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This work proposes a method to accelerate the acquisition of high-quality edited magnetic resonance spectroscopy (MRS) scans using machine learning models taking the sample covariance matrix as input. The method is invariant to the number of transients and robust to noisy input data for both synthetic as well as in-vivo scenarios.

This literature review presents a comprehensive overview of machine learning (ML) applications in proton magnetic resonance spectroscopy (MRS). As the use of ML techniques in MRS continues to grow, this review aims to provide the MRS community with a structured overview of the state-of-the-art methods. Specifically, we examine and summarize studies published between 2017 and 2023 from major journals in the magnetic resonance field. We categorize these studies based on a typical MRS workflow, including data acquisition, processing, analysis, and artificial data generation. Our review reveals that ML in MRS is still in its early stages, with a primary focus on processing and analysis techniques, and less attention given to data acquisition. We also found that many studies use similar model architectures, with little comparison to alternative architectures. Additionally, the generation of artificial data is a crucial topic, with no consistent method for its generation. Furthermore, many studies demonstrate that artificial data suffers from generalization issues when tested on in-vivo data. We also conclude that risks related to ML models should be addressed, particularly for clinical applications. Therefore, output uncertainty measures and model biases are critical to investigate. Nonetheless, the rapid development of ML in MRS and the promising results from the reviewed studies justify further research in this field.

We present a theoretical framework for the NMR and MRI measured Larmor frequency in media with magnetized microstructure using the mesoscopic Lorentz sphere and the principle of coarse graining. We obtain an analytical expression for infinite cylinders with arbitrary orientation dispersion and show how it depends on the fiber orientation distribution, measurable using diffusion MRI. Through simulations, we scrutinize the framework including the effect of the shape and size of the Lorentz cavity and validate our result for cylinders., Comment: 57 pages, 13 figures

Eye lens dosimetry has been an important field of research in the last decade. Dose measurements with a prototype of an active personal eye lens dosemeter based on the Dosepix detector are presented. The personal dose equivalent at $3\,$mm depth of soft tissue, $H_\text{p}(3)$, was measured in the center front of a water-filled cylinder phantom with a height and diameter of $20\,$cm. The energy dependence of the normalized response is investigated for mean photon energies between $12.4\,$keV and $248\,$keV for continuous reference radiation fields (N-series) according to ISO 4037. The response normalized to N-60 ($\overline{E}=47.9\,\text{keV}$) at $0^\circ$ angle of irradiation stays within the approval limits of IEC 61526 for angles of incidence between $-75^\circ$ and $+75^\circ$. Performance in pulsed photon fields was tested for varying dose rates from $0.1\,\frac{\text{Sv}}{\text{h}}$ up to $1000\,\frac{\text{Sv}}{\text{h}}$ and pulse durations from $1\,\text{ms}$ up to $10\,\text{s}$. The dose measurement works well within the approval limits (acc. to IEC 61526) up to $1\,\frac{\text{Sv}}{\text{h}}$. No significant influence of the pulse duration on the measured dose is found. Reproducibility measurements yield a coefficient of variation which does not exceed $1\,\%$ for two tested eye lens dosemeter prototypes., Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. 9 pages, 10 figures

Injecting with needles causes fear, pain and contamination risks. Billions of injections every year also cause environmental burden in terms of material consumption and waste. Controlled microfluidic-jet injection systems offer a needle-free alternative. However, understanding the relation between jet parameters and resulting injection depth are needed to enable targeting specific skin layers, and enhance the pharmacokinetics of various therapeutic compounds. The complexity of skin, its opacity and non-linear mechanical properties, pose a technological challenge. Hence the use of surrogates is instrumental to understand how to inject without needles. In particular, reducing undesired splashing upon jet impact and liquid squeeze-out after injection are needed to minimize infection risks and ensure accurate dosage. Therefore, in this paper we explore how microfluidic jet characteristics influence the impact outcome on a range of materials as skin surrogate. Jets with velocities between 7 - 77 m/s and diameters 35 - 130 $\mu$m were directed at substrates with shear moduli between 0.2 kPa and 26 GPa. We found seven different regimes depending on jet inertia and substrate shear modulus. Furthermore, three distinct transition regions were identified as the thresholds between regimes: i) spreading/splashing threshold, ii) dimple formation threshold, and iii) plastic/elastic deformation threshold. These thresholds allow predicting the required jet velocity and diameter to inject substrates with known shear modulus. We found that jet velocity is a better predictor for the injection depth compared to the Weber number, as the jet diameter does not influence the injection depth. Our findings are relevant for advancing needle-free injection research, because the shear modulus of skin depends on multiple factors, such as ethnicity, body part and environmental conditions.

BACKGROUND AND OBJECTIVE: Advanced ultrasound computed tomography techniques like full-waveform inversion are mathematically complex and orders of magnitude more computationally expensive than conventional ultrasound imaging methods. This computational and algorithmic complexity, and a lack of open-source libraries in this field, represent a barrier preventing the generalised adoption of these techniques, slowing the pace of research, and hindering reproducibility. Consequently, we have developed Stride, an open-source Python library for the solution of large-scale ultrasound tomography problems. METHODS: On one hand, Stride provides high-level interfaces and tools for expressing the types of optimisation problems encountered in medical ultrasound tomography. On the other, these high-level abstractions seamlessly integrate with high-performance wave-equation solvers and with scalable parallelisation routines. The wave-equation solvers are generated automatically using Devito, a domain-specific language, and the parallelisation routines are provided through the custom actor-based library Mosaic. RESULTS: We demonstrate the modelling accuracy achieved by our wave-equation solvers through a comparison (1) with analytical solutions for a homogeneous medium, and (2) with state-of-the-art modelling software applied to a high-contrast, complex skull section. Additionally, we show through a series of examples how Stride can handle realistic numerical and experimental tomographic problems, in 2D and 3D, and how it can scale robustly from a local multi-processing environment to a multi-node high-performance cluster. CONCLUSIONS: Stride enables researchers to rapidly and intuitively develop new imaging algorithms and to explore novel physics without sacrificing performance and scalability. This will lead to faster scientific progress in this field and will significantly ease clinical translation.

Synthetic digital twins based on medical data accelerate the acquisition, labelling and decision making procedure in digital healthcare. A core part of digital healthcare twins is model-based data synthesis, which permits the generation of realistic medical signals without requiring to cope with the modelling complexity of anatomical and biochemical phenomena producing them in reality. Unfortunately, algorithms for cardiac data synthesis have been so far scarcely studied in the literature. An important imaging modality in the cardiac examination is three-directional CINE multi-slice myocardial velocity mapping (3Dir MVM), which provides a quantitative assessment of cardiac motion in three orthogonal directions of the left ventricle. The long acquisition time and complex acquisition produce make it more urgent to produce synthetic digital twins of this imaging modality. In this study, we propose a hybrid deep learning (HDL) network, especially for synthetic 3Dir MVM data. Our algorithm is featured by a hybrid UNet and a Generative Adversarial Network with a foreground-background generation scheme. The experimental results show that from temporally down-sampled magnitude CINE images (six times), our proposed algorithm can still successfully synthesise high temporal resolution 3Dir MVM CMR data (PSNR=42.32) with precise left ventricle segmentation (DICE=0.92). These performance scores indicate that our proposed HDL algorithm can be implemented in real-world digital twins for myocardial velocity mapping data simulation. To the best of our knowledge, this work is the first one in the literature investigating digital twins of the 3Dir MVM CMR, which has shown great potential for improving the efficiency of clinical studies via synthesised cardiac data., 9 pages, 14 figures, Accepted by IEEE JBHI

We present a theoretical framework for the NMR and MRI measured Larmor frequency in media with magnetized microstructure using the mesoscopic Lorentz sphere and the principle of coarse graining. We obtain an analytical expression for infinite cylinders with arbitrary orientation dispersion and show how it depends on the fiber orientation distribution, measurable using diffusion MRI. Through simulations, we scrutinize the framework including the effect of the shape and size of the Lorentz cavity and validate our result for cylinders.