arXiv subjects

Sebastian Kozerke

Publications and source records attributed to Sebastian Kozerke.

16 recordsLinked to original sources

arXiv2026

Physics-Informed Implicit Neural Representations for Improved Myocardial Perfusion MRI Quantification

Quantifying myocardial perfusion from cardiac magnetic resonance (CMR) can be achieved by fitting tracer-kinetic models to the dynamic contrast-enhanced MR data. However, fitting the observed data with multi-compartment exchange models, which describe the evolution of the contrast agent in the tissue, to estimate perfusion parameters is a challenging inverse problem that is sensitive to noise and acquisition variability. Previously, physics-informed neural networks (PINNs) have been proposed as an alternative to conventional non-linear least squares fitting methods with promising results for quantitative perfusion CMR. In this work, we extend the previously proposed PINN framework with spatiotemporal implicit neural representations (INRs) to represent the MR signal as a continuous spatiotemporal function and to improve the accuracy, smoothness, and physical consistency of the PINN model. In realistic simulated CMR datasets, our proposed PINN with INRs demonstrates improved robustness and parameter estimation accuracy over the previously established methods. The code is available at https://github.com/q-cardIA/pinn-inr.

eess.IV↗

arXiv2026

A Latent ODE Approach to Spatiotemporal Modeling of Cine Cardiac MRI

Cardiac magnetic resonance imaging (CMR) captures rich spatiotemporal information about ventricular structure and motion, but conventional risk models use only a few image-derived indices from selected cardiac phases. We present a latent dynamical model that encodes bi-ventricular anatomy and full-cycle cine motion as a continuous latent trajectory, using heart-rate-aware neural ordinary differential equation (ODE) dynamics and a graph-based mesh autoencoder to reconstruct anatomically consistent 3D+t ventricular motion. A covariate-conditioned prior defines the expected end-diastolic latent state, and a Cox proportional hazards model tests whether deviations from this prior predict incident heart failure. We studied 72,386 UK Biobank participants without baseline cardiovascular disease, including 367 incident heart failure events. In a held-out evaluation subset, adding the latent score to refitted pooled cohort equations improved the stratified C-index from 0.704 to 0.785, compared with 0.764 for seven established cardiac markers. Compared with non-graph and non-ODE approaches, the proposed model gave the best trade-off between reconstruction fidelity, generative realism, and downstream prognostic performance. These results suggest that continuous full-cycle modeling of ventricular motion provides informative cardiac phenotypes beyond conventional CMR summaries, while external validation in more representative patient cohorts is required before clinical risk-prediction use.

cs.AI↗

arXiv2026

Time-resolved aortic 3D shape reconstruction from a limited number of cine 2D MRI slices

Background and Objective: To assess the feasibility and accuracy of reconstructing time-resolved, three-dimensional, subject-specific aortic geometries from a limited number of standard cine 2D magnetic resonance imaging (MRI) acquisitions. This is achieved by coupling a statistical shape model with a differentiable volumetric mesh optimization algorithm. Methods: Cine 2D MRI slices were manually segmented and used to reconstruct subject-specific aortic geometries via a differentiable mesh optimization algorithm, constrained by a statistical shape model. Optimal slice positioning was first evaluated on synthetic data, followed by in-vivo acquisition in 30 subjects (19 volunteers and 11 aortic stenosis patients). Time-resolved aortic geometries were reconstructed, from which geometric descriptors and radial strain were derived. In a subset of 10 subjects, 4D flow MRI data was acquired to provide volumetric reference for peak-systolic shape comparison. Results: Accurate reconstruction was achieved using as few as six cine 2D MRI slices. Agreement with 4D flow MRI reference data yielded a Dice score of (89.9 +/- 1.6) %, Intersection over Union of (81.7 +/- 2.7) %, Hausdorff distance of (7.3 +/- 3.3) mm, and Chamfer distance of (3.7 +/- 0.6) mm. The mean absolute radius error along the aortic arch was (0.8 +/- 0.6) mm. Secondary analysis demonstrated significant differences in geometric features and radial strain across age groups, with strain decreasing progressively with age at values of (11.00 +/- 3.11) x 10-2 vs. (3.74 +/- 1.25) x 10-2 vs. (2.89 +/- 0.87) x 10-2 for the young, mid-age, and elderly groups, respectively. Conclusion: The proposed framework enables reconstruction of time-resolved, subject-specific aortic geometries from a limited number of standard cine 2D MRI acquisitions, providing a practical basis for downstream computational analysis.

eess.IV↗

arXiv2025

Localized FNO for Spatiotemporal Hemodynamic Upsampling in Aneurysm MRI

Hemodynamic analysis is essential for predicting aneurysm rupture and guiding treatment. While magnetic resonance flow imaging enables time-resolved volumetric blood velocity measurements, its low spatiotemporal resolution and signal-to-noise ratio limit its diagnostic utility. To address this, we propose the Localized Fourier Neural Operator (LoFNO), a novel 3D architecture that enhances both spatial and temporal resolution with the ability to predict wall shear stress (WSS) directly from clinical imaging data. LoFNO integrates Laplacian eigenvectors as geometric priors for improved structural awareness on irregular, unseen geometries and employs an Enhanced Deep Super-Resolution Network (EDSR) layer for robust upsampling. By combining geometric priors with neural operator frameworks, LoFNO de-noises and spatiotemporally upsamples flow data, achieving superior velocity and WSS predictions compared to interpolation and alternative deep learning methods, enabling more precise cerebrovascular diagnostics.

cs.CV↗

arXiv2025

Consensus Recommendations for Hyperpolarized [1-13C]pyruvate MRI Multi-center Human Studies

Magnetic resonance imaging of hyperpolarized (HP) [1-13C]pyruvate allows in-vivo assessment of metabolism and has translated into human studies across diseases at 15 centers worldwide. Consensus on best practice for multi-center studies is required to develop clinical applications. This paper presents the results of a 2-round formal consensus building exercise carried out by experts with HP [1-13C]pyruvate human study experience. Twenty-nine participants from 13 sites brought together expertise in pharmacy methods, MR physics, translational imaging, and data-analysis; with the goal of providing recommendations and best practice statements on conduct of multi-center human studies of HP [1-13C]pyruvate MRI. Overall, the group reached consensus on approximately two-thirds of 246 statements in the questionnaire, covering 'HP 13C-Pyruvate Preparation', 'MRI System Setup, Calibration, and Phantoms', 'Acquisition and Reconstruction', and 'Data Analysis and Quantification'. Consensus was present across categories, examples include that: (i) different HP pyruvate preparation methods could be used in human studies, but that the same release criteria have to be followed; (ii) site qualification and quality assurance must be performed with phantoms and that the same field strength must be used, but that the rest of the system setup and calibration methods could be determined by individual sites; (iii) the same pulse sequence and reconstruction methods were preferable, but the exact choice should be governed by the anatomical target; (iv) normalized metabolite area-under-curve (AUC) values and metabolite AUC were the preferred metabolism metrics. The work confirmed areas of consensus for multi-center study conduct and identified where further research is required to ascertain best practice.

physics.med-ph↗

arXiv2024

Temperature-dependent dynamic nuclear polarization of diamond

Dynamic nuclear polarization (DNP) can increase nuclear magnetic resonance (NMR) signals by orders of magnitude. DNP in diamond proceeds through different DNP mechanisms with a possible temperature-dependence. We report on 13C dynamic nuclear polarization (DNP) experiments in diamonds at 3.4 T and 7 T between 300 K and 1.7 K. Nuclear polarization enhancements between 100 and 600 were measured for all temperatures, corresponding to polarizations at 7 T between 0.1% (300 K) and 38% (1.7 K). A strong temperature dependence of the DNP profiles was observed. Longitudinal-detected (LOD) electron paramagnetic resonance (EPR) experiments revealed an additional broad temperature-dependent electron line centered around the mI = 0 line of the P1 triplet transitions. Our results suggest that nuclei are preferentially polarized via a direct hyperfine mediated polarization transfer while spin diffusion in the sample plays a minor role.

cond-mat.mtrl-sci↗

arXiv2024

The role of nuclear spin diffusion in dynamic nuclear polarization of crystalline nanoscale silicon particles

Hyperpolarized nanoparticles (NPs) offer high polarization levels with room temperature relaxation times exceeding half an hour. In this work, we demonstrate that the achievable hyperpolarization enhancement and relaxation (decay) time at room temperature are largely independent of the particle size contrary to previous assumptions. This is explained through first-principles spin-diffusion coefficient calculations and finite-element polarization simulations. The simulated zero-quantum (flip-flop) line width governing the spin diffusion is found to agree with the experimentally accessible single-quantum (single spin flip, e.g. radio-frequency pulse) line width. The transport of hyperpolarization from strongly hyperfine-coupled spins towards the bulk is most likelybelieved to be responsible for the slow polarization dynamics including long room temperature decay time. The line width and spin-diffusion simulations are extended to other cubic crystal structures and analytical expressions, which only require insertion of the gyromagnetic ratio, lattice constant, isotope abundance and measured spectral density distribution (nuclear line width), are fitted. The presented simulations can be adjusted to study spin diffusion in other materials.

quant-ph↗

arXiv2024

FlowMRI-Net: A Generalizable Self-Supervised 4D Flow MRI Reconstruction network

Background: Image reconstruction from highly undersampled 4D flow MRI data can be very time consuming and may result in significant underestimation of velocities depending on regularization, thereby limiting the applicability of the method. The objective of the present work was to develop a generalizable self-supervised deep learning-based framework for fast and accurate reconstruction of highly undersampled 4D flow MRI and to demonstrate the utility of the framework for aortic and cerebrovascular applications. Methods: The proposed deep-learning-based framework, called FlowMRI-Net, employs physics-driven unrolled optimization using a complex-valued convolutional recurrent neural network and is trained in a self-supervised manner. The generalizability of the framework is evaluated using aortic and cerebrovascular 4D flow MRI acquisitions acquired on systems from two different vendors for various undersampling factors (R=8,16,24) and compared to state-of-the-art compressed sensing (CS-LLR) and deep learning-based (FlowVN) reconstructions. Evaluation includes an ablation study and a qualitative and quantitative analysis of image and velocity magnitudes. Results: FlowMRI-Net outperforms CS-LLR and FlowVN for aortic 4D flow MRI reconstruction, resulting in significantly lower vectorial normalized root mean square error and mean directional errors for velocities in the thoracic aorta. Furthermore, the feasibility of FlowMRI-Net's generalizability is demonstrated for cerebrovascular 4D flow MRI reconstruction, where no FlowVN can be trained due to the lack of high-quality reference data. Reconstruction times ranged from 3 to 7 minutes on commodity CPU/GPU hardware. Conclusion: FlowMRI-Net enables fast and accurate reconstruction of highly undersampled aortic and cerebrovascular 4D flow MRI, with possible applications to other vascular territories.

physics.med-ph↗

arXiv2024

Optimal OnTheFly Feedback Control of Event Sensors

Event-based vision sensors produce an asynchronous stream of events which are triggered when the pixel intensity variation exceeds a predefined threshold. Such sensors offer significant advantages, including reduced data redundancy, micro-second temporal resolution, and low power consumption, making them valuable for applications in robotics and computer vision. In this work, we consider the problem of video reconstruction from events, and propose an approach for dynamic feedback control of activation thresholds, in which a controller network analyzes the past emitted events and predicts the optimal distribution of activation thresholds for the following time segment. Additionally, we allow a user-defined target peak-event-rate for which the control network is conditioned and optimized to predict per-column activation thresholds that would eventually produce the best possible video reconstruction. The proposed OnTheFly control scheme is data-driven and trained in an end-to-end fashion using probabilistic relaxation of the discrete event representation. We demonstrate that our approach outperforms both fixed and randomly-varying threshold schemes by 6-12% in terms of LPIPS perceptual image dissimilarity metric, and by 49% in terms of event rate, achieving superior reconstruction quality while enabling a fine-tuned balance between performance accuracy and the event rate. Additionally, we show that sampling strategies provided by our OnTheFly control are interpretable and reflect the characteristics of the scene. Our results, derived from a physically-accurate simulator, underline the promise of the proposed methodology in enhancing the utility of event cameras for image reconstruction and other downstream tasks, paving the way for hardware implementation of dynamic feedback EVS control in silicon.

cs.CV↗

arXiv2024

Two-electron two-nucleus effective Hamiltonian and the spin diffusion barrier

Dynamic nuclear polarization (DNP) involves a polarization transfer from unpaired electrons to hyperfine coupled nuclei and can increase the sensitivity of nuclear magnetic resonance (NMR) signals by several orders of magnitude. The hyperfine coupling is considered to suppress nuclear dipolar flip-flop transitions, hindering the transport of nuclear hyperpolarization into the bulk (''spin-diffusion barrier''). Possible polarization-transfer pathways leading to DNP and subsequent spin diffusion between hypershifted nuclei in a two-electron two-nucleus four-spin system are investigated. The Schrieffer-Wolff transformation is applied to characterize transitions that are only possible as second-order effects. An energy-conserving electron-nuclear four-spin flip-flop is identified, which combines an electron dipolar with a nuclear dipolar flip-flop process, describing spin diffusion close to electrons. The relevance of this process is supported by two-compartment model fits of HypRes-on experimental data. This suggests that all nuclear spins can contribute to the hyperpolarization of the bulk and the concept of a spin-diffusion barrier has to be reconsidered for samples with significant electron and nuclear dipolar couplings.

physics.chem-ph↗

arXiv2024

Controlled synthesis and characterization of porous silicon nanoparticles for dynamic nuclear polarization

Si nanoparticles (NPs) have been actively developed as a hyperpolarized magnetic resonance imaging (MRI) contrast agent with an imaging window close to one hour. However, the progress in the development of NPs has been hampered by the incomplete understanding of their structural properties that correspond to efficient hyperpolarization buildup and long polarization decays. In this work we study dynamic nuclear polarization (DNP) of single crystal porous Si (PSi) NPs with defined doping densities ranging from nominally undoped to highly doped with boron or phosphorus. To develop such PSi NPs we perform low-load metal-assisted catalytic etching for electronic grade Si powder followed by thermal oxidation to form the dangling bonds in the Si/SiO$_2$ interface, the $P_b$ centers. $P_b$ centers are the endogenous source of the unpaired electron spins necessary for DNP. The controlled fabrication and oxidation procedures allow us to thoroughly investigate the impact of the magnetic field, temperature and doping on the DNP process. We argue that the buildup and decay rate constants are independent of size of Si crystals between approximately 10 and 60 nm. Instead, the rates are limited by the polarization transfer across the nuclear spin diffusion barrier determined by the large hyperfine shift of the central $^{29}$Si nuclei of the $P_b$ centers. The size-independent rates are then weakly affected by the doping degree for low and moderately doped Si although slight doping is required to achieve the highest polarization. Thus, we find the room temperature relaxation of low boron doped PSi NPs reaching $75 \pm 3$ minutes and nuclear polarization levels exceeding $\sim 6$ % when polarized at 6.7 T and 1.4 K. Our study thus establishes solid grounds for further development of Si NPs as hyperpolarized contrast agents.

cond-mat.mtrl-sci↗

arXiv2023

Relaxation enhancement by microwave irradiation may limit dynamic nuclear polarization

Dynamic nuclear polarization enables the hyperpolarization of nuclear spins beyond the thermal-equilibrium Boltzmann distribution. However, it is often unclear why the experimentally measured hyperpolarization is below the theoretical achievable maximum polarization. We report a (near-) resonant relaxation enhancement by microwave (MW) irradiation, leading to a significant increase in the nuclear polarization decay compared to measurements without MW irradiation. For example, the increased nuclear relaxation limits the achievable polarization levels to around 35% instead of hypothetical 60%, measured in the DNP material TEMPO in 1H glassy matrices at 3.3 K and 7 T. Applying rate-equation models to published build-up and decay data indicates that such relaxation enhancement is a common issue in many samples when using different radicals at low sample temperatures and high Boltzmann polarizations of the electrons. Accordingly, quantification and a better understanding of the relaxation processes under MW irradiation might help to design samples and processes towards achieving higher nuclear hyperpolarization levels.better understanding of the relaxation processes under MW irradiation might help to design samples and processes towards achieving higher nuclear hyperpolarization levels.

physics.chem-ph↗

arXiv2022

MRXCAT-CDTI: A Numerical Cardiac Diffusion Tensor Imaging Phantom

Magnetic Resonance cardiac diffusion tensor imaging (cDTI) and cardiac intravoxel incoherent motion imaging enables probing of in vivo myofiber architecture and myocardial perfusion surrogates. To study the impact of experimental parameters such as resolution, off-resonances and heart-rate variations, we propose a numerical open-source framework called MRXCAT-CDTI. It allows simulating diffusion and perfusion contrast for spin-echo (SE) and stimulated echo acquisition mode (STEAM) cDTI sequences. The Fourier encoder supports in-plane and/or through-slice off-resonance effects, as well as T2* effects during single-shot image encoding. Optional lesions are included to mimic ischemic and infarcted myocardial regions. MRXCAT-CDTI allows assessing realistic influences on data acquisition, and how these affect the data encoding process and subsequent data processing. As an example, heart-rate variations lead to differences in partial saturation and relaxation of magnetization that end up in errors of 9 to 30% for cDTI angle metrics if not accounted for. For SE echo-planar cDTI, in-plane off-resonance effects more adversely affect cDTI metrics compared to through-slice off-resonances. With this work we propose an open-source MRXCAT-CDTI numerical simulation framework that offers realistic image encoding effects found in cardiac diffusion and perfusion data to systematically study influences of data encoding, reconstruction, and post-processing to promote reproducible research.

physics.med-ph↗

arXiv2020

Simulation of Intravoxel Incoherent Perfusion Signal Using a Realistic Capillary Network of a Mouse Brain

Purpose: To simulate the intravoxel incoherent perfusion magnetic resonance magnitude signal from the motion of blood particles in three realistic vascular network graphs from a mouse brain. Methods: In three networks generated from the cortex of a mouse scanned by two-photon laser microscopy, blood flow in each vessel was simulated using Poiseuille law. The trajectories, flow speeds and phases acquired by a fixed number of simulated blood particles during a Stejskal-Tanner monopolar pulse gradient scheme were computed. The resulting magnitude signal as a function of b-value was obtained by integrating all phases and the pseudo-diffusion coefficient D* was estimated by fitting an exponential signal decay. To better understand the anatomical source of the IVIM perfusion signal, the above was repeated by restricting the simulation to various types of vessels. Results: The characteristics of the three microvascular networks were respectively: vessel lengths [mean +/- std. dev.]: 67.2 +/- 53.6 um, 59.8 +/- 46.2 um, and 64.5 +/- 50.9 um; diameters: 6.0 +/- 3.5 um, network 2: 5.7 +/- 3.6 um, and network 3: 6.1 +/- 3.7 um; simulated blood velocity: 0.9 +/- 1.7 um/ms, 1.4 +/- 2.5 um/ms and 0.7 +/- 2.1 um/ms. Exponential fitting of the simulated signal decay as a function of b-value resulted in the following D* [10-3 mm2/s]: 31.7, 40.4 and 33.4. The signal decay for low b-values was the largest in the larger vessels, but the smaller vessels and the capillaries accounted more to the total volume of the networks. Conclusion:This simulation improves the theoretical understanding of the IVIM perfusion estimation method by directly linking the MR IVIM perfusion signal to an ultra-high resolution measurement of the microvascular network and a realistic blood flow simulation.

physics.med-ph↗

arXiv2020

Improved Segmentation and Detection Sensitivity of Diffusion-Weighted Brain Infarct Lesions with Synthetically Enhanced Deep Learning

Purpose: To compare the segmentation and detection performance of a deep learning model trained on a database of human-labelled clinical diffusion-weighted (DW) stroke lesions to a model trained on the same database enhanced with synthetic DW stroke lesions. Methods: In this institutional review board approved study, a stroke database of 962 cases (mean age 65+/-17 years, 255 males, 449 scans with DW positive stroke lesions) and a normal database of 2,027 patients (mean age 38+/-24 years,1088 females) were obtained. Brain volumes with synthetic DW stroke lesions were produced by warping the relative signal increase of real strokes to normal brain volumes. A generic 3D U-Net was trained on four different databases to generate four different models: (a) 375 neuroradiologist-labeled clinical DW positive stroke cases(CDB);(b) 2,000 synthetic cases(S2DB);(c) CDB+2,000 synthetic cases(CS2DB); or (d) CDB+40,000 synthetic cases(CS40DB). The models were tested on 20%(n=192) of the cases of the stroke database, which were excluded from the training set. Segmentation accuracy was characterized using Dice score and lesion volume of the stroke segmentation, and statistical significance was tested using a paired, two-tailed, Student's t-test. Detection sensitivity and specificity was compared to three neuroradiologists. Results: The performance of the 3D U-Net model trained on the CS40DB(mean Dice 0.72) was better than models trained on the CS2DB (0.70,P <0.001) or the CDB(0.65,P<0.001). The deep learning model was also more sensitive (91%[89%-93%]) than each of the three human readers(84%[81%-87%],78%[75%-81%],and 79%[76%-82%]), but less specific(75%[72%-78%] vs for the three human readers (96%[94%-97%],92%[90%-94%] and 89%[86%-91%]). Conclusion: Deep learning training for segmentation and detection of DW stroke lesions was significantly improved by enhancing the training set with synthetic lesions.

physics.med-ph↗

arXiv2020

Deep variational network for rapid 4D flow MRI reconstruction

Phase-contrast magnetic resonance imaging (MRI) provides time-resolved quantification of blood flow dynamics that can aid clinical diagnosis. Long in vivo scan times due to repeated three-dimensional (3D) volume sampling over cardiac phases and breathing cycles necessitate accelerated imaging techniques that leverage data correlations. Standard compressed sensing reconstruction methods require tuning of hyperparameters and are computationally expensive, which diminishes the potential reduction of examination times. We propose an efficient model-based deep neural reconstruction network and evaluate its performance on clinical aortic flow data. The network is shown to reconstruct undersampled 4D flow MRI data in under a minute on standard consumer hardware. Remarkably, the relatively low amounts of tunable parameters allowed the network to be trained on images from 11 reference scans while generalizing well to retrospective and prospective undersampled data for various acceleration factors and anatomies.

eess.IV↗