Microstructure with Magnetic Resonance: Problems and Solutions responds to the challenge of how to see the invisible with magnetic resonance imaging. Technically, the goal is to quantify cellular-level properties of biological tissues and microarchitecture of porous media orders of magnitude below the achievable resolution of MR. While the interest in this area has grown exponentially, current research involves physics outside the scope of standard NMR and MRI textbooks. Microstructure with Magnetic Resonance: Problems and Solutions introduces readers to methods of describing complex media in statistical terms, and covers the effects of complex microenvironments on the MR signal phase, on the transverse relaxation, and on different facets of the diffusion-weighted signal. The book presents the material as a set of problems with detailed solutions, that build on each other, stimulating a hands-on approach to learning. Each chapter begins with a short introduction to the topic, followed by problems, solutions, and a summary of key points. The problems start from the basics, and bring the reader step-by-step to the frontier of current knowledge. The overall focus is on gaining physical insight, by drawing on simple physical analogies and dimensional analysis, which help to reproduce the essence of the results obtained in classical and recent studies. The necessary mathematics is collected in dedicated appendices. With this book the reader will: • Understand the classic and current literature on microstructure mapping with NMR and MRI; • Become familiar with the modern trends in microstructure MR; • Be able to design new experiments using MR based on a solid theoretical foundation. • Explains physics necessary to understand how the microscopic structure of biological tissues and porous media manifests itself in different magnetic resonance contrasts (phase, relaxation, diffusion). • Uses a unique problem/solution structure to provide for efficient learning from the basics to the frontiers of knowledge. • Tested through numerous teaching courses for trainees.

Abstract: n the past, changes of the Apparent Diffusion Coefficient in glioblastoma multiforme have been shown to be related to specific genes and described as being associated with survival. The purpose of this study was to investigate diffusion imaging parameters in combination with genome-wide expression data in order to obtain a comprehensive characterisation of the transcriptomic changes indicated by diffusion imaging parameters. Diffusion-weighted imaging, molecular and clinical data were collected prospectively in 21 patients. Before surgery, MRI diffusion metrics such as axial (AD), radial (RD), mean diffusivity (MD) and fractional anisotropy (FA) were assessed from the contrast enhancing tumour regions. Intraoperatively, tissue was sampled from the same areas using neuronavigation. Transcriptional data of the tissue samples was analysed by Weighted Gene Co-Expression Network Analysis (WGCNA) thus classifying genes into modules based on their network-based affiliations. Subsequent Gene Set Enrichment Analysis (GSEA) identified biological functions or pathways of the expression modules. Network analysis showed a strong association between FA and epithelial-to-mesenchymal-transition (EMT) pathway activation. Also, patients with high FA had a worse clinical outcome. MD correlated with neural function related genes and patients with high MD values had longer overall survival. In conclusion, FA and MD are associated with distinct molecular patterns and opposed clinical outcomes

Abstract: Filter-exchange imaging (FEXI) has already been utilized in several biomedical studies for evaluating the permeability of cell membranes. The method relies on suppressing the extracellular signal using strong diffusion weighting (the mobility filter causing a reduction in the overall diffusivity) and monitoring the subsequent diffusivity recovery. Using Monte Carlo (MC) simulations, we demonstrate that FEXI is not uniquely sensitive to the transcytolemmal exchange but also to the geometry of involved compartments: Complex geometry offers locations where spins remain unaffected by the mobility filter; moving to other locations afterward, such spins contribute to the diffusivity recovery without actually permeating any membrane. This exchange mechanism warns those who aim to use FEXI in complex media such as brain gray matter and opens large room for investigation towards crystallizing the genuine membrane permeation and characterizing the compartment geometry

Abstract: Purpose Estimating magnetic susceptibility using MRI depends on inverting a forward relationship between the susceptibility and measured Larmor frequency. However, an often-overlooked constraint in susceptibility fitting is that the Larmor frequency is only measured inside the sample, and after successful background field removal, susceptibility sources should only reside inside the same sample. Here, we test the impact of accounting for these constraints in susceptibility fitting. Theory and Methods Two different digital brain phantoms with scalar susceptibility were examined. We used the MEDI phantom, a simple phantom with no background fields, to examine the effect of the imposed constraints for various levels of SNR. Next, we considered the QSM reconstruction challenge 2.0 phantom with and without background fields. We estimated the parameter accuracy of openly-available QSM algorithms by comparing fitting results to the ground truth. Next, we implemented the mentioned constraints and compared to the standard approach. Results Including the spatial distribution of frequencies and susceptibility sources decreased the RMS-error compared to standard QSM on both brain phantoms when background fields were absent. When background field removal was unsuccessful, as is presumably the case in most in vivo conditions, it is better to allow sources outside the brain. Conclusion Informing QSM algorithms about the location of susceptibility sources and where Larmor frequency was measured improves susceptibility fitting for realistic SNR levels and efficient background field removal. However, the latter remains the bottleneck of the algorithm. Allowing for external sources regularizes unsuccessful background field removal and is currently the best strategy in vivo

Abstract: Purpose An open-source spatially resolved phase graph framework is proposed for simulating arbitrary pulse sequences in the presence of piece-wise constant gradients with arbitrary orientations in three dimensions. It generalizes the extended phase graph algorithm for analysis of nonperiodic sequences while preserving its efficiency, and is able to estimate the signal modulation in the 3D spatial domain. Methods The framework extends the recursive magnetization-evolution algorithm to account for anisotropic diffusion and exploits a novel 3D k-space grid-merging method to balance the computational effort and memory requirements against acceptable simulation errors. A new postsimulation module is proposed to track and visualize the signal evolution both in the k-space and in the image domain, which can be used for simulating image artifacts or finding frequency-response profiles. To illustrate the developed technique, three examples are presented: (1) fast off-resonance calculation for dictionary building in MR fingerprinting, (2) validation of a steady-state sequence with quasi-isotropic diffusion weighting, and (3) investigation of the magnetization evolution in PRESS-based spectroscopic imaging. Results The grid-merging algorithm of the proposed framework demonstrates high calculation efficiency exemplified by frequency-response simulation of pseudo steady-state or diffusion-weighted steady-state sequences. It further helps to visualize the signal evolution in PRESS-based sequences. Conclusions The proposed simulation framework has been validated based on several different example applications for analyzing signal evolution in the frequency and spatial domain