Single Shot Research Articles

Laser driven FLASH radiobiology using a high dose and ultra high dose rate single pulse proton source

Laser-driven proton sources have long been developed with an eye on their potential for medical application to radiation therapy. These sources are compact, versatile, and show peculiar characteristics such as extreme instantaneous dose rates, short duration and broad energy spectrum. Typical temporal modality of laser-driven irradiation, the so-called fast-fractionation, results from the composition of multiple, temporally separated, ultra-short dose fractions. In this paper we present the use of a high-energy laser system for delivering the target dose in a single nanosecond pulse, for ultra-fast irradiation of biological samples. A transport line composed by two permanent-magnet quadrupoles and a scattering system is used to improve the dose profile and to control the delivered dose-per-pulse. A single-shot dosimetry protocol for the broad-spectrum proton source using Monte Carlo simulations was developed. Doses as high as 20 Gy could be delivered in a single shot, lasting less than 10 ns over a 1 cm diameter biological sample, at a dose-rate exceeding . Exploratory application of extreme laser-driven irradiation conditions, falling within the FLASH irradiation protocol, are presented for irradiation in vitro and in vivo. A reduction of radiation-induced oxidative stress in vitro and radiation-induced developmental damage compatible with the onset of FLASH effect were observed in vivo, whereas anti-tumoral efficacy was confirmed by cell survival assay.

Comparative Analysis of CNN-Based Object Detection Models: Faster R-CNN, SSD, and YOLO

Target detection is widely used in the current environment. With the rapid development of deep learning, innovative models like Convolutional Neural Networks (CNNs) were born. CNNs have been widely used in many practical applications for object detection, since CNNS outperform traditional models in terms of speed and accuracy. This paper first introduces three well-known CNN-based target detection models: Region-based Convolutional Neural Network (Faster R-CNN), Single Shot Multibox Detector (SSD) and You Only Look Once (YOLO). Then this paper gives data on speed, accuracy and resource consumption. Based on these data, the advantages and disadvantages of these three models in different scenarios are systematically and comprehensively evaluated and analyzed. The purpose of the paper is to give researchers a deep understanding of the different characteristics of the three models and which model should be used best in what situations by examining their strengths and limitations. Finally, this paper analyzes the different characteristics of the three models to facilitate the researcher's possible subsequent improvements.

Squat kinematics of osteoarthritic knees after intra-articular viscosupplementation: an analysis of secondary outcomes from a double-blinded randomized controlled trial

BackgroundViscosupplementation for knee osteoarthritis (OA) aims to minimize pain and improve joint function. However, its effects on knee biomechanics during squat activities have not been investigated. This study aimed to assess the effects of viscosupplementation on squat biomechanics of older adults with late-stage knee osteoarthritis utilizing three-dimensional (3D) motion capture technology.MethodsThis study is a multiple-blinded, randomized, single-center, placebo-controlled trial with a 1:1 allocation ratio. Forty-two older individuals (72.6 ± 6.5 years) with advanced knee OA were randomly allocated into two groups to receive viscosupplementation or placebo (saline injection). Kinematic data were collected by a 3D motion analysis system 1 week before and 1, 6, and 12 weeks after the intervention. Dependent variables included maximal vertical displacement of center of mass (CoM), CoM position in the mediolateral axis, knee range of motion between initial and lowest CoM vertical position, and knee angles at lowest vertical CoM position in sagittal, coronal and axial planes (primary outcomes). Data were compared between groups using mixed linear models, with a significance level of 0.05. A repeated measures ANOVA was conducted within each group to assess changes over time if significant differences between groups were observed.ResultsThe viscosupplementation group showed a statistically significant difference in maximum knee internal rotation at lowest vertical CoM position (4.1° 95%CI [0.6 to 7.5]– p = 0.02) during squat at 12 weeks. None of the other variables showed statistically significant results (p > 0.05). There was no difference in knee internal rotation angle at 1, 6, or 12 weeks compared to baseline in the viscosupplementation group (p = 0.307).ConclusionThis study suggests that a single shot of intra-articular viscosupplementation may help preserve knee biomechanics during squatting in patients with late-stage knee OA in the medium term. Future studies should explore the relationship between biomechanical improvements and clinical symptoms.Trial registrationThe trial was registered in the Brazilian Clinical Trials Registry (No. RBR-3n52h4). Date of registration: 08/30/2017.

Evaluation of the Temporal Characteristics of Ultrafast Imaging Methods Using Continuous Chirped Pulse Illumination

Ultrafast imaging based on chirped pulse illumination has opened new frontiers, offering a frame rate beyond 1 Tfps for the acquisition of multiple frames in a single–shot. However, the temporal resolving capability is implicitly influenced by parameters in stages of pulse illumination and data acquisition. This study delivers a mathematical model to produce a precise investigation, sorting the dominating factors, including the illumination pulse’s bandwidth λFWHM, dispersive propagation length z, and framing module’s spectral resolution Δλ. For a different λFWHM, z has a lower bound to ensure the covered signal is resolved; meanwhile, the time resolution decreases with a larger z. Frame extraction with a narrower Δλ leads to a higher time resolution; however, Δλ must be broad enough for a reasonable signal-to-noise ratio. The theoretical and experimental approaches to evaluate temporal characteristics are discussed, enabling a precise quantitative determination for the community to produce, use, and exploit single-shot ultrafast imaging systems.

Optimization of MR acoustic radiation force imaging (MR-ARFI) for human transcranial focused ultrasound.

MR acoustic radiation force imaging (MR-ARFI) is an exceptionally promising technique to non-invasively confirm targeting accuracy and estimate exposure of low-intensity transcranial focused ultrasound applications. Implementing MR-ARFI in the human brain has been hindered by (1) sensitivity to subject motion, and (2) insufficient SNR at low (<1.0 MPa) ultrasound pressures. The purpose of this study was to optimize human MR-ARFI to allow reduced ultrasound exposure while at the same time being robust to bulk and physiological motion. We developed a novel timeseries approach to MR-ARFI with a single-shot spiral-out MRI sequence and correction for respiratory and cardiac motion artifacts. An MR-compatible four-element 500 kHz focused ultrasound transducer was coupled to the head and targeted to 60 mm depth in five participants. During spiral scans, two 6 ms focused ultrasound pulses (0.5-0.9 MPa in situ) were delivered in on-off blocks of 25 time frames. Our method generates ARFI maps that with correction are largely immune to bulk and pulsatile brain motion with reduced scan time (80 s per acquisition). Robust ARFI signals were observed at the expected target in four human participants, using low intensity ultrasound that does not produce significant tissue heating, confirmed both by simulation and MR thermometry. Single shot spiral MR-ARFI is motion robust in human applications, provides reduction in ultrasound exposure, and reduced scan time, enabling iteration for image-guided targeting. This provide persuasive proof-of-principle that MR-ARFI can be used as a tool to guide ultrasound-based precision neural circuit therapeutics.

Single-shot ultrafast imaging based on round-view projection (RVP)

Current ultrafast imaging techniques necessitate single-shot continuous recording capabilities to capture non-repetitive ultrafast phenomena. Among various methods, projection-based ultrafast imaging methods have garnered significant attention due to their ability to acquire multiple frames in a single exposure. However, the reconstruction accuracy of these methods is fundamentally constrained by the limited number of projection directions and partial angular coverage. In this study, we introduce a spectral-temporal ultrafast imaging system based on the round-view projection (RVP), which enables comprehensive data acquisition through multiple quasi-omnidirectional projections, facilitating effective compression and reconstruction of spatiotemporal data cubes. Numerical simulations demonstrate that the RVP achieves superior reconstruction fidelity by capturing quasi-omnidirectional characteristic information. In the experimental works, we captured the dynamics of laser-induced air plasma, such as shockwave propagations and plasma expansion, with 22 frames in a single shot. This work not only presents a robust ultrafast imaging methodology but also provides valuable insight for advancing related ultrafast imaging research.

Characterization of the energy of runaway electron bunches by analyzing emitted microwave radiation

A novel experimental approach to estimating the kinetic energy of a picosecond runaway electron bunch formed in an air electrode gap is proposed. When such an electron bunch travels in a longitudinal magnetic field through a hollow circular waveguide or a waveguide partially loaded with a dielectric, it emits microwave radiation having spectral peaks associated with cyclotron or Cherenkov excitation of the waveguide modes. The first mechanism cannot be invoked to estimate the bunch energy in a single shot, since such an estimation requires identification of the excited modes. The Cherenkov mechanism of excitation of slow modes in a dielectric-loaded waveguide, on the contrary, allows one to estimate the electron energy, since the dispersion curves of the lowest, predominantly excited TE11 and TM01 modes, degenerate starting from a certain frequency. Therefore, when radiation of a higher frequency is excited, the electron energy of hundreds of kiloelectronvolts can be determined for a single radio frequency pulse both at the axis and in the periphery of its radiation pattern. Application of a strong guide magnetic field makes it possible to tune out the parasitic electron cyclotron interaction and discriminate the spectral peaks of Cherenkov radiation, which has been proven to be coherent in this case.

High-resolution direct phase control in the spectral domain in ultrashort pulse lasers for pulse-shaping applications

Ultrafast laser systems, those with a pulse duration on the order of picoseconds or less, have enabled advancements in a wide variety of fields. Of particular interest to this work, these laser systems are the key component to many High Energy Density (HED) physics experiments. Despite this, previous studies on the shape of the laser pulse within the HED community have focused primarily on pulse duration due to the relationship between pulse duration and peak intensity, while leaving the femtosecond scale structure of the pulse shape largely unstudied. To broaden the variety of potential pulses available for study, a method of reliably adjusting the pulse shape at the femtosecond scale using sub-nanometer resolution Direct Phase Control has been developed. This paper examines the capabilities of this new method compared to more commonplace dispersion-based pulse shaping methods. It also will detail the capabilities of the core algorithm driving this technique when used in conjunction with the WIZZLER and DAZZLER instruments that are common in high intensity laser labs. Performance of the method and instrumentation is examined using data taken with a single shot FROG system. Finally, some discussion is given to possible applications on how the Direct Phase Control pulse shaping technique will be implemented in the future.

Rapid E-FISH measurements using a laser optical loop

Abstract Rapid electric field–induced second harmonic generation measurements are performed using a laser optical loop approach, which facilitates the use of each single laser shot several times. From a single laser shot, E-FISH signals at multiple points in the time domain are generated and recorded as a single waveform. It is also demonstrated experimentally that the polarization-sensitive nature of E-FISH allows to tailor the E-FISH response sensitivity within the pulse train traveling in the optical loop to optimize the measured signal waveform for a given electric field profile while avoiding the saturation of the detector and extending the dynamics range of the measurements.

Compressive phase-shifting fringe projection profilometry for accelerating 3D metrology.

Phase-shifting fringe projection profilometry (PSFPP) has emerged as an indispensable optical metrology technique for capturing the profiles of three-dimensional (3D) objects with high accuracy and low cost. However, the measurement speed of the existing PSFPP techniques is constrained by the frame rate of the cameras used, which limits its ability to capture high-speed 3D scenes. To address this limitation, we develop a novel compressive phase-shifting fringe projection profilometry (CPSFPP) technique, to our knowledge, that improves the imaging speed of conventional PSFPP by incorporating a compressive sensing (CS) method. By compressively sampling distorted fringe images with varying phase shifts in a single shot and subsequently recovering these images through CS-based image reconstruction algorithm, CPSFPP increases the 3D measurement speed by multi-fold. To demonstrate the high-speed imaging capabilities of CPSFPP, we experimentally record the dynamic evolutions of translational, rotational, and deformed 3D objects, achieving a threefold improvement in measurement speed. CPSFPP offers a versatile tool for high-speed 3D metrology, greatly enhancing the observation capabilities of dynamic 3D scenes and promoting applications in related fields.

Prostate Cancer Risk Stratification and Scan Tailoring Using Deep Learning on Abbreviated Prostate MRI.

MRI plays a critical role in prostate cancer (PCa) detection and management. Bi-parametric MRI (bpMRI) offers a faster, contrast-free alternative to multi-parametric MRI (mpMRI). Routine use of mpMRI for all patients may not be necessary, and a tailored imaging approach (bpMRI or mpMRI) based on individual risk might optimize resource utilization. To develop and evaluate a deep learning (DL) model for classifying clinically significant PCa (csPCa) using bpMRI and to assess its potential for optimizing MRI protocol selection by recommending the additional sequences of mpMRI only when beneficial. Retrospective and prospective. The DL model was trained and validated on 26,129 prostate MRI studies. A retrospective cohort of 151 patients (mean age 65 ± 8) with ground-truth verification from biopsy, prostatectomy, or long-term follow-up, alongside a prospective cohort of 142 treatment-naïve patients (mean age 65 ± 9) undergoing bpMRI, was evaluated. 3 T, Turbo-spin echo T2-weighted imaging (T2WI) and single shot EPI diffusion-weighted imaging (DWI). The DL model, based on a 3D ResNet-50 architecture, classified csPCa using PI-RADS ≥ 3 and Gleason ≥ 7 as outcome measures. The model was evaluated on a prospective cohort labeled by consensus of three radiologists and a retrospective cohort with ground truth verification based on biopsy or long-term follow-up. Real-time inference was tested on an automated MRI workflow, providing classification results directly at the scanner. AUROC with 95% confidence intervals (CI) was used to evaluate model performance. In the prospective cohort, the model achieved an AUC of 0.83 (95% CI: 0.77-0.89) for PI-RADS ≥ 3 classification, with 93% sensitivity and 54% specificity. In the retrospective cohort, the model achieved an AUC of 0.86 (95% CI: 0.80-0.91) for Gleason ≥ 7 classification, with 93% sensitivity and 62% specificity. Real-time implementation demonstrated a processing latency of 14-16 s for protocol recommendations. The proposed DL model identifies csPCa using bpMRI and integrates it into clinical workflows. 1. Stage 2.

Hollow silver nanoparticle formation under ultrafast laser irradiation via single- and multiple-shots.

The morphological changes induced in metal nanoparticles by the interaction with laser pulses have an important impact on their optical response. In this work, by means of an atomistic model, we have studied the formation of cavities in spherical silver nanoparticles embedded in amorphous silica using one or more femtosecond laser pulses. The model allows us to identify the different processes that lead to cavity formation and how they affect the variation of the aspect ratio, i.e., the relationship between the size of the cavity and that of the metallic sphere. The model is used to explain experiments revealing the conditions necessary to produce hollow metal nanoparticles. New information on hollow nanoparticle formation both in single shot and multiple shot regimes is reported. The atomistic model in combination with an optical model constitutes a tool to tune the properties of hollow nanoparticles, as shown in this paper. This way, we can achieve a fine control over the aspect ratio and, thus, about the localized surface plasmon resonance of the hollow nanoparticles.

Ultrafast Laser-Induced 1T'/2H-MoTe2 Nanopattern with Au-Nanoclusters for Raman Monitoring of Cellular Drug Metabolism.

The development of surface-enhanced Raman spectroscopy (SERS) as an ultrasensitive fingerprint analysis technique in precision medicine requires high-performance SERS substrates with controllable nanostructure (hot-spot) distribution, simple fabrication, superior stability, biocompatibility, and extraordinary optical responses. Unfortunately, fabrication of arbitrary nanostructures with high homogeneity on a large scale for SERS is still challenging. Herein, we report an ultrafast laser parallel fabrication protocol for Au/2D-transition-metal dichalcogenide hybrid SERS biosensors. The leveraged photonic nanojets (PNJs) are generated by a micron-sized microsphere monolayer to simultaneously trigger localized phase transition in 2H-MoTe2, achieving a 1T'-MoTe2 nanopattern array with a density of 1 million per mm2 by a single laser shot. The Au nanoparticle clusters (AuNCs) are subsequently grown in situ from the 1T' regions, creating a AuNCs on 1T'/2H-MoTe2 (AuNCs@1T'/2H-MoTe2) hybrid SERS substrate. The fabricated feature diameter and overlay accuracy of the patterned AuNCs are 210.1 ± 3.4 and 9.2 ± 1.7 nm, respectively. To eliminate background noise, we designed dimer-AuNCs@1T'/2H-MoTe2 (dAuNCs@1T'/2H-MoTe2), achieving a detection limit of 10-13 M with an enhancement factor of 4.9 × 108 for the methylene blue (MB) analyte. The strong localized surface plasmon resonances in the dAuNCs as well as efficient charge transfers between Au, 2H-MoTe2, and MB contribute to the majority of Raman enhancement. The multiscale dAuNCs@1T'/2H-MoTe2 array provides a powerful SERSome (comprising multiple SERS spectra) platform for therapeutic drug monitoring, by which we successfully identified the metabolic behaviors of living gastric adenocarcinoma cells administered with two drugs, i.e., capecitabine, oxaliplatin, and their combination. The present work establishes opportunities for creating a highly ordered nanopattern array for ultrasensitive SERSome analysis of cell metabolism in cancer therapy.

Retrospective Clinical Trial to Evaluate the Effectiveness of a New Tanner-Whitehouse-Based Bone Age Assessment Algorithm Trained with a Deep Neural Network System.

Background/Objectives: To develop an automated deep learning-based bone age prediction model using the Tanner-Whitehouse (TW3) method and evaluate its feasibility by comparing its performance with that of pediatric radiologists. Methods: The hand and wrist radiographs of 560 Korean children and adolescents (280 female, 280 male, mean age 9.43 ± 2.92 years) were evaluated using the TW3-based model and three pediatric radiologists. Images with bony destruction, congenital anomalies, or non-diagnostic quality were excluded. A commercialized AI solution built upon the Rotated Single Shot MultiBox Detector (SSD) and EfficientNet-B0 was used. Bone age measurements from the model and radiologists were compared using the paired t-tests. Linear regression analysis was performed and the coefficient of determination (r²), mean absolute error (MAE), and root mean square error (RMSE) were measured. A Bland-Altman analysis was conducted and the proportion of bone age predictions within 0.6 years of the radiologists' assessments was calculated. Results: The TW3-based model demonstrated no significant differences between bone age measurements and radiologists, except for participants <6 and >13 years old (overall, p = 0.874; 6-8 years, p = 0.737; 8-9 years, p = 0.093; 9-10 years, p = 0.301; 10-11 years, p = 0.584; 11-13 years, p = 0.976; <6 or >13 years, p < 0.001). There was a strong linear correlation between the model prediction and radiologist assessments (r2 = 0.977). The RMSE and MAE values of the model were 0.529 (95% CI, 0.482-0.575) and 0.388 (95% CI, 0.361-0.417) years. Overall, 82.3% of bone age model predictions were within 0.6 years of the radiologists' interpretation. Conclusions: Automated deep learning-based bone age assessment has the potential to reduce radiologists' workload and provide standardized measurements for clinical decision making.

Using deep learning target detection network to improve the ability to identify small targets in remote sensing images

ABSTRACT To address the problem of insufficient performance in detecting STs (small targets) in RS (remote sensing) images, this paper improves the Faster R-CNN (Faster Region-based Convolutional Neural Network) network to improve the ability to extract and locate ST, in order to meet the challenges of insufficient DA (detection accuracy) and generalization. By introducing FPN (Feature Pyramid Network), SE (Squeeze-and-Excitation) modules, context information fusion and DIoU (Distance Intersection over Union) loss function, detection network structure is optimized, significantly improving the accuracy and efficiency of small TD (target detection) in RS images. The model uses FPN to construct a multi-scale feature pyramid, which enhances the adaptability to targets of different sizes, especially in detection of ST. Through experiments on the DIOR (Dataset for Intelligent Object Recognition) dataset, the model outperforms traditional TD models such as SSD (Single Shot MultiBox Detector), YOLOv7 (You Only Look Once version 7) and CDA-YOLO (Contextual Data Augmentation for You Only Look Once) in terms of mAP (Mean Average Precision) and AR (Average Recall) indicators. This study not only provides effective method for small TD in RS images, but also has a wide range of practical application value, such as disaster monitoring, agricultural resource management and other fields.

Shaping ultrafast Bessel-like beamlets for single-pass laser chamfering of glass.

A methodology to micro-machine glass with a chamfered edge geometry using a single-pass ultrafast laser process is described. An input laser source is shaped into three spatially and temporally coordinated Bessel beamlets with controlled phases, each of which forms a single segment of a continuous C-chamfer shaped intensity profile. Beamlets are simultaneously deployed to induce damage (nanoperforation) extending through the thickness of a glass sample in a single laser shot. The methodology is experimentally demonstrated, generating controlled nanoperforation contours which produce C-chamfer shaped glass edges in a single laser cutting pass. Chamfered glass parts can be easily separated along nanoperforation contours via application of simple mechanical bend stress, with no need for additional post-process release steps, such as etching. This method has the potential to create laser-based chamfers for industrial glass processing applications with fast takt times, minimal waste, and low cost-to-throughput ratios.

Denoising 3D integral images by a single-shot unsupervised deep neural network.

Integral imaging is a passive three-dimensional imaging technique that captures the radiance field through an array of apertures, offering promising applications in various fields such as 3D microscopy, metrology, and augmented reality, amongst others. However, integral imaging often suffers from noise and deterioration of image quality that can compromise the 3D image feature extraction or visualization. Current state-of-the-art denoising methods for integral imaging rely heavily on deep neural networks. Yet, these methods face a significant hurdle due to the scarcity of ground-truth integral imaging databases required for supervised learning. Furthermore, they are limited only to the imaging conditions of the predetermined training data. In this paper, we propose a new unsupervised deep learning method for integral imaging denoising using a single integral image shot. Our single-shot Noise2Noise method exploits the inherent similarity between elemental and integral imaging. Thus, it adapts to the particular image conditions during the acquisition and can increase the image quality without needing clean images or prior knowledge of the noise model.

Boosting the Pixel Acquisition Rate of Elemental Mapping with Laser Ablation-ICP-Mass Spectrometry to 1000 Hz.

Novel low-dispersion ablation cell designs and highly efficient aerosol transport systems have enabled fast elemental mapping using laser ablation-ICP-mass spectrometry (LA-ICP-MS) at high spatial resolution and its application in various research fields. Nowadays, the fastest low-dispersion setups enable narrow single pulse responses (SPR, duration of the transient signal observed upon a single laser shot), which enhance the signal-to-noise ratio and boost the pixel acquisition rate attainable in elemental mapping applications. In this work, the analytical performance of a nanosecond 193 nm ArF* excimer-based kHz laser in combination with a low-dispersion tube-type ablation cell, coupled to an ICP-mass spectrometer equipped with a time-of-flight (ToF) analyzer, was evaluated. SPR profiles exhibited a duration below 1 ms (defined as full peak width at 10% of the peak maximum, FW0.1M) upon ablation of a NIST SRM 610 glass reference material (0.7 ± 0.1 ms) and a multielement-spiked gelatin droplet standard (0.6 ± 0.1 ms) using a 5 μm laser spot size and matrix-optimized laser energy density. Parameters such as pulse-to-pulse energy stability, linearity of the signal response, oxide ion formation, and elemental fractionation were evaluated. The duration of the SPR profiles determines the maximum achievable pixel acquisition rate, enabling up to 1000 pixels/s with the setup evaluated. As a proof of concept, this is illustrated via quantitative multielemental mapping of a highly primitive chondritic meteorite, displaying a fine mineral texture and heterogeneous elemental distributions, at high spatial resolution and firing only a single shot per pixel.

Development and characterization of a laser-gated, high resolution x-ray radiography platform for high energy density experiments using toroidally bent crystals

Bent crystal x-ray imagers are a well-established diagnostic tool to study high energy density (HED) objects by acquiring two-dimensional x-ray radiographs. Often, studying these HED objects requires very high spatial resolution, which is limited by astigmatism when using spherically bent crystals. By using toroidal-shaped crystals instead, astigmatism can be reduced and the overall spatial resolution of the instrument improved. Here, the development and characterization of a laser-gated x-ray radiography platform at the National Ignition Facility using a toroidal crystal is presented. This includes measurements of the spatial and temporal profile, which is determined by the x-ray source. To properly validate the reduced astigmatism, a new multi-plane grid approach was implemented, which, for the first time, allows a depth of field measurement, alignment verification and x-ray source size estimate in a single shot, demonstrating the current platform’s capability to provide sub-10 μm resolution over a 0.9 mm depth of field with a temporal resolution of 110 ps.

P080 Screening for osteoporosis in primary care using FRAX and pre-existing healthcare data: a pilot study

Abstract Background/Aims It is estimated that 1 in 2 adult women have a low trauma fracture during their lifetime. The ‘screening in the community to reduce fractures in older women’ (SCOOP) study identified women between the ages of 70-85 years at high risk of fracture by screening patients using the FRAX risk-assessment tool. Patients calculated to be at intermediate/high risk of hip fracture were referred for DXA scans. The results allowed refinement of risk and led to a recommendation for treatment in those most likely to fracture. The study demonstrated a 28% reduction in the incidence of hip fractures in the screened group over 5 years compared with usual care. A post-hoc analysis of SCOOP demonstrated that in individuals with the highest baseline estimated hip fracture risk, a 50% decrease in the incidence of hip fractures was achieved. In this pilot we aimed to assess the feasibility of implementing the SCOOP screening protocol in a real-world setting, using existing healthcare record data. Methods A GP (NJ) used System One to identify 100 women aged between 70 and 85 years. Those with a diagnosis of osteoporosis or who had received bisphosphonates or who had undergone DXA scanning within the last 3 years were excluded. A FRAX questionnaire was sent to the patients together with an explanatory note and stamped surgery addressed envelope. FRAX returns were then scored (NJ). Those at medium or high risk of fracture were offered a DXA scan. Results were transmitted to the referring GP and the patient advised to attend for review. Results 100 FRAX questionnaires were sent in early 2020. 71 were returned. FRAX assessment (1 hour of GP time) revealed 42 patients at low risk (reassured) and 29 patients referred for DXA scan. 14/29 (48%) aged 77-82 years attended. 15 cancelled or did not attend. 7/14 of scanned patients had osteoporotic bone and were recommended a bisphosphonate. In addition, a further 2/7 patients were recommended treatment, subject to confirmation of a previous low impact fracture. 5/14 patients were reassured and advised on lifestyle. Conclusion A single mail shot to women (70-85 years) resulted in a 71% response rate (SCOOP trial return rate 33%). Scoring returns and making the DXA referrals took one hour of GP time. However, only 14/29 women attended for DXA. The proportion may have been higher outside the COVID-19 pandemic. 9/14 (64%) of those referred for scan were recommended drug treatment. Bisphosphonate counselling and prescription would commit GPs (or potentially other practice staff) to additional patient contact time. The original SCOOP trial suggested 111 FRAX questionnaires need to be completed to save one hip fracture. A larger, real-world study would need to determine the health economics/costs around increased requirement for health professional input and patient concordance with drug therapy over time. Disclosure R. Ali: None. S.A. Hardcastle: None. J. Shipley: None. N. Jones: None. S. Clarke: None.