Monitoring Frequency of Intra‐Fraction Patient Motion Using the ExacTrac System for LINAC‐based SRS Treatments

Purpose: The aim of this study was to investigate the intra‐fractional patient motion using the ExacTrac system in LINAC‐based stereotactic radiosurgery (SRS). Method: A retrospective analysis of 104 SRS patients with kilovoltage image‐guided setup (Brainlab ExacTrac) data was performed. Each patient was imaged pre‐treatment, and at two time points during treatment (1st and 2nd mid‐treatment), and bony anatomy of the skull was used to establish setup error at each time point. The datasets included the translational and rotational setup error, as well as the time period between image acquisitions. After each image acquisition, the patient was repositioned using the calculated shift to correct the setup error. Only translational errors were corrected due to the absence of a 6D treatment table. Setup time and directional shift values were analyzed to determine correlation between shift magnitudes as well as time between acquisitions. Results: The average magnitude translation was 0.64 ± 0.59 mm, 0.79 ± 0.45 mm, and 0.65 ± 0.35 mm for the pre‐treatment, 1st mid‐treatment, and 2nd mid‐treatment imaging time points. The average time from pre‐treatment image acquisition to 1st mid‐treatment image acquisition was 7.98 ± 0.45 min, from 1st to 2nd mid‐treatment image was 4.87 ± 1.96 min. The greatest translation was 3.64 mm, occurring in the pre‐treatment image. No patient had a 1st or 2nd mid‐treatment image with greater than 2 mm magnitude shifts. Conclusion: There was no correlation between patient motion over time, in direction or magnitude, and duration of treatment. The imaging frequency could be reduced to decrease imaging dose and treatment time without significant changes in patient position

Deep Learning in Alzheimer's Disease: Diagnostic Classification and Prognostic Prediction Using Neuroimaging Data

Deep learning, a state-of-the-art machine learning approach, has shown outstanding performance over traditional machine learning in identifying intricate structures in complex high-dimensional data, especially in the domain of computer vision. The application of deep learning to early detection and automated classification of Alzheimer's disease (AD) has recently gained considerable attention, as rapid progress in neuroimaging techniques has generated large-scale multimodal neuroimaging data. A systematic review of publications using deep learning approaches and neuroimaging data for diagnostic classification of AD was performed. A PubMed and Google Scholar search was used to identify deep learning papers on AD published between January 2013 and July 2018. These papers were reviewed, evaluated, and classified by algorithm and neuroimaging type, and the findings were summarized. Of 16 studies meeting full inclusion criteria, 4 used a combination of deep learning and traditional machine learning approaches, and 12 used only deep learning approaches. The combination of traditional machine learning for classification and stacked auto-encoder (SAE) for feature selection produced accuracies of up to 98.8% for AD classification and 83.7% for prediction of conversion from mild cognitive impairment (MCI), a prodromal stage of AD, to AD. Deep learning approaches, such as convolutional neural network (CNN) or recurrent neural network (RNN), that use neuroimaging data without pre-processing for feature selection have yielded accuracies of up to 96.0% for AD classification and 84.2% for MCI conversion prediction. The best classification performance was obtained when multimodal neuroimaging and fluid biomarkers were combined. Deep learning approaches continue to improve in performance and appear to hold promise for diagnostic classification of AD using multimodal neuroimaging data. AD research that uses deep learning is still evolving, improving performance by incorporating additional hybrid data types, such as-omics data, increasing transparency with explainable approaches that add knowledge of specific disease-related features and mechanisms

UnionDet: Union-Level Detector Towards Real-Time Human-Object Interaction Detection

Recent advances in deep neural networks have achieved significant progress in detecting individual objects from an image. However, object detection is not sufficient to fully understand a visual scene. Towards a deeper visual understanding, the interactions between objects, especially humans and objects are essential. Most prior works have obtained this information with a bottom-up approach, where the objects are first detected and the interactions are predicted sequentially by pairing the objects. This is a major bottleneck in HOI detection inference time. To tackle this problem, we propose UnionDet, a one-stage meta-architecture for HOI detection powered by a novel union-level detector that eliminates this additional inference stage by directly capturing the region of interaction. Our one-stage detector for human-object interaction shows a significant reduction in interaction prediction time 4x~14x while outperforming state-of-the-art methods on two public datasets: V-COCO and HICO-DET.Comment: ECCV 202

Real-time observation of a coherent lattice transformation into a high-symmetry phase

Excursions far from their equilibrium structures can bring crystalline solids through collective transformations including transitions into new phases that may be transient or long-lived. Direct spectroscopic observation of far-from-equilibrium rearrangements provides fundamental mechanistic insight into chemical and structural transformations, and a potential route to practical applications, including ultrafast optical control over material structure and properties. However, in many cases photoinduced transitions are irreversible or only slowly reversible, or the light fluence required exceeds material damage thresholds. This precludes conventional ultrafast spectroscopy in which optical excitation and probe pulses irradiate the sample many times, each measurement providing information about the sample response at just one probe delay time following excitation, with each measurement at a high repetition rate and with the sample fully recovering its initial state in between measurements. Using a single-shot, real-time measurement method, we were able to observe the photoinduced phase transition from the semimetallic, low-symmetry phase of crystalline bismuth into a high-symmetry phase whose existence at high electronic excitation densities was predicted based on earlier measurements at moderate excitation densities below the damage threshold. Our observations indicate that coherent lattice vibrational motion launched upon photoexcitation with an incident fluence above 10 mJ/cm2 in bulk bismuth brings the lattice structure directly into the high-symmetry configuration for tens of picoseconds, after which carrier relaxation and diffusion restore the equilibrium lattice configuration.Comment: 22 pages, 4 figure

A Non-Collinear Mixing Technique to Measure the Acoustic Nonlinearity Parameter of Adhesive Bond from Only One Side of the Sample

The acoustic nonlinearity parameter (ANLP) of a material is often positively correlated with the damage in the material. Therefore, the ability to nondestructively measure the ANLP may enable the nondestructive characterization of the material’s remaining strength. In this work, we developed a non-collinear mixing technique to measure the ANLP of adhesive bonds. One of the most significant features of the new method is that it requires only one-side access to the adhesive bond being measured, which significantly increases it utility in field measurements. Specifically, the test sample considered in this study consists of two aluminum plates adhesively joined together through a commercial adhesive tape. The non-collinear wave mixing technique consists of generating a longitudinal wave and a shear wave by piezoelectric transmitters attached to the same surface of the sample under test. These waves are introduced into the sample in such an angle that they will mix at the adhesive bond region. Mixing of these two waves generates a shear wave that propagates back towards the surface where the two waves were generated. This mixing wave is then recorded by a shear wave receiver placed on the same surface where the longitudinal and shear wave transmitters are located. It was shown that amplitude of this mixing wave is proportional to the ANLP of the adhesive bond. To demonstrate the effectiveness of the newly developed technique, a freshly made adhesive sample was first measured using the non-collinear mixing technique to obtain the ANLP of the adhesive bond. This sample is then placed inside a thermal chamber for aging to change its ANLP. The sample was taken out the thermal chamber periodically to measure its ANLP. The measured results clearly show that the ANLP varies with aging time. Initially, the ANLP decreases with aging time, possibly due to further curing. Afterward, the ANLP begins to increase with aging time, likely due to aging induced damage in the polymer adhesive. To verify that the signals received from the shear wave receiver are indeed the mixing wave, the finite element method was used to simulate the wave motion in the test sample. The simulation results clearly show that the signals recorded by the shear wave receiver are indeed the desired mixing wave, whose amplitude is proportional to the ANLP of the adhesive bond

Motivational Factors Influencing Sport Spectator Involvement At NCAA Division II Basketball Games

The purpose of this study was to investigate the motivational factors affecting sport spectator involvement using 304 spectators from NCAA Division II men\u27s and women\u27s basketball games. Two aspects (behavioral and socio-psychological) of sport spectator involvement were examined. The results revealed that spectators at intercollegiate basketball games had a higher level of socio-psychological involvement than behavioral involvement. A series of multiple regression analyses were conducted to examine the affects of sociomotivational factors (perceived value, fan identification, involvement opportunity, and reference groups) on sport spectator involvement. Fan identification, involvement opportunity, and reference groups were identified as influential factors that had a significant impact on overall sport spectator involvement. The results also indicated that the four motivational factors predicted more variance for socio-psychological involvement (R2 = .33) than behavioral involvement (R2 = .22). The findings of this study provide valuable insight to Division II athletic administrators about how to attract additional spectators to collegiate basketball games

Accretion onto a Supermassive Black Hole Binary Before Merger

While supermassive binary black holes (SMBBHs) inspiral toward merger they may also accrete significant amounts of matter. To study the dynamics of such a system requires simultaneously describing the evolving spacetime and the dynamics of magnetized plasma. Here we present the first relativistic calculation simulating two equal-mass, non-spinning black holes as they inspiral from an initial separation of $20M$ ($G=c=1$) almost to merger, $\simeq 9M$, while accreting gas from a surrounding disk, where $M$ is the total binary mass. We find that the accretion rate $\dot M$ onto the black holes first decreases during this period and then reaches a plateau, dropping by only a factor of $\sim 3$ despite its rapid inspiral. An estimated bolometric light curve follows the same profile. The minidisks through which the accretion reaches the black holes are very non-standard. Reynolds, not Maxwell, stresses dominate, and they oscillate between two distinct structural states. In one part of the cycle, ``sloshing" streams transfer mass from one minidisk to the other through the L1 point at a rate $\sim 0.1\times$ the accretion rate, carrying kinetic energy at a rate that can be as large as the peak minidisk bolometric luminosity. We also discover that the minidisks have time-varying tilts with respect to the orbital plane similar in magnitude to the circumbinary disk's aspect ratio. The unsigned poloidal flux on the black hole event horizon is roughly constant at a dimensionless level $\phi\sim 2-3$, but doubles just before merger; if the black holes had significant spin, this flux could support jets whose power could approach the radiated luminosity. This simulation is the first to employ our multipatch infrastructure \pwmhd, decreasing computational expense per physical time to $\sim 3\%$ of similar runs using conventional single-grid methods.Comment: Comments welcom

Deep learning detection of informative features in tau PET for Alzheimer’s disease classification

Background: Alzheimer's disease (AD) is the most common type of dementia, typically characterized by memory loss followed by progressive cognitive decline and functional impairment. Many clinical trials of potential therapies for AD have failed, and there is currently no approved disease-modifying treatment. Biomarkers for early detection and mechanistic understanding of disease course are critical for drug development and clinical trials. Amyloid has been the focus of most biomarker research. Here, we developed a deep learning-based framework to identify informative features for AD classification using tau positron emission tomography (PET) scans. Results: The 3D convolutional neural network (CNN)-based classification model of AD from cognitively normal (CN) yielded an average accuracy of 90.8% based on five-fold cross-validation. The LRP model identified the brain regions in tau PET images that contributed most to the AD classification from CN. The top identified regions included the hippocampus, parahippocampus, thalamus, and fusiform. The layer-wise relevance propagation (LRP) results were consistent with those from the voxel-wise analysis in SPM12, showing significant focal AD associated regional tau deposition in the bilateral temporal lobes including the entorhinal cortex. The AD probability scores calculated by the classifier were correlated with brain tau deposition in the medial temporal lobe in MCI participants (r = 0.43 for early MCI and r = 0.49 for late MCI). Conclusion: A deep learning framework combining 3D CNN and LRP algorithms can be used with tau PET images to identify informative features for AD classification and may have application for early detection during prodromal stages of AD

Evaluation of a new secondary dose calculation software for Gamma Knife radiosurgery

Current available secondary dose calculation software for Gamma Knife radiosurgery falls short in situations where the target is shallow in depth or when the patient is positioned with a gamma angle other than 90°. In this work, we evaluate a new secondary calculation software which utilizes an innovative method to handle nonstandard gamma angles and image thresholding to render the skull for dose calculation. 800 treatment targets previously treated with our GammaKnife Icon system were imported from our treatment planning system (GammaPlan 11.0.3) and a secondary dose calculation was conducted. The agreement between the new calculations and the TPS were recorded and compared to the original secondary dose calculation agreement with the TPS using a Wilcoxon Signed Rank Test. Further comparisons using a Mann-Whitney test were made for targets treated at a 90° gamma angle against those treated with either a 70 or 110 gamma angle for both the new and commercial secondary dose calculation systems. Correlations between dose deviations from the treatment planning system against average target depth were evaluated using a Kendall\u27s Tau correlation test for both programs. The Wilcoxon Signed Rank Test indicated a significant difference in the agreement between the two secondary calculations and the TPS, with a P-value \u3c 0.0001. With respect to patients treated at nonstandard gamma angles, the new software was largely independent of patient setup, while the commercial software showed a significant dependence (P-value \u3c 0.0001). The new secondary dose calculation software showed a moderate correlation with calculation depth, while the commercial software showed a weak correlation (Tau = -.322 and Tau = -.217 respectively). Overall, the new secondary software has better agreement with the TPS than the commercially available secondary calculation software over a range of diverse treatment geometries

Mid-life Leukocyte Telomere Length and Dementia Risk: An Observational and Mendelian Randomization Study of 435,046 UK Biobank Participants

Telomere attrition is one of biological aging hallmarks and may be intervened to target multiple aging-related diseases, including Alzheimer\u27s disease and Alzheimer\u27s disease related dementias (AD/ADRD). The objective of this study was to assess associations of leukocyte telomere length (TL) with AD/ADRD and early markers of AD/ADRD, including cognitive performance and brain magnetic resonance imaging (MRI) phenotypes. Data from European-ancestry participants in the UK Biobank (n = 435,046) were used to evaluate whether mid-life leukocyte TL is associated with incident AD/ADRD over a mean follow-up of 12.2 years. In a subsample without AD/ADRD and with brain imaging data (n = 43,390), we associated TL with brain MRI phenotypes related to AD or vascular dementia pathology. Longer TL was associated with a lower risk of incident AD/ADRD (adjusted Hazard Ratio [aHR] per SD = 0.93, 95% CI 0.90–0.96, p = 3.37 × 10−7). Longer TL also was associated with better cognitive performance in specific cognitive domains, larger hippocampus volume, lower total volume of white matter hyperintensities, and higher fractional anisotropy and lower mean diffusivity in the fornix. In conclusion, longer TL is inversely associated with AD/ADRD, cognitive impairment, and brain structural lesions toward the development of AD/ADRD. However, the relationships between genetically determined TL and the outcomes above were not statistically significant based on the results from Mendelian randomization analysis results. Our findings add to the literature of prioritizing risk for AD/ADRD. The causality needs to be ascertained in mechanistic studies