Body Scan Research Articles

Hybrid Feature Extraction for Breast Cancer Classification Using the Ensemble Residual VGG16 Deep Learning Model

Introduction: Breast Cancer (BC) is a significant cause of high mortality amongst women globally and probably will remain a disease posing challenges about its detectability. Advancements in medical imaging technology have improved the accuracy and efficiency of breast cancer classification. However, tumor features' complexity and imaging data variability still pose challenges. Method: This study proposes the Ensemble Residual-VGG-16 model as a novel combination of the Deep Residual Network (DRN) and VGG-16 architecture. This model is purposely engineered with maximal precision for the task of breast cancer diagnosis based on mammography images. We assessed its performance by accuracy, recall, precision, and the F1-Score. All these metrics indicated the high performance of this Residual-VGG-16 model. The diagnostic residual-VGG16 performed exceptionally well with an accuracy of 99.6%, precision of 99.4%, recall of 99.7%, F1 score of 98.6%, and Mean Intersection over Union (MIoU) of 99.8% with MIAS datasets. Result: Similarly, the INBreast dataset achieved an accuracy of 93.8%, a precision of 94.2%, a recall of 94.5%, and an F1-score of 93.4% Conclusion: The proposed model is a significant advancement in breast cancer diagnosis, with high accuracy and potential as an automated grading.

Mindfulness Exercises Reduce Acute Physiologic Stress Among Female Clinicians.

Approximately 50% of clinicians experience excessive emotional, physical, and mental stress, with repercussions across the entire medical system. Mindfulness exercises may mitigate this excessive stress. Heart rate variability (HRV) is an objective stress measure that can quantify which mindfulness exercises provide the greatest stress reduction. To define the impact of specific mindfulness exercises on HRV, a surrogate for physiologic stress, and the relationship between physiologic (HRV) and subjective stress measured by the State-Trait Anxiety Inventory during a one-day mindfulness workshop. This was a prospective observational pilot study performed at a quaternary children's hospital with diverse subspecialists of pediatric nurses, nurse practitioners, and physicians. Our primary outcome measure was change in HRV from baseline during three mindfulness exercises. The grounding, deep breathing, and body scan exercises all produced statistically significant changes in HRV among our 13 female participants. The body scan exercise produced statistically significant changes in all studied HRV parameters compared with baseline. We observed significant increases in Root Mean Square of Successive Differences between normal heartbeats (p = 0.026), high frequency (p ≤ 0.001), and the parasympathetic nervous system index (p ≤ 0.001) reflecting increased parasympathetic tone (e.g., relaxation), whereas sd 2/sd 1 ratio (p ≤ 0.001) and the stress index (p = 0.004) were decreased reflecting sympathetic withdrawal (e.g., decreased stress). Subjective stress decreased after 1-day mindfulness training (44.6 to 27.2) (p < 0.001). Individuals with the largest decrease in subjective stress also had the most improvement in HRV during the body scan exercise. Clinician stress levels (HRV) improved after participating in grounding, deep breathing, and body scan meditations, which may highlight their importance as stress reduction tools for clinicians. Monitoring of HRV during mindfulness exercises may provide deeper understanding of which specific exercises produce the greatest physiologic stress reduction for individual participants and the trend of these changes over time.

Tunable Imaging Distance Focusing Module Directly Driven by a Thin‐plate Piezoelectric Actuator

AbstractWith the advancement of imaging technology, the performance of optical focusing modules becomes crucial for determining imaging quality. Conventional focusing modules frequently face challenges of excessive bulk and weight, complicating the integrated design and limiting application fields. Herein, this study presents a piezo‐actuated optical focusing module (POFM) and the construction strategy to alleviate these issues. A novel radial‐bending resonant‐type piezoelectric actuator (RB‐RPA) is developed to directly drive the POFM. The RB‐RPA, which features a hollow structure (27 × 27 × 7 mm3), utilizes both radial and bending vibration modes of a thin metal plate. It achieves a speed of 122.93 mm s−1, a thrust force of 0.14 N, and a displacement resolution of 0.52 µm. Employing the proposed construction strategy, RB‐RPA is integrated with imaging components to fabricate a POFM prototype. Furthermore, the micrometer‐level displacement resolution of POFM facilitates optical focusing on imaging targets, allowing the acquisition of sharp images within 70 ms. It achieves an image resolution of 119.57 lp mm−1 within a 42 mm segment of the captured image. Finally, a QT‐based interface for the POFM, enables real‐time image capture and sharpness evaluation, thereby enhancing imaging efficiency and integration. This work provides a potential candidate for next‐generation imaging devices.

Event Camera Denoising Using Asynchronous Spatio-Temporal Event Denoising Neural Network

Abstract. Compared to conventional cameras, event cameras represent a noteworthy advancement in neuromorphic imaging technology, garnering considerable attention from researchers due to their distinct advantages. However, event cameras are susceptible to significant levels of measurement noise, which can detrimentally affect the performance of algorithms reliant on event stream for tasks such as perception and navigation. In this study, we introduce a novel method for denoising event stream, aiming to filter out events that do not accurately reflect genuine logarithmic intensity changes within the observed scene. Our approach focuses on the asynchronous nature and spatiotemporal properties of events, culminating in the development of a novel Asynchronous Spatio- Temporal Event Denoising neural Network(ASTEDNet). This network operates directly on event streams, circumventing the need to convert event stream into denser formats like image frames, thereby preserving their inherent asynchronous nature. Drawing upon principles from graph encoding and temporal convolutional networks, we incorporate spatiotemporal feature attention mechanisms to capture the temporal and spatial correlations between events. This enables the classification of each active event pixel in the original stream as either representing a genuine intensity change or noise. Comparative evaluations conducted on multiple datasets against state-of-the-art methods demonstrate the remarkable efficacy and robustness of our proposed algorithm in noise removal while retaining meaningful event information within the scene.

Tumor therapeutics in the era of "RECIST": past, current insights, and future prospects.

In recent years, advancements in medical treatment and imaging technologies have revolutionized the assessment of tumor response. However, the Response Evaluation Criteria in Solid Tumors (RECIST) has long been established as the gold standard for evaluating tumor treatment. As treatment modalities evolve, the need for continuous refinement and adaptation of RECIST becomes increasingly apparent. This review explores the historical evolution, current applications, limitations, and future directions of RECIST. It discusses the challenges of distinguishing true progression from pseudo-progression in ICIs (immune checkpoint inhibitors), the integration of advanced imaging tools, and the necessity for RECIST criteria tailored to specific therapies like neoadjuvant treatments. The review highlights the ongoing efforts to enhance RECIST's accuracy and reliability in clinical decision-making and the potential for developing new standards to better evaluate treatment efficacy in the rapidly evolving landscape of oncology.

Rapid Assessment of Morphological Asymmetries Using 3D Body Scanner and Bioelectrical Impedance Technologies in Sports: A Case of Comparative Analysis Among Age Groups in Judo

(1) Background: The advancement of technologies has made morphological assessment rapid and reliable. A combination of 3D body scanning (3D-BS) and bioelectrical impedance (BIA) could be essential in monitoring the morphological status of athletes and the general population and their symmetries for coaches, researchers and medical professionals. (2) Methods: The current study presents the use of Inbody-720 BIA and 3D-BS NX-16 for analyzing the asymmetry profile of an athlete in 2 min on a sample of 106 male judo competitors from the following age categories: older boys—U14 (N = 24), younger cadets—U16 (N = 31), cadets—U18 (N = 17), juniors—U21 (N = 19) and seniors (N = 15). Variables observed were arm lean mass, upper arm, elbow, forearm and wrist girth, leg lean mass, thigh length, thigh, knee and calf girth. The paired sample t-test, asymmetry index (AI) and Kruskal–Wallis analysis were used at p ≤ 0.05; (3) Results: Morphological asymmetries were detected in all age categories: seniors—three, U21—four, U18—three, U16—five and U14—four. The most common asymmetrical variable in all categories was the forearm girth, while thigh length, knee girth and upper arm girth presented symmetrical variables in all age categories. AI showed that the size of the asymmetries did not differentiate between the age groups. (4) Conclusions: The current study demonstrated great potential for combining BIA and 3D-BS for rapid asymmetry detection that would allow for monitoring and quick adjustments to the training process in youth to senior age categories.

Cross-modality image translation of 3 Tesla Magnetic Resonance Imaging to 7 Tesla using Generative Adversarial Networks.

The rapid advancements in magnetic resonance imaging (MRI) technology have precipitated a new paradigm wherein cross-modality data translation across diverse imaging platforms, field strengths, and different sites is increasingly challenging. This issue is particularly accentuated when transitioning from 3 Tesla (3T) to 7 Tesla (7T) MRI systems. This study proposes a novel solution to these challenges using generative adversarial networks (GANs)-specifically, the CycleGAN architecture- to create synthetic 7T images from 3T data. Employing a dataset of 1112 and 490 unpaired 3T and 7T MR images, respectively, we trained a 2-dimensional (2D) CycleGAN model, evaluating its performance on a paired dataset of 22 participants scanned at 3T and 7T. Independent testing on 22 distinct participants affirmed the model's proficiency in accurately predicting various tissue types, encompassing cerebral spinal fluid, gray matter, and white matter. Our approach provides a reliable and efficient methodology for synthesizing 7T images, achieving a median Dice of 6.82%,7,63%, and 4.85% for Cerebral Spinal Fluid (CSF), Gray Matter (GM), and White Matter (WM), respectively, in the testing dataset, thereby significantly aiding in harmonizing heterogeneous datasets. Furthermore, it delineates the potential of GANs in amplifying the contrast-to-noise ratio (CNR) from 3T, potentially enhancing the diagnostic capability of the images. While acknowledging the risk of model overfitting, our research underscores a promising progression towards harnessing the benefits of 7T MR systems in research investigations while preserving compatibility with existent 3T MR data. This work was previously presented at the ISMRM 2021 conference (Diniz, Helmet, Santini, Aizenstein, & Ibrahim, 2021).

Preoperative Imaging-Guided Surgical Planning in Complex Pelvic Fractures - Innovations in Traumatology and Radiology for Enhanced Clinical Outcomes: A Systematic Review

This systematic review will explore role of preoperative imaging-guided surgical planning for the management of complex pelvic fractures. Pelvic fractures occurs in high-energy trauma and cause challenges for intricate anatomy and have a high association with vascular, visceral injuries. Traditional imaging methods frequently fail to capture injury complexity while increasing surgical risks. Recent advancements of imaging technology CT, MRI, 3D reconstructions along with and AI-based predictive models offer enhanced precision in diagnosing and planning surgical interventions with detailed insights into fracture patterns, bone density, and soft tissue damage which enable accurate surgical planning to reduce intraoperative complications and shorten recovery times. We aim to systematically investigate effectiveness of modern imaging techniques with potencies in clinical outcomes, with an emphasis on improved surgical accuracy and patient safety. Analyzing range of studies, we have established evidence-based recommendations for integrating advanced imaging technologies into routine trauma care showing critical role these innovations play in improving both short-term recovery and long-term clinical outcomes for patients having complex pelvic fractures.

Recent Advances in DNA-Based Nanoprobes for In vivo MiRNA Imaging.

As a post transcriptional regulator of gene expression, microRNAs (miRNA) is closely related to many major human diseases, especially cancer. Therefore, its precise detection is very important for disease diagnosis and treatment. With the advancement of fluorescent dye and imaging technology, the focus has shifted from in vitro miRNA detection to in vivo miRNA imaging. This concept review summarizes signal amplification strategies including DNAzyme catalytic reaction, hybrid chain reaction (HCR), catalytic hairpin assembly (CHA) to enhance detection signal of lowly expressed miRNAs; external stimuli of ultraviolet (UV) light or near-infrared region (NIR) light, and internal stimuli such as adenosine triphosphate (ATP), glutathione (GSH), protease and cell membrane protein to prevent nonspecific activation for the avoidance of false positive signal; and the development of fluorescent probes with emission in NIR for in vivo miRNA imaging; as well as rare earth nanoparticle based the second near-infrared window (NIR-II) nanoprobes with excellent tissue penetration and depth for in vivo miRNA imaging. The concept review also indicated current challenges for in vivo miRNA imaging including the dynamic monitoring of miRNA expression change and simultaneous in vivo imaging of multiple miRNAs.

A solution for the automatic detection of expansion joints in dam stilling pools using underwater robots

This research addresses the critical challenges of detecting and measuring underwater concrete structure expansion joints (CSEJ), with a particular focus on dam stilling basin environments. A full set of algorithms for detecting expansion joints with underwater robots is presented by the research, enabling real-time and offline analysis along with visualization. These algorithms introduce the use of Convolutional Neural Networks (CNNs) for image feature extraction and segmentation in CSEJ detection, significantly improving accuracy and adaptability over traditional methods. An automated approach known as the Underwater Dam Crack Detection Network (UDCD Network) was introduced by this research. And advanced attention gates and hybrid dilated convolutions to enhance its detection capabilities are incorporated in this system. Additionally, a three-phase deep transfer learning strategy during training was utilized in this research, which significantly improves the model's performance. The research suggests an underwater image restoration algorithm that uses a dark channel prior and dynamic white balance correction to improve image clarity. Furthermore, unique underwater environmental challenges were tackled by this research, such as non-contact measurement of expansion joint widths using a view-geometry-based algorithm and comprehensive detection of dam wall surfaces. Experimental validation demonstrates that the advanced imaging technologies of the proposed underwater robots hold significant potential for autonomous detection of expansion joints in dam stilling basins.

Canadian adult reference data for body composition, trabecular bone score and advanced hip analysis using DXA

DXA-derived reference data for visceral adipose tissue (VAT) and advanced hip analysis (AHA) parameters spanning the entire adult lifespan are limited. The purpose of this study was to develop age-, site- and sex-specific reference data for dual X-ray absorptiometry (DXA) -derived body composition, trabecular bone score (TBS) and advanced hip analysis (AHA) parameters across the adult lifespan. Adults (N = 908; female: 561 and male: 347) from Calgary and the surrounding area over the age of 20 years participated in this study. Participants received DXA scans of their hip (total hip [TH] and femoral neck [FN]), lumbar spine [LS], forearm [33 % site] and total body (iDXA, GE Lunar, GE Healthcare). Areal bone mineral density (aBMD, g/cm2) was captured at all sites, and body composition variables, including lean mass, fat mass and percent fat, were analyzed from the total body scan. VAT mass was assessed from total body DXA scans. Advanced hip analysis (AHA) was performed on hip scans and trabecular bone score (TBS) on the LS scans to assess bone quality. Site- and sex-specific centile curves and tables were generated using the Generalized Additive Models for Location, Scale, and Shape (GAMLSS) method. Clinicians and researchers can use these Canadian reference data as a tool to assess body composition, TBS and AHA parameters across the adult lifespan.

Advanced Imaging for Localized Prostate Cancer

Background/Objectives: Prostate cancer is a prevalent malignancy often presenting without early symptoms. Advanced imaging technologies have revolutionized its diagnosis and management. This review discusses the principles, benefits, and clinical applications of multiparametric magnetic resonance imaging (mpMRI), micro-ultrasound (microUS), and prostate-specific membrane antigen positron emission tomography–computed tomography (PSMA PET/CT) in localized prostate cancer. Methods: We conducted a comprehensive literature review of recent studies and guidelines on mpMRI, microUS, and PSMA PET/CT in prostate cancer diagnosis, focusing on their applications in biopsy-naïve patients, those with previous negative biopsies, and patients under active surveillance. Results: MpMRI has demonstrated high sensitivity and negative predictive value in detecting clinically significant prostate cancer (csPCa). MicroUS, a newer technology, has shown promising results in early studies, with sensitivity and specificity comparable to mpMRI. PSMA PET/CT has emerged as a highly sensitive and specific imaging modality, particularly valuable for staging and detecting metastatic disease. All three technologies have been incorporated into urologic practice for prostate cancer diagnosis and management, with each offering unique advantages in different clinical scenarios. Conclusions: Advanced imaging techniques, including mpMRI, microUS, and PSMA PET/CT, have significantly improved the accuracy of prostate cancer diagnosis, staging, and management. These technologies enable more precise targeting of suspicious lesions during biopsy and therapy planning. However, further research, especially randomized controlled trials, is needed to fully establish the optimal use and inclusion of these imaging modalities in various stages of prostate cancer care.

Image Analysis in Histopathology and Cytopathology: From Early Days to Current Perspectives.

Both pathology and cytopathology still rely on recognizing microscopical morphologic features, and image analysis plays a crucial role, enabling the identification, categorization, and characterization of different tissue types, cell populations, and disease states within microscopic images. Historically, manual methods have been the primary approach, relying on expert knowledge and experience of pathologists to interpret microscopic tissue samples. Early image analysis methods were often constrained by computational power and the complexity of biological samples. The advent of computers and digital imaging technologies challenged the exclusivity of human eye vision and brain computational skills, transforming the diagnostic process in these fields. The increasing digitization of pathological images has led to the application of more objective and efficient computer-aided analysis techniques. Significant advancements were brought about by the integration of digital pathology, machine learning, and advanced imaging technologies. The continuous progress in machine learning and the increasing availability of digital pathology data offer exciting opportunities for the future. Furthermore, artificial intelligence has revolutionized this field, enabling predictive models that assist in diagnostic decision making. The future of pathology and cytopathology is predicted to be marked by advancements in computer-aided image analysis. The future of image analysis is promising, and the increasing availability of digital pathology data will invariably lead to enhanced diagnostic accuracy and improved prognostic predictions that shape personalized treatment strategies, ultimately leading to better patient outcomes.

Progression in Near-Infrared Fluorescence Imaging Technology for Lung Cancer Management.

Lung cancer is a major threat to human health and a leading cause of death. Accurate localization of tumors in vivo is crucial for subsequent treatment. In recent years, fluorescent imaging technology has become a focal point in tumor diagnosis and treatment due to its high sensitivity, strong selectivity, non-invasiveness, and multifunctionality. Molecular probes-based fluorescent imaging not only enables real-time in vivo imaging through fluorescence signals but also integrates therapeutic functions, drug screening, and efficacy monitoring to facilitate comprehensive diagnosis and treatment. Among them, near-infrared (NIR) fluorescence imaging is particularly prominent due to its improved in vivo imaging effect. This trend toward multifunctionality is a significant aspect of the future advancement of fluorescent imaging technology. In the past years, great progress has been made in the field of NIR fluorescence imaging for lung cancer management, as well as the emergence of new problems and challenges. This paper generally summarizes the application of NIR fluorescence imaging technology in these areas in the past five years, including the design, detection principles, and clinical applications, with the aim of advancing more efficient NIR fluorescence imaging technologies to enhance the accuracy of tumor diagnosis and treatment.

Unenhanced CT as an Alternative to Contrast-Enhanced CT in Evaluating Renal Cryoablation Zones.

Background Advances in imaging technology and the increased use of abdominal imaging have led to a rise in renal cell carcinoma (RCC) detection. While surgery remains the primary treatment for small RCCs, minimally invasive procedures like cryoablation are gaining popularity, particularly for patients with comorbidities or renal dysfunction. CT-guided cryoablation offers advantages, including high spatial resolution and real-time visualization during the procedure. Post-procedure imaging is essential for assessing treatment success, with contrast-enhanced CT (CE-CT) typically considered vital. However, many patients, especially older individuals, have renal dysfunction that limits the use of contrast agents. In such cases, unenhanced CT (UE-CT) presents a viable alternative for post-procedural evaluation. This study explored the effectiveness of UE-CT in assessing cryoablation zones as a substitute for CE-CT. Materials and Methods This retrospective study included 54 patients (58 tumors) who underwent cryoablation at a single institution between 2014 and 2024. Only patients with available early follow-up CT (within three days post-cryoablation) and subsequent follow-up were included. Tumors marked with lipiodol prior to cryoablation and cases requiring transcatheter arterial embolization due to extravasation immediately after cryoablation were excluded. Percutaneous renal cryoablation was performed under CT fluoroscopy, and the ablation zone was assessed using a 64-channel multi-slice CT scanner. UE-CT was conducted before the procedure, followed by both UE-CT and CE-CT within three days after cryoablation. CT attenuation values were measured for pre-procedure UE-CT (kidneys and tumor), post-procedure UE-CT (kidneys, cryoablation zone, and tumor), and post-procedure CE-CT (kidneys, cryoablation zone, and tumor). Tumor volumes in the post-procedure regions were evaluated on both UE-CT and CE-CT. Statistical analyses were performed using Wilcoxon's signed-rank test and Spearman's rank correlation coefficient, with interobserver agreement determined by the intraclass correlation coefficient. Results The median tumor diameter was 1.56 cm (IQR: 1.33-2.00 cm). On UE-CT, the cryoablation zone exhibited high attenuation, while it showed low attenuation on CE-CT. The median attenuation values of the kidneys on UE-CT before and after cryoablation were not significantly different (33.6 Hounsfield unit (HU) vs. 34.3 HU, P = 0.17). However, on CE-CT, the median attenuation values of normal kidneys and the cryoablation zone significantly differed (171.7 HU vs. 55.7 HU, P < 0.0001). Similarly, on UE-CT, there was a significant difference in the median attenuation values between normal kidneys and the cryoablation zone (34.3 HU vs. 47.4 HU, P < 0.0001). The median renal volumes of the unenhanced regions on CE-CT and those with attenuation changes on UE-CT were not significantly different (26.52 cm³ vs. 28.83 cm³, P = 0.86). These values showed a strong correlation (r = 0.95; 95% CI: 0.91-0.97). Conclusions This study showed that UE-CT can reliably estimate the ablation zone in RCC patients post-cryoablation. While the contrast between the ablation zone and normal renal parenchyma was lower on UE-CT compared to CE-CT, the ablation zone was still detectable and highly correlated with CE-CT results. Further research with larger sample sizes is needed to validate the clinical utility of UE-CT and assess the reproducibility of these findings.

The peritumoral brain zone in glioblastoma: a review of the pretreatment approach.

Glioblastomas are the most common and aggressive form of malignant primary brain tumors in adults. The standard treatment is surgical resection followed by radiotherapy and chemotherapy. Despite optimal treatment methods, the prognosis for patients remains poor. Preoperative determination of glioblastoma margins remains beneficial for the complete removal of the tumor mass. Radiotherapy is essential for post-surgery treatment, but radioresistance is a significant challenge contributing to high mortality rates. Advanced imaging technologies are used to analyze the changes in the peritumoral brain zone (PTZ). Consequently, they may lead to the development of novel therapeutic options, especially targeting the marginal parts of a tumor, which could improve the prognosis of glioblastoma patients. The clinical presentation of glioblastoma is heterogeneous and mostly depends on the location and size of a tumor. Glioblastomas are characterized by both intratumoral cellular heterogeneity and an extensive, diffuse infiltration into the normal tissue bordering a tumor called the PTZ. Neuroimaging techniques, such as diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), perfusion-weighted imaging (PWI), proton magnetic resonance spectroscopy (1H MRS), and chemical exchange saturation transfer (CEST) are useful methods in the evaluation of the tumor infiltration and thus the resection margin.

Effect of Virtual Casts Superimposition Strategies on the Estimation of IOS Accuracy in Complete-Arch Scans.

This study aims to assess the impact of three different strategies of best-fit (BF) alignments of virtual casts on the estimation of the accuracy of intraoral scanner (IOS) in complete-arch scans. A maxillary typodont, modified with an Implant Scan Body (ISB) in the retroincisive area, was digitized using a desktop scanner (SW Optor Lab) to obtain a reference STL file. The typodont was then scanned 10 times using two IOSs (Trios4, Itero). Each STL file obtained from the IOS was superimposed onto the reference cast using three methods in CloudCompare: full-arch BF (BF-full), BF at the starting tooth (BF-tooth), and BF at the ISB (BF-ISB). Discrepancies from the reference were recorded, and trueness and precision were compared for each method. Statistical analysis with the Kruskall-Wallis nonparametric test was performed (α = 0.05). The Kruskall-Wallis test (p ⟨0.05) revealed statistically significant differences in trueness and precision among the alignment methods. Post Hoc multiple comparison test p-values were all below the critical alpha value. Differences in BF methods lead to significantly different accuracy values of IOS complete-arch scans as different virtual casts' alignment discrepancy. BF-full had the highest accuracy followed by BF-ISB and BF-tooth.

Retrospective Observational Analysis of Computed Tomography Scans of Trauma Patients in Overseas Operations (SCANOPEX Study).

High-intensity conflicts are on Europe's doorstep. The French expertise in the medical management of frontline casualties in overseas operations is well established. However, in the management of severe trauma, we lack data on the injuries identified by body scanners in the field. Understanding the associations between injury mechanisms and radiological lesions would enable us to anticipate medical and surgical management. To study this possible link, we collected and interpreted scanogaphic data and analyzed them according to lesion mechanisms, following the algorithm MARCH used to implement the concept of Damage Control Resuscitation, which includes life-saving measures to ensure that the wounded reach medical-surgical facilities alive. This retrospective monocentric study collected data from body scanners performed in overseas operations between June 2011 and September 2023. Inclusion criteria were to be French military personnel and to have undergone a whole-body scanner in a theater of overseas operations. Exclusion criteria were to have died before the scan, to be foreign, non-military and a minor. Of 164 available files, 96 were eligible, 1 patient declared aged 70 years was excluded, and 95 files were retained. In our population, 18% of injured patients had a spinal fracture. Compared with road traffic accident casualties, improvised explosive device casualties were the most severely injured patients arriving alive at computed tomography, with a relative risk of Injury Severity Score > 8 of 2.29 [1.09-4.80] (P = .019). Improvised explosive device casualties had a relative risk of airway injuries of 2.57 [1.03-6.39] (P = .030), injuries leading to functional impairment of 3.21 [1.17-8.82] (P =.013), injuries leading to infection of 2.14 [1.21-3.76] (P = .0045), and injuries leading to shock of 3.21 [0.96-10.70] (P = .039). Deep metal splinters were only found in the improvised explosive device group. Preparing the medical corps to deal with war casualties is fundamental. Our study shows that it is essential to consider the mechanism of injury to understand the casualty better and predict potential injuries. In addition, the study of postmortem scans could greatly help analyze potentially avoidable deaths.

The Relationship Between the Percentage of Skeletal Muscle (SMP) and the Emergence of Degenerative Diseases (Hypertension) in Posyandu for the Elderly Mugi Sehat

Degenerative disease is a condition of continuous decline in body function with increasing age. Hypertension is a degenerative disease that is often suffered by the elderly. The aging process results in changes in body composition, often without being accompanied by weight changes. Increasing age in the elderly will experience functional limitations caused by changes in body composition, one of which is the Percentage of Skeletal Muscle. The purpose of this study was to determine the relationship between the percentage of skeletal muscle and the emergence of degenerative diseases (hypertension) in the elderly at the Mugi Healthy Elderly Posyandu. The instruments in this study used a Digital Sphygmomanometer to measure blood pressure and an Omron Karada Scan Body Composition Monitor Model HBF-375 to measure Skeletal Muscle Percentage. This research method uses a cross-sectional research design. The sample in this study is 58 people. The research was conducted at the Mugi Sehat Elderly Posyandu in the working area of the 2022 UPTD Kembar I health center in May-June 2023. The sampling technique in this study uses total sampling and data analysis using the Spearman Rank test. The results of this study from the bivariate analysis showed that there was no relationship between the Percentage of Skeletal Muscle and the emergence of degenerative diseases (hypertension) in the elderly at the Mugi Healthy Elderly Posyandu, namely the Percentage of Skeletal Muscle with hypertension P value = 0.576 (CC=-0.075).

Using total-body PET to investigate relationships between excretory organs time activity curves and the metabolite fraction for [18F]SynVesT-1

Accurate quantification of dynamic PET data using kinetic modelling requires assessment of the metabolism of the tracer to obtain the quantity of unchanged parent tracer in the blood. Several image derived input functions methods have been demonstrated to be effective for kinetic modelling; however, currently no image derived metabolite correction method exists. Finding an image derived metabolite correction would remove the need for invasive blood sampling. This study uses 7 full body 18F-SynVesT-1 total body mouse scans. Initially, the time activity curves (TACs) for a range of organs including the liver, kidney and bladder were studied. Factor analysis (FA) was investigated to accurately separate contributions from the kidney – cortex, medulla and collecting duct. Following this, the relationships between each of the organ TACs and the parent plasma fraction (PPf) was investigated. FA analysis successfully discriminates between contributions from the cortex, medulla and collecting duct of the kidneys. In the context of total body scans, FA removes the need for manual drawing of volumes of interest allowing for more consistency amongst studies. By separating out these regions, a relationship between the kidney medulla and the metabolites also came to light. In addition, the average of the seven bladder time activity curves against the plasma parent fraction [Figure 1] shows a potential relationship between the excretion of the tracer and its metabolism. Combining excretory organ relationships with the parent fraction and potentially liver data opens the possibility for a model to be produced to fit a metabolite curve when we have access to a total body PET scan. While this research is in its early stages, it has shown relationships between excretory organ TACs and the metabolite fraction. This has the potential to allow for metabolite correction models to be created directly from the image data. Model architectures are under investigation. Please click on the 'PDF' for the full abstract!