Random Scans Research Articles

Precise Image-level Localization of Intracranial Hemorrhage on Head CT Scans with Deep Learning Models Trained on Study-level Labels.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To develop a highly generalizable weakly supervised model to automatically detect and localize image- level intracranial hemorrhage (ICH) using study-level labels. Materials and Methods In this retrospective study, the proposed model was pretrained on the image-level RSNA dataset and fine-tuned on a local dataset using attention-based bidirectional long-short-term memory networks. This local training dataset included 10,699 noncontrast head CT scans from 7469 patients with ICH study-level labels extracted from radiology reports. Model performance was compared with that of two senior neuroradiologists on 100 random test scans using the McNemar test, and its generalizability was evaluated on an external independent dataset. Results The model achieved a positive predictive value (PPV) of 85.7% (95% CI: [84.0%, 87.4%]) and an AUC of 0.96 (95% CI: [0.96, 0.97]) on the held-out local test set (n = 7243, 3721 female) and 89.3% (95% CI: [87.8%, 90.7%]) and 0.96 (95% CI: [0.96, 0.97]), respectively, on the external test set (n = 491, 178 female). For 100 randomly selected samples, the model achieved performance on par with two neuroradiologists, but with a significantly faster (P < .05) diagnostic time of 5.04 seconds per scan (versus 86 seconds and 22.2 seconds for the two neuroradiologists, respectively). The model's attention weights and heatmaps visually aligned with neuroradiologists' interpretations. Conclusion The proposed model demonstrated high generalizability and high PPVs, offering a valuable tool for expedited ICH detection and prioritization while reducing false-positive interruptions in radiologists' workflows. ©RSNA, 2024.

A new computed tomography protocol to assess the olfactory cleft

INTRODUCTION: The olfactory cleft is a region of significant importance within the nasal cavity, given the heightened focus on olfactory disorders in recent years. It is considered a 3-dimensional space that starts at the most anterior olfactory filament and ends just anteriorly to the sphenoid sinus. This study aimed to outline the profile of the olfactory cleft as an anatomical area of extreme relevance in the diagnosis of diseases of the nose, paranasal sinuses and olfactory disorders. The aim to establish parameters of radiological normality for the olfactory cleft enables us to recognize and differentiate nasal disorders by identifying the widening or narrowing of the olfactory cleft during the preoperative assessment, thus facilitating the planning of the optimal treatment. This supports a theory about the evolution and development of the nose (Evo Devo Theory) that shows the olfactory nose (ethmoid and olfactory cleft) represents the seat of most diseases of the nose and paranasal sinuses. MATERIALS AND METHODS: 700 random CT scans of the nose and sinuses have been studied. A homogeneous group of CT scans of patients without paranasal sinus diseases were included in this study, seeking to define a reference anatomical measure as a criterion for tomographic normality. Measurements of the olfactory cleft were taken in the lateral-lateral direction between the middle concha and the septum on each side of the nose cavity at three specific anatomical points in the anterior skull base in the anteroposterior direction, corresponding to the beginning, middle and end of the olfactory cleft. For data analysis, exploratory statistical techniques have been used that allowed a better visualization of the general characteristics of the data. The appropriate statstical analysis was performed using Statistical Package for Social Sciences (SPSS). RESULTS: According to the statistical analysis performed, the reference values that were found evaluating and studying the three points were: at the first point - First Olfactory Neuron Point (FONP) – was 1.3 to 2.1 mm, at the second point - Middle Point (MP) was 1.1 to 1.6 mm, and at the third point – Sphenoethmoidal recess point (SERP) was 1.1 to 1.8 mm. The average in the first point was 1.78 mm, while in the second and third points were 1.41 and 1.48 mm, respectively. CONCLUSION: It is concluded that the tomographic olfactory cleft reference values are respectively: at the first point – First Olfactory Neuron Point- 1.3 to 2.1 mm, at the second point - Middle Point - 1.1 to 1.6 mm, and at the third point – Sphenoethmoidal recess point -1.1 to 1.8 mm.

String Model Building on Quantum Annealers

AbstractFor the first time the direct construction of string models on quantum annealers has been explored and has been investigated their efficiency and effectiveness in the model discovery process. Through a thorough comparison with traditional methods such as simulated annealing, random scans, and genetic algorithms, it is highlighted the potential advantages offered by quantum annealers, which in this study promised to be roughly 50 times faster than random scans and genetic algorithm and approximately four times faster than simulated annealing.

Walk-through flat panel total-body PET: a patient-centered design for high throughput imaging at lower cost using DOI-capable high-resolution monolithic detectors

PurposeLong axial field-of-view (LAFOV) systems have a much higher sensitivity than standard axial field-of-view (SAFOV) PET systems for imaging the torso or full body, which allows faster and/or lower dose imaging. Despite its very high sensitivity, current total-body PET (TB-PET) throughput is limited by patient handling (positioning on the bed) and often a shortage of available personnel. This factor, combined with high system costs, makes it hard to justify the implementation of these systems for many academic and nearly all routine nuclear medicine departments. We, therefore, propose a novel, cost-effective, dual flat panel TB-PET system for patients in upright standing positions to avoid the time-consuming positioning on a PET-CT table; the walk-through (WT) TB-PET. We describe a patient-centered, flat panel PET design that offers very efficient patient throughput and uses monolithic detectors (with BGO or LYSO) with depth-of-interaction (DOI) capabilities and high intrinsic spatial resolution. We compare system sensitivity, component costs, and patient throughput of the proposed WT-TB-PET to a SAFOV (= 26 cm) and a LAFOV (= 106 cm) LSO PET systems.MethodsPatient width, height (= top head to start of thighs) and depth (= distance from the bed to front of patient) were derived from 40 randomly selected PET-CT scans to define the design dimensions of the WT-TB-PET. We compare this new PET system to the commercially available Siemens Biograph Vision 600 (SAFOV) and Siemens Quadra (LAFOV) PET-CT in terms of component costs, system sensitivity, and patient throughput. System cost comparison was based on estimating the cost of the two main components in the PET system (Silicon Photomultipliers (SiPMs) and scintillators). Sensitivity values were determined using Gate Monte Carlo simulations. Patient throughput times (including CT and scout scan, patient positioning on bed and transfer) were recorded for 1 day on a Siemens Vision 600 PET. These timing values were then used to estimate the expected patient throughput (assuming an equal patient radiotracer injected activity to patients and considering differences in system sensitivity and time-of-flight information) for WT-TB-PET, SAFOV and LAFOV PET.ResultsThe WT-TB-PET is composed of two flat panels; each is 70 cm wide and 106 cm high, with a 50-cm gap between both panels. These design dimensions were justified by the patient sizes measured from the 40 random PET-CT scans. Each panel consists of 14 × 20 monolithic BGO detector blocks that are 50 × 50 × 16 mm in size and are coupled to a readout with 6 × 6 mm SiPMs arrays. For the WT-TB-PET, the detector surface is reduced by a factor of 1.9 and the scintillator volume by a factor of 2.2 compared to LAFOV PET systems, while demonstrating comparable sensitivity and much better uniform spatial resolution (< 2 mm in all directions over the FOV). The estimated component cost for the WT-TB-PET is 3.3 × lower than that of a 106 cm LAFOV system and only 20% higher than the PET component costs of a SAFOV. The estimated maximum number of patients scanned on a standard 8-h working day increases from 28 (for SAFOV) to 53–60 (for LAFOV in limited/full acceptance) to 87 (for the WT-TB-PET). By scanning faster (more patients), the amount of ordered activity per patient can be reduced drastically: the WT-TB-PET requires 66% less ordered activity per patient than a SAFOV.ConclusionsWe propose a monolithic BGO or LYSO-based WT-TB-PET system with DOI measurements that departs from the classical patient positioning on a table and allows patients to stand upright between two flat panels. The WT-TB-PET system provides a solution to achieve a much lower cost TB-PET approaching the cost of a SAFOV system. High patient throughput is increased by fast patient positioning between two vertical flat panel detectors of high sensitivity. High spatial resolution (< 2 mm) uniform over the FOV is obtained by using DOI-capable monolithic scintillators.

(445) A Full Bladder Is Not Needed To Grade Male Stress Incontinence For Prosthesis Patient Selection

Abstract Introduction Stress urinary incontinence (SUI) resulting from prostate surgery can have devastating consequences in men’s quality of life. Patient selection is crucial for successful treatment of SUI with urethral sling or artificial urinary sphincter (AUS). The ideal SUI evaluation tool for determining severity and guiding surgical treatment remains unknown. The Male Stress Incontinence Grading Scale (MSIGS) uses a standing cough test to provide a simple, non-invasive assessment of post-prostatectomy SUI severity, and can facilitate procedure selection. However, uncertainty remains as to whether MSIGS underestimates SUI severity if performed with insufficiently full bladder, increasing the risk of treatment failure. Objective We present our sling and AUS outcomes using the MSIGS for selection. We assessed bladder fullness with random bladder scans (BS) at the time of evaluation to determine if emptier bladder carries increased risk of failure. Methods We conducted a retrospective chart review of male patients undergoing sling and AUS placement. MSIGS and BS were documented at initial evaluation. Additional collected data included age, pads per day (PPD) pre- and post-surgery, post-operative MSIGS, and need for revision or conversion. Success and failure groups were defined as the post-operative use of ≤1 PPD or &amp;gt;1 PPD, respectively. Descriptive and comparative statistics were performed using SAS software. Results From April 2017 to September 2021, 40 male patients underwent sling and 43 naïve AUS placement. Average age was 71.9 for slings and 70.3 for AUS. 80/83 had prior prostatic surgery. Average follow up was 12.5 months. 34/40 slings had complete SUI resolution or reduction of pad use to no more than one PPD vs 40/43 of AUS (85 vs 93%, p=0.4). 4 sling patients (10%) had persistence or recurrence of SUI, of whichone patient underwent AUS placement. MSIGS scores were similar between sling success (83% patients grade 0, 3% grade 1, 14% grade 2) and failure groups (67% grade 0, 33% grade 1). 73% of AUS patient mostly had MSIGS scores 3 or 4. BS was available for 35 sling and 25 AUS patients. Average was 18.5cc in the sling failure group (range 17-161) and 38cc in the sling success, (17-161), with 32% of success patients having a BS of 0cc, and 80% of &amp;lt;60cc. Average BS for AUS patients was 45cc (14-224). 10 patients with BS 0 and 7 patients with BS&amp;lt;30cc demonstrated MSIGS 4 on exam. Conclusions MSIGS is a non-invasive, reliable test to assess SUI severity. Our preliminary data suggests that a full bladder at time of assessment is not necessary to accurately select patients for male sling vs AUS, with high rate of success and patient satisfaction. Disclosure No

Efficient labelling for efficient deep learning: the benefit of a multiple-image-ranking method to generate high volume training data applied to ventricular slice level classification in cardiac MRI.

Getting the most value from expert clinicians' limited labelling time is a major challenge for artificial intelligence (AI) development in clinical imaging. We present a novel method for ground-truth labelling of cardiac magnetic resonance imaging (CMR) image data by leveraging multiple clinician experts ranking multiple images on a single ordinal axis, rather than manual labelling of one image at a time. We apply this strategy to train a deep learning (DL) model to classify the anatomical position of CMR images. This allows the automated removal of slices that do not contain the left ventricular (LV) myocardium. Anonymised LV short-axis slices from 300 random scans (3,552 individual images) were extracted. Each image's anatomical position relative to the LV was labelled using two different strategies performed for 5 hours each: (I) 'one-image-at-a-time': each image labelled according to its position: 'too basal', 'LV', or 'too apical' individually by one of three experts; and (II) 'multiple-image-ranking': three independent experts ordered slices according to their relative position from 'most-basal' to 'most apical' in batches of eight until each image had been viewed at least 3 times. Two convolutional neural networks were trained for a three-way classification task (each model using data from one labelling strategy). The models' performance was evaluated by accuracy, F1-score, and area under the receiver operating characteristics curve (ROC AUC). After excluding images with artefact, 3,323 images were labelled by both strategies. The model trained using labels from the 'multiple-image-ranking strategy' performed better than the model using the 'one-image-at-a-time' labelling strategy (accuracy 86% vs. 72%, P=0.02; F1-score 0.86 vs. 0.75; ROC AUC 0.95 vs. 0.86). For expert clinicians performing this task manually the intra-observer variability was low (Cohen's κ=0.90), but the inter-observer variability was higher (Cohen's κ=0.77). We present proof of concept that, given the same clinician labelling effort, comparing multiple images side-by-side using a 'multiple-image-ranking' strategy achieves ground truth labels for DL more accurately than by classifying images individually. We demonstrate a potential clinical application: the automatic removal of unrequired CMR images. This leads to increased efficiency by focussing human and machine attention on images which are needed to answer clinical questions.

The Z7 model of three-component scalar dark matter

We investigate, for the first time, a scenario where the dark matter consists of three complex scalar fields that are stabilized by a single Z7 symmetry. As an extension of the well-known scalar Higgs-portal, this Z7 model is also subject to important restrictions arising from the relic density constraint and from direct detection experiments. Our goal in this paper is to find and characterize the viable regions of this model, and to analyze its detection prospects in future experiments. First, the processes that affect the relic densities are identified (they include semiannihilations and conversions) and then incorporated into the Boltzmann equations for the dark matter abundances, which are numerically solved with micrOMEGAs. By means of random scans of the parameter space, the regions consistent with current data, including the recent direct detection limit from the LZ experiment, are selected. Our results reveal that the Z7 model is indeed viable over a wide range of dark matter masses and that both conversions and semiannihilations play an important role in determining the relic densities. Remarkably, we find that in many cases all three of the dark matter particles give rise to observable signals in future direct detection experiments, providing a suitable way to test this scenario.

Image life trails based on contrast reduction models for face counter-spoofing

Natural face images are both content and context-rich, in the sense that they carry significant immersive information via depth cues embedded in the form of self-shadows or a space varying blur. Images of planar face prints, on the other hand, tend to have lower contrast and also suppressed depth cues. In this work, a solution is proposed, to detect planar print spoofing by enhancing self-shadow patterns present in face images. This process is facilitated and siphoned via the application of a non-linear iterative functional map, which is used to produce a contrast reductionist image sequence, termed as an image life trail. Subsequent images in this trail tend to have lower contrast in relation to the previous iteration. Differences taken across this image sequence help in bringing out the self-shadows already present in the original image. The proposed solution has two fronts: (i) a calibration and customization heavy 2-class client specific model construction process, based on self-shadow statistics, in which the model has to be trained with respect to samples from the new environment, and (ii) a subject independent and virtually environment independent model building procedure using random scans and Fourier descriptors, which can be cross-ported and applied to new environments without prior training. For the first case, where calibration and customization is required, overall mean error rate for the calibration-set (reduced CASIA dataset) was found to be 0.3106%, and the error rates for other datasets such OULU-NPU and CASIA-SURF were 1.1928% and 2.2462% respectively. For the second case, which involved building a 1-class and 2-class model using CASIA alone and testing completely on OULU, the error rates were 5.86% and 2.34% respectively, comparable to the customized solution for OULU-NPU.

Tracked 3D ultrasound and deep neural network-based thyroid segmentation reduce interobserver variability in thyroid volumetry.

Thyroid volumetry is crucial in the diagnosis, treatment, and monitoring of thyroid diseases. However, conventional thyroid volumetry with 2D ultrasound is highly operator-dependent. This study compares 2D and tracked 3D ultrasound with an automatic thyroid segmentation based on a deep neural network regarding inter- and intraobserver variability, time, and accuracy. Volume reference was MRI. 28 healthy volunteers (24—50 a) were scanned with 2D and 3D ultrasound (and by MRI) by three physicians (MD 1, 2, 3) with different experience levels (6, 4, and 1 a). In the 2D scans, the thyroid lobe volumes were calculated with the ellipsoid formula. A convolutional deep neural network (CNN) automatically segmented the 3D thyroid lobes. 26, 6, and 6 random lobe scans were used for training, validation, and testing, respectively. On MRI (T1 VIBE sequence) the thyroid was manually segmented by an experienced MD. MRI thyroid volumes ranged from 2.8 to 16.7ml (mean 7.4, SD 3.05). The CNN was trained to obtain an average Dice score of 0.94. The interobserver variability comparing two MDs showed mean differences for 2D and 3D respectively of 0.58 to 0.52ml (MD1 vs. 2), −1.33 to −0.17ml (MD1 vs. 3) and −1.89 to −0.70ml (MD2 vs. 3). Paired samples t-tests showed significant differences for 2D (p = .140, p = .002 and p = .002) and none for 3D (p = .176, p = .722 and p = .057). Intraobsever variability was similar for 2D and 3D ultrasound. Comparison of ultrasound volumes and MRI volumes showed a significant difference for the 2D volumetry of all MDs (p = .002, p = .009, p <.001), and no significant difference for 3D ultrasound (p = .292, p = .686, p = 0.091). Acquisition time was significantly shorter for 3D ultrasound. Tracked 3D ultrasound combined with a CNN segmentation significantly reduces interobserver variability in thyroid volumetry and increases the accuracy of the measurements with shorter acquisition times.

Acoustic Shadowing Aware Robotic Ultrasound: Lighting up the Dark

Ultrasound imaging is becoming more prevalent in clinical practice and research. To counteract the drawbacks of high user-dependency and difficult interpretability, ultrasound probes can be attached to robotic arms, enabling an increase in accuracy and repeatability. Currently, robotic ultrasound scans are mainly performed in a perpendicular manner. However, these scans create shadows below high-attenuation structures like bones due to acoustic shadowing, leading to an information loss in the scan. To counteract this and improve the compounding quality of robotic ultrasound scans, we introduce an ultrasound pose optimization method. In this initial work, we focus on the volume coverage of a region of interest and an acoustic shadowing reduction in this volume. Our proposed method is compared against perpendicular scans and random scans. Results show that the volume coverage sweep achieves higher coverage with the trade-off of more performed poses. In addition, the acoustic shadow reduction consistently leads to a higher coverage and confidence of the volumes when applied after a random or perpendicular scan, with a relatively small number of additional poses. Such context-aware volume scanning and path optimization can pave the path to standardized, fully automatic high-quality robotic ultrasound scans without the need for pre-acquired data and systematically reduces the occurrence of acoustic shadows.

An Objective Assessment of Lumbar Spine Degeneration/Ageing Seen on MRI Using An Ensemble Method-A Novel Approach to Lumbar MRI Reporting.

Retrospective, randomized, radiographic study assessing age-related changes (ARCs) on lumbar magnetic resonance imaging (MRI) using an ensemble method. This study proposed to develop a novel reporting method to calculate a predicted "age estimate" for the ARC seen on lumbar MRI. Lumbar MRI reports include pathological findings but usually not the prevalence data of common findings which has been shown to decrease the need for narcotics in the management of non-specific lower back pain (NSLBP). Comparing the normal age estimation for lumbar spine degenerative changes/ARC on MRI and comparing this to the patient's real age may improve patient outcome in the management of NSLBP. A total of 60 lumbar MRI were taken from patients aged between 0 and 100 years. Lumbar MRI features reported as associated with age on review of the literature were measured on each MRI and statistically evaluated for correlation with age. Factors found to be associated were then entered into an ensemble model consisting of several machine learning techniques. The resulting ensemble model was then tested to predict age for a further 10 random lumbar MRI scans. One further lumbar MRI was then assessed for observer variability. Features that correlated with age were disc signal intensity, the appearance of paravertebral and psoas muscle, disc height, facet joint size, ligamentum flavum thickness, Schmorl nodes, Modic changes, vertebral osteophytes, and high-intensity zones. With the ensemble model, 80% of estimated spinal age were within 11 years of the subjects' physical age. It would appear that the intervertebral discs, and many other structures that are subjected to loading in and around the lumbar spine change their lumbar MRI appearance in a predictable way with increasing age. ARC on lumbar MRI can be assessed to predict an "expected age" for the subject.Level of Evidence: 2.

Electroweak phase transition in an inert complex triplet model

We study the dynamics of electroweak phase transition in a simple extension of the Standard Model where the Higgs sector is extended by adding an $SU(2)_L$-triplet with hypercharge Y=2. By making random scans over the parameters of the model, we show that there are regions consistent with constraints from collider experiments and the requirement for a strong first-order electroweak phase transition which is needed for electroweak baryogenesis. Further, we also study the power spectrum of the gravitational waves which can be generated due to the first-order phase transitions. Moreover, the detectability of these gravitational waves, via future space-based detectors, is discussed.

The Z5 model of two-component dark matter

Scenarios for multi-component scalar dark matter based on a single ZN (N ≥ 4) symmetry are simple and well-motivated. In this paper we investigate, for the first time, the phenomenology of the Z5 model for two-component dark matter. This model, which can be seen as an extension of the well-known singlet scalar model, features two complex scalar fields — the dark matter particles — that are Standard Model singlets but have different charges under a Z5 symmetry. The interactions allowed by the Z5 give rise to novel processes between the dark matter particles that affect their relic densities and their detection prospects, which we study in detail. The key parameters of the model are identified and its viable regions are characterized by means of random scans. We show that, unlike the singlet scalar model, dark matter masses below the TeV are still compatible with present data. Even though the dark matter density turns out to be dominated by the lighter component, we find that current and future direct detection experiments may be sensitive to signals from both dark matter particles.

Face anti-spoofing by identity masking using random walk patterns and outlier detection

Existing architectures used in face anti-spoofing tend to deploy registered spatial measurements to generate feature vectors for spoof detection. This means that the ordering or sequence in which specific statistics are computed cannot be changed, as one moves from one facial profile to another. While this arrangement works in a person-specific setting, it becomes a major drawback when single-sided training is done based on the natural face class alone. To mitigate subject identity linked content interference within the anti-spoofing frame, we propose a identity-independent architecture based on random correlated scans of natural face images. The same natural face image can be scanned multiple times through independent correlated random walks before deriving simple differential features on the 1D scanned vectors. This proposed frame tends to capture the pixel correlation statistics with minimal content interference and shows great promise, particularly when trained on natural face sets, using a one-class support vector machine and cross-validated on other databases. Performance measured in terms of EER for detection of spoof face is found to be $$3.8291\%$$ with proposed random scan features, and $$2.02\%$$ with auto-population samples for inter database.

Can there be a more precise co-relation between grades of sinusitis, concha bullosa and deviated nasal septum- A CBCT evaluation

Computerized tomographic imaging of the nasal and paranasal regions is frequently used for sinonasal surgery. CBCT is another less explored modality which can provide accurate anatomic details. A retrograde study was done on 204 random scans to find out incidence and prevalence of sino-nasal anatomical variations and associated pathologies. A positive correlation was found between deviated nasal septum and concha bullosa. CBCT can sure act as a guide in future for sinonasal surgeries with lesser cost and radiation exposure. Keywords: CBCT, Deviated nasal septum, Choncha bullosa, Chronic sinusitis.

Anatomic Sphenoid Cell Variants: Introduction of the Retrosphenoid Cell and Relevance in the Presentation and Management of Sinus Disease.

Background Despite the well-appreciated variability in sphenoid sinus anatomy, there are no documented cases of retrosphenoid cells in the literature to date. Objective This study defines and determines the prevalence of retrosphenoid cells as identified on computed tomography (CT) imaging and intraoperative endoscopy and reviews the prevalence of other related anatomical variants of the sphenoid sinus. Methods Retrospective study of 300 random noncontrast sinus CT scans of patients with chronic rhinosinusitis presenting to a tertiary rhinology center. All identifiable anatomic variations and any presence of retrosphenoid cells and their pneumatization patterns were recorded. The prevalence of various anatomic variations of the sphenoid sinus was also calculated. Results A total of 300 sinus CT scans were included in the study. Protrusion of both the internal carotid artery (42.6%) and optic nerve (19.7%) into the sinus was more prevalent than the dehiscence of either one. A retrosphenoid cell was identified in 2% of CT scans. Other anatomic variants were less prevalent. Conclusion Meticulous review of preoperative imaging is key in identifying rare and complex sphenoid cell variations in planning surgical approaches and potential treatment strategies for the unusually pneumatized sphenoid air cells. Various manifestations of sinus disease can be localized to this area, and suspicion of a retrosphenoid cell should be raised in patients presenting with recalcitrant headache.

Measurement of Pectoralis Muscle Quantity and Attenuation by Computed Tomography Using Routinely Available Software is Feasible and Predicts Mortality after LVAD Implantation

Purpose We have previously demonstrated that pectoralis muscle mass and tissue attenuation measured on preoperative CT scans were powerful predictors of mortality after LVAD implantation. These measurements were made with specialized software (Slice-O-Matic) which is not accessible to advanced heart failure teams. The purpose of this analysis was to determine the correlation between pectoralis muscle measures made with specialized software vs. routinely available CT software (PACS). Methods A random sample CT scans (n=57) was selected from our larger LVAD cohort with previously quantified pectoralis muscle measures by Slice-O-Matic Software (larger cohort n=141). Unilateral pectoralis muscle mass indexed to body surface area (PMI) and attenuation (approximated by mean Hounsfield units; PHUm) were re-measured on preoperative chest CT scans by a local radiologist using PACS software. Correlation between Slice-O-Matic and PACS pectoralis muscle measures was performed with Pearson analysis. These measures (PACS) were then assessed as predictors of mortality after LVAD implantation using multivariate cox regression. Results We observed a high correlation between pectoralis muscle measures using both techniques (R = 0.95 for PMI, R = 0.94 for PHUm). For the pectoralis measures quantified by PACS, each unit increase in PMI was associated with a 31% reduction in the hazard of death after LVAD (adjusted HR 0.69, 95% CI 0.48-0.99, p = 0.046). Each 5-unit increase in PHUm was associated with a 39% reduction in the hazard of death after LVAD (adjusted HR 0.61, 95% CI 0.38-0.96, p Conclusion Pectoralis muscle measurements can be performed by local radiologists using routinely available software. These measures are predictive of morality following LVAD. This is an important first step in delivering a predictive model incorporating this measure into real world practice.

Abstract TP79: Do Early Ischemic Changes Occur on CT Within the First Hour of Acute Ischemic Stroke

Introduction: Early ischemic changes (EIC) on non-contrast computed tomography (NCCT) can appear within 6 hours of last known normal (LKN), and can be quantified using the Alberta Stroke Program Early CT Score (ASPECTS). However, there is lack of data describing when EIC first appear. We leveraged our Mobile Stroke Unit (MSU) to determine the incidence of EIC on NCCT within 1 hour of LKN. Methods: Prospectively derived data were analyzed from patients on our MSU who were independently adjudicated as tissue plasminogen activator (tPA) eligible, had NCCT within 1 hour of LKN, and had definite strokes based on subsequent testing. EIC, defined as ASPECTS ≤ 7, was measured and correlated to time from LKN, stroke severity (National Institutes of Health Stroke Scale, NIHSS), and presence of large vessel occlusion (LVO) on neuroimaging. All scans were obtained on an 8 slice Ceretom (Neurologica Corp) and graded by a Vascular Neurology fellow, with random scans compared with a Vascular Neurology attending (κ=0.69). Results: 80 tPA eligible patients with NCCT within 1 hour of LKN were identified. 57 had definite strokes and/or strokes reversed by tPA. Of these, 54 (95%) had NCCT with sufficient diagnostic quality. Mean ASPECTS was 9.2 (median 10, interquartile range (IQR) 9-10) with a mean of 45.3 minutes (median 46, IQR 39-52) from LKN. Average NIHSS was 14.9. EIC (e.g. ASPECTS 6, 6, 7, 7) was identified in 4 patients (7%). There was no association between ASPECTS and time from LKN to CT (p=0.63), stroke severity (p=0.12) or presence of LVO (p= 0.09); the LVO analysis was limited by the small number of EIC patients (n=4). Conclusions: Based on our experience, EIC may be present but ASPECTS is not &lt; 6 within the first hour after LKN. Close scrutiny of NCCT for EIC within this timeframe may not be necessary for determining eligibility for tPA or endovascular thrombectomy.

A singlet doublet dark matter model with radiative neutrino masses

We present a detailed study of a combined singlet-doublet scalar and singlet-doublet fermion model for dark matter. These models have only been studied separately in the past. We show that their combination allows for the radiative generation of neutrino masses, but that it also implies the existence of lepton-flavour violating (LFV) processes. We first analyse the dark matter, neutrino mass and LFV aspects separately. We then perform two random scans for scalar dark matter imposing Higgs mass, relic density and neutrino mass constraints, one over the full parameter space, the other over regions where scalar-fermion coannihilations become important. In the first case, a large part of the new parameter space is excluded by LFV, and the remaining models will be probed by XENONnT. In the second case, direct detection cross sections are generally too small, but a substantial part of the viable models will be tested by future LFV experiments. Possible constraints from the LHC are also discussed.

Automatic 3-dimensional cephalometric landmarking based on active shape models in related projections.

This article presents a novel technique for automatic cephalometric landmark localization on 3-dimensional (3D) cone-beam computed tomography (CBCT) volumes by using an active shape model to search for landmarks in related projections. Twenty-four random CBCT scans from a public data set were imported and processed into Matlab (MathWorks, Natick, Mass). Orthogonal coronal and sagittal projections (digitally reconstructed radiographs) were created, and 2 trained active shape models were used to locate cephalometric landmarks on each. Finally, by relating projections, 18 tridimensional landmarks were located on CBCT volume representations. From our 3D gold standard, a 3.64-mm mean error in localization of cephalometric landmarks was achieved with this method, with the highest localization errors in the porion and sella regions because of the low volume definition. The proposed algorithm for automatic 3D landmarking on CBCT volumes seems to be useful for 3D cephalometric analysis. This study shows that a fast 2-dimensional landmark search can be useful for 3D localization, which could save computational time compared with a full-volume analysis. Also, this research confirmsthat by using CBCT for cephalometry, there are no distortion projections, and full structure information of a virtual patient is manageable in a personal computer.