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Prof. Moritz Zaiss (Friedrich-Alexander University Erlangen–Nuremberg (FAU))20/05/2026, 09:30
In the MR-zero project [1,2], we approached to reinvent MRI from scratch by treating MR sequence design as a learning-based optimization problem. We formulate an end-to-end differentiable simulation pipeline, given a specific imaging task or even just a target image, the entire chain can be optimized jointly.
In this talk, I will introduce the MR-zero approach and illustrate it with...
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Dr Kurt Majewski (Siemens AG, DAI R ORD-DE)20/05/2026, 11:00
The well known direct signal control (DSC) approach allows us to optimize excitation voltages of a MRI pulse sequence in such a way that the overall transverse magnetization of a spin ensemble has desired values at a number of signal readout times. In this approach the overall transverse magnetization is calculated with the help of an approximate solution of the Bloch equations and...
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Ms Viktoria Buchegger (Institute of Biomedical Imaging, Graz University of Technology, Graz, Austria)20/05/2026, 11:25
Introduction Arterial spin labeling (ASL) is a promising non-invasive perfusion imaging technique, but its reproducibility across platforms remains limited due to the lack of standardized, end-to-end pipelines. Although consensus guidelines exist, implementation variability persists. The Berkeley Advanced Reconstruction Toolbox (BART) is a free and open-source computational MRI framework for...
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Mr Daniel Mackner (Graz University of Technology)20/05/2026, 11:50
This work presents an open-source framework that integrates MRI sequence design, execution, and model-based reconstruction into a single reproducible workflow within the BART toolbox. The motivation is to address reproducibility challenges in quantitative MRI (qMRI), where both sequence implementation and reconstruction details are often difficult to replicate due to proprietary software and...
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Mr Philip Schaten (TU Graz)20/05/2026, 12:15
Open-Source software such as pypulseq and BART have immensely improved MRI sequence design and image reconstruction. Nevertheless, working with a clinical MRI scanner still poses challenges. We present solutions for bringing open source sequences and image reconstruction to the scanner. An MRI sequence describes the sequence of events that is played out by the scanner hardware during an MRI...
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Prof. Barbara Kaltenbacher (University of Klagenfurt)20/05/2026, 14:05
In this talk we provide some uniqueness results for the (multi-)coefficient identification problem of reconstructing the spatially varying spin density as well as the spin-lattice and spin-spin relaxation times and the local field inhomogeneity in the Bloch-Torrey equation, as relevant in magnetic resonance imaging MRI.
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To this end, we follow two approaches:
(a) Relying on sampling of the... -
Mr Pablo Muñoz (Klagenfurt Universität)20/05/2026, 14:30
We present a computational framework for model-based quantitative imaging through the identification of spatially varying relaxation parameters in the Bloch-Torrey equation, a time dependent PDE governing magnetization dynamics in magnetic resonance imaging. The equation couples RF induced precession, longitudinal and transverse relaxation, and diffusion advection transport, a structure that...
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Dr Jyrki Jauhiainen (University of Graz)20/05/2026, 14:55
This talk gives a nontechnical introduction to PDE-constrained bilevel optimization problems with nonsmooth lower levels. The PDE constraints considered here are simplifications of the Bloch--Torrey equation from MRI, with the bilevel structure modelling a parameter learning problem. We discuss how complementarity conditions arising from first-order information are connected to the...
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Mr Jakob Wagner (Technical University of Munich)20/05/2026, 15:20
Modern 4D-MRI imaging techniques are able to capture fully space-time resolved blood flow velocity data. This data facilitates a diagnosis of the severity of cardiovascular diseases, provided that, in a first step, additional information such as pressure, wall-shear stresses, etc., is inferred from the measured data. In this talk, we treat the inverse problem of deducing pressure values from...
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Mr Rui Tian20/05/2026, 16:15
MRI mainly relies on linear gradients for spatial encoding. However, nonlinear spatial encoding functions – such as RF receivers’ sensitivities in parallel imaging, actively produced nonlinear gradient modulations in FRONSAC, or naturally occurred shot-to-shot phase variations in multi-shot EPI – can significantly impact acquisition speed and spatial resolution. Inspired by the reproducing...
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Dr Bastien Milani20/05/2026, 16:40
This abstract aims to describe MRI reconstructions in the general framework of finite-dimensional inner-product spaces. We explain that image-space preconditioning (ISP) and data-space preconditioning (DSP) can be formulated as non-conventional inner-products. This allows to introduce image-space preconditioning in the variational formulation of the MRI reconstruction problem (in an...
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Mr Xin Zhao (Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences)20/05/2026, 17:05
State space models (SSMs) have recently emerged as efficient alternatives to Transformers for MRI reconstruction, offering linear complexity while maintaining global receptive fields. LMO (Linear Mamba Operator, Li et al., CVPR 2025) formulates MRI reconstruction as operator learning in bandlimited function spaces, achieving competitive results on standard benchmarks. We identify a physical...
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Prof. Michael Unser (Ecole Polytechnique Fédérale de Lausanne (EPFL))21/05/2026, 09:00
The two dominant CNN-based paradigms for consistent biomedical image reconstruction are: (i) approaches based on trainable regularizers; and (ii) proximal-gradient-type architectures, such as plug-and-play (PnP) methods, that rely on a learned denoiser. A major challenge in both settings is the control of stability and convergence, which typically requires restrictive conditions such as...
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Dr Markus Huemer (Institute of Biomedical Imaging - Graz University of Technology)21/05/2026, 10:00
This work proposes a method for the simultaneous estimation of the exchange-dependent relaxation rate Rex and the longitudinal relaxation time T1 from a single acquisition. A novel acquisition scheme was developed that combines CEST saturation with an inversion pulse and a Look-Locker readout to capture the magnetization evolution starting from the inverse transient Z-spectrum. The...
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Mr Štěpán Zapadlo (University of Graz)21/05/2026, 10:25
Magnetic Resonance Imaging (MRI) is a key non-invasive imaging modality offering large versatility in creating high-resolution images. Reconstructing MR images from measurements requires knowledge of the MR physics involved in the measurement process, which are highly complex (e.g., involving quantum-mechanical effects) and are commonly modeled via the Bloch equations. Recent investigations...
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Mr Alexander Falk (TU Graz)21/05/2026, 11:20
We present a novel method for drawing samples from Gibbs distributions with densities of the form π(x)∝exp(−U(x)). The method accelerates the unadjusted Langevin algorithm by introducing an inertia term similar to Polyak's heavy ball method, together with a corresponding noise rescaling. Interpreting the scheme as a discretization of kinetic Langevin dynamics, we prove ergodicity (in...
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Dr Andreas Habring21/05/2026, 11:45
In this talk we consider time- and space-dependent preconditioning for Langevin sampling from posteriors arising in Bayesian inverse imaging problems, in particular, MRI. Inspired by quasi-Newton methods in optimization, we consider learned preconditioners which use curvature information to accelerate sampling. We provide a careful theoretical analysis of the sampling methods based on the...
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Ms Tina Holliber (Graz University of Technology, Institute of Biomedical Imaging)21/05/2026, 12:10
Introduction Diffusion models employed as prior knowledge have demonstrated strong performance in MRI reconstruction. A principal advantage of this probabilistic framework is that the variability across generated samples enables uncertainty quantification. Incorporating the likelihood term directly into the diffusion process, however, yields intractable expressions that are typically...
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Dr Kostas Papafitsoros (Queen Mary University of London)21/05/2026, 14:30
Data-driven reconstruction methods, mostly based on deep learning, have emerged as the undisputed state-of-the-art across many different inverse problems, including MRI reconstruction. However, they are also notorious for requiring large and diverse datasets to be trained successfully, preferably in a supervised manner. When target reconstructions cannot be obtained, zero-shot self-supervised...
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Mr Wenqi Huang (Technical University of Munich)21/05/2026, 14:55
Accelerated cardiac cine MRI requires reconstructing spatiotemporal images from highly undersampled k-space data. Implicit neural representations (INRs) enable scan-specific reconstruction without large training datasets, but encode content implicitly in network weights without physically interpretable parameters. Gaussian primitives provide an explicit and geometrically interpretable...
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Mr Nil Stolt-Ansó (Technical University of Munich)21/05/2026, 15:50
Recovering high-fidelity 3D volumes from sparse or degraded 2D images is a fundamental challenge in magnetic resonance imaging with broad clinical applications. Implicit neural representations (INRs) offer a resolution-agnostic solution to modelling a volume directly from a scanner’s coordinate system. This allows for continuous 3D representations to be built from arbitrarily oriented sets of...
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Dr Kathrin Lisa Kapper (University of Graz, Austria)21/05/2026, 16:15
Accurate 3D cardiac model construction relies on high-quality segmentation and registration of 2D+t cardiac cine MRI (cMRI) data, with patient-specific ventricular models serving as a key building block for cardiac digital twins. 2D+t cMRI offers higher resolution compared to 3D acquisitions, but the 2D slices are prone to spatial misalignment caused by patient movement and inconsistent breath...
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Prof. Gerlind Plonka-Hoch (University of Göttingen)22/05/2026, 09:00
One of the biggest innovations in magnetic resonance imaging (MRI) within the last years was the concept of parallel MRI. In this setting, the use of multiple receiver coils allows the reconstruction of high-resolution images from undersampled Fourier data such that the acquisition time can be substantially reduced. Mathematically, the parallel MRI reconstruction problem can be seen as a...
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Mr Pierre-Antoine Comby (CEA/Neurospin)22/05/2026, 10:00
MRI reconstruction from non-Cartesian k-space undersampling is naturally formulated as an inverse problem (y = Ax + n), where recovering the image x from data y critically depends on the accuracy and efficiency of the forward model A. MRI-NUFFT is an open-source Python library that provides a unified, high-performance implementation of this physics-based forward model for non-Cartesian MRI...
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Mr Matthias Höfler (University of Graz)22/05/2026, 10:25
Magnetic resonance imaging (MRI) has become a standard diagnostic tool in medical assessments. However, long acquisition times remain one of its main drawbacks. This makes dynamic imaging, as needed in cineMRI, particularly challenging: motion artefacts and limited data acquisition per time point lead to an inherently ill-posed reconstruction problem. Typical solution strategies include...
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Mr Tim Höpfner (University Hospital Würzburg)22/05/2026, 11:20
Magnetic Resonance Fingerprinting (MRF) allows to determine MR relaxation times quantitatively, but its performance depends on the design of acquisition schemes to enable robust parameter estimation from highly undersampled data [1]. Earlier work by Zhao et al. introduced a smoothness constraint in a Cramér-Rao lower bound-based optimization framework, motivated by the observation that...
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Dr Mohammad Golbabaee (University of Bristol)22/05/2026, 11:45
Magnetic Resonance Fingerprinting (MRF) and other highly accelerated transient-state parameter mapping techniques enable simultaneous quantification of multiple tissue properties, but often suffer from aliasing artifacts due to compressed sampling. Incorporating spatial image priors can mitigate these artifacts, and deep learning has shown strong potential when large training datasets are...
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