arXiv subjects

Artyom Tsanda

Publications and source records attributed to Artyom Tsanda.

3 recordsLinked to original sources

arXiv2026

Rapid quantitative chemical composition mapping using model-based MRI reconstruction with field inhomogeneity correction

Magnetic resonance spectroscopic imaging methods are particularly attractive for chemical engineering applications, including the monitoring of chemical reactions, where a rapid assessment of spatial variations in chemical composition is required. Conventional approaches, such as chemical shift imaging, introduce an additional spectral-encoding dimension, which substantially increases acquisition time. Consequently, fast spatially resolved spectroscopy remains an active research topic. This work uses a model-based reconstruction framework that embeds a priori spectral knowledge of the involved chemical components into the forward model to accelerate composition mapping. It allows for the reconstruction of molar ratio maps for individual chemical components without acquiring high-resolution spectra. Extending from previous studies, the proposed model accounts for inhomogeneities of the main field, which become more pronounced in systems with larger bores relevant for process engineering. Phantom experiments employing a 2D multi-gradient echo sequence demonstrate the ability to determine molar ratios for chemical components with single peaks as well as multiple peaks in their spectra. The bias and precision of the method remain around 0.01 mol/mol and 0.09 mol/mol, respectively, for a 20 s scan, indicating suitability for dynamic processes. Finally, acquisition time can be reduced further by applying sparse k-space sampling, potentially shortening the scan to 5 s with only minor degradation in quantitative performance.

eess.IV↗

arXiv2026

Comprehensive Study of 3D Liquid Flow Fields in Additive Manufactured Structures for SMART Reactors Using Large-Scale Vertical Magnetic Resonance Imaging and Computational Fluid Dynamics

Triply Periodic Minimal Surface (TPMS) structures have emerged as a new class of porous materials with variable geometries and favourable transport properties, making them promising for reactor internals in chemical engineering. However, experimental data on internal TPMS flow behaviour are still limited. To address this gap, the flow behaviour in additively manufactured TPMS structures is analysed using three-dimensional Magnetic Resonance Imaging (MRI) velocimetry in a large-bore vertical 3 T MRI system, in cylindrical columns of 38 mm diameter and Reynolds numbers between 50 and 300. Three different TPMS geometries are investigated, and consistency between Computational Fluid Dynamics (CFD) simulations and experimentally measured MRI velocity fields is established through cross-validation. The MRI system provides fully three-dimensional velocity fields with a divergence deviation below 6 %. MRI revealed distinct flow features: the Gyroid TPnS exhibited pronounced channelling, while the Schwarz-Diamond TPSf showed merge-split behaviour, achieving a 46 % increase in lateral mixing compared to the Gyroid TPnS structures. Numerical simulations reproduce the flow features and show agreement with the MRI data. The combined methodology demonstrates the suitability of MRI velocimetry for the experimental validation of CFD simulations and establishes a robust foundation for future studies of heat and mass transfer, as well as reactive flow, in structured reactor systems.

physics.flu-dyn↗

arXiv2025

Deep Learning for Restoring MPI System Matrices Using Simulated Training Data

Magnetic particle imaging reconstructs tracer distributions using a system matrix obtained through time-consuming, noise-prone calibration measurements. Methods for addressing imperfections in measured system matrices increasingly rely on deep neural networks, yet curated training data remain scarce. This study evaluates whether physics-based simulated system matrices can be used to train deep learning models for different system matrix restoration tasks, i.e., denoising, accelerated calibration, upsampling, and inpainting, that generalize to measured data. A large system matrices dataset was generated using an equilibrium magnetization model extended with uniaxial anisotropy. The dataset spans particle, scanner, and calibration parameters for 2D and 3D trajectories, and includes background noise injected from empty-frame measurements. For each restoration task, deep learning models were compared with classical non-learning baseline methods. The models trained solely on simulated system matrices generalized to measured data across all tasks: for denoising, DnCNN/RDN/SwinIR outperformed DCT-F baseline by >10 dB PSNR and up to 0.1 SSIM on simulations and led to perceptually better reconstuctions of real data; for 2D upsampling, SMRnet exceeded bicubic by 20 dB PSNR and 0.08 SSIM at $\times 2$-$\times 4$ which did not transfer qualitatively to real measurements. For 3D accelerated calibration, SMRnet matched tricubic in noiseless cases and was more robust under noise, and for 3D inpainting, biharmonic inpainting was superior when noise-free but degraded with noise, while a PConvUNet maintained quality and yielded less blurry reconstructions. The demonstrated transferability of deep learning models trained on simulations to real measurements mitigates the data-scarcity problem and enables the development of new methods beyond current measurement capabilities.

eess.IV↗