Identifying Gravity-Related Artifacts on Ballistocardiography Signals by Comparing Weightlessness and Normal Gravity Recordings (ARTIFACTS): Protocol for an Observational Study.

BACKGROUND: Modern ballistocardiography (BCG) and seismocardiography (SCG) use acceleration sensors to measure oscillating recoil movements of the body caused by the heartbeat and blood flow, which are transmitted to the body surface. Acceleration artifacts occur through intrinsic sensor roll, pitch, and yaw movements, assessed by the angular velocities of the respective sensor, during measurements that bias the signal interpretation. OBJECTIVE: This observational study aims to generate hypotheses on the detection and elimination of acceleration artifacts due to the intrinsic rotation of accelerometers and their differentiation from heart-induced sensor accelerations. METHODS: Multimodal data from 4 healthy participants (3 male and 1 female) using BCG-SCG and an electrocardiogram will be collected and serve as a basis for signal characterization, model modulation, and location vector derivation under parabolic flight conditions from µg to 1.8g. The data will be obtained during a parabolic flight campaign (3 times 30 parabolas) between September 24 and July 25 (depending on the flight schedule). To detect the described acceleration artifacts, accelerometers and gyroscopes (6-degree-of-freedom sensors) will be used for measuring acceleration and angular velocities attributed to intrinsic sensor rotation. Changes in acceleration and angular velocities will be explored by conducting descriptive data analysis of resting participants sitting upright in varying gravitational states. RESULTS: A multimodal data set will serve as a basis for research into a noninvasive and gentle method of BCG-SCG with the aid of low-noise and synchronous 3D gyroscopes and 3D acceleration sensors. Hypotheses will be generated related to detecting and eliminating acceleration artifacts due to the intrinsic rotation of accelerometers and gyroscopes (6-degree-of-freedom sensors) and their differentiation from heart-induced sensor accelerations. Data will be collected entirely and exclusively during the parabolic flights, taking place between September 2024 and July 2025. Thus, as of June 2024, no data have been collected yet. The data will be analyzed until December 2025. The results are expected to be published by June 2026. CONCLUSIONS: The study will contribute to understanding artificial acceleration bias to signal readings. It will be a first approach for a detection and elimination method. TRIAL REGISTRATION: Deutsches Register Klinische Studien DRKS00034402; https://drks.de/search/en/trial/DRKS00034402. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/63306.

Enhancing photoacoustic imaging for lung diagnostics and BCI communication: simulation of cavity structures artifact generation and evaluation of noise reduction techniques.

Pandemics like COVID-19 have highlighted the potential of Photoacoustic imaging (PAI) for Brain-Computer Interface (BCI) communication and lung diagnostics. However, PAI struggles with the clear imaging of blood vessels in areas like the lungs and brain due to their cavity structures. This paper presents a simulation model to analyze the generation and propagation mechanism within phantom tissues of PAI artifacts, focusing on the evaluation of both Anisotropic diffusion filtering (ADF) and Non-local mean (NLM) filtering, which significantly reduce noise and eliminate artifacts and signify a pivotal point for selecting artifact-removal algorithms under varying conditions of light distribution. Experimental validation demonstrated the efficacy of our technique, elucidating the effect of light source uniformity on artifact-removal performance. The NLM filtering simulation and ADF experimental validation increased the peak signal-to-noise ratio by 11.33% and 18.1%, respectively. The proposed technique adds a promising dimension for BCI and is an accurate imaging solution for diagnosing lung diseases.

Clinical, laboratory and ultrasonographic correlates of prostate calcifications in patients with chronic prostatitis/chronic pelvic pain syndrome.

Introduction: The research aim was to determine the role of clinical, laboratory, immunological and sonographic parameters in the development of an assessment tool for the symptomatic manifestations of prostate calcifications in chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS). Material and methods: All men underwent a transabdominal ultrasonographic examination using a grayscale B-mode and color Doppler mapping, the evaluation of the National Institutes of Health-Chronic Prostatitis Symptom Index and the Patient Health Questionnaire-9, spermogram. Vascular endothelial growth factor (VEGF), serotonin and gamma-aminobutyrate (GABA), interleukins 1ß and 10 were determined in blood serum and ejaculate. Results: This study included 102 men aged 18-45 years. Group 1 (n = 34) consisted of patients with CP/CPPS. Group 2 included patients (n = 34) with asymptomatic prostatitis. Group 3 consisted of healthy volunteers (n = 34). More severe symptoms of prostatitis and depression, as well as frequent exacerbations in patients with CP/CPPS, were associated with ultrasound evidence of prostate calcifications, and especially the twinkling artifact (Spearman's r = 0.481; р <0.001; Spearman's r = 0.437; р <0.001, respectively).The presence of prostate calcifications in both CP/CPPS and asymptomatic prostatitis was accompanied by a significantly higher concentration of pro-inflammatory cytokine IL-1ß and a lower concentration of anti-inflammatory cytokine IL-10 in the ejaculate (p < 0.05 in both cases, Kolmogorov-Smirnov test). The clinical manifestations observed in patients with CP/CPPS and asymptomatic prostatitis were not correlated with the leukocyte count in the ejaculate or the levels of VEGF, GABA, and serotonin in both blood and ejaculate. Conclusions: Twinkling artifact potentially could serve as a valuable tool for evaluating the condition of patients with CP/CPPS and prostate calcifications.

Enhancing EEG data quality and precision for cloud-based clinical applications: an evaluation of the SLOG framework.

Automation is revamping our preprocessing pipelines, and accelerating the delivery of personalized digital medicine. It improves efficiency, reduces costs, and allows clinicians to treat patients without significant delays. However, the influx of multimodal data highlights the need to protect sensitive information, such as clinical data, and safeguard data fidelity. One of the neuroimaging modalities that produces large amounts of time-series data is Electroencephalography (EEG). It captures the neural dynamics in a task or resting brain state with high temporal resolution. EEG electrodes placed on the scalp acquire electrical activity from the brain. These electrical potentials attenuate as they cross multiple layers of brain tissue and fluid yielding relatively weaker signals than noise-low signal-to-noise ratio. EEG signals are further distorted by internal physiological artifacts, such as eye movements (EOG) or heartbeat (ECG), and external noise, such as line noise (50 Hz). EOG artifacts, due to their proximity to the frontal brain regions, are particularly challenging to eliminate. Therefore, a widely used EOG rejection method, independent component analysis (ICA), demands manual inspection of the marked EOG components before they are rejected from the EEG data. We underscore the inaccuracy of automatized ICA rejection and provide an auxiliary algorithm-Second Layer Inspection for EOG (SLOG) in the clinical environment. SLOG based on spatial and temporal patterns of eye movements, re-examines the already marked EOG artifacts and confirms no EEG-related activity is mistakenly eliminated in this artifact rejection step. SLOG achieved a 99% precision rate on the simulated dataset while 85% precision on the real EEG dataset. One of the primary considerations for cloud-based applications is operational costs, including computing power. Algorithms like SLOG allow us to maintain data fidelity and precision without overloading the cloud platforms and maxing out our budgets.

Anthropogenic modification of a giant ground sloth tooth from Brazil supported by a multi-disciplinary approach.

Identifying evidence of human modification of extinct animal remains, such as Pleistocene megafauna, is challenging due to the similarity of anthropogenic and non-anthropogenic taphonomic features observed under optical microscopy. Here, we re-investigate a Late Pleistocene ground sloth tooth from northeast Brazil, previously suggested as human-modified based only on optical observation. To characterize the macro- and micro-morphological characteristics of the marks preserved in this tooth and evaluate potential human modification, we used stereomicroscope and scanning electron microscopy (SEM) supplemented by energy dispersive spectroscopy (EDS), UV photoluminescence (UV/PL), synchrotron-based X-ray fluorescence (SR-XRF), and synchrotron micro-computed tomography (SR-µCT). These methods allowed us to discriminate non-anthropogenic taphonomic features (root and sedimentary damage), anthropogenic marks, and histological features. The latter shows the infiltration of exogenous elements into the dentine from the sediments. Our evidence demonstrates the sequence of anthropogenic and non-anthropogenic taphonomic modification of this tooth and supports its initial intentional modification by humans. We highlight the benefits of emerging imaging and spectral imaging techniques to investigate and diagnose human modification in fossil and archaeological records and propose that human modification of tooth tissues should be further considered when studying possibly anthropogenically altered fossil remains.

Magnetic Resonance Imaging of Total Ankle Arthroplasty: State-of-The-Art Assessment of Implant-Related Pain and Dysfunction.

Total ankle arthroplasty (TAA) is an effective alternative for treating patients with end-stage ankle degeneration, improving mobility, and providing pain relief. Implant survivorship is constantly improving; however, complications occur. Many causes of pain and dysfunction after total ankle arthroplasty can be diagnosed accurately with clinical examination, laboratory, radiography, and computer tomography. However, when there are no or inconclusive imaging findings, magnetic resonance imaging (MRI) is highly accurate in identifying and characterizing bone resorption, osteolysis, infection, osseous stress reactions, nondisplaced fractures, polyethylene damage, nerve injuries and neuropathies, as well as tendon and ligament tears. Multiple vendors offer effective, clinically available MRI techniques for metal artifact reduction MRI of total ankle arthroplasty. This article reviews the MRI appearances of common TAA implant systems, clinically available techniques and protocols for metal artifact reduction MRI of TAA implants, and the MRI appearances of a broad spectrum of TAA-related complications.

Evaluation of a Metal Artifact Reduction Algorithm for Image Reconstruction on a Novel CBCT Platform.

PURPOSE: The presence of metal implants can produce artifacts and distort Hounsfield units (HU) in patient computed tomography (CT) images. The purpose of this work was to characterize a novel metal artifact reduction (MAR) algorithm for reconstruction of CBCT images obtained by the HyperSight imaging system. METHODS: Three tissue-equivalent phantoms were fitted with materials commonly used in medical applications. The first consisted of a variety of metal samples centered within a solid water block, the second was an Advanced Electron Density phantom with metal rods, and the third consisted of hip prostheses positioned within a water tank. CBCT images of all phantoms were acquired and reconstructed using the MAR and iCBCT Acuros algorithms on the HyperSight system. The signal-to-noise ratio (SNR), artifact index (AI), structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and mean-square error (MSE) were computed to assess the image quality in comparison to artifact-free reference images. The mean HU at various VOI positions around the cavity was calculated to evaluate the artifact dependence on distance and angle from the center of the cavity. The artifact volume of the phantom (excluding the cavity) was estimated by summing the volume of all voxels with HU values outside the 5th and 95th percentiles of the phantom CBCT with no artifact. RESULTS: The SNR, AI, SSIM, PSNR, and MSE metrics demonstrated significantly higher similarity to baseline when using MAR compared to iCBCT Acuros for all high-density materials, except for aluminum. Mean HU returned to expected solid water background at a shorter distance from metal sample in the MAR images, and the standard deviation remained lower for the MAR images at all distances from the insert. The artifact volume decreased using the novel MAR algorithm for all metal samples excluding aluminum (p < 0.001) and all hip prostheses (p < 0.05). CONCLUSION: Varian's HyperSight MAR reconstruction algorithm shows a reduction in metal artifact metrics, motivating the use of MAR reconstruction for patients with metal implants.

Lesion-mimicking DIXON swap artifact in contrast-enhanced subtraction breast MRI.

Breast cancer is the most common cancer in women; approximately 1 in 8 women is diagnosed with breast cancer in their lifetime. Some women are at significantly higher risk of developing breast cancer, including women carrying mutations in the BRCA1/2, TP53, or other genes and women with other risk factors. Women with a high lifetime risk for breast cancer are frequently offered annual breast magnetic resonance imaging (MRI) examinations for early breast cancer detection. Breast MRI is commonly performed using a multiparametric imaging protocol, including dynamic contrast-enhanced T1-weighted acquisitions. The dynamic contrast-enhanced T1-weighted acquisitions are frequently transformed into subtraction series, allowing the focused visualization of areas with high signal intensity and masses associated with elevated contrast agent uptake, which are among the hallmarks of suspicious findings. Here, we report a case in which a suspicious lesion-mimicking swap artifact occurred using a T1-weighted contrast-enhanced DIXON acquisition technique in a high-risk breast cancer screening MRI examination.

I see artifacts: ICA-based EEG artifact removal does not improve deep network decoding across three BCI tasks.

\textit{Objective.} In this paper, we conduct a detailed investigation on the effect of IC-based noise rejection methods in neural network classifier-based decoding of electroencephalography (EEG) data in different task datasets. \textit{Approach.} We apply a pipeline matrix of two popular different Independent Component (IC) decomposition methods (Infomax, AMICA) with three different component rejection strategies (none, ICLabel, and MARA) on three different EEG datasets (Motor imagery, long-term memory formation, and visual memory). We cross-validate processed data from each pipeline with three architectures commonly used for EEG classification (two convolutional neural networks (CNN) and one long short term memory (LSTM) based model. We compare decoding performances on within-participant and within-dataset levels. \textit{Main Results.} Our results show that the benefit from using IC-based noise rejection for decoding analyses is at best minor, as component-rejected data did not show consistently better performance than data without rejections---especially given the significant computational resources required for ICA computations. \textit{Significance.} With ever growing emphasis on transparency and reproducibility, as well as the obvious benefits arising from streamlined processing of large-scale datasets, there has been an increased interest in automated methods for pre-processing EEG data. One prominent part of such pre-processing pipelines consists of identifying and potentially removing artifacts arising from extraneous sources. This is typically done via Independent Component (IC) based correction for which numerous methods have been proposed, differing not only in the decomposition of the raw data into ICs, but also in how they reject the computed ICs. While the benefits of these methods are well established in univariate statistical analyses, it is unclear whether they help in multivariate scenarios, and specifically in neural network based decoding studies. As computational costs for pre-processing large-scale datasets are considerable, it is important to consider whether the tradeoff between model performance and available resources is worth the effort.

Premature ventricular contraction arising from the left coronary sinus cusp: Which signal is the target of ablation?

We described a premature ventricular contraction arising from the left coronary sinus cusp, in which we discussed about the interpretations of the signals recorded there. Our case provided further insights into the interpretation of signals recorded at the coronary sinus cusp during premature ventricular contraction ablation.