64-slice CT Research Articles

A rare finding of left innominate artery arising from the left pulmonary artery in a case of tetralogy of fallot

Right-sided aortic arch with an aberrant left innominate artery arising from the left pulmonary artery is a very rare finding in the tetralogy of fallot (TOF). A 2-year-old boy presented for routine intracardiac repair of TOF. A routine angiogram raised suspicion of the left innominate artery with abnormal origin. 640-slice CT was done to delineate the anatomy. The left innominate artery was re-implanted in the descending aorta and the intracardiac repair was performed.

Assessment of Computed Tomography Dose Index (CTDI) During CT Pelvimetry Using Monte Carlo Simulation

A pelvimetry examination is sometimes prescribed to a pregnant woman at the end of her pregnancy in order to assess the dimensions of her pelvis prior to childbirth. This examination has long been performed by using X-ray, but is now increasingly being replaced by CT-scan The objective of this study is to assess the radiation doses received during a practical CT pelvimetry examination performed using a Hitashi Supria 16-slice CT scanner. The radiation doses were estimated using Monte Carlo (MC)-based simulation with GATE code to model the 16-slice CT scanner machine. The GATE code operates using GEANT4 libraries. A polyymethyl metacrylate (PMMA) acrylic phantom of 32 cm diameter was modeled to represent the patient's body. X-ray energy spectrum generated using the SRS-78 spectrum processor was used for simulation. The simulation was executed with the same exposure parameters as the practical CT pelvimetry examination with dose parameters of 1 mGy, 0.9 mGy, and 36.6 mGy.cm, respectively, for the weighted CT dose index (CTDIw), the volume CT dose index (CTDIvol), and dose-length product (DLP). The MC simulation results provide dose parameters of 1.16 mGy, 1.07 mGy, and 43.6 mGy.cm, respectively, for the CTDIw, CTDIvol, and DLP. The differences between the simulation and the practical examination were 16 %, 18 %, and 18 %, respectively. These differences are considered in a quite good agreement. The results were also consistent with other similar studies. This work proves that the Monte Carlo simulation with the GATE code is usable to assess the patient doses during a CT pelvimetry examination.

Deep-Learning Segmentation of Urinary Stones in Noncontrast Computed Tomography.

Background: Noncontrast CT (NCCT) relies on labor-intensive examinations of CT slices to identify urolithiasis in the urinary tract, and, despite the use of deep-learning algorithms, false positives remain. Materials and Methods: A total of 410 NCCT axial scans from patients undergoing surgical treatment for urolithiasis were used for model development. The deep learning model was customized to combine a urolithiasis segmentation with per-slice classification for screening. Prediction models of the axial, coronal, and sagittal views were trained, and an additive model with an intersection of the coronal and sagittal predictions added to the axial outcome was introduced. Automated quantification of clinical metrics was evaluated in three-dimensional models of urinary stones. Results: The axial model detected 88.92% of urinary stones and produced a dice similarity coefficient of 87.56% in the urolithiasis segmentation. For urolithiasis (>5 mm), the sensitivity of the axial model reached 95.10%. False positives were reduced to 0.34 per patient using an ensemble of individual models. The additive model improved the sensitivity to 90.97% by detecting more small urolithiasis (<5 mm). All clinical metrics of size, long-axis diameter, volume, mean stone density, stone heterogeneity index, and skin-to-stone distance showed a strong correlation of R2 > 0.964. Conclusions: The proposed system could reduce the burden on the physician for imaging diagnosis and help determine treatment strategies for urinary stones through automated quantification of clinical metrics with high accuracy and reproducibility.

Association of myosteatosis with treatment response and survival in patients with hepatocellular carcinoma undergoing chemoembolization: a retrospective cohort study

Patients with hepatocellular carcinoma (HCC) have poor prognosis and have frequent treatment-related toxicities resulting in cancer-associated cachexia. This study aimed to determine the association of myosteatosis and sarcopenia on mortality in patients with HCC treated with transarterial chemoembolization (TACE). Six hundred and eleven patients diagnosed with HCC and underwent TACE at a tertiary care center between 2008 and 2019 were included. Body composition was assessed using axial CT slices at level L3 to calculate the skeletal muscle density for myosteatosis and skeletal muscle index for sarcopenia. The primary outcome was overall survival while the secondary outcome was TACE response. Patients with myosteatosis had a poorer TACE response than patients without myosteatosis (56.12% vs. 68.72%, adjusted odds ratio [OR] 0.49, 95% confidence interval [CI] 0.34–0.72). The rate of TACE response in patients with sarcopenia was not different from those without sarcopenia (60.91% vs. 65.22%, adjusted OR 0.79, 95% CI 0.55–1.13). Patients with myosteatosis had shorter overall survival than without myosteatosis (15.9 vs. 27.1 months, P < 0.001). In the multivariable Cox regression analysis, patients with myosteatosis or sarcopenia had higher risk of all-cause mortality than their counterparts (adjusted hazard ratio [HR] for myosteatosis versus no myosteatosis 1.66, 95% CI 1.37–2.01, adjusted HR for sarcopenia versus no sarcopenia 1.26, 95% CI 1.04–1.52). Patients with both myosteatosis and sarcopenia had the highest 7 year mortality rate at 94.45%, while patients with neither condition had the lowest mortality rate at 83.31%. The presence of myosteatosis was significantly associated with poor TACE response and reduced survival. Identifying patients with myosteatosis prior to TACE could allow for early interventions to preserve muscle quality and might improve prognosis in HCC patients.

Design of Proposed Software System for Prediction of Iliosacral Screw Placement for Iliosacral Joint Injuries Based on X-ray and CT Images.

One of the crucial tasks for the planning of surgery of the iliosacral joint is placing an iliosacral screw with the goal of fixing broken parts of the pelvis. Tracking of proper screw trajectory is usually done in the preoperative phase by the acquisition of X-ray images under different angles, which guide the surgeons to perform surgery. This approach is standardly complicated due to the investigation of 2D X-ray images not showing spatial perspective. Therefore, in this pilot study, we propose complex software tools which are aimed at making a simulation model of reconstructed CT (DDR) images with a virtual iliosacral screw to guide the surgery process. This pilot study presents the testing for two clinical cases to reveal the initial performance and usability of this software in clinical conditions. This model is consequently used for a multiregional registration with reference intraoperative X-ray images to select the slide from the 3D dataset which best fits with reference X-ray. The proposed software solution utilizes input CT slices of the pelvis area to create a segmentation model of individual bone components. Consequently, a model of an iliosacral screw is inserted into this model. In the next step, we propose the software CT2DDR which makes DDR projections with the iliosacral screw. In the last step, we propose a multimodal registration procedure, which performs registration of a selected number of slices with reference X-ray, and based on the Structural Similarity Index (SSIM) and index of correlation, the procedure finds the best match of DDR with X-ray images. In this pilot study, we also provide a comparative analysis of the computational costs of the multimodal registration upon various numbers of DDR slices to show the complex software performance. The proposed complex model has versatile usage for modeling and surgery planning of the pelvis area in fractures of iliosacral joints.

A Study on Ureteric Abnormalities in Single Contrast Bolus CT Urography at A Tertiary Care Centre

Introduction: CT urography is an imaging technique in which the i.v contrast is injected and phases of CT scan are taken to study the entire urinary system. It has essentially replaced intravenous urography (IVU) in most imaging practices. CT Urography has evolved as an ideal imaging tool for comprehensive imaging assessment of patients with flank pain, malignancies, hematuria, trauma, renal calculi, suspected congenital malformations and in various clinical settings. Other CT advantages include the use of multiplanar reconstruction (MPR), curved planar reformatted images, maximum intensity projection (MIP) and three-dimensional (3D) reconstruction which gives valuable information to the clinician about the extent of the lesions and helps in management.&#x0D; Aim: To study various types of ureteric abnormalities in CT Urography in various clinical presentation.&#x0D; Materials and methods: This is a tertiary hospital based observational Descriptive Cross sectional study conducted on 360 patients referred for CT Urography with Clinical Signs &amp; Symptoms of urinary tract abnormalities, having ureteric imaging abnormalities. Siemens somatum High Definition 128 slice CT scanner was used for single bolus CT urography.&#x0D; Results: Males were more as compared to females with age group 31 to 50 years (44.4%). Abdominal pain with urinary complaints was seen in maximum number of patients (203 cases- 56.4%). Urinary complaints in known abdomino-pelvic malignancies was seen in 18% cases. 285 patients had obstructive urography findings like hydronephrosis or hydroureter, and 75 cases had non obstructive findings like ureteral filling defects due to calculi, clots, etc or abnormal ureteric course or only congenital abnormality. Most hydroureter cases were secondary to obstructive calculus followed by obstruction by external malignant mass.&#x0D; Conclusion: This study outlines the most common ureteric abnormalities in patients referred for CT urography which were mostly obstructive findings most cases were secondary to obstructive calculus followed by obstruction by external malignant mass. The non obstructive findings like ureteral filling defects were due to calculi, clots, etc or abnormal ureteric course or only congenital abnormalities. Delayed phase of CT urography can also be applied in Contrast CT study of abdomen &amp; Pelvic scans on case to case basis to study ureteric involvements &amp; pathologies.

4DCT is long overdue for improvement.

4DCT is long overdue for improvement.

Cardiovascular risk factors and pericardial fat volume as predictors of coronary atherosclerosis burden in postmenopausal women: A retrospective, single center study.

Little is known about the clinical relevance of pericardial fat volume (PFV), compared to other conventional cardiovascular risk factors, as predictors of coronary atherosclerosis severity among postmenopausal women. Our main aim was to assess the association of PFV and cardiovascular risk factors with coronary atherosclerosis burden, including occlusive coronary artery disease (CAD) (luminal stenosis >50%), coronary artery calcium score (CAC)>100, and plaque types in postmenopausal women. Patients who had chest pain of angina and referred for 64-slice multi-detector CT (MDCT) angiography examination to exclude occlusive CAD presence were enrolled in this retrospective study. A total of 241 women with absence of menses for more than 2 consecutive years and not taking hormone replacement therapy were enrolled in the present study. Increased PFV(OR[CI] = 1.1 [1-1.3],p = 0.004), age (OR [CI] = 1.1 [1-1.4],p = 0.002), and diabetes mellitus (OR [CI] = 2 [1-4],p = 0.025)were significant predictors of occlusive CAD presence. Increased PFV(OR [CI] = 1.1 [1-1.4],p = 0.017)and increased age (OR [CI] = 1.2 [1.1-1.4],p < 0.001)were better predictors of CAC>100 than other cardiovascular risk factors. Regarding coronary plaque types, increased age (OR [CI] = 1.1 [1.1-1.3],p = 0.001)showed a significant association with coronary calcified plaque presence while increased body mass index(OR [CI] = 1.2 [1-1.4],p = 0.018)showed a significant association with coronary non-calcified plaque presence. Higher PFV values showed an independent association with occlusive CAD and significant coronary calcification. This may imply that increased PFV is likely associated with the development of advanced and severe phenotype of coronary atherosclerosis among postmenopausal women with suspected CAD.

Effect of 320-Row Computed Tomography Acquisition Technology on Coronary Computed Tomography Angiography-Derived Fractional Flow Reserve Based on Machine Learning: Systolic and Diastolic Scan Acquisition.

The aim of the study is to investigate the performance of coronary computed tomography angiography (CCTA)-derived fractional flow reserve (CT-FFR) in the same patient evaluated by different systolic and diastolic scans, aiming to explore whether 320-slice CT scanning acquisition protocol has an impact on CT-FFR value. One hundred forty-six patients with suspected coronary artery stenosis who underwent CCTA examination were included into the study. The prospective electrocardiogram gated trigger sequence scan was performed and electrocardiogram editors selected 2 optimal phases of systolic phase (preset collection trigger at 25% of R-R interval) and diastolic phase (preset collection trigger at 75% of R-R interval) for reconstruction. The lowest CT-FFR value (the CT-FFR value at the distal end of each vessel) and the lesion CT-FFR value (at 2 cm distal to the stenosis) after coronary artery stenosis were calculated for each vessel. The difference of CT-FFR values between the 2 scanning techniques was compared using paired Wilcoxon signed-rank test. Pearson correlation value and Bland-Altman were performed to evaluate the consistency of CT-FFR values. A total of 366 coronary arteries from the remaining 122 patients were analyzed. There was no significant difference regarding the lowest CT-FFR values between systole phase and diastole phase across all vessels. In addition, there was no significant difference in the lesion CT-FFR value after coronary artery stenosis between systole phase and diastole phase across all vessels. The CT-FFR value between the 2 reconstruction techniques had excellent correlation and minimal bias in all groups. The correlation coefficient of the lesion CT-FFR values for left anterior descending branch, left circumflex branch, and right coronary artery were 0.86, 0.84, and 0.76, respectively. Coronary computed tomography angiography-derived fractional flow reserve based on artificial intelligence deep learning neural network has stable performance, is not affected by the acquisition phase technology of 320-slice CT scan, and has high consistency with the evaluation of hemodynamics after coronary artery stenosis.

Computed tomography in skull base fractures in infants and young children

Aim of the study is to show the possibilities of multiplanar reformation and 3D reconstructions of computed tomography in the diagnosis of skull base fractures in young children.&#x0D; Materials and methods. In one thousand three hundred thirty four children under 3 years of age with traumatic brain injury (TBI) CT was performed on a 128-slice Philips Ingenuity CT scanner; in 707 (53%) in the first 6 hours, in 254 (19%) — after 6 hours, but during the first 24 hours, in 205 (15%) children within 3 days and in 168 (13%) children later on 3 days after injury. Scanning of the area of interest (head + cervical spine) was made with the maximum possible reduction in parameters to minimize the radiation dose. Contrast was not used in children from 0 to 3 years of age with TBI. Post-processing included isotropic multi-planar reformatted (MPR) and 3D images.&#x0D; Results. Of the 1334 children examined, 730 were boys and 604 were girls. In 448 (33.58%) children, fractures of the skull bones were diagnosed, in 366 (81.7%) of them, fractures were combined with intracranial injuries. Fractures of the skull base were in 83 (18.52%) of 448 children. In 65% (n = 54) of cases, basal fractures were combined with fractures of the temporal bones, 31.5% &#x0D; (n = 17) of these children had liquorrhea. Fractures of the anterior fossa (12% of the total number of fractures of the base of the skull) of the base of the skull or fronto-basal fractures were accompanied by additional fractures of the orbits and/or other bones of the facial skull in 56.6% of cases. Fractures of the middle cranial fossa were diagnosed in 54 (65%) children. Fractures of the posterior cranial fossa were found in 19 (23%) of 83 children. In addition to fractures of the bones of the base of the skull, 32 (38.6%) children were diagnosed with fractures of the bones of the vault and intracranial injuries.&#x0D; Conclusion. The use of multiplanar reformation and 3D reconstruction increased the sensitivity and specificity of diagnosing skull base fractures in children compared to conventional axial CT. The essential advantages of using 3D reconstruction are the availability of the technique, the absence of additional scanning time and radiation exposure.

ROLE OF 128 SLICE MDCT IN PANCREATIC TRAUMA DIAGNOSIS AND MANAGEMENT

Aims and Objectives:This study aimed to detail how multi-detector computed tomography (MDCT) can be utilised to the study diagnosis of pancreatic trauma and its management. Material and Methods:This Hospital-based prospective study was carried out over 10 months from December 2020 to SEP 2021 at the Department of Radiodiagnosis, Narayana medical college, Nellore. 30 patients with pancreatic injury were included in study. All patients underwent Contrast enhanced computed tomography abdomen on128 slice CT(GE OPTIMA 660).Grading of pancreatic injury done using American Association for the surgery of trauma organ injury scale .One patient underwent MRI abdomen and MRCP. 20 patients are treated conservatively. 10 out of 30 patients underwentsurgery.Intraoperative findings noted. Results: 1. According to American Association for the surgery of trauma organ injury scale 19/30 patients are of grade I &amp;grade II injury. 6/30 in grade III and 5/30 were grade IV injuries. 2. Grade I &amp;II patients were managed conservatively and improved remarkably on followup. 3. Grade III&amp;IV patients underwent surgery and CT findings are confirmed intraoperatively. 4. 6 patients with grade III injury undergone Distal pancreatectomy + Splenectomy and 4 patients undergone Spleen preserving Distal Pancreatectomy. 5. Grade IV patients initially managed conservatively in which 4 patients developed pseudocyst they were treated with cystogastrostomy and one patient developed fistula .This fistula was initially controlled with Percutaneous catheter drainage followed by roux -en- y fistulojejunostomy. Conclusion: 1. CT represents an accurate tool for diagnosing pancreatic trauma, it provides useful information to plan therapeutic approach .

Novel Corona Virus 2019 Disease: Assessment on High-Resolution Computed Tomography Thorax.

Objectives This particular study was undertaken to assess the role of high-resolution computed tomography (HRCT) thorax in diagnosing patients with novel Corona virus-2019 disease and screening suspected COVID-19 cases. It also involves an assessment of the severity of bilateral lung involvement in proven and suspected cases of COVID-19 infection. Materials and methods Two hundred and fourteen symptomatic cases referred to the department of radio-diagnosis were evaluated in this study. HRCT thorax was performed on SIEMENS Somatom Emotion 16-slice spiral CT. Initially, a tomogram was taken, followed by sections in the lung window at B90s, kVp 130, with a pitch of 1.15. The images are then reconstructed into 1.0-mm-thin slices. Radiologists then interpreted the scans for features of COVID-19 disease. Various imaging features and the severity of the disease were analysed in all patients. Results We observed that the male population was more affected by the disease (72% of the total cases). The most consistent and common HRCT finding is that of ground-glass opacity (GGO), which was present in 172 cases, corresponding to 78.4% of the cases. Crazy pavement appearance was seen in 41.2 % of the cases. Other findings included consolidation, discrete nodules surrounded by ground-glass opacification, subpleural linear opacities, and tubular bronchiectasis. Conclusion HRCT thorax plays an ideal role in diagnosing COVID-19 disease with high sensitivity and also provides prompt results as compared to RT-PCR. It also helps in grading the severity of the disease based on various patterns and the extent of lung parenchyma involved. Therefore, because of the immediate results and the ability to grade the disease, HRCT became invaluable in directing the treatment of COVID-19 disease.

VP shunt entry area recommender (VPSEAR): A computer-assisted system for VP shunt operation

Hydrocephalus is a condition with an abnormal cerebrospinal fluid (CSF) accumulation in the brain's ventricles resulting in ventricular enlargement. One of the most common surgical treatments for hydrocephalus is the ventriculoperitoneal (VP) Shunt operation. A freehand technique using surface anatomy ventricular catheter placement has been widely used in VP Shunt operations because of its simplicity and low cost. However, this technique trades off with moderate accuracy. To improve accuracy, most existing freehand techniques involved using tools or software to manually measure distances and/or drilling angles from CT or MRI slides. In this work, we developed the first fully automated system VP shunt entry area recommender (VPSEAR) for a pre-planned freehand placement. The program with a user- interface took the patient's CT slides, calculated a circular entry site on a skull, and reported a unique circular entry region. The program integrated several mathematical knowledge and 3-D data processing techniques to ensure high accuracy and acceptable running time. We tested the invented programs on a collection of CT slices of 15 patients with 30 head sides and evaluated the system's accuracy against the traditional Keen's method using 3D Slicer software. We achieved an average accuracy of 95.33% using five internal points evaluation, with accuracy improvement over Keen's method up to 40.33%. The program running time was less than 15 min per head side.

Deep learning for automatic head and neck lymph node level delineation provides expert-level accuracy.

Deep learning-based head and neck lymph node level (HN_LNL) autodelineation is of high relevance to radiotherapy research and clinical treatment planning but still underinvestigated in academic literature. In particular, there is no publicly available open-source solution for large-scale autosegmentation of HN_LNL in the research setting. An expert-delineated cohort of 35 planning CTs was used for training of an nnU-net 3D-fullres/2D-ensemble model for autosegmentation of 20 different HN_LNL. A second cohort acquired at the same institution later in time served as the test set (n = 20). In a completely blinded evaluation, 3 clinical experts rated the quality of deep learning autosegmentations in a head-to-head comparison with expert-created contours. For a subgroup of 10 cases, intraobserver variability was compared to the average deep learning autosegmentation accuracy on the original and recontoured set of expert segmentations. A postprocessing step to adjust craniocaudal boundaries of level autosegmentations to the CT slice plane was introduced and the effect of autocontour consistency with CT slice plane orientation on geometric accuracy and expert rating was investigated. Blinded expert ratings for deep learning segmentations and expert-created contours were not significantly different. Deep learning segmentations with slice plane adjustment were rated numerically higher (mean, 81.0 vs. 79.6, p = 0.185) and deep learning segmentations without slice plane adjustment were rated numerically lower (77.2 vs. 79.6, p = 0.167) than manually drawn contours. In a head-to-head comparison, deep learning segmentations with CT slice plane adjustment were rated significantly better than deep learning contours without slice plane adjustment (81.0 vs. 77.2, p = 0.004). Geometric accuracy of deep learning segmentations was not different from intraobserver variability (mean Dice per level, 0.76 vs. 0.77, p = 0.307). Clinical significance of contour consistency with CT slice plane orientation was not represented by geometric accuracy metrics (volumetric Dice, 0.78 vs. 0.78, p = 0.703). We show that a nnU-net 3D-fullres/2D-ensemble model can be used for highly accurate autodelineation of HN_LNL using only a limited training dataset that is ideally suited for large-scale standardized autodelineation of HN_LNL in the research setting. Geometric accuracy metrics are only an imperfect surrogate for blinded expert rating.

A spatial squeeze and multimodal feature fusion attention network for multiple tumor segmentation from PET–CT Volumes

Tumor segmentation is a key step in computer-aided diagnosis. The PET–CT co-segmentation method combines the high sensitivity of PET images and the anatomical information of CT images. For whole-body multiple tumors, such as soft tissue sarcoma, lymphoma, etc., due to the different lesion location and size, it is necessary to segment the tumor area according to the whole body anatomical information. How to effectively leverage whole-body contextual information and the fusion of multimodal information is the key to the problem. To address this issue, we propose a spatial squeeze and multimodal feature fusion attention network for whole-body multiple tumors segmentation based on PET–CT volumes. Our proposed method consists of two parts, a Coronal-Spatial Squeeze Attention Extraction Network (CSAE-Net) and a Precise PET–CT Fusion Attention Segmentation Network (PFAS-Net), respectively. In CSAE-Net, we squeeze a 3D PET–CT volume along the coronal plane into m 2D images, and obtain 3D Coronal Spatial Squeeze Attention Volume based on these 2D images. In PFAS-Net, the input is a 2D axial PET–CT slice, and the previously obtained coronal spatial squeeze attention map is used to guide the segmentation. Moreover, a Multimodal Fusion Attention (MFA) module is proposed to fuse the metabolic information of PET and the anatomical information of CT. We perform experiments on PET–CT datasets of two whole-body multiple tumors, Soft Tissue Sarcoma (STS) and Lymphoma. The results show that our proposed method improved Dice values by 8.03% in STS and 1.74% in Lymphoma. Also the visualization results show that our proposed method is able to suppress high-uptake regions of normal tissues.

Deep-Learning-Based Automatic Segmentation of Parotid Gland on Computed Tomography Images.

This study aims to develop an algorithm for the automatic segmentation of the parotid gland on CT images of the head and neck using U-Net architecture and to evaluate the model's performance. In this retrospective study, a total of 30 anonymized CT volumes of the head and neck were sliced into 931 axial images of the parotid glands. Ground truth labeling was performed with the CranioCatch Annotation Tool (CranioCatch, Eskisehir, Turkey) by two oral and maxillofacial radiologists. The images were resized to 512 × 512 and split into training (80%), validation (10%), and testing (10%) subgroups. A deep convolutional neural network model was developed using U-net architecture. The automatic segmentation performance was evaluated in terms of the F1-score, precision, sensitivity, and the Area Under Curve (AUC) statistics. The threshold for a successful segmentation was determined by the intersection of over 50% of the pixels with the ground truth. The F1-score, precision, and sensitivity of the AI model in segmenting the parotid glands in the axial CT slices were found to be 1. The AUC value was 0.96. This study has shown that it is possible to use AI models based on deep learning to automatically segment the parotid gland on axial CT images.

Prototyping and 3D Printing of Computed Tomography Images with an Emphasis on Soft Tissues, Especially Muscles, for Teaching Human Anatomy

The study on cadavers, although considered fundamental in the teaching of human anatomy, is limited in several universities, mainly due to the acquisition and manipulation of cadaveric material. Throughout history, several artificial anatomical models have been used to complement the real anatomical pieces. The present study offers a new alternative: the making of three-dimensional models from Computed Tomography (3D-CT) patient image acquisition. CT images from the USP University Hospital database were used. Patients underwent examinations for reasons other than the present study and were anonymized to maintain confidentiality. The CT slices obtained in thin cross-sections (approximately 1.0 mm thick) were converted into three-dimensional images by a technique named Volume Rendering for visualization of soft tissue and bone. The reconstructions were then converted to an STL (Standard Triangle Language) model and printed through two printers (LONGER LK4 Pro® and Sethi S3®), using PLA and ABS filaments. The 3D impressions of the thigh and leg muscles obtained better visual quality, being able to readily identify the local musculature. The images of the face, heart, and head bones, although easily identifiable, although seemed to present lower quality aesthetic results. This pilot study may be one of the first to perform 3D impressions of images from CT to visualize the musculature in Brazil and may become an additional tool for teaching.

CT SEVERITY SCORE –A TOOL FOR CLINICAL ASSESSMENT IN COVID 19 PATIENTS ?

Background: COVID-19 is an infection that has widely and rapidly spread all over the world. The clinical presentations being asymptomatic carriers to requiring assisted ventilatory support with increased mortality. Clinical parameters like oxygen saturation, respiratory rate and laboratory parameters like CRP, ferritin, d-dimer etc have been used to classify patients according to disease severity. CT chest manifestations in COVID pneumonia resemble to those seen in any viral infection To establish Aims &amp; Objectives: correlation between CT severity score and Clinico-laboratory parameters in RTPCR proven positive COVID 19 patients and study the role of HRCT chest in clinical management of COVID 19 patients. We collected clinical and laboratory data for analysis of pati Materials and Methods: ents who had RTPCR positive COVID-19 infection and underwent a chest HRCT chest scan with 128 slice CT scanner in our tertiary care hospital. Three radiologists evaluated the scan to determine the CT severity score (CTSS) and studied typical, intermediate &amp; atypical imaging features in each patient and assigned CT severity score for oxygen dependency , the patients were classied radiologically into negative, mild and severe disease The statistical analysis was performed using IBM SPSS and Microsoft excel. Descriptive statistics of patients clinical, and laboratory results were reported as numbers and relative frequencies. Frequencies of CT scores were calculated and compared with other clinical variables. The Pearson correlation coefcient test was used for correlations, and p value less than 0.01 was dened statistically signicant. A Chi square analysis was done to establish relationship between CTSS oxygen dependency, remdesivir and steroid usage. CTSS and oxygen dependency had Results: strong negative Pearson's association. Severe CTSS (10 &amp; above) had 98.3% sensitivity in relation to requirement of O2 in patients, with high negative predictive value 99.3%. Positive likelihood ratio 4.33, negative likelihood ratio 0.02. The Pearson's correlation coefcient showed moderately positive signicant association between CTSS &amp; S.CRP levels, CTSS &amp; S.ferritin level. Signicant P value is seen in severe patients having higher CTSS, treated with steroids and remdesivir. A CT-SS score less than 10 could rule out oxygen dependency Conclusion: and severe form of disease with a high negative predictive value of 99.3% in our cohort. Clinical intervention in patients according to CT severity score can be of value as evident by signicant association demonstrated in our study.

Factors Associated with Coronary Artery Calcification and Cardiovascular Outcome Among Pre-Dialysis Chronic Kidney Disease Patients in Phramongkutklao hospital

Abstract Funding Acknowledgements Type of funding sources: None. Background Cardiovascular disease is the leading cause of death in chronic kidney disease (CKD) patients. Calcified coronary artery is highly prevalent and more severe among these group. Coronary calcium score was used as a risk predictor for coronary heart disease in general population but data among CKD patients were less known. Objective To evaluate factors associated with coronary calcium score in pre-dialysis CKD patients. Methods Prospective cohort study was conducted in CKD patients who have intermediate risk for coronary heart disease between February 2021 and December 2021. Patients who had received dialysis or coronary revascularization were excluded. GFR was categorized into 4 groups including Group 1: GFR ≥ 60 ml/min/1.73m2, Group 2: GFR 45-59 ml/min/1.73m2, Group 3: GFR 30-44 ml/min/1.73m2 and Group 4: GFR &amp;lt; 30 ml/min/1.73m2. Baseline geographical data and prespecified factors that might be correlated with coronary calcium score were collected and Agaston coronary calcium score was performed by 640-slice CT scanner with standard protocol. Primary outcome was evaluation of factors associated with coronary calcium score. Secondary outcome was incidence of composited cardiovascular events which defined as death, MI or heart failure within 6 months after enrollment. Results Total 53 males and 35 females were enrolled. Patients in Group 1, 2, 3 and 4 were 22, 24, 21 and 21 patients, respectively. Mean age, underlying hypertension, T2DM with target organ damage, PTH, ESR, Osteocalcin and coronary calcium score were significantly different between each group of GFR. Coronary calcium score showed inversely correlation with GFR level, p-value &amp;lt; 0.001. Factors that significantly correlated with coronary calcium score were mean age (r=0.548, p-value &amp;lt;0.001), underlying hypertension (r=0.466, p-value &amp;lt;0.001), CAVI (r=0.436, p-value &amp;lt;0.001) and Osteocalcin (r=0.83, p &amp;lt;0.001). In multivariate analysis, Osteocalcin was significantly correlated with coronary calcium score, p-value &amp;lt; 0.001. Increasing Osteocalcin 1 ng/ml correlated with increasing coronary calcium score 52, p-value &amp;lt; 0.001. Composited cardiovascular events were found in 11 patients, 6 patients in Group 3, and was not demonstrated correlation with coronary calcium score. Conclusions Coronary calcium score showed inversely correlation with GFR level. Increasing Osteocalcin was significantly correlated with increasing coronary calcium score in pre-dialysis CKD patients.

Semi-supervised medical image segmentation via cross-guidance and feature-level consistency dual regularization schemes.

Semi-supervised learning is becoming an effective solution for medical image segmentation because of the lack of a large amount of labeleddata. Consistency-based strategy is widely used in semi-supervised learning. However, it is still a challenging problem because of the coupling of CNN-based isomorphic models. In this study, we propose a new semi-supervised medical image segmentation network (DRS-Net) based on a dual-regularization scheme to address thischallenge. The proposed model consists of a CNN and a multidecoder hybrid Transformer, which adopts two regularization schemes to extract more generalized representations for unlabeled data. Considering the difference in learning paradigm, we introduce the cross-guidance between CNN and hybrid Transformer, which uses the pseudo label output from one model to supervise the other model better to excavate valid representations from unlabeled data. In addition, we use feature-level consistency regularization to effectively improve the feature extraction performance. We apply different perturbations to the feature maps output from the hybrid Transformer encoder and keep an invariance of the predictions to enhance the encoder'srepresentations. We have extensively evaluated our approach on three typical medical image datasets, including CT slices from Spleen, MRI slices from the Heart, and FM Nuclei. We compare DRS-Net with state-of-the-art methods, and experiment results show that DRS-Net performs better on the Spleen dataset, where the dice similarity coefficient increased by about 3.5%. The experimental results on the Heart and Nuclei datasets show that DRS-Net also improves the segmentation effect of the twodatasets. The proposed DRS-Net enhances the segmentation performance of the datasets with three different medical modalities, where the dual-regularization scheme extracts more generalized representations and solves the overfittingproblem.