate these parameters to the size of a separator of G as well as the size of an expanding subgraph of G. Our results imply that if one of these parameters is sublinear in the number of vertices of G then so are all the others. This implies for example that graphs of fixed genus have sublinear bandwidth or, more generally, a corresponding result for graphs with any fixed forbidden minor. As a consequence we establish a simple criterion for universality for such classes of graphs and show for example that for each gamma>0 every n-vertex graph with minimum degree ((3/4)+gamma)n contains a copy of every bounded-degree planar graph on n vertices if n is sufficiently large.

math.CO↗