Magnetic nanoparticles (MNP) are employed in many technical and clinical applications for which the knowledge of their magnetic properties is mandatory. This comprises not only the intrinsic magnetic parameters, but especially in biomedical applications the changes of the MNP behavior in a certain environment or binding state such as the immobilization of the MNP after cellular uptake, blood contact or injection into tissue. Therefore, the magnetic properties of MNP samples that have been immobilized are considered to mimic the binding of MNP to human tissue. However, the procedure and materials used for immobilization differently impact their magnetic properties. Here, we investigated common immobilization methods regarding reproducibility and variation in terms of the magnetic properties of the commercial nanoparticle system perimag® for the three available surface coatings. We considered immobilization by polyacrylamide embedding, freeze drying, gypsum crystallization, filter paper as well as cotton wool drying. We used the two magnetic measurement techniques magnetic particle spectroscopy (MPS) and magnetorelaxometry (MRX) to assess the magnetic properties and changes thereof for all immobilization methods. We found highest reproducibility and less variation in magnetic parameters for freeze dried and gypsum immobilization. A higher variation of magnetic properties was observed for evaporation-based methods filter paper and cotton wool attributed to unstructured arrangement of MNP on the fibers of the immobilization materials. The surface modification of the MNP system shows a minor impact on immobilization procedure. From our results, we deduce recommendations and practicability considerations for immobilization methods in preparation and handling of MNP reference samples.

Simultaneous hearing and balance restoration through combined cochlear-vestibular implants (CVIs) offers a promising treatment for patients with dual sensory deficits. However, the effects of electrical stimulation on neighboring neural structures in the inner ear remain poorly understood. In this study, we present a detailed computational model of the human inner ear that simulates electrical stimulation of cochlear and vestibular nerves under clinically relevant conditions. The model integrates high-resolution micro-CT-based geometry, anisotropic tissue conductivities, and myelinated fiber models to predict neural activation patterns across a wide range of clinically relevant stimulation parameters. Simulation results suggest that vestibular stimulation at clinically relevant amplitudes can influence cochlear nerve activation thresholds, particularly in basal cochlear regions. Conversely, cochlear stimulation had a comparatively weaker effect on vestibular activation. Interleaved stimulation with short interstimulus intervals (smaller 100 s) resulted in increased excitability in the non-targeted nerve population, suggesting the potential for undesired cross-talk effects. Pulse waveform characteristics, including phase duration and symmetry, further modulated the degree of crosstalk observed. This model allows for comprehensive evaluation of scenarios that cannot be tested in humans due to ethical, practical, or technical limitations. As a result, it provides a valuable tool for exploring new combined simulation scenarios and may aid the development of newly designed implants.

Objective. Magnetic nanoparticles (MNPs) are an exciting tool in various biomedical applications. In particular, monitoring the movement and distribution of MNPs in body organs, especially in the brain, can help diagnose some life-threatening conditions such as cerebral aneurysms.Approach. In this study, we explore the potential of magnetorelaxometry imaging (MRXI) to monitor the movement of a fluid MNP bolus within a phantom model with different flow rates ranging from 0.6 to 1.7 ml min-1. To carry out this experiment, we developed an MRXI setup comprising a 72-channel low-Tccurrent sensing SQUID system, six excitation coils and a tube phantom, mimicking a vessel. The sensitivity of our setup was evaluated with different iron concentrations in the range of 25-100 mmol l-1.Main result. For all concentrations and flow rates, the localization of the MNP bolus aligns well with the ground truth. Moreover, the MRXI setup allowed the observation of the bolus movement over a specific coil to reveal details of the flow.Significance. These results demonstrate the ability of MRXI to monitor a MNP bolus, while passing through the tube phantom, with a temporal resolution of 1.4 sand an image resolution of approximately (6.3 × 6.3 × 2.1) mm3.

N -Interval Fourier Transform Analysis ( N -FTA) allows for spectral separation of an evoked target signal from uncorrelated background activity. It computes the frequency-dependent evoked-to-background ratio (EBR). The developed method allows for conversion of the spectral EBR into expected values for improvement of signal-to-noise ratio with progressing sweep count. Our study presents the mathematical basis for this conversion along with a validation for simulated and recorded data. The major findings are: •Three factors enter the calculus of the expected signal-to-noise ratio (SNR): the ratio of durations of the single sweep cycle and the evoked response window, the mean EBR in the spectral target band, and the sweep count. By conversion of all factors to dB, the expected SNR is defined by their sum.•The two fundamental theories governing the improvement of SNR with increasing sweep count, the law of large numbers and the uncertainty principle of signal processing, deliver identical results.•Conversion of EBR to expected SNR was successfully validated by simulated and recorded data and can be applied to all types of evoked data.•A median sweep count of about 2000 (range approximately 600 to 6000) is required for extracting an HFO response at an SNR of 10dB.

Purpose:To extract conjunctival bulbar redness from standardized high-resolution ocular surface photographs of a novel imaging system by implementing an image analysis pipeline. Methods:Data from two trials (healthy; outgoing ophthalmic clinic) were collected, processed, and used to train a machine learning model for ocular surface segmentation. Various regions of interest were defined to globally and locally extract a redness biomarker based on color intensity. The image-based redness scores were correlated to clinical gradings (Efron) for validation. Results:The model to determine the regions of interest was verified for a segmentation performance, yielding mean intersections over union of 0.9639 (iris) and 0.9731 (ocular surface). All trial data were analyzed and a digital grading scale for the novel imaging system was established. Photographs and redness scores from visits weeks apart showed good feasibility and reproducibility. For scores within the same session, a mean coefficient of variation of 4.09% was observed. A moderate positive Spearman correlation (0.599) was found with clinical grading. Conclusions:The proposed conjunctival bulbar redness extraction pipeline demonstrates that by using standardized imaging, a segmentation model and image-based redness scores' external eye photography can be classified and evaluated. Therefore, it shows the potential to provide eye care professionals with an objective tool to grade ocular redness and facilitate clinical decision-making in a high-throughput manner. Translational Relevance:To empower clinicians and researchers with a high-throughput workflow by standardized imaging combined with an analysis tool based on artificial intelligence to objectively determine an image-based redness score.

The influence of inter-individual variations of tissue conductivities on MEG source analysis is generally assumed to be small in comparison to EEG source analysis and the resulting effects on MEG source analysis have therefore been investigated much less. We perform an in-depth analysis of this influence, so that the results of this study are of importance to better interpret results of MEG source analysis and to improve applications that make use of both EEG and MEG, e.g., combined source analysis. MEG forward solutions for dipole sources regularly distributed in the gray matter compartment were simulated in a detailed five-compartment head model for three realistic sensor configurations using the FEM multipole approach. Subsequently, a generalized polynomial chaos approach (gPC) was employed to calculate MEG leadfields for varying tissue conductivities. Based on these gPC expansions, the sensitivity of MEG forward solutions towards tissue conductivity uncertainties and the influence on MEG source analysis was investigated. In general, our study shows that the influence of tissue conductivity uncertainties on MEG forward solutions and source analysis is clearly weaker than for the EEG, and confirms that MEG is fairly robust against tissue conductivity uncertainties. For all three investigated sensor configurations, we find very similar sensitivity distributions. A strong influence of tissue conductivity uncertainties on the topography of MEG forward solutions is found especially for quasi-radial sources as they are for example found on top of gyri. Furthermore, a strong influence of gray and white matter conductivity variations on the signal magnitude is found especially for sources on sulcal walls. For MEG source analysis, mean localization errors are below 2 mm in most regions, but significant errors are found in deep and temporal areas with mean localization errors of up to 5 mm. Significant effects on reconstructed source orientation and magnitude are particularly strong when no rank reduction is performed, i.e., the quasi-radial source orientation, which has a comparatively small contribution to the MEG signal, is not excluded. On the other hand, rank reduction makes it impossible to reconstruct the actual source orientation as long as this source is not exactly quasi-tangential. Even though the sensitivity of MEG source analysis towards tissue conductivity uncertainties is clearly weaker than for the EEG, it should not be completely neglected. The effects found for quasi-radial sources have little practical implications, since these sources have a very weak MEG signal and can therefore usually not be properly detected, but the effects found for quasi-tangential sources, such as variations of reconstructed source magnitude and orientation, could have significant effects in practice, e.g., in a combined analysis of EEG and MEG.

Magnetic nanoparticles (MNPs) are emerging as key tools in biomedical and technical applications due to their tunable magnetic properties and responsiveness to external magnetic fields. However, the effectiveness of MNPs in applications such as targeted drug delivery, magnetic imaging and magnetic hyperthermia critically depends on achieving a narrow particle size distribution. Conventional gradient magnetic separation techniques often fall short in delivering high resolution size separation, particularly in the challenging 20 to 200 nm range, where the interplay between Brownian motion and magnetophoretic forces reduces separation precision. Therefore, in this study, we propose an enhanced gradient magnetic separation (GMS) method that superimposes a homogeneous alternating magnetic field onto an inhomogeneous gradient field and makes use of size-dependent magnetization dynamics. The proposed dual-field method is first verified in a simple test case, confirming that the desired separation behavior can principally be achieved. Simulations show that the magnetization ratio between particles of different sizes can be significantly increased beyond the predictions of the Langevin function. By systematically varying offset and alternating field strengths, an optimal combination maximizing this ratio is identified. Additionally, the influence of the alternating field frequency is investigated, showing that separation efficiency improves with increasing frequency up to a saturation point. To translate this behavior into effective spatial separation, particle trajectories are simulated while dynamically optimizing the alternating field strength over time to maximize the travelled distance ratio between large and small particles. The results demonstrate that large particles maintain strong alignment with the field, while smaller particles experience reduced time averaged magnetization, resulting in notably reduced mobility. Additionally, travelled distance ratios between particle sizes increase significantly compared to using a gradient field alone. The introduced dual-field method is also shown to remain effective for various particle sizes and under more realistic conditions where hydrodynamic and magnetic radii differ due to surface coatings. Finally, it is shown that the separation cut-off radius can be chosen arbitrarily, confirming the size independence of the method. These findings demonstrate that the proposed method substantially enhances size based separation, enabling improved control over particle size distributions and potentially advancing biomedical applications.

Background: Tele-ophthalmology is gaining recognition for its role in improving eye care accessibility via cloud-based solutions. The Google Cloud Platform (GCP) Healthcare API enables secure and efficient management of medical image data such as high-resolution ophthalmic images. Objectives: This study investigates cloud-based solutions’ effectiveness in tele-ophthalmology, with a focus on GCP’s role in data management, annotation, and integration for a novel imaging device. Methods: Leveraging the Integrating the Healthcare Enterprise (IHE) Eye Care profile, the cloud platform was utilized as a PACS and integrated with the Open Health Imaging Foundation (OHIF) Viewer for image display and annotation capabilities for ophthalmic images. Results: The setup of a GCP DICOM storage and the OHIF Viewer facilitated remote image data analytics. Prolonged loading times and relatively large individual image file sizes indicated system challenges. Conclusion: Cloud platforms have the potential to ease distributed data analytics, as needed for efficient tele-ophthalmology scenarios in research and clinical practice, by providing scalable and secure image management solutions.

An essential bio-marker to detect ocular surface diseases like dry eye disease is ocular redness. In clinical routine, this marker is graded by visual comparison to reference image scales. We aim at supporting clinicians in this time-consuming and subjective task by determining a redness score from images, obtained with a novel device for standardized ocular surface photography (Cornea Dome Lens, Occyo GmbH, Innsbruck, Austria). Therefore, in a previous work [1], we presented a baseline pipeline to automatically determine eye redness. Regions of interest were cropped from the recordings based on the iris center and split up into smaller squared sub-regions called tiles. Each of these tiles was classified by a machine learning model and the redness is extracted for the relevant regions. Using the pipeline, images from 36 healthy and 37 pathological eyes were divided into 5840 tiles (80 per eye). A typical split of 80/10/10 % was used as training, validation and test set, respectively, to train the machine learning model. Hereby, the Random Forest model employed in the baseline was replaced by a deep learning model (ResNet50) to improve the performance. This model showed an accuracy of 0.920 and an F1-score of 0.919 on the test data set compared to an accuracy of 0.856 and an F1-score of 0.855 for the Random Forest [2]. In a follow-up work, we were able to relate the resulting redness scores with gradings from clinicians [3]. A positive relation between the scores and the gradings was observed. In the future, we will expand our data set and include more features (e.g., vessel density) to define a meaningful indicator for eye redness grading, which can be used as support in the clinical routine.

The analysis of electroencephalography (EEG)/magnetoencephalography (MEG) functional connectivity has become an important tool in neuroscience. Especially the high time resolution of EEG/MEG enables important insight into the functioning of the human brain. To date, functional connectivity is commonly estimated offline, that is, after the conclusion of the experiment. However, online computation of functional connectivity has the potential to enable unique experimental paradigms. For example, changes of functional connectivity due to learning processes could be tracked in real time and the experiment be adjusted based on these observations. Furthermore, the connectivity estimates can be used for neurofeedback applications or the instantaneous inspection of measurement results. In this study, we present the implementation and evaluation of online sensor and source space functional connectivity estimation in the open-source software MNE Scan. Online capable implementations of several functional connectivity metrics were established in the Connectivity library within MNE-CPP and made available as a plugin in MNE Scan. Online capability was achieved by enforcing multithreading and high efficiency for all computations, so that repeated computations were avoided wherever possible, which allows for a major speed-up in the case of overlapping intervals. We present comprehensive performance evaluations of these implementations proving the online capability for the computation of large all-to-all functional connectivity networks. As a proof of principle, we demonstrate the feasibility of online functional connectivity estimation in the evaluation of somatosensory evoked brain activity.

Magnetic nanoparticles have the potential to be used in various biomedical applications, including magnetic drug targeting and hyperthermia for cancer treatment. In both cases, precise prediction of the local particle concentration is required to plan a successful therapy, which is a challenging task due to several complex flow phenomena. Computational macroscopic and microscopic models have been developed to support this process, but they have limitations in terms of interactions implementation, micromagnetic dynamics or computational costs. This study aims to develop a model that can represent micromagnetic dynamics and being employed to study equilibrium properties of particle ensembles in viscous media. Therefore, a kinetic Monte Carlo method in combination with Langevin equations is used. So, magnetization dynamics and mechanical motion can be simulated in combination very efficiently. The new model is validated with another model based on the stochastic Landau-Lifshitz-Gilbert equation. Various interaction potentials like Van der Waals, steric and electrostatic interactions are included to study their specific influence on structural properties. Also, methods to control the errors while integrating the equations of motion and using the Ewald method for calculating interaction quantities are implemented. As a validation we studied equilibrium and time dependent properties of non-interacting particle ensembles as well as errors made by the Ewald-method used for interaction quantities calculation. In all studies we found very good agreement of the simulation results with the theoretical predictions. The developed models can now be used for investigating equilibrium and dynamic properties of ferrofluids, or more general for biomedical research for magnetic drug targeting, hyperthermia, or imaging techniques. Furthermore, the new hybrid model can be also used for simulations in the range of milliseconds with reasonable computational effort.

N-Interval Fourier Analysis (N-FTA) allows for simultaneous spectral assessment of evoked and spontaneous activity in the frequency domain. We applied this method to signals following peripheral electrical nerve stimulation and performed analysis of cortical somatosensory evoked potentials within the 400 to 750Hz band. For median nerve stimulation, data from eleven volunteers were analyzed. For tibial nerve stimulation, three subjects were investigated. For both stimulation sites, evoked high frequency oscillations (HFOs) components were identified. Furthermore, two kinds of background HFO activity were detected in sham stimulation trials. Spectral component models were applied for quantifying signal properties.Evoked spectral components reflected HFOs being time-locked to the stimulus. The detected spectral components were distributed over the entire investigated spectral band. Their spectral amplitude was close to the limit of the resolution of N-FTA. The experimentally observed spectral amplitude were in quantitative agreement with a model using a Morlet morphology.Within the HFO band, a flat noise floor was observed. Spontaneous physiological background activity contributes significantly to the spectral amplitude. This random activity is the dominant source of interference when extracting evoked HFOs.Within the HFO band, narrow spectral peaks in background activity were detected – both for real and sham stimulation. In the data sampled at 9.6kHz, such peaks were observed in all recordings. For the 5.0kHz sampling rate, these peaks were visible in about half of the recordings, and their amplitude was reduced. Based on a mathematical model, these peaks may be generated by organized spontaneous HFO activity producing a stable background wave.

Magnetic nanoparticles (MNP) offer exciting biomedical applications, e.g. magnetic hyperthermia for tumor treatment. The knowledge of the quantitative spatial distribution of MNP in the human body is a key for treatment safety and efficiency. Here we experimentally demonstrate the reconstruction of the quantitative MNP distribution of a human head phantom by magnetorelaxometry imaging (MRXI). The MRXI setup is composed of a 50 channel optically pumped magnetometer (OPM) system and 72 excitation coils, which are operated inside a magnetically shielded room. With our setup, we were able to reconstruct a glioblastoma phantom with a clinically relevant iron amount. A simulation study reveals, that currently the main limitations in terms of reconstruction quality arise from geometrical uncertainties of the setup. Summing up, we demonstrate the feasibility of OPM-MRXI for large regions of interest, potentially advancing the monitoring of MNP in related treatments.