fRegGAN with K-space Loss Regularization for Medical Image Translation

Generative adversarial networks (GANs) have shown remarkable success in generating realistic images and are increasingly used in medical imaging for image-to-image translation tasks. However, GANs tend to suffer from a frequency bias towards low frequencies, which can lead to the removal of important structures in the generated images. To address this issue, we propose a novel frequency-aware image-to-image translation framework based on the supervised RegGAN approach, which we call fRegGAN. The framework employs a K-space loss to regularize the frequency content of the generated images and incorporates well-known properties of MRI K-space geometry to guide the network training process. By combine our method with the RegGAN approach, we can mitigate the effect of training with misaligned data and frequency bias at the same time. We evaluate our method on the public BraTS dataset and outperform the baseline methods in terms of both quantitative and qualitative metrics when synthesizing T2-weighted from T1-weighted MR images. Detailed ablation studies are provided to understand the effect of each modification on the final performance. The proposed method is a step towards improving the performance of image-to-image translation and synthesis in the medical domain and shows promise for other applications in the field of image processing and generation

DNA methylation-based classification of central nervous system tumours.

Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging-with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology

Minimally invasive computer-navigated total hip arthroplasty, following the concept of femur first and combined anteversion: design of a blinded randomized controlled trial

<p>Abstract</p> <p>Background</p> <p>Impingement can be a serious complication after total hip arthroplasty (THA), and is one of the major causes of postoperative pain, dislocation, aseptic loosening, and implant breakage. Minimally invasive THA and computer-navigated surgery were introduced several years ago. We have developed a novel, computer-assisted operation method for THA following the concept of "femur first"/"combined anteversion", which incorporates various aspects of performing a functional optimization of the cup position, and comprehensively addresses range of motion (ROM) as well as cup containment and alignment parameters. Hence, the purpose of this study is to assess whether the artificial joint's ROM can be improved by this computer-assisted operation method. Second, the clinical and radiological outcome will be evaluated.</p> <p>Methods/Design</p> <p>A registered patient- and observer-blinded randomized controlled trial will be conducted. Patients between the ages of 50 and 75 admitted for primary unilateral THA will be included. Patients will be randomly allocated to either receive minimally invasive computer-navigated "femur first" THA or the conventional minimally invasive THA procedure. Self-reported functional status and health-related quality of life (questionnaires) will be assessed both preoperatively and postoperatively. Perioperative complications will be registered. Radiographic evaluation will take place up to 6 weeks postoperatively with a computed tomography (CT) scan. Component position will be evaluated by an independent external institute on a 3D reconstruction of the femur/pelvis using image-processing software. Postoperative ROM will be calculated by an algorithm which automatically determines bony and prosthetic impingements.</p> <p>Discussion</p> <p>In the past, computer navigation has improved the accuracy of component positioning. So far, there are only few objective data quantifying the risks and benefits of computer navigated THA. Therefore, this study has been designed to compare minimally invasive computer-navigated "femur first" THA with a conventional technique for minimally invasive THA. The results of this trial will be presented as soon as they become available.</p> <p>Trial registration number</p> <p>DRKS00000739</p

Detection of SARS-CoV-2 in Air and on Surfaces in Rooms of Infected Nursing Home Residents

There is an ongoing debate on airborne transmission of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) as a risk factor for infection. In this study, the level of SARS-CoV-2 in air and on surfaces of SARS-CoV-2 infected nursing home residents was assessed to gain insight in potential transmission routes. During outbreaks, air samples were collected using three different active and one passive air sampling technique in rooms of infected patients. Oropharyngeal swabs (OPS) of the residents and dry surface swabs were collected. Additionally, longitudinal passive air samples were collected during a period of 4 months in common areas of the wards. Presence of SARS-CoV-2 RNA was determined using RT-qPCR, targeting the RdRp- and E-genes. OPS, samples of two active air samplers and surface swabs with Ct-value ≤35 were tested for the presence of infectious virus by cell culture. In total, 360 air and 319 surface samples from patient rooms and common areas were collected. In rooms of 10 residents with detected SARS-CoV-2 RNA in OPS, SARS-CoV-2 RNA was detected in 93 of 184 collected environmental samples (50.5%) (lowest Ct 29.5), substantially more than in the rooms of residents with negative OPS on the day of environmental sampling (n = 2) (3.6%). SARS-CoV-2 RNA was most frequently present in the larger particle size fractions [>4 μm 60% (6/10); 1-4 μm 50% (5/10); <1 μm 20% (2/10)] (Fischer exact test P = 0.076). The highest proportion of RNA-positive air samples on room level was found with a filtration-based sampler 80% (8/10) and the cyclone-based sampler 70% (7/10), and impingement-based sampler 50% (5/10). SARS-CoV-2 RNA was detected in 10 out of 12 (83%) passive air samples in patient rooms. Both high-touch and low-touch surfaces contained SARS-CoV-2 genome in rooms of residents with positive OPS [high 38% (21/55); low 50% (22/44)]. In one active air sample, infectious virus in vitro was detected. In conclusion, SARS-CoV-2 is frequently detected in air and on surfaces in the immediate surroundings of room-isolated COVID-19 patients, providing evidence of environmental contamination. The environmental contamination of SARS-CoV-2 and infectious aerosols confirm the potential for transmission via air up to several meters

Relating Polarized Light Imaging Data Across Scales

Polarized light imaging (PLI) (Axer et al. (2011a,b)) enables scanning of individual histological human brain sections with two independent setups: a large-area polarimeter (LAP, “object space resolution”, which is referred to as “resolution” in the remainder of this abstract: 64 × 64 μm²/px) and a polarizing microscope (PM, resolution: 1.6 × 1.6 μm²/px). While PM images are of high resolution (HR) containing complex information, the LAP provides low resolution (LR) overview-like data. The information contained in an LR image is a mixture of the information of its HR counterpart (Koenderink (1984)). Each resolution yields valuable information, which multiplies if they are combined.Image registration algorithms, for example, handle multiple resolutions (1) in case of several modalities with special metrics, and (2) in multi-resolution approaches (e.g. Trottenberg et al. (2001)) to increase the stability of the optimization process of automatic image registration. In the latter case, the data is coarsened synthetically. Our goal is to directly relate measured HR to LR data of the same object, avoiding artificial intermediate steps.All images show the average light intensity, that is transmitted through a thin brain slice (Axer et al. (2011a,b)), and depict a region from the human occipital pole. The images were manually segmented and smoothed by a Gaussian kernel suitable for noise reduction and adapted to each resolution.We selected octave 2 at LR and octave 7 at HR for SURF extraction (Bay et al. (2006)), where one octave denotes a decrease in resolution by a factor of 2. Features with corresponding scales were matched with FLANN (Muja and Lowe (2009)). Homography estimation from the resulting feature point pairs used RANSAC (Fischler and Bolles (1981)). The homography and a linear interpolation scheme were applied to transfer information from LR to HR and vice versa.Localization of the HR ROI in the LR ROI is plausible (figure 1(B)), while localization in the LAP image fails, because the matched feature point positions in HR and LR do not correspond. Numerical and feature point matching inaccuracies become evident in figure 1(C).The experiments were performed with one HR ROI (figure 1(A)), one LAP ROI (figure 1(B)) and one LAP image. We plan to improve the algorithm and to obtain complete HR data sets for further exploration of the method’s performance.Figure 1. This figure shows input data and results of the experiment. The arrows indicate the flow of information and the color by which it is displayed at its destination. Subfigure (A) shows the down-scaled PM ROI (original size: 20604 px × 17157 px). (B) shows the up-scaled LAP ROI (original size: 916 px × 510 px) with estimated PM ROI location (green frame). Note, that only part of the HR ROI is contained in the LR ROI. Also, most of the fine white structures depicted in (A) vanished due to the low resolution of (B). (C) shows the down-scaled overlay image (original size: 20604 px × 17157 px) of LR data (enclosed in the green frame in (B)) transferred to HR versus PM ROI data of (A), where HR data is labeled green and transferred LR data is labeled red. HR data and transferred LR data were normalized. Numerical and feature point matching inaccuracies become evident. Also, displacement and distortion compared to HR data is visible

Estimating Fiber Orientation Distribution Functions in 3D-Polarized Light Imaging

Research of the human brain connectome requires multiscale approaches derived from independent imaging methods ideally applied to the same object. Hence, comprehensible strategies for data integration across modalities and across scales are essential. We have successfully established a concept to bridge the spatial scales from microscopic fiber orientation measurements based on 3D-Polarized Light Imaging (3D-PLI) to meso- or macroscopic dimensions. By creating orientation distribution functions (pliODFs) from high-resolution vector data via series expansion with spherical harmonics utilizing high performance computing and supercomputing technologies, data fusion with Diffusion Magnetic Resonance Imaging has become feasible, even for a large-scale dataset such as the human brain. Validation of our approach was done effectively by means of two types of datasets that were transferred from fiber orientation maps into pliODFs: simulated 3D-PLI data showing artificial, but clearly defined fiber patterns and real 3D-PLI data derived from sections through the human brain and the brain of a hooded seal