On the transverse relaxation enhancement effect in H-MRI of the lung.

Purpose: Presenting a technique to quantify the transverse relaxation time T, which is associated with the diffusion of water molecules through the internal magnetic field gradients of the lung in-vivo.

Methods: A Half-Fourier-Acquired Single-shot Turbo spin-Echo (HASTE) pulse sequence with Hahn-echo preparation was implemented and used for image acquisition. Quantification of T was performed by acquiring multiple images with identical TE, but with a different number of refocusing pulses between excitation and signal acquisition.

Towards a zero-shot low-latency navigation for open surgery augmented reality applications.

Purpose: Augmented reality (AR) enhances surgical navigation by superimposing visible anatomical structures with three-dimensional virtual models using head-mounted displays (HMDs). In particular, interventions such as open liver surgery can benefit from AR navigation, as it aids in identifying and distinguishing tumors and risk structures. However, there is a lack of automatic and markerless methods that are robust against real-world challenges, such as partial occlusion and organ motion.

Image space formalism of convolutional neural networks for k-space interpolation.

Purpose: Noise resilience in image reconstructions by scan-specific robust artificial neural networks for k-space interpolation (RAKI) is linked to nonlinear activations in k-space. To gain a deeper understanding of this relationship, an image space formalism of RAKI is introduced for analyzing noise propagation analytically, identifying and characterizing image reconstruction features and to describe the role of nonlinear activations in a human-readable manner.

Theory And Methods: The image space formalism for RAKI inference is employed by expressing nonlinear activations in k-space as element-wise multiplications with activation masks, which transform into convolutions in image space.

Utilizing X-ray radiography for non-destructive assessment of paddy rice grain quality traits.

Background: Agricultural systems are under extreme pressure to meet the global food demand, hence necessitating faster crop improvement. Rapid evaluation of the crops using novel imaging technologies coupled with robust image analysis could accelerate crops research and improvement. This proof-of-concept study investigated the feasibility of using X-ray imaging for non-destructive evaluation of rice grain traits.

Outcome measures of instrumented gait analysis in hereditary spastic paraplegia: a systematic review.

Background: Hereditary spastic paraplegias (HSPs) comprise a group of genetic movement disorders characterized by progressive spasticity and weakness of the lower limbs leading to gait deficits. Instrumented gait measures are applied to quantify gait patterns in HSP objectively. However, there is no consensus on the most relevant HSP-specific digital outcome measures for future clinical studies.

Controlling sharpness, SNR, and specific absorption rate for 3D fast-spin echo at 7T by end-to-end learning.

Purpose: To non-heuristically identify dedicated variable flip angle (VFA) schemes optimized for the point-spread function (PSF) and SNR of multiple tissues in 3D FSE sequences with very long echo trains at 7T.

Methods: The proposed optimization considers predefined specific absorption rate (SAR) constraints and target contrast using an end-to-end learning framework. The cost function integrates components for contrast fidelity (SNR) and a penalty term to minimize image blurring (PSF) for multiple tissues.

Cocreating the Visualization of Digital Mobility Outcomes: Delphi-Type Process With Patients.

Background: Recent technological advances in wearable devices offer new potential for measuring mobility in real-world contexts. Mobilise-D has validated digital mobility outcomes to provide novel outcomes and end points in clinical research of 4 different long-term health conditions (Parkinson disease, multiple sclerosis, chronic obstructive pulmonary disease, and proximal femoral fracture). These outcomes also provide unique information that is important to patients; however, there is limited literature that explores the optimal methods to achieve this, such as the best way to visualize patients' data.

Comparing sparse inertial sensor setups for sagittal-plane walking and running reconstructions.

Estimating spatiotemporal, kinematic, and kinetic movement variables with little obtrusion to the user is critical for clinical and sports applications. One possible approach is using a sparse inertial sensor setup, where sensors are not placed on all relevant body segments. Here, we investigated if movement variables can be estimated similarly accurate from sparse sensor setups as from a full lower-body sensor setup.

Towards interactive AI-authoring with prototypical few-shot classifiers in histopathology.

A vast multitude of tasks in histopathology could potentially benefit from the support of artificial intelligence (AI). Many examples have been shown in the literature and first commercial products with FDA or CE-IVDR clearance are available. However, two key challenges remain: (1) a scarcity of thoroughly annotated images, respectively the laboriousness of this task, and (2) the creation of robust models that can cope with the data heterogeneity in the field (domain generalization).

Introducing ProsperNN-a Python package for forecasting with neural networks.

We present the package prosper_nn, that provides four neural network architectures dedicated to time series forecasting, implemented in PyTorch. In addition, prosper_nn contains the first sensitivity analysis suitable for recurrent neural networks (RNN) and a heatmap to visualize forecasting uncertainty, which was previously only available in Java. These models and methods have successfully been in use in industry for two decades and were used and referenced in several scientific publications.

Investigating Gold Deposition with High-Power Impulse Magnetron Sputtering and Direct-Current Magnetron Sputtering on Polystyrene, Poly-4-vinylpyridine, and Polystyrene Sulfonic Acid.

Fabricating thin metal layers and particularly observing their formation process in situ is of fundamental interest to tailor the quality of such a layer on polymers for organic electronics. In particular, the process of high power impulse magnetron sputtering (HiPIMS) for establishing thin metal layers has sparsely been explored in situ. Hence, in this study, we investigate the growth of thin gold (Au) layers with HiPIMS and compare their growth with thin Au layers prepared by conventional direct current magnetron sputtering (dcMS).

The Artificial Neural Twin - Process optimization and continual learning in distributed process chains.

Industrial process optimization and control is crucial to increase economic and ecologic efficiency. However, data sovereignty, differing goals, or the required expert knowledge for implementation impede holistic implementation. Further, the increasing use of data-driven AI-methods in process models and industrial sensory often requires regular fine-tuning to accommodate distribution drifts.

Rotary excitation of non-sinusoidal pulsed magnetic fields: Towards non-invasive direct detection of cardiac conduction.

Purpose: There is a need for high resolution non-invasive imaging methods of physiologic magnetic fields. The purpose of this work is to develop a MRI detection approach for non-sinusoidal magnetic fields based on the rotary excitation (REX) mechanism which was previously successfully applied for the detection of oscillating magnetic fields in the sub-nT range.

Methods: The new detection concept was examined by means of Bloch simulations, evaluating the interaction effect of spin-locked magnetization and low-frequency pulsed magnetic fields.

Selected annotated instance segmentation sub-volumes from a large scale CT data-set of a historic aircraft.

The Me 163 was a Second World War fighter airplane and is currently displayed in the Deutsches Museum in Munich, Germany. A complete computed tomography (CT) scan was obtained using a large scale industrial CT scanner to gain insights into its history, design, and state of preservation. The CT data enables visual examination of the airplane's structural details across multiple scales, from the entire fuselage to individual sprockets and rivets.

X-ray transmission imaging of waste printed circuit boards for value estimation in recycling using machine learning.

The growing amount of electronic waste is a global challenge: on one hand, it poses a threat to the environment as it may contain toxic or hazardous substances, on the other hand it is a valuable 'urban mine' containing metals like gold and copper. Thus, recycling of electronic waste is not only a measure to reduce environmental pollution but also economically reasonable as prices for raw materials are rising. Within electronic waste, printed circuit boards (PCBs) occupy a prominent position, as they contain most of the valuable material.

Mobile digital gait analysis captures effects of botulinum toxin in hereditary spastic paraplegia.

Background And Purpose: Hereditary spastic paraplegias (HSPs) comprise a group of inherited neurodegenerative disorders characterized by progressive spasticity and weakness. Botulinum toxin has been approved for lower limb spasticity following stroke and cerebral palsy, but its effects in HSPs remain underexplored. We aimed to characterize the effects of botulinum toxin on clinical, gait, and patient-reported outcomes in HSP patients and explore the potential of mobile digital gait analysis to monitor treatment effects and predict treatment response.

Unraveling progression subtypes in people with Huntington's disease.

Background: Huntington's disease (HD) is a progressive neurodegenerative disease caused by a CAG trinucleotide expansion in the huntingtin gene. The length of the CAG repeat is inversely correlated with disease onset. HD is characterized by hyperkinetic movement disorder, psychiatric symptoms, and cognitive deficits, which greatly impact patient's quality of life.

Machine learning-based detection of acute psychosocial stress from body posture and movements.

Investigating acute stress responses is crucial to understanding the underlying mechanisms of stress. Current stress assessment methods include self-reports that can be biased and biomarkers that are often based on complex laboratory procedures. A promising additional modality for stress assessment might be the observation of body movements, which are affected by negative emotions and threatening situations.

Simulating room transfer functions between transducers mounted on audio devices using a modified image source method.

The image source method (ISM) is often used to simulate room acoustics due to its ease of use and computational efficiency. The standard ISM is limited to simulations of room impulse responses between point sources and omnidirectional receivers. In this work, the ISM is extended using spherical harmonic directivity coefficients to include acoustic diffraction effects.

Towards fully automated inner ear analysis with deep-learning-based joint segmentation and landmark detection framework.

Automated analysis of the inner ear anatomy in radiological data instead of time-consuming manual assessment is a worthwhile goal that could facilitate preoperative planning and clinical research. We propose a framework encompassing joint semantic segmentation of the inner ear and anatomical landmark detection of helicotrema, oval and round window. A fully automated pipeline with a single, dual-headed volumetric 3D U-Net was implemented, trained and evaluated using manually labeled in-house datasets from cadaveric specimen ([Formula: see text]) and clinical practice ([Formula: see text]).

The effects of an individualized smartphone-based exercise program on self-defined motor tasks in Parkinson's disease: a long-term feasibility study.

Background: Exercise therapy is considered effective for the treatment of motor impairment in patients with Parkinson's disease (PD). During the COVID-19 pandemic, training sessions were cancelled and the implementation of telerehabilitation concepts became a promising solution. The aim of this controlled interventional feasibility study was to evaluate the long-term acceptance and to explore initial effectiveness of a digital, home-based, high-frequency exercise program for PD patients.

Cost-effective 3D scanning and printing technologies for outer ear reconstruction: current status.

Current 3D scanning and printing technologies offer not only state-of-the-art developments in the field of medical imaging and bio-engineering, but also cost and time effective solutions for surgical reconstruction procedures. Besides tissue engineering, where living cells are used, bio-compatible polymers or synthetic resin can be applied. The combination of 3D handheld scanning devices or volumetric imaging, (open-source) image processing packages, and 3D printers form a complete workflow chain that is capable of effective rapid prototyping of outer ear replicas.

Longitudinal trajectories of memory among middle-aged and older people with hearing loss: the influence of cochlear implant use on cognitive functioning.

Introduction: Cochlear implants (CI) are the gold standard intervention for severe to profound hearing loss, a known modifiable risk factor for dementia. However, it remains unknown whether CI use might prevent the age-related cognitive decline. Recent studies are encouraging but are limited, mainly by short follow-up periods and, for ethical reasons, lack of appropriate control groups.

Automated assessment of foot elevation in adults with hereditary spastic paraplegia using inertial measurements and machine learning.

Background: Hereditary spastic paraplegias (HSPs) cause characteristic gait impairment leading to an increased risk of stumbling or even falling. Biomechanically, gait deficits are characterized by reduced ranges of motion in lower body joints, limiting foot clearance and ankle range of motion. To date, there is no standardized approach to continuously and objectively track the degree of dysfunction in foot elevation since established clinical rating scales require an experienced investigator and are considered to be rather subjective.

Tumor-Stroma Ratio in Colorectal Cancer-Comparison between Human Estimation and Automated Assessment.

The tumor-stroma ratio (TSR) has been repeatedly shown to be a prognostic factor for survival prediction of different cancer types. However, an objective and reliable determination of the tumor-stroma ratio remains challenging. We present an easily adaptable deep learning model for accurately segmenting tumor regions in hematoxylin and eosin (H&E)-stained whole slide images (WSIs) of colon cancer patients into five distinct classes (tumor, stroma, necrosis, mucus, and background).