Common Artifacts Research Articles

An Open-Source Toolkit That Powers the Genome-Wide Analysis of Mature B-Cell Lymphomas (GAMBL) Project

Introduction: Genome- and transcriptome-wide analyses continue to enhance our understanding of the molecular pathogenesis of cancer. In lymphomas, this has enabled the identification of hundreds of recurrently mutated genes, highlighting genetic heterogeneity and relationships both within and among clinical entities. While the growing availability of lymphoma genomic data sets can be leveraged to integrate genomic analyses into diagnostic testing and clinical trials, the ability to rapidly process genomic data sets in a reproducible manner serves as a barrier to this goal. To this end, we developed a suite of tools Lymphoid Cancer Research modules (LCR-modules) to facilitate the discovery of novel drivers and molecular features in lymphoma cancers and perform quantitative comparisons between disease entities. We demonstrate here how this toolkit enabled a meta-analysis of lymphoma genomic data involving genome-wide profiles of 3330 patients.Methods: We assembled a collection of whole genome, whole exome, and RNA sequencing data from a combination of controlled-access repositories and ongoing projects at BC Cancer. The scope of genomic analysis of mature B-cell lymphomas (GAMBL) project includes cell lines and patient tumors from all common mature B cell neoplasms, comprising a total of 4612 samples from 3330 patients. To facilitate the project, we developed a suite of open-source and custom bioinformatics tools (https://github.com/LCR-BCCRC/lcr-modules) that leverages the Snakemake workflow management system and includes lymphoma-centric modules for the discovery and annotation of common mutation types, analysis of B-cell receptor repertoires and discovery of novel aSHM targets and relevant non-coding mutations, and RNA-seq analysis with batch correction and normalization. Individual modules are configured to create an automated, scalable, and reproducible workflow that runs each step as dictated by the availability of new data. The cohort-level integrative analysis and comparisons across entities are handled by our custom R package GAMBLR, which facilitates open-ended data analysis and custom visualizations.Results: Simple somatic mutations (SSM) were detected using a workflow that utilizes four algorithms to identify high-confidence variants with validated default thresholds for filtering of germline variants and common FFPE-associated artifacts, allowing for processing of samples without matched normal tissue. This automated and reproducible workflow facilitated the discovery of novel genes significantly mutated across lymphomas and broadened our understanding of the scope of aberrant somatic hypermutation (aSHM) and other non-coding mutations. Specifically, HNRNPU, STAT3, TFAP4, RRAGC were found to be mutated at relatively low frequencies, and their presence is a distinct feature of certain lymphomas or novel genetic subgroups within lymphoma types (Figure 1A).The aSHM analysis and discovery of novel hypermutated regions is handled by a custom tool Rainstorm. As a result, we were able to detect sites preferentially hypermutated in a single entity, such as the transcription start site of BACH2, mutated at lower rates than the other common target sites but significantly more in BL compared to other entities (Figure 1B). Combining aSHM at target sites discovered using our toolkit with other genetic features allowed us to explore and establish novel genetic subgroups within Burkitt lymphoma and follicular lymphoma.SV analysis can be conducted using Manta, GRIDSS, and JaBbA modules with downstream processing in GAMBLR. In B-cell lymphomas, the most common SVs identified using the automated workflow were targeting MYC, BCL2, and CCND1. Unsurprisingly, the most common translocation partner among B-cell lymphomas was the immunoglobulin heavy chain, but the novel BCL6-FOXP1, CD274-BACH2, BCL6-RHOH translocations in DLBCLs and MYC-BCL6 translocations in BLs were identified, among others (Figure 1C).Conclusions: We present here the modularized workflow for scalable and automated analysis of genomic and transcriptomic data and demonstrate that it can be successfully deployed across thousands of tumour samples for the discovery of known and novel lymphoma biology. This represents an important advancement in reproducibility that will facilitate clinical translation of genomic discoveries. [Display omitted] DisclosuresGrande: Sage Bionetworks: Current Employment. Coyle: Allakos, Inc.: Consultancy. Steidl: AbbVie: Consultancy; Trillium Therapeutics: Research Funding; Epizyme: Research Funding; Seattle Genetics: Consultancy; Curis Inc.: Consultancy; Bayer: Consultancy; Bristol-Myers Squibb: Research Funding. Scott: Abbvie: Consultancy; NanoString Technologies: Patents & Royalties: Patent describing measuring the proliferation signature in MCL using gene expression profiling.; Celgene: Consultancy; AstraZeneca: Consultancy; Incyte: Consultancy; Janssen: Consultancy, Research Funding; Rich/Genentech: Research Funding; BC Cancer: Patents & Royalties: Patent describing assigning DLBCL COO by gene expression profiling–licensed to NanoString Technologies. Patent describing measuring the proliferation signature in MCL using gene expression profiling. . Morin: Epizyme: Patents & Royalties; Celgene: Consultancy; Foundation for Burkitt Lymphoma Research: Membership on an entity's Board of Directors or advisory committees.

BACILLARY LAYER DETACHMENT: MULTIMODAL IMAGING AND HISTOLOGIC EVIDENCE OF A NOVEL OPTICAL COHERENCE TOMOGRAPHY TERMINOLOGY: Literature Review and Proposed Theory.

To clarify the histologic basis of bacillary layer detachment (BALAD) through a review of the current literature and an analysis of retinal imaging. The literature for previous reports of BALAD were reviewed. An analysis of retinal images was performed to support anatomical conclusions. A total of 164 unique patients with BALAD on optical coherence tomography (OCT) were identified from the published literature. Twenty-two underlying etiologies, all associated with subretinal exudation, were identified. Forty-one different OCT terminologies were found. The defining OCT feature of BALAD was a split at the level of the photoreceptor inner segment myoid creating a distinctive intraretinal cavity. Resolution of BALAD was followed by a rapid restoration of the ellipsoid zone. Histology of age-related macular degeneration eyes suggests that individual photoreceptors can shed inner segments. Furthermore, detachment of the entire layer of inner segments is a common postmortem artifact. It is proposed that BALAD occurs when outwardly directed forces promoting attachment of photoreceptor outer segments to the retinal pigment epithelium exceed the tensile strength of the photoreceptor inner segment myoid. This review serves to strengthen the OCT nomenclature "bacillary layer detachment," based on specific reflectance information obtained by OCT and previously published histologic observations.

Using a Blur Metric to Estimate Linear Motion Blur Parameters.

Motion blur is a common artifact in image processing, specifically in e-health services, which is caused by the motion of a camera or scene. In linear motion cases, the blur kernel, i.e., the function that simulates the linear motion blur process, depends on the length and direction of blur, called linear motion blur parameters. The estimation of blur parameters is a vital and sensitive stage in the process of reconstructing a sharp version of a motion blurred image, i.e., image deblurring. The estimation of blur parameters can also be used in e-health services. Since medical images may be blurry, this method can be used to estimate the blur parameters and then take an action to enhance the image. In this paper, some methods are proposed for estimating the linear motion blur parameters based on the extraction of features from the given single blurred image. The motion blur direction is estimated using the Radon transform of the spectrum of the blurred image. To estimate the motion blur length, the relation between a blur metric, called NIDCT (Noise-Immune Discrete Cosine Transform-based), and the motion blur length is applied. Experiments performed in this study showed that the NIDCT blur metric and the blur length have a monotonic relation. Indeed, an increase in blur length leads to increase in the blurriness value estimated via the NIDCT blur metric. This relation is applied to estimate the motion blur. The efficiency of the proposed method is demonstrated by performing some quantitative and qualitative experiments.

Magnetic resonance cholangiopancreatography: pitfalls in interpretation.

Magnetic resonance cholangiopancreatography (MRCP) has become a widely accepted noninvasive diagnostic tool in the assessment of pancreatic and biliary disease. MRCP essentially exploits extended T2 relaxation times of slow-moving fluid and delineates the outline of biliary and pancreatic ducts on T2-weighted images. In order to maximize the clinical implication of MRCP, it is of utmost importance for radiologists to optimize the acquisition technique, be aware of patient-related factors and physiologic changes than can affect its performance and interpretation. It is critical to understand the most common artifacts and pitfalls encountered during acquisition and interpretation of MRCP. We provide a general overview of the different pitfalls encountered in MRCP and pearls on how to manage them in real-world practice.

Wide-Field Pixel Super-Resolution Colour Lensfree Microscope for Digital Pathology.

Whole slide imaging enables scanning entire stained-glass slides with high resolution into digital images for the tissue morphology/molecular pathology assessment and analysis, which has increased in adoption for both clinical and research applications. As an alternative to conventional optical microscopy, lensfree holography imaging, which offers high resolution and a wide field of view (FOV) with digital focus, has been widely used in various types of biomedical imaging. However, accurate colour holographic imaging with pixel super-resolution reconstruction has remained a great challenge due to its coherent characteristic. In this work, we propose a wide-field pixel super-resolution colour lensfree microscopy by performing wavelength scanning pixel super-resolution and phase retrieval simultaneously on the three channels of red, green and blue (RGB), respectively. High-resolution RGB three-channel composite colour image is converted to the YUV space for separating the colour component and the brightness component, keeping the brightness component unchanged as well as enhancing the colour component through average filter, which not only eliminates the common rainbow artifacts of holographic colour reconstruction but also maintains the high-resolution details collected under different colour illuminations. We conducted experiments on the reconstruction of a USAF1951, stained lotus root and red bone marrow smear for performance evaluation of the spatial resolution and colour reconstruction with an imaging FOV >40 mm2.

Report on UG[formula omitted] challenge Track 1: Assessing algorithms to improve video object detection and classification from unconstrained mobility platforms

How can we effectively engineer a computer vision system that is able to interpret videos from unconstrained mobility platforms like UAVs? One promising option is to make use of image restoration and enhancement algorithms from the area of computational photography to improve the quality of the underlying frames in a way that also improves automatic visual recognition. Along these lines, exploratory work is needed to find out which image pre-processing algorithms, in combination with the strongest features and supervised machine learning approaches, are good candidates for difficult scenarios like motion blur, weather, and mis-focus — all common artifacts in UAV acquired images. This paper summarizes the protocols and results of Track 1 of the UG2+ Challenge held in conjunction with IEEE/CVF CVPR 2019. The challenge looked at two separate problems: (1) object detection improvement in video, and (2) object classification improvement in video. The challenge made use of new protocols for the UG2 (UAV, Glider, Ground) dataset, which is an established benchmark for assessing the interplay between image restoration and enhancement and visual recognition. In total, 16 algorithms were submitted by academic and corporate teams, and a detailed analysis of them is reported here.

A Novel Method Based on Combination of Independent Component Analysis and Ensemble Empirical Mode Decomposition for Removing Electrooculogram Artifacts From Multichannel Electroencephalogram Signals

Electrooculogram (EOG) is one of common artifacts in recorded electroencephalogram (EEG) signals. Many existing methods including independent component analysis (ICA) and wavelet transform were applied to eliminate EOG artifacts but ignored the possible impact of the nature of EEG signal. Therefore, the removal of EOG artifacts still faces a major challenge in EEG research. In this paper, the ensemble empirical mode decomposition (EEMD) and ICA algorithms were combined to propose a novel EEMD-based ICA method (EICA) for removing EOG artifacts from multichannel EEG signals. First, the ICA method was used to decompose original EEG signals into multiple independent components (ICs), and the EOG-related ICs were automatically identified through the kurtosis method. Then, by performing the EEMD algorithm on EOG-related ICs, the intrinsic mode functions (IMFs) linked to EOG were discriminated and eliminated. Finally, artifact-free IMFs were projected to obtain the ICs without EOG artifacts, and the clean EEG signals were ultimately reconstructed by the inversion of ICA. Both EOGs correction from simulated EEG signals and real EEG data were studied, which verified that the proposed method could achieve an improved performance in EOG artifacts rejection. By comparing with other existing approaches, the EICA obtained the optimal performance with the highest increase in signal-to-noise ratio and decrease in root mean square error and correlation coefficient after EOG artifacts removal, which demonstrated that the proposed method could more effectively eliminate blink artifacts from multichannel EEG signals with less error influence. This study provided a novel promising method to eliminate EOG artifacts with high performance, which is of great importance for EEG signals processing and analysis.

Initial insights into ceramic production and exchange at the early Bronze Age citadel at Shimao, Shaanxi, China

Over the last decade, excavations at the late Neolithic to early Bronze Age site of Shimao in northern Shaanxi Province have transformed our understanding of the archaeology of early China. What was previously seen as an area that was peripheral to the development of early dynastic centers in northern China is now being heralded by some scholars as a precursor of Chinese civilization. However, despite incredible finds of large-scale stone architecture, bronze working, thousands of jade artifacts, and elaborate stone carvings, our overall understanding of the economic and political organization of the inhabitants of Shimao is still very limited. In this study we examine the most common artifact class at the site, pottery, using petrographic analysis, in order to explore production methods as well as potential production organization and exchange. As our results demonstrate, most of the pottery used at Shimao was likely produced locally, potentially by multiple production groups at or near the site, but the ceramics were not particularly standardized in regard to paste recipes. These results likely reflect that ceramic production was not tightly controlled or formalized, but instead took place in local households or workshops, much like ceramic production in many other parts of northern China at the time.

On the Replica of US Pulmonary Artifacts by Means of Physical Models

Currently, the diagnostic value of the artefactual information provided by lung ultrasound images is widely recognized by physicians. In particular, the existence of a correlation between the visual characteristics of the vertical artifacts, which arise from the pleura line, and the genesis (pneumogenic or cardiogenic) of a pulmonary disorder is commonly accepted. Physicians distinguish vertical artifacts from vertical artifacts which extend to the bottom of the screen (B-lines) and common vertical artifacts from well-structured artifacts (modulated B-lines). However, the link between these visual characteristics and the causes which determine them is still unclear. Moreover, the distinction between short and long artifacts and the distinction between common and structured artifacts are not on/off, and their classification can be critical. In order to derive further information from the visual inspection of the vertical artifacts, the mechanisms which control the artifact formation must be identified. In this paper, the link between the visual characteristics of the vertical artifacts (the observed effect) and the distribution of the aerated spaces at the pleural level (the cause) is addressed. Plausible mechanisms are suggested and illustrated through experimental results.

Ensemble averaging laser speckle contrast imaging: statistical model of improvement as function of static scatterers.

The appearance of the common artifacts of laser speckle contrast imaging (LSCI), namely the granularity in flow rate estimation caused by static scatterers, is a well-known phenomenon. This artifact can be greatly reduced in spatial speckle contrast calculation using interframe decorrelated illumination, forcing true ensemble averaging. We propose a statistical model, which describes the effect of multiple image acquisitions on the contrast map quality when the illumination stable and when the illumination is decorrelated frame by frame. We investigate the improvement as a function of the ratio of dynamic and static scatterers by formulating a statistical distribution based model, using in simulation, flow phantom and in vivo experiments. Our main finding is that the ensemble averaging yields limited improvement in several practical cases due to the highly heterogeneous scatterer structure of living tissues.

Excitation light-induced phototoxicity during fluorescence imaging

Fluorescence microscopy has revolutionized life science research. It is a powerful technique to visualize molecular structures, physiological functions, and dynamic processes in living cells, tissues, and organisms. Excitation-light and/or excited-fluorescent proteins-mediated dysregulation in cell physiology are widely known but poorly considered. Although there are vast applications of fluorescence microscopy in research, imaging results may suffer from the excitation-light-induced artefacts. Here, we highlight potential excitation light-induced alterations in cell functions thereby misinterpretation of imaging results and strategies to minimize common artefacts that can be produced during imaging experiments.

Geometric rectification‐based neural network architecture for image manipulation detection

Determination of image authenticity usually requires the identification and localization of the manipulated regions of images. Hence, image manipulation detection has become one of the most important tasks in the field of multimedia forensics. Recently, Convolutional Neural Networks (CNNs) have achieved promising performance in image manipulation detection. However, it is hard for the existing CNN-based manipulation detection approaches to accurately identify and localize the manipulated regions that have undergone geometric transformations, since CNNs are limited by their inability to be geometrically invariant. To address this issue, we propose a geometric rectification-based neural network architecture for image manipulation detection. In this type of network architecture, following the detection of a set of potential manipulated regions (PMRs) using Region Proposal Network, the Spatial Transformer Network is employed to geometrically rectify the convolutional feature maps (CFMs) of these regions to obtain the geometrically rectified CFMs (GR-CFMs). Subsequently, the residual feature maps (RFMs) are computed to capture the characteristic inconsistency between the CFMs and GR-CFMs of each PMR. Finally, the computed RFMs are automatically integrated with the GR-CFMs by a designed attention module to determine whether each PMR is a manipulated region and to localize the manipulated part at the pixel-level. Extensive experiments on the public data set as well as on our challenging data set demonstrate that the proposed network architecture achieves desirable performance in identifying and localizing regions with common tampering artifacts, which involve geometric transformations.

Determination of Dynamic Brain Connectivity via Spectral Analysis.

Spectral analysis based on neural field theory is used to analyze dynamic connectivity via methods based on the physical eigenmodes that are the building blocks of brain dynamics. These approaches integrate over space instead of averaging over time and thereby greatly reduce or remove the temporal averaging effects, windowing artifacts, and noise at fine spatial scales that have bedeviled the analysis of dynamical functional connectivity (FC). The dependences of FC on dynamics at various timescales, and on windowing, are clarified and the results are demonstrated on simple test cases, demonstrating how modes provide directly interpretable insights that can be related to brain structure and function. It is shown that FC is dynamic even when the brain structure and effective connectivity are fixed, and that the observed patterns of FC are dominated by relatively few eigenmodes. Common artifacts introduced by statistical analyses that do not incorporate the physical nature of the brain are discussed and it is shown that these are avoided by spectral analysis using eigenmodes. Unlike most published artificially discretized “resting state networks” and other statistically-derived patterns, eigenmodes overlap, with every mode extending across the whole brain and every region participating in every mode—just like the vibrations that give rise to notes of a musical instrument. Despite this, modes are independent and do not interact in the linear limit. It is argued that for many purposes the intrinsic limitations of covariance-based FC instead favor the alternative of tracking eigenmode coefficients vs. time, which provide a compact representation that is directly related to biophysical brain dynamics.

Image- versus histogram-based considerations in semantic segmentation of pulmonary hyperpolarized gas images.

To characterize the differences between histogram-based and image-based algorithms for segmentation of hyperpolarized gas lung images. Four previously published histogram-based segmentation algorithms (ie, linear binning, hierarchical k-means, fuzzy spatial c-means, and a Gaussian mixture model with a Markov random field prior) and an image-based convolutional neural network were used to segment 2 simulated data sets derived from a public (n = 29 subjects) and a retrospective collection (n = 51 subjects) of hyperpolarized 129Xe gas lung images transformed by common MRI artifacts (noise and nonlinear intensity distortion). The resulting ventilation-based segmentations were used to assess algorithmic performance and characterize optimization domain differences in terms of measurement bias and precision. Although facilitating computational processing and providing discriminating clinically relevant measures of interest, histogram-based segmentation methods discard important contextual spatial information and are consequently less robust in terms of measurement precision in the presence of common MRI artifacts relative to the image-based convolutional neural network. Direct optimization within the image domain using convolutional neural networks leverages spatial information, which mitigates problematic issues associated with histogram-based approaches and suggests a preferred future research direction. Further, the entire processing and evaluation framework, including the newly reported deep learning functionality, is available as open source through the well-known Advanced Normalization Tools ecosystem.

Electric Fence Artifact on Ambulatory EEG and Review of Common EEG Electrical Artifacts

ABSTRACT Ambulatory electroencephalogram (AEEG) can be a cost-effective and valuable alternative to in-patient long-term EEG monitoring. A potential benefit of AEEG is that it allows monitoring in the patient’s unique home environment. While this can be more affordable and convenient for the patient, it can also present unique challenges for the reviewer. Unlike long-term monitoring in an epilepsy unit, the AEEG recording occurs in a less controlled environment and most often without immediate EEG technical assistance during the recording. As a result, unique EEG artifacts can occur with AEEG. Their recognition and correct interpretation are crucial for proper EEG analysis. This report presents a case of a patient who underwent a 72-hour AEEG to evaluate symptoms initially concerning for subclinical seizures. During the AEEG recording, the patient had a tactile encounter with an electric fence. This tactile event resulted in a unique, not previously reported, pattern clouding an otherwise normal study. By conducting a brief review of the most common non-physiologic environmental artifacts encountered in modern EEG monitoring, we aim to emphasize the importance of patient education to prevent artifactual pollution. This knowledge can facilitate planning and help avoid environmental influences that may create artifacts when recording in an uncontrolled setting.

Ultra Deep Whole Exome Sequencing of Classical Hodgkin Lymphoma (cHL) Reveals Novel Recurrent Somatic Mutations

Background: Although cHL patients who receive standard therapy have a high rate of event-free and overall survival, 5% - 10% of patients will be refractory to initial therapy and up to 1/3 will relapse. Thus, improved prognostication and identification of new treatment targets are needed. High throughput sequencing can identify recurrent somatic mutations that potentially drive lymphomagenesis and impact treatment response. However, Hodgkin-Reed-Sternberg (HRS) cells have a low (~1%) abundance in cHL biopsies, creating a challenge for comprehensive detection of somatic mutations in bulk lymphoma biopsies. We hypothesized that ultra deep exome sequencing can address this challenge, by providing a more widely available approach to HRS cell characterization compared to HRS cell purification.Methods: We performed exome sequencing on 32 fresh frozen samples from 31 cHL patients obtained prior to treatment (27) or after relapse (4) with paired normal skin samples (31). Sequencing was performed using the Illumina HiSeq platform (2 x 150bp reads) with a >1,000X median coverage goal. Sequence data was aligned to GRC38 and SNVs and INDELs were called with multiple algorithms. We employed several variant filtering strategies, including manual review, to remove common polymorphisms, sequencing artifacts, and false positives.Results: In total 7,000Gb were sequenced across all samples with an average of 1,100Gb/sample. The median depth of coverage was 1,009X for lymphoma and 926X for normal tissue. After filtering, 4,734 somatic SNVs and INDELs were identified, with an average of 33 protein-coding variants/case, excluding a single hyper-mutated case. The hyper-mutated patient had 3,684 variants, several of which are in DNA mismatch repair genes including: EXO1, MLH3, and MUS81 . We identified 296 genes with mutations observed in two or more patients. We confirmed previously identified genes with recurrent mutations in cHL (e.g., TNFAIP3 [18.8%], B2M [12.5%], XPO1 [9.4%]). We identified one B2M start loss mutation, which is consistent with other studies (Reichel et al. Blood 2015). However, our overall detection of B2M mutations was lower than previous reports (70%; Reichel et al. Blood 2015). Several recurrently mutated genes in our dataset are not well characterized in cHL and may contribute to disease development or progression. IGLL5 [21.9%] has been shown to be mutated in DLBCL and CLL, with a mutation pattern consistent with off-target activation-induced deaminase (AID) activity. ITPKB [12.5%], which functions as a negative regulator of PI3K, has been recently discussed as a tumor suppressor in DLBCL and detected in cfDNA from cHL patients. RYR1 [15.6%] encodes a Ca+ channel expressed in normal B cells, and was previously reported as recurrently mutated in DLBCL, Burkitt, and mantle cell lymphoma. Our work confirmed that components of the JAK/STAT pathway including SOCS1 [18.8%] and STAT6 [9.4%] are mutated in cHL. We also identified mutations associated with SWI/SNF chromatin remodeling complexes (e.g., BCL7A [6.3%], ARID1A [6.3%], SMARCA4 [3.1%], SMARCA2 [3.1%], SMARCD1/2 [3.1%]), which have not previously been reported as recurrently mutated in cHL. Finally, we observed mutations in 11 histone genes (e.g., HIST1H1B [6.3%]), extending the importance of histone gene mutations in lymphomas from germinal center B cells.Conclusions: Recurrent somatic mutations withincHL were identified via ultra deep exome sequencing, providing a strategy to identify somatic variation that does not require HRS cell purification. We have confirmed known recurrent genes within cHL and identified previously unreported mutated genes and pathways. These data suggest that cHL genomes harbor recurrent SNVs and INDELs that can inform new targets for cHL treatment and prognostication [Display omitted] DisclosuresMehta-Shah:Celgene: Research Funding; Verastem: Research Funding; Bristol Myers-Squibb: Research Funding. Kahl:Gilead: Consultancy; Celgene: Consultancy; ADC Therapeutics: Research Funding; Seattle Genetics: Consultancy; Genentech: Consultancy.

Common computed tomography artifact: source and avoidance

BackgroundArtifacts have significantly degraded the quality of computed tomography (CT) images, to the extent of making them unusable for diagnosis. The types of artifact that could be used are as follows: (a) streaking, which is commonly due to a discrepancy in a single measurement, (b) shading, which is due to a group of channels deviating gradually from the true measurement, (c) rings, which are due to errors in individual detector calibration and (d) distortion, which is due to helical reconstruction. It is occasionally possible to avoid scanning of a bony area, by means of changing the postion of the patient. Thus, this study aimed to evaluate the common artifacts that affect image quality and the method of correction to improve image quality.ResultsThe data were collected by distributing a questionnaire to the CT technologist at different hospitals about the most common type of artifacts in the CT images, source of artifacts and methods of correction. A total of 95 CT technologists responded to the questionnaire, which included 67% males and 33% females. Most of the participants (70%) were experienced CT technologists, and 61% of the participants had not done any subspecialty CT scan courses. The most common artifact used in the CT departments was motion artifact in brain CT (73%), and the best method to reduce motion artifact was patient preparation (87%).ConclusionsThe most common shown artifact in this study was motion artifact, and the common cause was the patient-based artifact. It is important to understand why objects occur and how they could be prevented or suppressed to improve image quality.

Frequency-Domain Optical Coherence Tomography for Intracranial Atherosclerotic Stenosis: Feasibility, Safety, and Preliminary Experience.

Background: Despite advances in non-invasive imaging, the characterization of atherosclerotic plaque remains superior with frequency-domain optical coherence tomography (FD-OCT) in the clinical coronary and experimental cerebrovascular literature. An assessment of the feasibility and safety of FD-OCT for intracranial atherosclerotic stenosis (ICAS) is desirable.Methods: We analyzed a cohort of all consecutive FD-OCT evaluations for ICAS performed at our institution from April 2017 to August 2018 (16 months) in patients who suffered from transient ischemic attack (TIA) or non-disabling stroke despite optimal medical management within 90 days of admission attributable to angiographically verified 70–99% stenosis of an intracranial artery.Results: Thirty-three patients harboring 36 lesions with an average age of (57.6 ± 7.1) years (male sex 27 cases) comprising nine cases of lesions located within the anterior circulation and 24 cases within the posterior circulation were identified. Of the 33 patients with 36 lesions, the FD-OCT imaging catheter detected 35/36 (97%) lesions except in one case in which the FD-OCT catheter failed to navigate excessively tortuous vessels, and FD-OCT images in 27 patients (81.8%) were finally obtained successful, where the target lesion was fully visible, and image quality under at least one pullback was graded 2 or 3. There were no symptomatic complications. Blood flow was the most common artifact encountered (51.9%).Conclusion: FD-OCT is safe and feasible for the assessment of ICAS in the anterior and posterior circulation. The use of diagnostic interferometry will have to be weighed against its cost, and these preliminary findings should be verified by prospective large-scale studies.

The Le Rozel footprints: snapshots of Neandertal groups in the Late Pleistocene. A combined morphometric and experimental approach

Hominin footprints represent brief moments of life and provide access to the locomotor and biological features of the individuals who left them. Because of the particular time scale that they represent and the information they provide, they make it possible to approach the size and composition of hominin groups in a different way from the methods used with the more common archaeological artefacts and osteological assemblages. Since 2012, several hundred Neandertal footprints have been discovered at Le Rozel (Normandy, France), within 5 stratigraphic sub-units dated to about 80,000 years ago. Analyses of the ichnological assemblage discovered at Le Rozel between 2012 and 2017 have identified 257 footprints, 132 of which have been digitized in 3D. This represents the largest ichnological assemblage attributed to Neandertals to date. Morphometric analyses of the 3D models of the footprints, combined with an experimental study under substrate conditions similar to those at Le Rozel, have quantified the size and composition of the groups that left them. The footprints from the most ichnologically dense stratigraphic sub-unit were left by a small group and represent 90% of children and adolescents. These results not only enhance our understanding of the Palaeolithic occupations at Le Rozel but also provide new methods and data for assessing the size and composition of Neandertal groups.

ECG Analysis for HRV Detection

In this paper an algorithm is proposed for estimation of HRV with better accuracy and results. We are making use of Auto Regressive Model (AR Model) for the estimation. Since ECG wave is also contaminated with a lot of noise such as Power Line Interference (PLI), EMG and just some common artifacts like breathing disturbance’s, so to filter out all this noise from the wave we are using Cumulant based AR model for filtering the wave. Using IoT we will later use real time ECG waves to estimate HRV.