Actual Scans Research Articles

Assessment of accuracy and repeatability of quantitative parameter mapping in MRI.

We aimed to evaluate the accuracy and repeatability of the T1, T2*, and proton density (PD) values obtained by quantitative parameter mapping (QPM) using the ISMRM/NIST MRI system phantom and compared them with computer simulations. We compared the relaxation times and PD obtained through QPM with the reference values of the ISMRM/NIST MRI system phantom and conventional methods. Furthermore, we evaluated the presence or absence of influences other than noise in T1 and T2* values obtained by QPM by comparing the obtained coefficient of variation (CV) with simulation results. The T1, T2*, and PD values by QPM showed a strong correlation with the measured values and the referenced values. The simulated CVs of QPM calculated for each sphere showed similar trends to those of the actual scans.

TS-AI: A deep learning pipeline for multimodal subject-specific parcellation with task contrasts synthesis

Accurate mapping of brain functional subregions at an individual level is crucial. Task-based functional MRI (tfMRI) captures subject-specific activation patterns during various functions and behaviors, facilitating the individual localization of functionally distinct subregions. However, acquiring high-quality tfMRI is time-consuming and resource-intensive in both scientific and clinical settings. The present study proposes a two-stage network model, TS-AI, to individualize an atlas on cortical surfaces through the prediction of tfMRI data. TS-AI first synthesizes a battery of task contrast maps for each individual by leveraging tract-wise anatomical connectivity and resting-state networks. These synthesized maps, along with feature maps of tract-wise anatomical connectivity and resting-state networks, are then fed into an end-to-end deep neural network to individualize an atlas. TS-AI enables the synthesized task contrast maps to be used in individual parcellation without the acquisition of actual task fMRI scans. In addition, a novel feature consistency loss is designed to assign vertices with similar features to the same parcel, which increases individual specificity and mitigates overfitting risks caused by the absence of individual parcellation ground truth. The individualized parcellations were validated by assessing test-retest reliability, homogeneity, and cognitive behavior prediction using diverse reference atlases and datasets, demonstrating the superior performance and generalizability of TS-AI. Sensitivity analysis yielded insights into region-specific features influencing individual variation in functional regionalization. Additionally, TS-AI identified accelerated shrinkage in the medial temporal and cingulate parcels during the progression of Alzheimer's disease, suggesting its potential in clinical research and applications.

Evaluating the accuracy of one-piece and three-piece 3D-printed indirect bonding transfer trays: An in vitro study.

Recent developments in computer-aided design/computer-aided manufacturing (CAD/CAM) and 3D printing have enabled the fabrication of digital indirect bonding (IDB) transfer trays. These modern products require thorough investigation. The aim of the study was to determine the accuracy of one-piece and three-piece IDB transfer trays in vitro. An initial dental scan (IDS) of a randomly selected patient with digitally positioned brackets served as the master scan (MS) for designing 16 IDB transfer trays of each type. They were 3D printed and used for bonding 448 brackets to the models. Subsequently, the models were scanned with a TRIOS® 3 Intraoral Scanner (3Shape A/S, Copenhagen, Denmark), producing actual scans (ASs). The accuracy of bracket positioning was measured digitally on both MSs and ASs. The measurements were compared to the Objective Grading System for dental casts provided by the American Board of Orthodontics (ABO). The 2 types of IDB transfer trays showed comparable accuracy. All linear errors were within the clinically acceptable range, whereas the angular measurements demonstrated significant variability, resulting in clinically unacceptable transfer errors that ranged from 3.3% to 90.3%. The study results cannot be unconditionally extrapolated to other types of IDB transfer trays due to the diversity of their properties and features. The study evaluated the in vitro accuracy of IDB transfer trays. The revealed number of errors may be even higher in vivo due to limitations in visibility, salivary flow, interference from the tongue, and difficulties in achieving a proper fit of the IDB transfer tray to the teeth.

Machine learning framework for simulation of artifacts in paranasal sinuses diagnosis using CT images.

In the medical field, diagnostic tools that make use of deep neural networks have reached a level of performance never before seen. A proper diagnosis of a patient's condition is crucial in modern medicine since it determines whether or not the patient will receive the care they need. Data from a sinus CT scan is uploaded to a computer and displayed on a high-definition monitor to give the surgeon a clear anatomical orientation before endoscopic sinus surgery. In this study, a unique method is presented for detecting and diagnosing paranasal sinus disorders using machine learning. The researchers behind the current study designed their own approach. To speed up diagnosis, one of the primary goals of our study is to create an algorithm that can accurately evaluate the paranasal sinuses in CT scans. The proposed technology makes it feasible to automatically cut down on the number of CT scan images that require investigators to manually search through them all. In addition, the approach offers an automatic segmentation that may be used to locate the paranasal sinus region and crop it accordingly. As a result, the suggested method dramatically reduces the amount of data that is necessary during the training phase. As a result, this results in an increase in the efficiency of the computer while retaining a high degree of performance accuracy. The suggested method not only successfully identifies sinus irregularities but also automatically executes the necessary segmentation without requiring any manual cropping. This eliminates the need for time-consuming and error-prone human labor. When tested with actual CT scans, the method in question was discovered to have an accuracy of 95.16 percent while retaining a sensitivity of 99.14 percent throughout.

A multichannel analysis of imbalanced computed tomography data for lung cancer classification

Lung cancer holds the highest fatality rate among cancers, emphasizing the importance of early detection. Computer algorithms have gained prominence across various domains, including lung cancer diagnosis. These algorithms assist specialists, especially in medical imaging, yet current efforts lack comprehensive CT data analysis; especially in handling imbalanced datasets and fully exploiting spatial information. The lack of spatial analysis hinders the ability to identify subtle variations in texture and structure that are crucial for detecting lung cancer early and accurately. Therefore, this study uses a multichannel analysis of computed tomography (CT) images and deep learning-based ensemble learning (MC-ECNN) to find lung cancer even when the data is not balanced. Firstly, the data imbalance issue is tackled through the synthetic minority oversampling technique (SOMTE); afterwards, a multi-channel analysis of the data is performed to explore a distinct set of abstract features. Lastly, a deep ensemble learning method is used to classify the extracted distinct abstract feature set into the appropriate classes. The proposed method uses the discrete Fast Fourier transform (DFFT) and discrete cosine transform (DCT), along with the actual CT scans, for the multi-channel analysis of the data in different domains. The proposed model yielded 99.60% test accuracy on unseen data, which is at least 3% better than the other state-of-the-art studies considered for the comparison. In addition to the classification accuracy, the efficacy of the proposed model has also been justified through precision, recall, F1-score, support value, and misclassification rate.

Synthetic PET from CT improves diagnosis and prognosis for lung cancer: Proof of concept

[18F]Fluorodeoxyglucose positron emission tomography (FDG-PET) and computed tomography (CT) are indispensable components in modern medicine. Although PET can provide additional diagnostic value, it is costly and not universally accessible, particularly in low-income countries. To bridge this gap, we have developed a conditional generative adversarial network pipeline that can produce FDG-PET from diagnostic CT scans based on multi-center multi-modal lung cancer datasets (n= 1,478). Synthetic PET images are validated across imaging, biological, and clinical aspects. Radiologists confirm comparable imaging quality and tumor contrast between synthetic and actual PET scans. Radiogenomics analysis further proves that the dysregulated cancer hallmark pathways of synthetic PET are consistent with actual PET. We also demonstrate the clinical values of synthetic PET in improving lung cancer diagnosis, staging, risk prediction, and prognosis. Taken together, this proof-of-concept study testifies to the feasibility of applying deep learning to obtain high-fidelity PET translated from CT.

Minkowski functional evaluation of representative elementary volume of rock microtomography images at multiple resolutions

Pore-scale properties can be obtained by building a reliable digital twin of porous media through the digital rock physics (DRP) workflow. The two prerequisites of DRP are reliable imaging and computing power. Determining a proper image resolution that can reveal the actual pore-scale properties is challenging as there is a trade-off between the resolution and the representative elementary volume (REV). The REV is the smallest volume that reproduces the properties of the whole porous medium. The REV is a function of heterogeneities on the pore scale, the parameter of interest, and the scale range. Although the REV analysis for hydraulic properties is straightforward, it is computationally expensive. This study aims to estimate hydraulic pore-scale properties during REV evaluations by the geometric characterization of porous media using the Minkowski morphological functionals. Two sandstone and one carbonate rock samples were scanned at multiple imaging resolutions by both laboratory and synchrotron tomography. The REVs of various parameters of interest (porosity, permeability, surface area, tortuosity, Minkowski functionals) were computed, and the effect of image resolution and artificial rebinning on the final REV values was examined. After reaching the REV for porosity, the REV for the integrals of mean and total curvature agreed well with the permeability REV for large-volume image sizes. At constant porosity, the Minkowski integrals were found to be indicators for pore throat sizes. We also showed that the properties obtained from the rebinned (or coarsened) images differ entirely from that of actual scans at the same resolution.

Deep Learning Modeling for Prediction of Cognitive Task Related Features from Resting-state fMRI Data

In recent years, several neural network methods, such as brain functional networks, have been proposed to efficiently learn non-Euclidean graph structures. In this study, we modified the connectivity-based graph convolutional network (cGCN) developed by Wang et al. (2021) for autism spectrum disorder (ASD) classification into a regression model and used resting-state functional magnetic resonance imaging (rs-fMRI) data to predict the scores on the offline Kohs block-design test for a total of 615 subjects aged 33–89 years. To convert from discrimination to regression, we employed a technique that introduces a fully connected layer in the cGCN and connects the long short-term memory (LSTM) in the last output phase instead of the Softmax layer. The results showed that our cGCN–LSTM was more accurate than the baseline LASSO regression model, and that the predictions correlated significantly with the measured scores of the cognitive function test. Moreover, we used the leave-one-out and leave-two-out occlusion methods to extract important regions of interest (ROIs), as well as networks from the model. It was acknowledged that the Kohs block-design test scores were negatively correlated with age, but the results suggested the possibility of age-related cognitive decline that could not be captured by age prediction models alone. We found that only the nodes of the default mode network and cerebellum contain some significant within-networks; however, overall, between-network connectivity overwhelmingly contributed to the prediction regardless of the weight of the role in the age projection. This model and the leave-two-out occlusion method allowed us to identify the regions and networks involved in further task-based fMRI experiments in advance. Our methodology has the potential to make the design of task fMRI experiments more rational and accurate before planning and conducting actual scans.

Development of 3D printed human brain models using thermoplastic Polyurethane/Rubber blends for anatomy teaching promoting kinesthetic learning purposes

Kinesthetic appreciation of anatomy models serves to activate a concrete sensory experience that enhances 3D visuospatial mental mapping of anatomical structures. However, most commercially available plastic anatomy models are costly and do not provide tactile sensation similar to the organ or structures. Polymeric materials have been used widely in additive manufacturing to produce human brain models, however, none of the models resembles accurate feeling. The aim of this study is to produce 3D printed anatomical models using customized soft 3D printing filament from thermoplastic Polyurethane (TPU) and two types of rubber, which are ethylene propylene diene monomer (EPDM) and styrene butadiene rubber (SBR) compounds. Actual human brain MRI scans are used to form 3D-printed anatomy models. The MRI image obtained was in DICOM format, exported into 3D slicer software for specific brain parts extraction (whole brain, brain ventricles, circle of Willis, and dural venous sinuses), and converted into STL format for 3D printing. For material processing, the blending of TPU/EPDM and TPU/SBR were successfully fabricated using an internal mixer (Haake) and extruded into filament using a single screw extruder with the aid of a filament winder. The human brain model was then printed using 3D Printer (Ender 3). It was found that all TPU blends were able to be printed into brain anatomy models but the properties of the materials were greatly affected by rubber types and compositions of the added fillers. The addition of more than 30% of the fillers gave difficulties in processing, probably due to inconsistent viscosity that led to phase separation.

Implementation and Assessment of Lung Ultrasound Training Curriculum for Physiotherapists With a Focus on Image Acquisition and Calculation of an Aeration Score.

Described here is the implementation of a lung ultrasound course for physiotherapists focused on the acquisition and retention of knowledge and skills. Initially, we provided online lectures in a virtual learning environment (VLE), in which we taught the semiquantification of edema through a lung ultrasound score (LUS). Afterward, the physiotherapists participated in face-to-face lectures (which resumed the online lectures), followed by hands-on training and simulation with ultrasound. We assessed knowledge acquisition through a multiple-choice test with 30 questions (totaling 10 points). The test was applied before accessing the VLE (pre-VLE), before the face-to-face course and at its end (pre- and post-course). Physiotherapists collected actual patients' ultrasound scans, which were uploaded to the VLE and assessed by three supervisors, who performed a consensus LUS calculation and gave virtual written feedback. Thirteen physiotherapists collected 59 exams. The test results were 3.60 ± 1.58 (pre-VLE), 5.94 ± 1.45 (pre-course) and 8.50 ± 0.71 (post-course), with p < 0.001 for all. The intraclass correlation coefficient for LUS between physiotherapists and supervisors was 0.814 (p < 0.001), with moderate-to-weak agreement for LUS of the lung apical, median and basal zones, with κ=0.455.334, and 0.417 (p < 0.001 for all). Trainees were found to have increased short-term acquisition and retention of knowledge and skills, with a good intraclass correlation coefficient between them and the consensus of supervisors for the LUS of actual patients.

Magic angle effect on diffusion tensor imaging in ligament and brain

PurposeTo evaluate the magic angle effect on diffusion tensor imaging (DTI) measurements in rat ligaments and mouse brains. MethodsThree rat knee joints and three mouse brains were scanned at 9.4 T using a modified 3D diffusion-weighted spin echo pulse sequence with the isotropic spatial resolution of 45 μm. The b value was 1000 s/mm2 for rat knee and 4000 s/mm2 for mouse brain. DTI model was used to investigate the quantitative metrics at different orientations with respect to the main magnetic field. The collagen fiber structure of the ligament was validated with polarized light microscopy (PLM) imaging. ResultsThe signal intensity, signal-to-noise ratio (SNR), and DTI metrics in the ligament were strongly dependent on the collagen fiber orientation with respect to the main magnetic field from both simulation and actual MRI scans. The variation of fractional anisotropy (FA) was about ~32%, and the variation of mean diffusivity (MD) was ~11%. These findings were further validated with the numerical simulation at different SNRs (~10.0 to 86.0). Compared to the ligament, the DTI metrics showed little orientation dependence in mouse brains. ConclusionMagic angle effect plays an important role in DTI measurements in the highly ordered collagen-rich tissues, while MD showed less orientation dependence than FA.

Estimation of vertical plant area density from single return terrestrial laser scanning point clouds acquired in forest environments

Plant area density (PAD in m2·m−3) defines the total one-sided total plant surface area within a given volume. It is a key variable in characterizing exchange processes between the atmosphere and land surface. Terrestrial laser scanning (TLS) provides unprecedented detail of the 3D structure of forest canopies. Yet, signal occlusion and uneven sampling density of the TLS point clouds limit our capacity to characterize the 3D distribution of canopy components. Recent studies have made use of statistical estimators of PAD that are applied to TLS point clouds subdivided into three-dimensional (3D) cubes, or voxels. Computation of such metrics under actual field conditions with point clouds containing several millions of returns is challenging. Moreover, rigorous assessment of the estimated PAD and effects of occlusions in forests remain unclear due to laborious, time-consuming, and inaccurate field measurements. In the present study, we present L-Vox, a software that computes PAD per voxel for TLS scans acquired in forest environments, which is based upon recent development of unbiased estimators derived from maximum likelihood. Two applications are presented. First, the software is evaluated for virtual forest plots, which are detailed 3D models of individual trees with corresponding simulated TLS scans, for which reference data are known. Second, L-Vox is applied to actual scans that were acquired in hardwood and coniferous plots in New Brunswick and Newfoundland, Canada. Both test cases were used to investigate the effects of occlusion and the uneven sampling in estimating PAD. The test cases were also used to assess the influence of voxel size and the number of scans per plot on PAD estimates. Our results showed strong correlations between the estimated PAD profile from L-Vox and simulated PAD for virtual forest plots, with a mean R2 = 0.98 and a mean coefficient of variation (CV) = 15.6%. We demonstrated that comparing multi-scan to single scan TLS acquisitions in real forest plots substantially reduced signal occlusion, resulting in an increase up to 50% in PAD values. Effects of voxel size on PAD estimates greatly depended upon the relative size of foliar and woody elements, with an optimal size around 10 cm in coniferous plots. L-Vox proved to be an efficient and accurate tool for computing 3D distributions of PAD from TLS measurements in natural forest environments.

PIMA-CT: Physical Model-Aware Cyclic Simulation and Denoising for Ultra-Low-Dose CT Restoration.

A body of studies has proposed to obtain high-quality images from low-dose and noisy Computed Tomography (CT) scans for radiation reduction. However, these studies are designed for population-level data without considering the variation in CT devices and individuals, limiting the current approaches' performance, especially for ultra-low-dose CT imaging. Here, we proposed PIMA-CT, a physical anthropomorphic phantom model integrating an unsupervised learning framework, using a novel deep learning technique called Cyclic Simulation and Denoising (CSD), to address these limitations. We first acquired paired low-dose and standard-dose CT scans of the phantom and then developed two generative neural networks: noise simulator and denoiser. The simulator extracts real low-dose noise and tissue features from two separate image spaces (e.g., low-dose phantom model scans and standard-dose patient scans) into a unified feature space. Meanwhile, the denoiser provides feedback to the simulator on the quality of the generated noise. In this way, the simulator and denoiser cyclically interact to optimize network learning and ease the denoiser to simultaneously remove noise and restore tissue features. We thoroughly evaluate our method for removing both real low-dose noise and Gaussian simulated low-dose noise. The results show that CSD outperforms one of the state-of-the-art denoising algorithms without using any labeled data (actual patients' low-dose CT scans) nor simulated low-dose CT scans. This study may shed light on incorporating physical models in medical imaging, especially for ultra-low level dose CT scans restoration.

Comparison of Simulated Low-Dose and Conventional-Dose CT for Preoperative Planning in Shoulder Arthroplasty.

Shoulder computed tomography (CT) is commonly utilized in preoperative planning for total shoulder arthroplasty. Conventional-dose shoulder CT may expose patients to more ionizing radiation than is necessary to provide high-quality images for this procedure. The purpose of this study was to evaluate the utility of simulated low-dose CT images for preoperative planning using manual measurements and common preoperative planning software. Eighteen shoulder CT scans obtained for preoperative arthroplasty planning were used to generate CT images as if they had been acquired at reduced radiation dose (RD) levels of 75%, 50%, and 25% using a simulation technique that mimics decreased x-ray tube current. This technique was validated by quantitative comparison of simulated low-dose scans of a cadaver with actual low-dose scans. Glenoid version, glenoid inclination, and humeral head subluxation were measured using 2 commercially available software platforms and were also measured manually by 3 physicians. These measurements were then analyzed for agreement across RD levels for each patient. Tolerances of 5° of glenoid version, 5° of glenoid inclination, and 10% humeral head subluxation were used as equivalent for preoperative planning purposes. At all RD levels evaluated, the preoperative planning software successfully segmented the CT images. Semiautomated software measurement of 25% RD images was within tolerances in 99.1% of measurements; for 50% RD images, within tolerances in 96.3% of measurements; and for 75% RD images, within tolerances in 100% of measurements. Manual measurements of 25% RD images were within these tolerances in 95.1% of measurements; for 50% RD images, in 98.8% of measurements; and for 75% RD images, in 99.4% of measurements. Simulated low-dose CT images were sufficient for reliable measurement of glenoid version, glenoid inclination, and humeral head subluxation by preoperative planning software as well as by physician-observers. These findings suggest the potential for substantial reduction in RD in preoperative shoulder CT scans without compromising surgical planning. The adoption of low-dose techniques in preoperative shoulder CT may lower radiation exposure for patients undergoing shoulder arthroplasty, without compromising image quality.

Three-dimensional (3D) Visualization Educational Modeling for Ophthalmology Residents' Training: Viewpoints.

Background: Three-dimensional models are used to guide residents and physicians in accessing specific anatomical areas and types of fractures and better diagnosis of anomalies. These models are useful for illuminating complex anatomical areas, such as orbit, especially limited space with sensitive access. The aim of this study was to design a three-dimensional visualization educational modeling for ophthalmology residents' training. Methods: This study is a product-oriented application that uses radiological images of anatomy, anomalies, and orbital fractures based on actual CT scans of patients. These CT scans were carefully selected from the Picture Archiving and Communication System of Ghaem Hospital of Mashhad University of Medical Sciences. Results: To produce twelve 3D models, the CT scan files were converted to 3D printer output. Then, the models were presented to residents at a training session by an ophthalmologist. These models created all major fractures associated with the orbit area and most disorders, anomalies of this area and several normal anatomical. The features of 3D models were mentioned. The strengths and weaknesses of the educational modeling, the level of satisfaction with the use of three-dimensional models, suggestions and criticisms were assessed qualitatively by the residents. Satisfaction was reported 100% by residents. Suggestions for future 3D models were presented, and the only criticism was fear of exams and grades. Conclusion: Real-size 3D modeling help to understand the spatial and mental imagery of anatomy and orbital pathology and to touch different anatomical areas creates a clear image in the minds of residents, especially in the orbit.

Real-time virtual intraoperative CT in endoscopic sinus surgery.

Endoscopic sinus surgery (ESS) is typically guided under preoperative computed tomography (CT), which increasingly diverges from actual patient anatomy as the surgery progresses. Studies have reported that the revision surgery rate in ESS ranges between 28 and 47%. This paper presents a method that can update the preoperative CT in real time to improve surgical completeness in ESS. The work presents and compares three novel methods that use instrument motion data and anatomical structures to predict surgical modifications in real time. The methods use learning techniques, such as nonparametric filtering and Gaussian process regression, to correlate surgical modifications with instrument tip positions, tip trajectories, and instrument shapes. Preoperative CT image sets are updated with modification predictions to serve as a virtual intraoperative CT. The three methods were compared in eight ESS cadaver cases, which were performed by five surgeons and included the following representative ESS operations: maxillary antrostomy, uncinectomy, anterior and posterior ethmoidectomy, and sphenoidotomy. Experimental results showed accuracy metrics that were clinically acceptable with dice similarity coefficients > 86%, with F-score > 92% and precision > 89.91% in surgical completeness evaluation. Among the three methods, the tip trajectory-based estimator had the highest precision of 96.87%. This work demonstrated that virtually modified intraoperative CT scans improved the consistency between the actual surgical scene and the reference model, and could lead to improved surgical completeness in ESS. Compared to actual intraoperative CT scans, the proposed method has no impact on existing surgical protocols, does not require extra hardware, does not expose the patient to radiation, and does not lengthen time under anesthesia.

Dual-energy CT of acute bowel ischemia.

Acute bowel ischemia is a condition with high mortality and requires rapid intervention to avoid catastrophic outcomes. Swift and accurate imaging diagnosis is essential because clinical findings are commonly nonspecific. Conventional contrast enhanced CT of the abdomen has been the imaging modality of choice to evaluate suspected acute bowel ischemia. However, subtlety of image findings and lack of non-contrast or arterial phase images can make correct diagnosis challenging. Dual-energy CT provides valuable information toward assessing bowel ischemia. Dual-energy CT exploits the differential X-ray attenuation at two different photon energy levels to characterize the composition of tissues and reveal the presence or absence of faint intravenous iodinated contrast to improve reader confidence in detecting subtle bowel wall enhancement. With the same underlying technique, virtual non-contrast images can help to show non-enhancing hyperdense hemorrhage of the bowel wall in intravenous contrast-enhanced scans without the need to acquire actual non-contrast scans. Dual-energy CT derived low photon energy (keV) virtual monoenergetic images emphasize iodine contrast and provide CT angiography-like images from portal venous phase scans to better evaluate abdominal arterial patency. In Summary, dual-energy CT aids diagnosing acute bowel ischemia in multiple ways, including improving visualization of the bowel wall and mesenteric vasculature, revealing intramural hemorrhage in contrast enhanced scans, or possibly reducing intravenous contrast dose.

Through The Back Door: Expiratory Accumulation of SARS-Cov-2 in the Olfactory Mucosa as Mechanism for CNS Penetration

Introduction: SARS-CoV-2 is a respiratory virus supposed to enter the organism through aerosol or fomite transmission to the nose, eyes and oropharynx. It is responsible for various clinical symptoms, including hyposmia and other neurological ones. Current literature suggests the olfactory mucosa as a port of entry to the CNS, but how the virus reaches the olfactory groove is still unknown. Because the first neurological symptoms of invasion (hyposmia) do not correspond to first signs of infection, the hypothesis of direct contact through airborne droplets during primary infection and therefore during inspiration is not plausible. The aim of this study is to evaluate if a secondary spread to the olfactory groove in a retrograde manner during expiration could be more probable.Methods: Four three-dimensional virtual models were obtained from actual CT scans and used to simulate expiratory droplets. The volume mesh consists of 25 million of cells, the simulated condition is a steady expiration, driving a flow rate of 270 ml/s, for a duration of 0.6 seconds. The droplet diameter is of 5 µm.Results: The analysis of the simulations shows the virus to have a high probability to be deployed in the rhinopharynx, on the tail of medium and upper turbinates. The possibility for droplets to access the olfactory mucosa during the expiratory phase is lower than other nasal areas, but consistent.Discussion: The data obtained from these simulations demonstrates the virus can be deployed in the olfactory groove during expiration. Even if the total amount in a single act is scarce, it must be considered it is repeated tens of thousands of times a day, and the source of contamination continuously acts on a timescale of several days. The present results also imply CNS penetration of SARS-CoV-2 through olfactory mucosa might be considered a complication and, consequently, prevention strategies should be considered in diseased patients.

Low-dose CT with metal artifact reduction in arthroplasty imaging: a cadaveric and clinical study.

To determine whether a simulated low-dose metal artifact reduction (MAR) CT technique is comparable with a clinical dose MAR technique for shoulder arthroplasty evaluation. Two shoulder arthroplasties in cadavers and 25 shoulder arthroplasties in patients were scanned using a clinical dose (140kVp, 300qrmAs); cadavers were also scanned at half dose (140kVp, 150qrmAs). Images were reconstructed using a MAR CT algorithm at full dose and a noise-insertion algorithm simulating 50% dose reduction. For the actual and simulated half-dose cadaver scans, differences in SD for regions of interest were assessed, and streak artifact near the arthroplasty was graded by 3 blinded readers. Simulated half-dose scans were compared with full-dose scans in patients by measuring differences in implant position and by comparing readers' grades of periprosthetic osteolysis and muscle atrophy. The mean difference in SD between actual and simulated half-dose methods was 2.42 HU (95% CI [1.4, 3.4]). No differences in streak artifact grades were seen in 13/18 (72.2%) comparisons in cadavers. In patients, differences in implant position measurements were within 1° or 1mm in 149/150 (99.3%) measurements. The inter-reader agreement rates were nearly identical when readers were using full-dose (77.3% [232/300] for osteolysis and 76.9% [173/225] for muscle atrophy) and simulated half-dose (76.7% [920/1200] for osteolysis and 74.0% [666/900] for muscle atrophy) scans. A simulated half-dose MAR CT technique is comparable both quantitatively and qualitatively with a standard-dose technique for shoulder arthroplasty evaluation, demonstrating that this technique could be used to reduce dose in arthroplasty imaging.

DeepSPIO: Super Paramagnetic Iron Oxide Particle Quantification Using Deep Learning in Magnetic Resonance Imaging.

The susceptibility of super paramagnetic iron oxide (SPIO) particles makes them a useful contrast agent for different purposes in MRI. These particles are typically quantified with relaxometry or by measuring the inhomogeneities they produced. These methods rely on the phase, which is unreliable for high concentrations. We present in this study a novel Deep Learning method to quantify the SPIO concentration distribution. We acquired the data with a new sequence called View Line in which the field map information is encoded in the geometry of the image. The novelty of our network is that it uses residual blocks as the bottleneck and multiple decoders to improve the gradient flow in the network. Each decoder predicts a different part of the wavelet decomposition of the concentration map. This decomposition improves the estimation of the concentration, and also it accelerates the convergence of the model. We tested our SPIO concentration reconstruction technique with simulated images and data from actual scans from phantoms. The simulations were done using images from the IXI dataset, and the phantoms consisted of plastic cylinders containing agar with SPIO particles at different concentrations. In both experiments, the model was able to quantify the distribution accurately.