Generalized 3D registration algorithm for enhancing retinal optical coherence tomography images.

Significance: Optical coherence tomography (OCT) has emerged as the standard of care for diagnosing and monitoring the treatment of various ocular disorders due to its noninvasive nature and in vivo volumetric acquisition capability. Despite its widespread applications in ophthalmology, motion artifacts remain a challenge in OCT imaging, adversely impacting image quality. While several multivolume registration algorithms have been developed to address this issue, they are often designed to cater to one specific OCT system or acquisition protocol. Aim: We aim to generate an OCT volume free of motion artifacts using a system-agnostic registration algorithm that is independent of system specifications or protocol. Approach: We developed a B-scan registration algorithm that removes motion and corrects for both translational eye movements and rotational angle differences between volumes. Tests were carried out on various datasets obtained from two different types of custom-built OCT systems and one commercially available system to determine the reliability of the proposed algorithm. Additionally, different system specifications were used, with variations in axial resolution, lateral resolution, signal-to-noise ratio, and real-time motion tracking. The accuracy of this method has further been evaluated through mean squared error (MSE) and multiscale structural similarity index measure (MS-SSIM). Results: The results demonstrate improvements in the overall contrast of the images, facilitating detailed visualization of retinal vasculatures in both superficial and deep vasculature plexus. Finer features of the inner and outer retina, such as photoreceptors and other pathology-specific features, are discernible after multivolume registration and averaging. Quantitative analyses affirm that increasing the number of averaged registered volumes will decrease MSE and increase MS-SSIM as compared to the reference volume. Conclusions: The multivolume registered data obtained from this algorithm offers significantly improved visualization of the retinal microvascular network as well as retinal morphological features. Furthermore, we have validated that the versatility of our methodology extends beyond specific OCT modalities, thereby enhancing the clinical utility of OCT for the diagnosis and monitoring of ocular pathologies.

Influence of CBCT filters and contrast adjustments on peri-implant buccal bone thickness measurement and blooming expression.

OBJECTIVES: To assess whether filter and contrast adjustments can improve the accuracy of CBCT in measuring the buccal bone thickness (BBT) adjacent to dental implants by reducing blooming artifacts. MATERIALS AND METHODS: Homogeneous bone blocks with peri-implant BBT of 0.3 mm, 0.5 mm, and 1 mm were scanned using the Orthophos SL system. Three dentists measured the BBT in 234 CBCT scans under different settings of contrast adjustments and 'Sharpen' filter activation. Additionally, implant diameter measurements were taken to assess blooming artifact expression. The differences between tomographic and actual measurements of BBT and implant diameter [(CBCT - actual) * 100 / actual] were subjected to Mixed ANOVA (α = 0.05). RESULTS: The group with the thinnest BBT (0.3 mm) had the greatest difference between tomographic and actual measurements (79.9% ± 29.0%). Conversely, the 0.5 mm (36.1% ± 38.4%) and 1 mm (29.4% ± 12.3%) groups exhibited lower differences (p < 0.05). 'Sharpen' filter activation reduced blooming expression since it resulted in a lower difference for implant diameter (p < 0.05), but it did not influence BBT measurements (p = 0.673). Contrast settings had no impact on BBT (p = 0.054) or implant diameter measurements (p = 0.079). CONCLUSION: Although filter activation reduced blooming artifacts, neither filter nor contrast adjustments improved the accuracy of CBCT in measuring peri-implant BBT; actual BBT influenced this task. CLINICAL RELEVANCE: When assessing the peri-implant buccal bone plate in the CBCT system studied, dental surgeons may find it beneficial to adjust contrast and apply filters according to their preferences, since such adjustments were found to have no adverse effects on the diagnostic accuracy of this task. The use of the 'Sharpen' filter may lead to improved representation of implant dimensions.

Aluminium Chloride instead of Ferric chloride for inducing superior sagittal sinus thrombosis to reduce ferromagnetic artifacts on MRI-imaging in experimental models.

Using ferric chloride (FeCl3) to induce experimental superior sagittal sinus (SSS) thrombosis might interfere with magnetic resonance imaging (MRI)-assisted visualization and evaluation of the thrombus, the brain parenchyma, and the quality of the occlusion. The aim of this study was to investigate whether aluminum chloride (AlCl3)-induced thrombosis of the SSS has comparable properties to those of FeCl3 without causing artifacts in MRI. SSS thrombosis was induced in 14 male Wistar rats by exposure of the SSS and subsequent topical application of a filter paper strip soaked in AlCl3 (n = 7) or FeCl3 (n = 7) over a period of 15 min. The animals with AlCl3-induced SSS thrombosis showed a constant and complete occlusion with in histological analysis large thrombi. Blood flow measurements indicated a significant reduction on the first and seventh postoperative day compared to preoperative measurements. MRI enabled visualization and subsequent evaluation of the thrombus and the surrounding parenchyma. In comparison, FeCl3-induced SSS thrombosis could not be evaluated by MRI due to artifacts caused by the paramagnetic properties and increased susceptibility of FeCl3. The occluded sinus and the surrounding area appeared hypointense. The quality of SSS occlusion by AlCl3 was comparable to that of FeCl3. AlCl3 therefore represents a significant alternative substance in experimental SSS thrombosis ideally suited for studies using MRI.

Pro: "Is Spread Through Air Spaces an In Vivo Phenomenon or an Inducible Artifact?"

In this PRO-CON debate, you will read very different perspectives about a simple question regarding an observation under the microscope: What is the significance of tumor cells in the air spaces of the lung parenchyma beyond the tumor edge of a resected lung cancer? An important underlying question is whether this entire PRO-CON debate is a mere academic exercise or whether spread through air spaces (STAS), as currently defined, describes a clinically useful phenomenon. The journey of STAS began with a complete paradigm shift to reverse the thinking that all air space tumor cells beyond the edge of lung cancers are an artifact. This led to a new concept where STAS could be separated from artifacts with a definition that has proven to be clinically useful. As with any major change in thinking, it is understandable that there would be some disagreement with this paradigm shift. Nevertheless, after a decade since it was described, many pathologists and clinicians around the world have found STAS to provide important information about the behavior of lung cancer. Numerous PRO-STAS articles supporting the usefulness of STAS have been published with clinical data on many thousands of patients from numerous institutions all over the world. In contrast, for the CON-STAS articles, widespread international representation and data are limited. It is now difficult to ignore the numerous reports and is reasonable to consider how to use the presence of STAS in clinical decisions. Hopefully, this PRO-CON debate will further stimulate clinical and scientific investigations aimed at a better understanding of STAS.

Spinal magnetic resonance imaging artifacts in lumboperitoneal shunt surgery using adjustable valve implantation on the paravertebral spinal muscles.

BACKGROUND: Adjustable shunt valves that have been developed for managing hydrocephalus rely on intrinsically magnetic components ; thus, artifacts with these valves on magnetic resonance imaging (MRI) are inevitable. No studies on valve-induced artifacts in lumboperitoneal shunt (LPS) surgery have been published. Therefore, this study aimed to evaluate valve-induced artifacts in LPS. METHODS: We retrospectively reviewed all MRIs obtained between January 2023 and June 2023 in patients with an implanted Codman CERTAS Plus adjustable shunt valve (Integra Life Sciences, Princeton, New Jersey, USA). The valve was placed <1 cm subcutaneously on the paravertebral spinal muscle of the back, with its long axis perpendicular to the body axis. The scans were performed using a Toshiba Medical Systems 1.5 Tesla scanner. The in-plane artifact sizes were assessed as the maximum distance of the artifact from the expected region of the back. RESULTS: All spinal structures or spinal cords can be recognized, even with valve-induced artifacts. The median maximum valve-induced artifact distance on T1-weighted axial imaging was 25.63 mm (mean, 25.98 mm ; range, 22.24-30.94 mm). The median maximum valve-induced artifact distance on T2-weighted axial imaging was 25.56 mm (mean, 26.27 mm ; range, 21.83-29.53 mm). CONCLUSION: LPS surgery with adjustable valve implantation on paravertebral muscles did not cause valve-induced artifacts in the spine and spinal cord. We considered that LPS could simplify the postoperative care of these patients. J. Med. Invest. 71 : 154-157, February, 2024.

Overview of artificial intelligence in point-of-care ultrasound. New horizons for respiratory system diagnoses.

Throughout the past decades ultrasonography did not prove to be a procedure of choice if regarded as part of the routine bedside examination. The reason was the assumption defining the lungs and the bone structures as impenetrable by ultrasound. Only during the recent several years has the approach to the use of such tool in clinical daily routines changed dramatically to offer so-called point-of-care ultrasonography (POCUS). Both vertical and horizontal artefacts became valuable sources of information about the patient's clinical condition, assisting therefore the medical practitioner in differential diagnosis and monitoring of the patient. What is important is that the information is delivered in real time, and the procedure itself is non-invasive. The next stage marking the progress made in this area of diagnostic imaging is the development of arti-ficial intelligence (AI) based on machine learning algorithms. This article is intended to present the available, innovative solutions of the ultrasound systems, including Smart B-line technology, to ensure automatic identification process, as well as interpretation of B-lines in the given lung area of the examined patient. The article sums up the state of the art in ultrasound artefacts and AI applied in POCUS.

Image quality evaluation of a new high-performance ring-gantry cone-beam computed tomography imager.

Objective. Newer cone-beam computed tomography (CBCT) imaging systems offer reconstruction algorithms including metal artifact reduction (MAR) and extended field-of-view (eFoV) techniques to improve image quality. In this study a new CBCT imager, the new Varian HyperSight CBCT, is compared to fan-beam CT and two CBCT imagers installed in a ring-gantry and C-arm linear accelerator, respectively.Approach. The image quality was assessed for HyperSight CBCT which uses new hardware, including a large-size flat panel detector, and improved image reconstruction algorithms. The decrease of metal artifacts was quantified (structural similarity index measure (SSIM) and root-mean-squared error (RMSE)) when applying MAR reconstruction and iterative reconstruction for a dental and spine region using a head-and-neck phantom. The geometry and CT number accuracy of the eFoV reconstruction was evaluated outside the standard field-of-view (sFoV) on a large 3D-printed chest phantom. Phantom size dependency of CT numbers was evaluated on three cylindrical phantoms of increasing diameter. Signal-to-noise and contrast-to-noise were quantified on an abdominal phantom.Main results. In phantoms with streak artifacts, MAR showed comparable results for HyperSight CBCT and CT, with MAR increasing the SSIM (0.97-0.99) and decreasing the RMSE (62-55 HU) compared to iterative reconstruction without MAR. In addition, HyperSight CBCT showed better geometrical accuracy in the eFoV than CT (Jaccard Conformity Index increase of 0.02-0.03). However, the CT number accuracy outside the sFoV was lower than for CT. The maximum CT number variation between different phantom sizes was lower for the HyperSight CBCT imager (∼100 HU) compared to the two other CBCT imagers (∼200 HU), but not fully comparable to CT (∼50 HU).Significance. This study demonstrated the imaging performance of the new HyperSight CBCT imager and the potential of applying this CBCT system in more advanced scenarios by comparing the quality against fan-beam CT.

ECG signal quality in intermittent long-term dry electrode recordings with controlled motion artifacts.

Wearable long-term monitoring applications are becoming more and more popular in both the consumer and the medical market. In wearable ECG monitoring, the data quality depends on the properties of the electrodes and on how they interface with the skin. Dry electrodes do not require any action from the user. They usually do not irritate the skin, and they provide sufficiently high-quality data for ECG monitoring purposes during low-intensity user activity. We investigated prospective motion artifact-resistant dry electrode materials for wearable ECG monitoring. The tested materials were (1) porous: conductive polymer, conductive silver fabric; and (2) solid: stainless steel, silver, and platinum. ECG was acquired from test subjects in a 10-min continuous settling test and in a 48-h intermittent long-term test. In the settling test, the electrodes were stationary, whereas both stationary and controlled motion artifact tests were included in the long-term test. The signal-to-noise ratio (SNR) was used as the figure of merit to quantify the results. Skin-electrode interface impedance was measured to quantify its effect on the ECG, as well as to leverage the dry electrode ECG amplifier design. The SNR of all electrode types increased during the settling test. In the long-term test, the SNR was generally elevated further. The introduction of electrode movement reduced the SNR markedly. Solid electrodes had a higher SNR and lower skin-electrode impedance than porous electrodes. In the stationary testing, stainless steel showed the highest SNR, followed by platinum, silver, conductive polymer, and conductive fabric. In the movement testing, the order was platinum, stainless steel, silver, conductive polymer, and conductive fabric.

Effect of different cone beam computed tomography settings on artifact production in titanium and zirconia dental implants: An in vitro study.

BACKGROUND: The use of dental implants in the treatment of edentulous patients is increasing. Zirconia implants are an alternative to titanium implants, offering advantages in terms of aesthetics and biological compatibility. However, the number of artifacts observed on radiographic images with zirconia implants compared to titanium implants is yet to be determined. OBJECTIVES: The purpose of this study was to evaluate the impact of different cone-beam computed tomography (CBCT) parameters on the production of artifacts in zirconia and titanium implants. MATERIAL AND METHODS: A dry human mandible was coated with wax to simulate human soft tissues and examined. Subsequently, titanium and zirconia implants were placed at the same points in the posterior region of the mandible. The production of artifacts on CBCT scans was evaluated using 2 parameters. The first parameter, the standard deviation within the region of interest (SDROI), is based on a comparison of the gray levels at implant and control areas. The second parameter was the contrast-to-noise ratio (CNR), which was evaluated for different protocols created by various combinations of the field of view (FOV) area, milliampere [mA] intensity and metal artifact reduction (MAR) programs. RESULTS: The study found that zirconia implants produced more artifacts than titanium implants. However, the production of artifacts in zirconia implants could be significantly reduced by increasing the mA values, performing CBCT scans with smaller FOV areas, and enabling MAR programs. CONCLUSIONS: The production of artifacts is a disadvantage of zirconia implants, but this drawback can be mitigated by selecting appropriate protocols for the CBCT device.

Estimate and compensate head motion in non-contrast head CT scans using partial angle reconstruction and deep learning.

BACKGROUND: Patient head motion is a common source of image artifacts in computed tomography (CT) of the head, leading to degraded image quality and potentially incorrect diagnoses. The partial angle reconstruction (PAR) means dividing the CT projection into several consecutive angular segments and reconstructing each segment individually. Although motion estimation and compensation using PAR has been developed and investigated in cardiac CT scans, its potential for reducing motion artifacts in head CT scans remains unexplored. PURPOSE: To develop a deep learning (DL) model capable of directly estimating head motion from PAR images of head CT scans and to integrate the estimated motion into an iterative reconstruction process to compensate for the motion. METHODS: Head motion is considered as a rigid transformation described by six time-variant variables, including the three variables for translation and three variables for rotation. Each motion variable is modeled using a B-spline defined by five control points (CP) along time. We split the full projections from 360° into 25 consecutive PARs and subsequently input them into a convolutional neural network (CNN) that outputs the estimated CPs for each motion variable. The estimated CPs are used to calculate the object motion in each projection, which are incorporated into the forward and backprojection of an iterative reconstruction algorithm to reconstruct the motion-compensated image. The performance of our DL model is evaluated through both simulation and phantom studies. RESULTS: The DL model achieved high accuracy in estimating head motion, as demonstrated in both the simulation study (mean absolute error (MAE) ranging from 0.28 to 0.45 mm or degree across different motion variables) and the phantom study (MAE ranging from 0.40 to 0.48 mm or degree). The resulting motion-corrected image, I D L , P A R ${I}_{DL,\ PAR}$ , exhibited a significant reduction in motion artifacts when compared to the traditional filtered back-projection reconstructions, which is evidenced both in the simulation study (image MAE drops from 178 ± $ \pm $ 33HU to 37 ± $ \pm $ 9HU, structural similarity index (SSIM) increases from 0.60 ± $ \pm $ 0.06 to 0.98 ± $ \pm $ 0.01) and the phantom study (image MAE drops from 117 ± $ \pm $ 17HU to 42 ± $ \pm $ 19HU, SSIM increases from 0.83 ± $ \pm $ 0.04 to 0.98 ± $ \pm $ 0.02). CONCLUSIONS: We demonstrate that using PAR and our proposed deep learning model enables accurate estimation of patient head motion and effectively reduces motion artifacts in the resulting head CT images.

A Wavelet-Based Approach for Motion Artifact Reduction in Ambulatory Seismocardiography.

Wearable sensing has become a vital approach to cardiac health monitoring, and seismocardiography (SCG) is emerging as a promising technology in this field. However, the applicability of SCG is hindered by motion artifacts, including those encountered in practice of which the strongest source is walking. This holds back the translation of SCG to clinical settings. We therefore investigated techniques to enhance the quality of SCG signals in the presence of motion artifacts. To simulate ambulant recordings, we corrupted a clean SCG dataset with real-walking-vibrational noise. We decomposed the signal using several empirical-mode-decomposition methods and the maximum overlap discrete wavelet transform (MODWT). By combining MODWT, time-frequency masking, and nonnegative matrix factorization, we developed a novel algorithm which leveraged the vertical axis accelerometer to reduce walking vibrations in dorsoventral SCG. The accuracy and applicability of our method was verified using heart rate estimation. We used an interactive selection approach to improve estimation accuracy. The best decomposition method for reduction of motion artifact noise was the MODWT. Our algorithm improved heart rate estimation from 0.1 to 0.8 r-squared at -15 dB signal-to-noise ratio (SNR). Our method reduces motion artifacts in SCG signals up to a SNR of -19 dB without requiring any external assistance from electrocardiography (ECG). Such a standalone solution is directly applicable to the usage of SCG in daily life, as a content-rich replacement for other wearables in clinical settings, and other continuous monitoring scenarios. In applications with higher noise levels, ECG may be incorporated to further enhance SCG and extend its usable range. This work addresses the challenges posed by motion artifacts, enabling SCG to offer reliable cardiovascular insights in more difficult scenarios, and thereby facilitating wearable monitoring in daily life and the clinic.

AcquisitionFocus: Joint Optimization of Acquisition Orientation and Cardiac Volume Reconstruction Using Deep Learning.

In cardiac cine imaging, acquiring high-quality data is challenging and time-consuming due to the artifacts generated by the heart's continuous movement. Volumetric, fully isotropic data acquisition with high temporal resolution is, to date, intractable due to MR physics constraints. To assess whole-heart movement under minimal acquisition time, we propose a deep learning model that reconstructs the volumetric shape of multiple cardiac chambers from a limited number of input slices while simultaneously optimizing the slice acquisition orientation for this task. We mimic the current clinical protocols for cardiac imaging and compare the shape reconstruction quality of standard clinical views and optimized views. In our experiments, we show that the jointly trained model achieves accurate high-resolution multi-chamber shape reconstruction with errors of <13 mm HD95 and Dice scores of >80%, indicating its effectiveness in both simulated cardiac cine MRI and clinical cardiac MRI with a wide range of pathological shape variations.

Evaluation and Utilization of Flow Artifacts at CT.

Flow artifacts are commonly encountered at contrast-enhanced CT and can be difficult to discern from true pathologic conditions. Therefore, radiologists must be comfortable distinguishing flow artifacts from true pathologic conditions. This is of particular importance when evaluating the pulmonary arteries and aorta, as a flow artifact may be mistaken for a pulmonary embolism or dissection flap. Understanding the mechanics of flow artifacts and how these artifacts are created can help radiologists in several ways. First, this knowledge can help radiologists appreciate how the imaging characteristics of flow artifacts differ from true pathologic conditions. This information can also help radiologists better recognize the clinical conditions that predispose patients to flow artifacts, such as pneumonia, chronic lung damage, and altered cardiac output. By understanding when flow artifacts may be confounding the interpretation of an examination, radiologists can then know when to pursue other troubleshooting methods to assist with the diagnosis. In these circumstances, the radiologist can consider several troubleshooting methods, including adjusting the imaging protocols, recommending when additional imaging may be helpful, and suggesting which imaging study would be the most beneficial. Finally, flow artifacts can also be used as a diagnostic tool when evaluating the vascular anatomy, examples of which include the characterization of shunts, venous collaterals, intimomedial flaps, and alternative patterns of blood flow, as seen in extracorporeal membrane oxygenation circuits. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material.

Simulating rigid head motion artifacts on brain magnitude MRI data-Outcome on image quality and segmentation of the cerebral cortex.

Magnetic Resonance Imaging (MRI) datasets from epidemiological studies often show a lower prevalence of motion artifacts than what is encountered in clinical practice. These artifacts can be unevenly distributed between subject groups and studies which introduces a bias that needs addressing when augmenting data for machine learning purposes. Since unreconstructed multi-channel k-space data is typically not available for population-based MRI datasets, motion simulations must be performed using signal magnitude data. There is thus a need to systematically evaluate how realistic such magnitude-based simulations are. We performed magnitude-based motion simulations on a dataset (MR-ART) from 148 subjects in which real motion-corrupted reference data was also available. The similarity of real and simulated motion was assessed by using image quality metrics (IQMs) including Coefficient of Joint Variation (CJV), Signal-to-Noise-Ratio (SNR), and Contrast-to-Noise-Ratio (CNR). An additional comparison was made by investigating the decrease in the Dice-Sørensen Coefficient (DSC) of automated segmentations with increasing motion severity. Segmentation of the cerebral cortex was performed with 6 freely available tools: FreeSurfer, BrainSuite, ANTs, SAMSEG, FastSurfer, and SynthSeg+. To better mimic the real subject motion, the original motion simulation within an existing data augmentation framework (TorchIO), was modified. This allowed a non-random motion paradigm and phase encoding direction. The mean difference in CJV/SNR/CNR between the real motion-corrupted images and our modified simulations (0.004±0.054/-0.7±1.8/-0.09±0.55) was lower than that of the original simulations (0.015±0.061/0.2±2.0/-0.29±0.62). Further, the mean difference in the DSC between the real motion-corrupted images was lower for our modified simulations (0.03±0.06) compared to the original simulations (-0.15±0.09). SynthSeg+ showed the highest robustness towards all forms of motion, real and simulated. In conclusion, reasonably realistic synthetic motion artifacts can be induced on a large-scale when only magnitude MR images are available to obtain unbiased data sets for the training of machine learning based models.

Multi-Scale Masked Autoencoders for Cross-Session Emotion Recognition.

Affective brain-computer interfaces (aBCIs) have garnered widespread applications, with remarkable advancements in utilizing electroencephalogram (EEG) technology for emotion recognition. However, the time-consuming process of annotating EEG data, inherent individual differences, non-stationary characteristics of EEG data, and noise artifacts in EEG data collection pose formidable challenges in developing subject-specific cross-session emotion recognition models. To simultaneously address these challenges, we propose a unified pre-training framework based on multi-scale masked autoencoders (MSMAE), which utilizes large-scale unlabeled EEG signals from multiple subjects and sessions to extract noise-robust, subject-invariant, and temporal-invariant features. We subsequently fine-tune the obtained generalized features with only a small amount of labeled data from a specific subject for personalization and enable cross-session emotion recognition. Our framework emphasizes: 1) Multi-scale representation to capture diverse aspects of EEG signals, obtaining comprehensive information; 2) An improved masking mechanism for robust channel-level representation learning, addressing missing channel issues while preserving inter-channel relationships; and 3) Invariance learning for regional correlations in spatial-level representation, minimizing inter-subject and inter-session variances. Under these elaborate designs, the proposed MSMAE exhibits a remarkable ability to decode emotional states from a different session of EEG data during the testing phase. Extensive experiments conducted on the two publicly available datasets, i.e., SEED and SEED-IV, demonstrate that the proposed MSMAE consistently achieves stable results and outperforms competitive baseline methods in cross-session emotion recognition.

The wooden artifacts from Schöningen's Spear Horizon and their place in human evolution.

Ethnographic records show that wooden tools played a pivotal role in the daily lives of hunter-gatherers including food procurement tools used in hunting (e.g., spears, throwing sticks) and gathering (e.g. digging sticks, bark peelers), as well as, domestic tools (e.g., handles, vessels). However, wood rarely survives in the archeological record, especially in Pleistocene contexts and knowledge of prehistoric hunter-gatherer lifeways is strongly biased by the survivorship of more resilient materials such as lithics and bones. Consequently, very few Paleolithic sites have produced wooden artifacts and among them, the site of Schöningen stands out due to its number and variety of wooden tools. The recovery of complete wooden spears and throwing sticks at this 300,000-y-old site (MIS 9) led to a paradigm shift in the hunter vs. scavenger debate. For the first time and almost 30 y after their discovery, this study introduces the complete wooden assemblage from Schöningen 13 II-4 known as the Spear Horizon. In total, 187 wooden artifacts could be identified from the Spear Horizon demonstrating a broad spectrum of wood-working techniques, including the splitting technique. A minimum of 20 hunting weapons is now recognized and two newly identified artifact types comprise 35 tools made on split woods, which were likely used in domestic activities. Schöningen 13 II-4 represents the largest Pleistocene wooden artifact assemblage worldwide and demonstrates the key role woodworking had in human evolution. Finally, our results considerably change the interpretation of the Pleistocene lakeshore site of Schöningen.

EEG complexity measures for detecting mind wandering during video-based learning.

This study explores the efficacy of various EEG complexity measures in detecting mind wandering during video-based learning. Employing a modified probe-caught method, we recorded EEG data from participants engaged in viewing educational videos and subsequently focused on the discrimination between mind wandering (MW) and non-MW states. We systematically investigated various EEG complexity metrics, including metrics that reflect a system's regularity like multiscale permutation entropy (MPE), and metrics that reflect a system's dimensionality like detrended fluctuation analysis (DFA). We also compare these features to traditional band power (BP) features. Data augmentation methods and feature selection were applied to optimize detection accuracy. Results show BP features excelled (mean area under the receiver operating characteristic curve (AUC) 0.646) in datasets without eye-movement artifacts, while MPE showed similar performance (mean AUC 0.639) without requiring removal of eye-movement artifacts. Combining all kinds of features improved decoding performance to 0.66 mean AUC. Our findings demonstrate the potential of these complexity metrics in EEG analysis for mind wandering detection, highlighting their practical implications in educational contexts.

[Features of detection and interpretation of intravital and postmortem changes according to the results of traditional X-ray and X-ray computed tomography of objects from historical graves and artefacts]. / Osobennosti vyyavleniya i interpretatsii prizhiznennykh i posmertnykh izmenenii po rezul'tatam traditsionnoi rentgenografii i rentgenovskoi komp'yuternoi tomografii ob"ektov iz istoricheskikh zakhoronenii i artefaktov.

OBJECTIVE: To study emergence mechanism, physical nature, pattern of intravital and postmortem changes of biological and non-biological objects originated in the period from 1550 to 1918 yr. using traditional X-ray and X-ray computed tomography. MATERIAL AND METHODS: The relics of Saint Macarius the Roman of Novgorod, the remains of the First Reverend of the Resurrection Novodevichy Convent in Saint Petersburg Mother Superior Theophania, damages on the chair leg on which Tsesarevich Alexey sat during the shooting of Russian Emperor Nicholas II, his family and entourage in 1918 in Yekaterinburg were stidued. RESULTS AND CONCLUSION: The application of highly informative methods of traditional X-ray and X-ray computed tomography of biological and non-biological objects showed their high informativity and allowed to correctly interpret the emergence mechanism, physical nature, pattern of intravital and postmortem changes of skeleton bones and historical artefact (chair legs) originated long ago. The necessity of special professional training and advanced training of experts in forensic radiology to prevent possible diagnostic and expert errors has been substantiated.

Pixel-by-pixel correction of beam hardening artifacts by bowtie filter in fan-beam CT.

Objective. Beam hardening (BH) artifacts in computed tomography (CT) images originate from the polychromatic nature of x-ray photons. In a CT system with a bowtie filter, residual BH artifacts remain when polynomial fits are used. These artifacts lead to worse visuals, reduced contrast, and inaccurate CT numbers. This work proposes a pixel-by-pixel correction (PPC) method to reduce the residual BH artifacts caused by a bowtie filter.Approach. The energy spectrum for each pixel at the detector after the photons pass through the bowtie filter was calculated. Then, the spectrum was filtered through a series of water slabs with different thicknesses. The polychromatic projection corresponding to the thickness of the water slab for each detector pixel could be obtained. Next, we carried out a water slab experiment with a mono energyE= 69 keV to get the monochromatic projection. The polychromatic and monochromatic projections were then fitted with a 2nd-order polynomial. The proposed method was evaluated on digital phantoms in a virtual CT system and phantoms in a real CT machine.Main results. In the case of a virtual CT system, the standard deviation of the line profile was reduced by 23.8%, 37.3%, and 14.3%, respectively, in the water phantom with different shapes. The difference of the linear attenuation coefficients (LAC) in the central and peripheral areas of an image was reduced from 0.010 to 0.003cm-1and 0.007cm-1to 0 in the biological tissue phantom and human phantom, respectively. The method was also validated using CT projection data obtained from Activion16 (Canon Medical Systems, Japan). The difference in the LAC in the central and peripheral areas can be reduced by a factor of two.Significance. The proposed PPC method can successfully remove the cupping artifacts in both virtual and authentic CT images. The scanned object's shapes and materials do not affect the technique.