Volumetric Scans Research Articles

Volumetric directional optical coherence tomography.

Photoreceptor loss and resultant thinning of the outer nuclear layer (ONL) is an important pathological feature of retinal degenerations and may serve as a useful imaging biomarker for age-related macular degeneration. However, the demarcation between the ONL and the adjacent Henle's fiber layer (HFL) is difficult to visualize with standard optical coherence tomography (OCT). A dedicated OCT system that can precisely control and continuously and synchronously update the imaging beam entry points during scanning has not been realized yet. In this paper, we introduce a novel imaging technology, Volumetric Directional OCT (VD-OCT), which can dynamically adjust the incident beam on the pupil without manual adjustment during a volumetric OCT scan. We also implement a customized spoke-circular scanning pattern to observe the appearance of HFL with sufficient optical contrast in continuous cross-sectional scans through the entire volume. The application of VD-OCT for retinal imaging to exploit directional reflectivity properties of tissue layers has the potential to allow for early identification of retinal diseases.

Agreement of different OCT scan directions for individual retinal-layer thickness measurements in multiple sclerosis subjects with prior unilateral optic neuritis

The similarities between horizontal and vertical Optical Coherence Tomography (OCT) scans for the individual retinal layer thickness measurements in the macula was evaluated. Two volumetric scans (B-scans oriented horizontally and vertically) were performed in 64 multiple sclerosis subjects with history of unilateral optic neuritis and 64 healthy controls. The agreement between the thickness measurements with horizontal and vertical OCT scans was evaluated in 3 groups of eyes: healthy controls, eyes with history of optic neuritis and the fellow eyes. The mean difference in individual layer thickness between the scans was smaller than the instrument’s axial resolution in all 3 groups. The limit of agreement (LoA) varied among the different layers and sectors analyzed and this trend was similar in all the groups. For the inner retinal layers (retinal nerve fiber layer to inner nuclear layer), the inner macular sectors had a larger LoA compared to the corresponding outer sectors. In the outer plexiform and nuclear layers, the central and inner sectors (except inner temporal) had LoA larger than the other sectors and layers. The larger LoA seen for different layers and sectors suggests that the scan direction must be same for the follow-up OCT measurements and in clinical studies.

Accurate brain‐age models for routine clinical MRI examinations

Convolutional neural networks (CNN) can accurately predict chronological age in healthy individuals from structural MRI brain scans. Potentially, these models could be applied during routine clinical examinations to detect deviations from healthy ageing, including early-stage neurodegeneration. This could have important implications for patient care, drug development, and optimising MRI data collection. However, existing brain-age models are typically optimised for scans which are not part of routine examinations (e.g., volumetric T1-weighted scans), generalise poorly (e.g., to data from different scanner vendors and hospitals etc.), or rely on computationally expensive pre-processing steps which limit real-time clinical utility.Here, we sought to develop a brain-age framework suitable for use during routine clinical head MRI examinations. Using a deep learning-based neuroradiology report classifier, we generated a dataset of 23,302 ‘radiologically normal for age’ head MRI examinations from two large UK hospitals for model training and testing (age range = 18–95 years), and demonstrate fast (< 5 s), accurate (mean absolute error [MAE] < 4 years) age prediction from clinical-grade, minimally processed axial T2-weighted and axial diffusion-weighted scans, with generalisability between hospitals and scanner vendors (Δ MAE < 1 year). The clinical relevance of these brain-age predictions was tested using 228 patients whose MRIs were reported independently by neuroradiologists as showing atrophy ‘excessive for age’. These patients had systematically higher brain-predicted age than chronological age (mean predicted age difference = +5.89 years, 'radiologically normal for age' mean predicted age difference = +0.05 years, p < 0.0001).Our brain-age framework demonstrates feasibility for use as a screening tool during routine hospital examinations to automatically detect older-appearing brains in real-time, with relevance for clinical decision-making and optimising patient pathways.

Use of the Fully Spectroscopic Pixelated Cadmium Telluride Detector for Benchtop X-Ray Fluorescence Computed Tomography.

In this work, we integrated a commercially-available fully-spectroscopic pixelated cadmium telluride (CdTe) detector system as a two-dimensional (2D) array detector into our existing benchtop cone-beam x-ray fluorescence computed tomography (XFCT) system. After integrating this detector, known as High-Energy X-ray Imaging Technology (HEXITEC), we performed quantitative imaging of gold nanoparticle (GNP) distribution in a small animal-sized phantom using our benchtop XFCT system. Owing to the upgraded detector component within our benchtop XFCT system, we were able to conduct this phantom imaging in an unprecedented manner by volumetric XFCT scans followed by XFCT image reconstruction in 3D. The current results showed that adoption of HEXITEC, in conjunction with a custom-made parallel-hole collimator, drastically reduced the XFCT scan time/dose. Compared with the previous work performed with our original benchtop XFCT system adopting a single crystal CdTe detector, the currently observed reduction was up to a factor of 5, while achieving comparable GNP detection limit under similar experimental conditions. Overall, we demonstrated, for the first time to the best our knowledge, the feasibility of benchtop XFCT imaging of small animal-sized objects containing biologically relevant GNP concentrations (on the order of 0.1 mg Au/cm3 or 100 parts-per-million/ppm), with the scan time (on the order of 1 minute)/x-ray dose (on the order of 10 cGy) that are likely meeting the minimum requirements for routine preclinical imaging applications.

Tooth segmentation in 3D cone-beam CT images using deep convolutional neural network

Segmentation of an individual tooth in dental radiographs has great significance in the process of orthodontics surgeries and dentistry. Machine learning techniques, especially deep convolutional neural networks can play a key role in revolutionizing the way orthodontics surgeons and dentists work. Lately, many researchers have been working on tooth segmentation in 3D volumetric dental scans with a great degree of success, but to the best of our knowledge, there is no pretrained neural network available publicly for performing tooth segmentation in 3D cone-beam dental CT scans. The methods which so far have been proposed by the researchers in this domain are based on complex multistep pipelines. This lack of the availability of a pre-trained model blocks the path for further explorations in this domain. In this research, we have produced a deep learning model for tooth segmentation from CBCT dental radiographs. The proposed model can segment teeth in CBCT scans in a single step. To train the proposed model, we obtained a dataset consisting of 70 3D CBCT volumes from a local health facility. We labeled the ground truth through a semi-automatic method and trained our neural network. The training yielded a validation accuracy of 95.57% on a binary class semantic segmentation of the 3D CBCT volumes. The model is successfully able to segment teeth, regardless of their type from the background in a single step. This eliminates the need of having a complex and lengthy pipeline which many researchers have been proposing. The proposed model can be extended by incorporating labeling schemes. The custom labeling schemes will help healthcare professionals to perform the labeling as per their needs. The produced model can also provide a basis for further research in this domain.

Improving the accuracy of motion quantification using area detector computed tomography for real-time tumor-tracking irradiation in stereotactic ablative radiotherapy.

CyberKnife radiotherapy enables tumor-tracking irradiation using positional information regarding the tumor and a fiducial marker in a patient's body. This positional information acts as a surrogate of tumor motion. Therefore, deviations in these movements should be quantitatively estimated and included as an internal margin for radiation treatment planning. This study aimed to investigate variations between the positions of fiducial markers and tumor regions using 320-row area detector computed tomography and to analyze the effectiveness of our proposed method in contouring tumor regions based on the fiducial marker position. To determine the moving tumor volume, a typical single-phase image was selected, and pixel values in other phase images were accumulated. Moreover, a maximum-intensity projection image was created to clarify motion deviations in the tumor volume. To evaluate the delineation accuracy, the dice similarity coefficient and mean distance to agreement were calculated in phase-selected and breath-holding computed tomography. Moving chest phantom images were acquired using helical scanning 4-dimensional computed tomography (H-4DCT) and volumetric scanning 4-dimensional computed tomography (V-4DCT), and the delineation accuracies were compared for each scanning type. The average dice similarity coefficient and mean distance to agreement were degraded in limited-phase images, which cannot represent the hysteretic motion of a tumor. Moreover, deviations in tumor volume with unstable motion reached 71.6% in H-4DCT but only 1.6% in V-4DCT. Our proposed method with V-4DCT using area detector computed tomography can achieve accurate moving tumor delineation and can clarify positional associations between the fiducial marker and tumor under respiratory motion.

Peripapillary retinal nerve fiber layer thinning in patients with progressive supranuclear palsy

ObjectivesTo investigate peripapillary retinal nerve fiber layer (pRNFL) changes in patients with progressive supranuclear palsy (PSP).MethodsWe included 21 PSP patients (36 eyes) who underwent peripapillary optical coherence tomography (OCT) scans at 2.5 ± 1.3 years of disease, without ophthalmologic co-morbidities. We compared pRNFL thicknesses in PSP eyes with age-matched 22 controls (22 eyes) using generalized estimating equation model adjusting for intra-subject inter-eye correlations, age and sex. We also analyzed the correlation between the pRNFL thickness and clinical severity using Spearman’s correlation. In twelve PSP patients with 3 T brain MRI volumetric scan within 1 year of OCT exam, we investigated the correlation between the pRNFL thickness and brain atrophy using Pearson’s correlation.ResultsPSP patients had global pRNFL thinning compared to controls (beta = − 6.436, p = 0.025). Global pRNFL thickness correlated with Hoehn & Yahr stages (r = − 0.487, p = 0.025), and nasal pRNFL thinning showed a trend of correlation (uncorrected p < 0.05). Exploratory correlation analysis between global pRNFL thickness and nonmotor items in the PSP rating scale showed a trend toward association with sleep disturbances (uncorrected p = 0.008) and urinary incontinence (uncorrected p = 0.031), although not significant after Bonferroni correction (all 28 items). The patients had significant atrophy in the posterior cingulate cortex, third ventricle, pallidum, and midbrain with reduced midbrain-to-pons ratio, but no correlation was found between pRNFL thickness and brain volumes.ConclusionThe pRNFL seems to be affected in PSP, which is more severe with advanced disease stages. Retinal investigation in a larger longitudinal cohort would help elucidate the pathophysiological role of retinal thinning in PSP.

Impact of Intra- and Subretinal Fluid on Vision Based on Volume Quantification in the HARBOR Trial

To investigate the functional associations of intraretinal fluid (IRF) and subretinal fluid (SRF) volumes at baseline and after the loading dose as well as fluid change after the first injection with best-corrected visual acuity (BCVA) in patients with neovascular age-related macular degeneration (nAMD) who received an anti-VEGF treatment over 24 months. Post hoc analysis of a phase III, randomized, multicenter trial in which ranibizumab was administered monthly or in a pro re nata regimen (HARBOR). Study eyes of 1094 treatment-naïve patients with nAMD. IRF and SRF volumes were segmented automatically on monthly spectral domain OCT images. Fluid volumes and changes thereof were included as covariates into longitudinal mixed-effects models, which modeled BCVA trajectories. BCVA estimates corresponding to baseline, follow-up, and persistent IRF/SRF volumes after the loading dose; BCVA estimates of change in fluid volumes after the first injection; and marginal and conditional R2. Analysis of 22 494 volumetric scans revealed that foveal IRF consistently shows a negative correlation with BCVA at baseline and subsequent visits (-3.23 and-4.32 letters/100 nL, respectively). After the first injection, BCVA increased by+2.13 letters/100 nL decrease in foveal IRF. Persistent IRF was associated with lower baseline BCVA and less improvement. Foveal SRF correlated with better BCVA at baseline and subsequent visits (+6.52 and+1.42 letters/100 nL, respectively). After the first injection, SRF decrease was associated with significant vision gain (+5.88 letters/100 nL). Foveal fluid correlated more with BCVA than parafoveal IRF/SRF. Although IRF consistently correlates with decreased function and recovery throughout therapy, SRF is associated with a more pronounced functional improvement. Moreover, SRF resolution provides increased benefit. Fluid-function correlation represents an essential base for the development of personalized treatment regimens, optimizing functional outcomes, and reducing treatment burden.

Retinal Changes before and after Silicone Oil Removal in Eyes with Rhegmatogenous Retinal Detachment Using Swept-Source Optical Coherence Tomography.

This study aimed to evaluate and compare the retinal and choroidal thickness and vessel density (VD) changes between silicone oil (SO) tamponade and after SO removal using swept-source optical coherence tomography (SS-OCT) and OCT angiography (OCTA). Thirty patients who underwent pars plana vitrectomy for retinal detachment (RD) with SO tamponade were included. SS-OCT and OCTA were conducted before RD surgery, during SO tamponade, and after SO removal. A 3-dimensional volumetric wide scan protocol was used for the analysis. The segmented retina, choroidal thickness map, and peripapillary thickness were then measured. For the OCTA analysis, 4.5 × 4.5 mm scans were used. Superficial and deep capillary plexus VDs in unaffected fellow eyes and eyes after SO removal were compared. During the SO tamponade period, the thickness of the parafoveal total retina, ganglion cell-inner plexiform layer, and peripapillary retinal nerve fiber layer (ppRNFL) were significantly thinner than those of unaffected fellow eyes (p < 0.05). The parafoveal layer thickness thinning recovered up to three to six months after SO removal. Moreover, six months after SO removal, the parafoveal thickness was not significantly different compared to that of unaffected fellow eyes (p > 0.05). However, the ppRNFL thickness was significantly decreased during SO tamponade and remained unrecovered six months after SO removal. There was no significant difference in the VD on the OCTA. Thus, SO tamponade and removal for RD resulted in a change in the retinal and peripapillary thickness. This may be due to the mechanical pressure effect of SO.

Assessing Foveal Structure in Individuals with TYR R402Q and S192Y Hypomorphic Alleles.

PurposeTo assess the impact of two TYR hypomorphic alleles (R402Q and S192Y) on foveal pit and foveal avascular zone (FAZ) morphology.DesignProspective, cross-sectional study.ParticipantsA total of 164 participants with normal vision (67 male and 97 female; mean ± standard deviation [SD] age = 30.5 ± 12.8 years) were recruited.MethodsSequencing of more than 100 pigmentation-related genes was performed, and results were reviewed for the presence or absence of the TYR polymorphisms R402Q (rs1126809) and S192Y (rs1042602). Volumetric scans of the macula were obtained for each participant using OCT, and retinal thickness maps were analyzed using custom software. OCT angiography was used to image the FAZ, which was manually segmented and measured. Linear mixed model analysis was used to assess associations between genotype and foveal pit morphology.Main Outcome MeasuresFoveal pit depth, diameter, volume, and FAZ area in relation to the presence of hypomorphic alleles R402Q and S192Y on the TYR gene.ResultsHeterozygosity for the TYR R402Q allele was associated with decreased pit diameter (P = 0.0094) and decreased FAZ area (P = 0.025). Homozygosity for the TYR R402Q allele was associated with reduced pit volume (P = 0.0005), decreased pit depth (P = 0.007), reduced pit diameter (P = 0.0052), and reduced FAZ area (P = 0.0012). Homozygosity for TYR S192Y was associated with reduced FAZ area (P = 0.016). Heterozygosity for the TYR S192Y allele was not associated with differences in foveal pit depth, diameter, volume, or FAZ area (P > 0.05).ConclusionsAlthough the role of the TYR R402Q and S192Y hypomorphic alleles in albinism remains controversial, our data suggest that these variants contribute to the extensive inter-individual variability in foveal morphology in the normal population. Our results contribute to the evolving picture of the relationship between ocular pigmentation and foveal morphology.

Multi-task learning approach for volumetric segmentation and reconstruction in 3D OCT images.

The choroid is the vascular layer of the eye that supplies photoreceptors with oxygen. Changes in the choroid are associated with many pathologies including myopia where the choroid progressively thins due to axial elongation. To quantize these changes, there is a need to automatically and accurately segment the choroidal layer from optical coherence tomography (OCT) images. In this paper, we propose a multi-task learning approach to segment the choroid from three-dimensional OCT images. Our proposed architecture aggregates the spatial context from adjacent cross-sectional slices to reconstruct the central slice. Spatial context learned by this reconstruction mechanism is then fused with a U-Net based architecture for segmentation. The proposed approach was evaluated on volumetric OCT scans of 166 myopic eyes acquired with a commercial OCT system, and achieved a cross-validation Intersection over Union (IoU) score of 94.69% which significantly outperformed (p<0.001) the other state-of-the-art methods on the same data set. Choroidal thickness maps generated by our approach also achieved a better structural similarity index (SSIM) of 72.11% with respect to the groundtruth. In particular, our approach performs well for highly challenging eyes with thinner choroids. Compared to other methods, our proposed approach also requires lesser processing time and has lower computational requirements. The results suggest that our proposed approach could potentially be used as a fast and reliable method for automated choroidal segmentation.

Developing diagnostic assessment of breast lumpectomy tissues using radiomic and optical signatures

High positive margin rates in oncologic breast-conserving surgery are a pressing clinical problem. Volumetric X-ray scanning is emerging as a powerful ex vivo specimen imaging technique for analyzing resection margins, but X-rays lack contrast between non-malignant and malignant fibrous tissues. In this study, combined micro-CT and wide-field optical image radiomics were developed to classify malignancy of breast cancer tissues, demonstrating that X-ray/optical radiomics improve malignancy classification. Ninety-two standardized features were extracted from co-registered micro-CT and optical spatial frequency domain imaging samples extracted from 54 breast tumors exhibiting seven tissue subtypes confirmed by microscopic histological analysis. Multimodal feature sets improved classification performance versus micro-CT alone when adipose samples were included (AUC = 0.88 vs. 0.90; p-value = 3.65e−11) and excluded, focusing the classification task on exclusively non-malignant fibrous versus malignant tissues (AUC = 0.78 vs. 0.85; p-value = 9.33e−14). Extending the radiomics approach to high-dimensional optical data—termed “optomics” in this study—offers a promising optical image analysis technique for cancer detection. Radiomic feature data and classification source code are publicly available.

A registration-free approach for statistical process control of 3D scanned objects via FEM

Recent work in on-line Statistical Process Control (SPC) of manufactured 3-dimensional (3-D) objects has been proposed based on the estimation of the spectrum of the Laplace-Beltrami (LB) operator, a differential operator that encodes the geometrical features of a manifold and is widely used in Machine Learning (i.e., Manifold Learning). The resulting spectra are an intrinsic geometrical feature of each part, and thus can be compared between parts to assess the state of the manufacturing process while avoiding part to part registration (or “part localization”) pre-processing or the need for equal size meshes, characteristics which are required in previous approaches for SPC of 3D parts. The recent spectral SPC methods, however, are limited to monitoring surface data from objects such that the scanned meshes have no boundaries, holes or missing portions. In this paper we extend spectral methods by first considering a more accurate and general estimator of the LB spectrum that is obtained by application of Finite Element Methods (FEM) to the solution of Helmholtz's equation with boundaries. It is shown how the new spectral FEM approach, while it retains the advantages of not requiring part localization/registration or equal size datasets scanned from each part, it provides more accurate spectrum estimates, which results in faster detection of out of control conditions than earlier methods, can be applied to both mesh or volumetric (solid) scans, and furthermore, it is shown how it can be applied to partial scans that result in open meshes (surface or volumetric) with boundaries, increasing the practical applicability of the methods. The present work brings SPC methods closer to contemporary research in Computer Graphics and Manifold Learning. MATLAB code that reproduces the examples of this paper is provided in the supplementary materials.

Association of Reproductive History With Brain MRI Biomarkers of Dementia Risk in Midlife.

To examine associations between indicators of estrogen exposure from women's reproductive history and brain MRI biomarkers of Alzheimer disease (AD) in midlife. We evaluated 99 cognitively normal women 52 ± 6 years of age and 29 men 52 ± 7 years of age with reproductive history data, neuropsychological testing, and volumetric MRI scans. We used multiple regressions to examine associations among reproductive history indicators, voxel-wise gray matter volume (GMV), and memory and global cognition scores, adjusting for demographics and midlife health indicators. Exposure variables were menopause status, age at menarche, age at menopause, reproductive span, hysterectomy status, number of children and pregnancies, and use of menopause hormonal therapy (HT) and hormonal contraceptives (HC). All menopausal groups exhibited lower GMV in AD-vulnerable regions compared to men, with perimenopausal and postmenopausal groups also exhibiting lower GMV in temporal cortex compared to the premenopausal group. Reproductive span, number of children and pregnancies, and use of HT and HC were positively associated with GMV, chiefly in temporal cortex, frontal cortex, and precuneus, independent of age, APOE ε4 status, and midlife health indicators. Although reproductive history indicators were not directly associated with cognitive measures, GMV in temporal regions was positively associated with memory and global cognition scores. Reproductive history events signaling more estrogen exposure such as premenopausal status, longer reproductive span, higher number of children, and use of HT and HC were associated with larger GMV in women in midlife. Further studies are needed to elucidate sex-specific biological pathways through which reproductive history influences cognitive aging and AD risk.

Data Stream Stabilization for Optical Coherence Tomography Volumetric Scanning

Optical Coherence Tomography (OCT) is an emerging medical imaging modality for luminal organ diagnosis. The non-constant rotation speed of optical components in the OCT catheter tip causes rotational distortion in OCT volumetric scanning. By improving the scanning process, this instability can be partially reduced. To further correct the rotational distortion in the OCT image, a volumetric data stabilization algorithm is proposed. The algorithm first estimates the Non-Uniform Rotational Distortion (NURD) for each B-scan by using a Convolutional Neural Network (CNN). A correlation map between two successive B-scans is computed and provided as input to the CNN. To solve the problem of accumulative error in iterative frame stream processing, we deploy an overall rotation estimation between reference orientation and actual OCT image orientation. We train the network with synthetic OCT videos by intentionally adding rotational distortion into real OCT images. As part of this article we discuss the proposed method in two different scanning modes: the first is a conventional pullback mode where the optical components move along the protection sheath, and the second is a self-designed scanning mode where the catheter is globally translated by using an external actuator. The efficiency and robustness of the proposed method are evaluated with synthetic scans as well as real scans under two scanning modes.

Magnetic Resonance Image Guided Adaptive Radiation Therapy: Clinical Applications in the Pediatric and Adolescent and Young Adult Population

<h3>Purpose/Objective(s)</h3> To demonstrate the feasibility of online adaptive magnetic resonance (MR) image guided radiotherapy in the pediatric, adolescent and young adult (AYA) population. <h3>Materials/Methods</h3> All pediatric (age < 18) and AYA patients (age < 30), treated on an MR linear accelerator (MRL) from 2019 to 2021 were enrolled onto a prospective registry. The system is used with a related treatment planning system which employs a Monte Carlo dose calculation algorithm to account for effects of the magnetic field on the dose distribution. Step-and-Shoot intensity-modulated radiation treatment (IMRT) plans were generated for all Unity patients with a dose grid of 3 mm and a statistical uncertainty of ≤ 1% per plan. CT and MR images were obtained for each patient for initial radiation treatment planning. Daily treatment setup consisted of a volumetric MRI scan followed by rigid registration used to evaluate the agreement between the reference plan contours and daily shifts in internal anatomy. If necessary, based on evaluation of the daily anatomy, online recontouring and plan re-optimization was performed. <h3>Results</h3> A total of 14 pediatric and AYA patients have been treated. The median patient age is 13 years (range: 14 mo -27 yrs). Four patients were ≤ 8 years old. Reasons for treatment on the MRL were improved visualization of the primary tumor, re-irradiation in a critical area, and need for daily adaptive replanning to minimize dose to critical structures The clinical applications of MR-guided radiotherapy included Ewing sarcoma (primary and metastatic, n = 3), recurrent diffuse intrinsic pontine glioma (DIPG, n = 2), nasopharyngeal carcinoma (n = 1), clival chordoma (n = 1), primitive neuroectodermal tumor of the pancreas (n = 1), recurrent gluteo-sacral germ cell tumor (n = 1), Wilms tumor with unresectable IVC thrombus (n = 1), C-spine ependymoma (n = 1), and posterior fossa ependymoma (n = 1). Only one child (age 14 mo. with Wilms tumor required anesthesia. One AYA patient with Ewing sarcoma of C1 could not complete the treatment course due to pain related to longer treatment times and was transferred to a conventional linear accelerator. No patient experienced treatment interruptions or unexpected toxicity. Reasons for plan adaptation included changes in stomach and small bowel anatomy, target shape change on a near-daily basis, critical location adjacent to the brainstem, and manipulation of hot spots near the spinal cord. <h3>Conclusion</h3> Radiotherapy on the MRL was well-tolerated by pediatric and AYA patients. There was no increased use of anesthesia outside of our usual practice. Significant dosimetric advantages were seen for patients with tumors in critical locations such as adjacent to or involving the brainstem, spinal cord or near abdominal organs that change shape and location daily.

Deep learning-based COVID-19 detection system using pulmonary CT scans

One of the most significant pandemics has been raised in the form of Coronavirus disease 2019 (COVID19). Many researchers have faced various types of challenges for finding the accurate model, which can automatically detect the COVID-19 using computed pulmonary tomography (CT) scans of the chest. This paper has also focused on the same area, and a fully automatic model has been developed, which can predict the COVID-19 using the chest CT scans. The performance of the proposed method has been evaluated by classifying the CT scans of community-acquired pneumonia (CAP) and other non-pneumonia. The proposed deep learning model is based on ResNet 50, named CORNet for the detection of COVID-19, and also performed the retrospective and multicenter analysis for the extraction of visual characteristics from volumetric chest CT scans during COVID-19 detection. Between August 2016 and May 2020, the datasets were obtained from six hospitals. Results are evaluated on the image dataset consisting of a total of 10,052 CT scan images generated from 7850 patients, and the average age of the patients was 50 years. The implemented model has achieved the sensitivity and specificity of 90% and 96%, per scanned image with an AUC of 0. 95.

Ultrasound coherent plane-wave compound imaging: Image quality evaluation in phantoms and breast lesions

Coherent plane-wave compound imaging (CPWCI) can achieve high temporal resolution (e.g., to prevent breathing artefacts in volumetric scanning). It has to be verified that CPWCI using a limited number of steering angles (na) can achieve at least similar image quality as conventional focused imaging (CFI). The aim is to compare image quality between CPWCI and CFI in phantom and in breast lesions. In CPWCI, plane-wave channel data were recorded (na = 15, ±11 deg) by a Sequoia Ultrasound system (10L4, 14L5; Siemens Healthineers); beamformed (delay-and-sum (DAS), Lu’s-fk and Stolt’s-fk) for each steering angle, and coherently compounded. For comparison, images were recorded by CFI (10, 35 mm focus). Image quality metrics were obtained in images of a multipurpose phantom (CIRS Model057). We have just initiated a reader study (n = 200) to investigate how these phantom results translate to in vivo. Phantom results showed that contrast sensitivity (CS) and resolution (CR), lateral resolution (LR) and contrast-to-noise ratio (CNR, depth &amp;lt; 45 mm) were similar for CPWCI and CFI (10, 35mm) for both transducers, whereas CNR (&amp;gt;45 mm), LR and penetration were improved. In CPWCI, Lu’s f-k and DAS resulted in optimal CS (10L4) and LR, and CNR, respectively. In the ongoing reader study, image quality of breast lesions will be evaluated.

Correlation Between CT Volumetric and Nuclear Renal Scans in Donors with Renal Asymmetry

Correlation Between CT Volumetric and Nuclear Renal Scans in Donors with Renal Asymmetry

MTU-COVNet: A hybrid methodology for diagnosing the COVID-19 pneumonia with optimized features from multi-net

PurposeThe aim of this study was to establish and evaluate a fully automatic deep learning system for the diagnosis of COVID-19 using thoracic computed tomography (CT). Materials and methodsIn this retrospective study, a novel hybrid model (MTU-COVNet) was developed to extract visual features from volumetric thoracic CT scans for the detection of COVID-19. The collected dataset consisted of 3210 CT scans from 953 patients. Of the total 3210 scans in the final dataset, 1327 (41%) were obtained from the COVID-19 group, 929 (29%) from the CAP group, and 954 (30%) from the Normal CT group. Diagnostic performance was assessed with the area under the receiver operating characteristic (ROC) curve, sensitivity, and specificity. ResultsThe proposed approach with the optimized features from concatenated layers reached an overall accuracy of 97.7% for the CT-MTU dataset. The rest of the total performance metrics, such as; specificity, sensitivity, precision, F1 score, and Matthew Correlation Coefficient were 98.8%, 97.6%, 97.8%, 97.7%, and 96.5%, respectively. This model showed high diagnostic performance in detecting COVID-19 pneumonia (specificity: 98.0% and sensitivity: 98.2%) and CAP (specificity: 99.1% and sensitivity: 97.1%). The areas under the ROC curves for COVID-19 and CAP were 0.997 and 0.996, respectively. ConclusionA deep learning–based AI system built on the CT imaging can detect COVID-19 pneumonia with high diagnostic efficiency and distinguish it from CAP and normal CT. AI applications can have beneficial effects in the fight against COVID-19.