IFIP TC7 System Modeling and Optimization

Session

MS 08: Mathematics and Magnetic Resonance Imaging

Aug 13, 2024, 9:30 AM

Seminarraum 211 (Von-Melle-Park 8)

75. Model-based Cardiac T1-Mapping

Daniel Mackner (Graz University of Technology)

8/13/24, 9:30 AM

MS 08: Mathematics and Magnetic Resonance Imaging

Minisymposium Contribution

The longitudinal relaxation time $T_1$ is an important biomarker in clinical cardiac MRI, e.g. for myocardial fibrosis. Conventional $T_1$ mapping is time-consuming and requires multiple breath holds. Therefore, we developed a sequence with a continuous radial readout which allows us to quantify $T_1$ within seconds. For reconstruction of the $T_1$ maps, the physical signal model of the MRI...

64. MRI in chemical engineering applications

Stefan Benders (Technische Universität Hamburg, Institut für Prozessbildgebung)

8/13/24, 10:00 AM

MS 08: Mathematics and Magnetic Resonance Imaging

Minisymposium Contribution

In the field of chemical engineering, reactors play a vital role in processing and producing many goods However, their understanding is often limited by measurement access. Tomographic techniques can overcome these limits, with MRI being especially suitable. MRI can measure velocity and chemical composition in opaque systems and has been applied in the medical field extensively. While these...

91. Quantitative MRI as a non-linear tomography problem: from mathematical principles to clinical demonstration

Oscar van der Heide (University Medical Center Utrecht)

8/14/24, 9:00 AM

MS 08: Mathematics and Magnetic Resonance Imaging

Minisymposium Contribution

Quantitative Magnetic Resonance Imaging (MRI) is based on a two-steps approach: estimation of the magnetic moments distribution inside the body, followed by a voxel-by-voxel quantification of the human tissue properties. This splitting simplifies the computations but poses several constraints on the measurement process, limiting its efficiency. Instead, we can perform quantitative MRI as a one...

47. Assessment of Deep Learning-based Reconstruction with Imperfect Ground Truth for MRCP

Jinho Kim (Friedrich-Alexander-Universität Erlangen-Nürnberg)

8/14/24, 9:30 AM

MS 08: Mathematics and Magnetic Resonance Imaging

Minisymposium Contribution

Magnetic resonance cholangiopancreatography (MRCP) is a non-invasive imaging technique to visualize the hepatobiliary system. However, acquiring MRCP using a triggered 3-D T2-weighted turbo spin echo sequence causes prolonged scan time and often provides undiagnostic image quality. Therefore, we aimed to accelerate MRCP acquisition using deep learning (DL)-based reconstruction.

We acquired...

97. Robustness of Deep Learning for Accelerated MRI: Benefits of Diverse Training Data

Reinhard Heckel (TUM)

8/14/24, 10:00 AM

MS 08: Mathematics and Magnetic Resonance Imaging

Minisymposium Contribution

Deep learning based methods for image reconstruction are state-of-the-art for a variety of imaging tasks. However, neural networks often perform worse if the training data differs significantly from the data they are applied to. For example, a network trained for accelerated magnetic resonance imaging (MRI) on one scanner performs worse on another scanner. In this work, we investigate the...

48. Mathematical background of ESPIRiT: ESPIRiT versus MOCCA

Gerlind Plonka (Universität Göttingen)

8/14/24, 11:00 AM

MS 08: Mathematics and Magnetic Resonance Imaging

Minisymposium Contribution

Recently, we have proposed a new model-based coil calibration (MOCCA) algorithm to reconstruct the coil sensitivities and the magnetization image from the given (incomplete) measurements. This method employs the assumption that the coil sensitivities are smooth functions which can be represented using bivariate trigonometric polynomials of small degree while the magnetization image is only...

96. Phase distribution graphs for differentiable and efficient simulations of arbitrary MRI sequences

Prof. Moritz Zaiss (FAU)

8/14/24, 11:30 AM

MS 08: Mathematics and Magnetic Resonance Imaging

Minisymposium Contribution

Introduction

We present an analytical Bloch simulation approach for arbitrary MRI sequence simulation called Phase Distribution Graphs. It is a general implementation of the Extended Phase Graph (EPG) concept, based on the Fourier-domain Bloch equation, but with arbitrary timing, and including the exact contribution of dephased states resulting from spatial encoding and T2’ relaxation...

128. Motion-compensated cardiac MRI reconstruction with stochastic optimisation

Letizia Protopapa (Science and Technology Facilities Council, UK)

8/14/24, 12:00 PM

MS 08: Mathematics and Magnetic Resonance Imaging

Minisymposium Contribution

A 3D whole-heart MR acquisition with high spatial resolution can take several minutes. During this time, the heart is constantly moving due to breathing and the heartbeat, which can cause severe motion artefacts in the final image. To minimize these artefacts, motion correction approaches have been proposed. In this work, we utilise a motion-corrected image reconstruction (MCIR) approach. For...

21. Mathematical Models in Quantitative MRI

Mariya Doneva (Philips Innovative Technologies)

MS 08: Mathematics and Magnetic Resonance Imaging

Minisymposium Contribution

Quantitative MRI is not a novelty, but it has inspired a lot of innovation in the last 10-15 years driven by two main needs: shorter scan times, improved accuracy and precision.
Quantitative MRI typically requires the acquisition of multiple images with different acquisition parameters and subsequent voxel-wise fitting to an appropriate signal model. A major challenge in the adoption of...