64-slice Scanner Research Articles

Evaluation of Prevalence and Features of Coronary Artery Anomalies using Multidetector-row Computed Tomography

Aim: To evaluate the course, origin and termination of coronary artery anomalies in subjects demonstrating various symptoms and undergoing multidetector CT coronary angiography for evaluation of coronary disease. Study Design: Retrospective study Place and duration: Ch.Pervaiz Elahi Institute of Cardiology Multan from January 2021 to January 2022. Methodology: The study included patients who were referred to the radiology department because of suspicion of coronary artery disease. The data of these patients were reviewed for the course and origin of coronary artery anomalies. A 64-slice scanner was used for MDCT coronary angiography. At the start of acquisition contrast arriving coronary arteries were synchronised using the bolus tracking technique. The whole heart was imaged during apnea using 80-90ml iodinated contrast material administered intravenously. Coronary arteries were evaluated using 3-D and multiplanar reformatted images Results: Total of 600 patients who were referred for MDCT coronary angiography. The subjects underwent screening because of various risk factors, chest pain, or similar symptoms. Coronary artery anomalies were diagnosed in 21 out of 600 (3.5%) patients, whose mean age was 52.6±10.8 years and 504 (84%) were male. 3(14%) patients had high take-off of RCA (right coronary right) and 4(19%) of LMCA (left main coronary artery). In 5(24%) patients left circumflex artery (LCx) and left anterior descending artery (LAD) originated separately from the left sinus of Valsalva (LSV). In 3(14%) patients RCA originated from the opposite sinus of Valsalva.RCA anomaly in 1 patient had a malignant course. Conclusion: The study shows that evaluation of coronary artery anomalies can viably be done using CT. Right diagnosis and treatment planning are based upon an understanding of the clinical significance and CT characteristics of different CAAs. Keywords: Coronary artery angiography, MDCT, prevalence

Ultra-low-dose computed tomography for torsion measurements of the lower extremities in children and adolescents

BackgroundQuantifying femoral and tibial torsion is crucial in the preoperative planning for derotation surgery in children and adolescents. The use of an ultra-low-dose computed tomography (CT) protocol might be possible for modern CT scanners and suitable for reliable torsion measurements even though the bones are not completely ossified.MethodsThis is a retrospective review of 77 children/adolescents (mean age 12.7 years) who underwent a lower extremity CT for torsion measurements on a 64-slice scanner. A stepwise dose reduction (70%, 50%, 30% of the original dose) was simulated. Torsion measurements were performed on all image datasets, and image noise, interrater agreement and subjective image quality were evaluated. Effective radiation dose of each original scan was estimated. As proof of concept, 24 children were scanned with an ultra-low-dose protocol, adapted from the 30% dose simulation, and the intra-class correlation coefficient (ICC) was determined. Ethics approval and informed consent were given.ResultsTorsion measurements at the simulated 30% dose level had equivalent interrater agreement compared to the 100% dose level (ICC ≥ 0.99 for all locations and dose levels). Image quality of almost all datasets was rated excellent, regardless of dose. The mean sum of the effective dose of the total torsion measurement was reduced by simulation from 0.460/0.490 mSv (boys/girls) at 100% dose to 0.138/0.147 mSv at 30%. The ICC of the proof-of-concept group was as good as that of the simulated 30% dose level.ConclusionPediatric torsion measurements of the lower extremities can be performed using an ultra-low-dose protocol without compromising diagnostic confidence.

The Impacts of Vertical Off-Centring, Localiser Direction, Phantom Positioning and Tube Voltage on CT Number Accuracy: An Experimental Study.

Background: This study investigates the effects of vertical off-centring, localiser direction, tube voltage, and phantom positioning (supine and prone) on computed tomography (CT) numbers and radiation dose. Methods: An anthropomorphic phantom was scanned using a Discovery CT750 HD—128 slice (GE Healthcare) scanner at different tube voltages (80, 120, and 140 kVp). Images employing 0° and 180° localisers were acquired in supine and prone positions for each vertical off-centring (±100, ±60, and ±30 mm from the iso-centre). CT numbers and displayed volume CT dose index (CTDIvol) were recorded. The relationship between dose variation and CT number was investigated. Results: The maximum changes in CT number between the two phantom positions as a function of vertical-off-centring were for the upper thorax 34 HU (0° localiser, 120 kVp), mid thorax 43 HU (180° localiser, 80 kVp), and for the abdominal section 31 HU (0° localiser, 80 kVp) in the prone position. A strong positive correlation was reported between the variation in dose and CT number (r = 0.969, p < 0.001); 95% CI (0.93, 0.99). Conclusions: Patient positioning demands an approach with a high degree of accuracy, especially in cases where clinical decisions depend on CT number accuracy for tissue lesion characterisation.

Abstract 6392: Predicting malignancy in indeterminate pulmonary nodules using quantitative CT imaging

Abstract With an increasing number of positive lung cancer screening trials and the growing utilization of low dose CT screening, the detection of indeterminate pulmonary nodules is an important clinical problem. A biomarker that will be able to differentiate between benign andmalignant nodules would help to accelerate diagnosis and reduce unnecessary and invasive procedures. Here, we investigated the utility of quantitative radiomics to predict malignancy of small pulmonary nodules. Data including cancer status, age, gender, smokingstatus and CT radiomics data, from 242 patients accrued from the Detection of Early Lung Cancer Among Military Personnel (DECAMP1) consortium was used. DECAMP1 is a prospective study of 500 current or former smokers with indeterminate pulmonary nodules (0.7-3.0 cm). A total of 446 quantitative radiomic features were extracted from CT images of each patient. For the prediction models, various feature selection methods and machine learning algorithms including Random Forest, Gradient Boosting Machine and Support Vector Machine were used. Data was split into train and test set with tenfold cross-validation.Models were evaluated using; Accuracy, Sensitivity, Specificity and Area under the curve - a receiver operating characteristic curve (AUC-ROC). In terms of predictive performance, the AUC value was 0.64 (95% CI: 0.50-0.78) for the clinical model and 0.77 (95% CI: 0.64-0.90) for the radiomics model. Adding clinical features to the radiomics model (gender, age, smoking status) did not improve the model. We have also investigated the slice thickness and scanner (PET vs PET-CT) variables on the models’ performance and saw a modest improvement of the AUC values when using more homogenous data. In this study, we showed the potential of radiomics for lung cancer prediction in DECAMP1.For future work, we are planning to test whether incorporating bronchial gene expression data improves lung cancer detection and to validate these findings in separate, independent cohorts. Citation Format: Kate M. Bloch, Xingyi Shi, Fenghai Duan, George R. Washko, Avrum Spira, Denise R. Aberle, Raul S. Estepar, Ehab Billatos, Marc E. Lenburg, on behalf of the DECAMP Investigators. Predicting malignancy in indeterminate pulmonary nodules using quantitative CT imaging [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6392.

The impacts of vertical off-centring, tube voltage, and phantom size on computed tomography numbers: An experimental study

IntroductionThis experimental study explored the effect of vertical off-centring on computed tomography (CT) numbers in combination with various tube voltages and phantom sizes for two CT units. MethodsCIRS Model 062 Electron Density and system performance phantoms were imaged on Siemens Emotion 16-slice CT and GEMINI-GXL scanners, respectively. Uniformity and accuracy were evaluated as a function of vertical off-centring (20, 40, 60, and 80 mm above the gantry isocentre) using different water phantom sizes (18, 20, and 30 cm) and tube voltages (80, 90, 110, 120, 130 and 140 kVp). ResultsVertical off-centring and phantom size accounted for 92% of the recorded variance and the resultant change in CT numbers. The uniformity test recorded maximum changes of 14 and 27.2 HU for peripheral ROIs across the X- and Y-axes for an 80 mm phantom shift above the gantry isocentre on the GEMINI GXL and Siemens scanners, respectively. The absolute CT number differences between the superior and inferior ROIs were 13.7 HU for the 30 cm phantom and 4.8 HU for the 20 cm phantom for 80 mm vertical off-centring. The largest differences were observed at lower tube voltages. ConclusionsIt is essential to highlight the significance of CT number variation in clinical decision-making. Phantom off-centring affected the uniformity of these numbers, which were further impacted by the ROI position in this experimental study. CT number variation was more evident in peripheral phantom areas, lower tube voltages and larger phantom sizes. Implications for practiceCT number is observed to be a variable under certain common conditions. This significantly impacts several applications where clinical decisions depend on CT number accuracy for tissue lesion characterisation.

Different CT slice thickness and contrast-enhancement phase in radiomics models on the differential performance of lung adenocarcinoma.

BackgroundTo investigate the effects of computed tomography (CT) reconstruction slice thickness and contrast‐enhancement phase on the differential diagnosis performance of radiomic signature in lung adenocarcinoma.MethodsA total of 187 patients who had been pathologically confirmed with lung adenocarcinoma and nonadenocarcinoma were divided into a training cohort (n = 149) and validation cohort (n = 38). All the patients underwent contrast‐enhanced CT and the images were reconstructed with different slice thickness. The radiomic features were extracted from different slice thickness and scan phase. The logistic regression (LR) algorithm was used to build a machine learning model for each group. The area under the curve (AUC) obtained from the receiver operating characteristic (ROC) curve and DeLong test was used to evaluate its discriminating performance.ResultsFinally, 34 image features and five semantic features were selected to establish a radiomics model. Based on the three contrast‐enhanced CT phases and four reconstruction slice thickness, 12 groups of radiomics models showed good discrimination ability with the AUCs range from 0.9287 to 0.9631, sensitivity range from 0.8349 to 0.9083, specificity range from 0.825 to 0.925 in the training group. Similar results were observed in the validation group. However, there was no statistical significance between the different CT scan phase groups and different slice thickness (p > 0.05).ConclusionsThe radiomic analysis of contrast‐enhanced CT can be used for the differential diagnosis of lung adenocarcinoma. Moreover, different slice thickness and contrast‐enhanced scan phase did not affect the discriminating ability in the radiomics models.

Detecting and Extracting Brain Hemorrhages from CT Images Using Generative Convolutional Imaging Scheme.

Purpose The need for computerized medical assistance for accurate detection of brain hemorrhage from Computer Tomography (CT) images is more mandatory than conventional clinical tests. Recent technologies and advanced computerized algorithms follow Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) techniques to improve medical diagnosis platforms. This technology is making the diagnosis practice of brain issues easier for medical practitioners to analyze and identify diseases with an assured degree of precision and performance. Methods As the existing CT image analysis models use standard procedures to detect hemorrhages, the need for DL-based data analysis is essential to provide more accurate results. Generally, the existing techniques are limited with image training efficiency, image filtering procedures, and runtime system tuning modules. On the scope, this work develops a DL-based automated analysis of CT scan slices to find various levels of brain hemorrhages. Notably, this proposed system integrates Convolutional Neural Network (CNN) and Generative Adversarial Network (GAN) architectures as Integrated Generative Adversarial-Convolutional Imaging Model (IGACM) for extracting the CT image features for detecting brain hemorrhages. Results This system produces good results and takes lesser training time than existing techniques. This proposed system effectively works over CT images and classifies the abnormalities with more accuracy than current techniques. The experiments and results deliver the optimal detection of hemorrhages with better accuracy. It shows that the proposed system works with 5% to 10% of the better performance compared to other diagnostic techniques. Conclusion The complex nature of CT images leads to noncorrelated feature complexities in diagnosis models. Considering the issue, the proposed system used GAN-based effective sampling techniques for enriching complex image samples into CNN training phases. This concludes the effective contribution of the proposed IGACM technique for detecting brain hemorrhages than the existing diagnosis models.

Extracardiac findings on cardiac computed tomography in patients undergoing atrial fibrillation catheter ablation

Abstract Background and aim To assess the prevalence of incidental extracardiac findings in patients who underwent cardiac CT for the evaluation of left atrial (LA) anatomy before atrial fibrillation (AF) catheter ablation. We also aimed to determine the independent predictors of relevant extracardiac alterations. Patients and methods We studied consecutive patients who underwent cardiac CT with a 256-slice scanner for the visualization of LA anatomy before AF ablation. Prevalence of clinically significant and not significant extracardiac findings were recorded. Moreover, we determined the variables associated with relevant extracardiac alterations with uni- and multivariate logistic regression analyses. Results In total, 1,952 consecutive patients who underwent cardiac CT examination between 2010 and 2020 were included in our study (mean age 61.2 ± 10.6 years; 66.2% male). Incidental extracardiac findings were detected in 820 (42.0%; 95%CI = 0.40–0.44%) patients, while clinically significant alterations were reported in 416 (21.3%; 95%CI = 20.0–23.2%) patients. When analyzing the predictors of clinically relevant alterations, age (OR = 1.04; 95%CI = 1.03–1.05), male sex (OR = 1.39; 95%CI = 1.12–1.73), chest pain (OR = 1.46; 95%CI = 1.09–1.93), hypertension (OR = 1.42; 95%CI = 1.12–1.81), heart failure (OR = 1.68; 95%CI = 1.09–2.53), obstructive CAD (OR = 1.56; 95%CI = 1.16–2.09) and prior stroke/TIA (OR = 1.56; 95%CI = 1.04–2.30) showed association with clinically significant incidental findings in the univariate analysis (all P &lt; 0.05). In the multivariate analysis, age (OR = 1.04; 95%CI = 1.02–1.06; P &lt; 0.001) proved to be the only significant predictor of clinically relevant extracardiac finding. Conclusion Cardiac CT performed before AF ablation is not only helpful in understanding LA anatomy, but might also identify clinically significant pathologies. These incidental findings might have further diagnostic or therapeutic consequences.

Towards the establishment of national imaging practice guidelines: A preliminary study of the basic computed tomography imaging protocols in Ghana

BackgroundThere is a need to harmonize imaging practices in computed tomography (CT) imaging. This study, therefore, investigated the variability of the basic imaging protocols used for CT imaging of common indications in Ghana in order to generate recommendations for the development of national imaging practice guidelines in CT imaging. MethodA cross-sectional study, utilizing a structured online questionnaire, was undertaken (between December 2018 to March 2019) to collect indication-based imaging protocol data (scan coverage, scan series, image quality requirement, slice thickness, reconstruction, scan mode and orientation, required window, AEC usage, scan and breath hold techniques etc.,) across the various CT facilities in the country. Data were analysed and with experts’ input, recommendations were made. ResultsThe imaging protocols used across the CT facilities in the country were largely similar, with a few variabilities for similar examinations. These variabilities were found in scan coverages, series and slice thicknesses. In particular, for a brain tumour examination, 92% of the 25 facilities used both non-contrast and contrast phases while 8% preferred only the IV contrast phase. Seventy percent of all the facilities (n=10) performing pulmonary angiograms in the country also used a two-sequence scan, and others (30%) worked with only the angiogram phase. A majority (89%) of the 19 facilities that were engaged in CT-IVU procedures also used 3-4 scan phases, while 11% preferred a split-bolus technique. None of the facilities employed the low-dose or ultra-low dose protocol for kidney stone examination. ConclusionsThe study's outcome provides an important preliminary roadmap that could lead to the development of imaging practice guidelines to ensure harmonization of imaging practices to improve the protection and safety of patients across the CT facilities.

An automated diagnosis and classification of COVID-19 from chest CT images using a transfer learning-based convolutional neural network

Researchers have developed more intelligent, highly responsive, and efficient detection methods owing to the COVID-19 demands for more widespread diagnosis. The work done deals with developing an AI-based framework that can help radiologists and other healthcare professionals diagnose COVID-19 cases at a high level of accuracy. However, in the absence of publicly available CT datasets, the development of such AI tools can prove challenging. Therefore, an algorithm for performing automatic and accurate COVID-19 classification using Convolutional Neural Network (CNN), pre-trained model, and Sparrow search algorithm (SSA) on CT lung images was proposed. The pre-trained CNN models used are SeresNext50, SeresNext101, SeNet154, MobileNet, MobileNetV2, MobileNetV3Small, and MobileNetV3Large. In addition, the SSA will be used to optimize the different CNN and transfer learning(TL) hyperparameters to find the best configuration for the pre-trained model used and enhance its performance. Two datasets are used in the experiments. There are two classes in the first dataset, while three in the second. The authors combined two publicly available COVID-19 datasets as the first dataset, namely the COVID-19 Lung CT Scans and COVID-19 CT Scan Dataset. In total, 14,486 images were included in this study. The authors analyzed the Large COVID-19 CT scan slice dataset in the second dataset, which utilized 17,104 images. Compared to other pre-trained models on both classes datasets, MobileNetV3Large pre-trained is the best model. As far as the three-classes dataset is concerned, a model trained on SeNet154 is the best available. Results show that, when compared to other CNN models like LeNet-5 CNN, COVID faster R–CNN, Light CNN, Fuzzy + CNN, Dynamic CNN, CNN and Optimized CNN, the proposed Framework achieves the best accuracy of 99.74% (two classes) and 98% (three classes).

Quantifying prognosis severity of COVID-19 patients from deep learning based analysis of CT chest images.

The COVID-19 pandemic has affected all the countries in the world with its droplet spread mode. The colossal amount of cases has strained all the healthcare systems due to the serious nature of infections especially for people with comorbidities. A very high specificity Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR) test is the principal technique in use for diagnosing the COVID-19 patients. Also, CT scans have helped medical professionals in patient severity estimation & progression tracking of COVID-19 virus. In study we present our own extensible COVID-19 viral infection tracking prognosis technique. It uses annotated dataset of CT chest scan slice images created with the help of medical professionals. The annotated dataset contains bounding box coordinates of different features for COVID-19 detection like ground glass opacities, crazy paving pattern, consolidations, lesions etc. We qualitatively identify the severity of the patient for later prognosis stages in our study to assist medical staff for patient prioritization. First we detected COVID-19 positive patients with pre-trained Siamese Neural Network (SNN) which obtained 87.6% accuracy, 87.1% F1-Score & 95.1% AUC scores. These metrics were achieved after removal of 40% quantitatively highly similar images from the COVID-CT dataset. This reduced dataset was further medically annotated with COVID-19 features for bounding box detection. After this we assigned severity scores to detected COVID-19 features and calculated the cumulative severity score for COVID-19 patients. For qualitative patient prioritization with prognosis clinical assistance information, we finally converted this score into a multi-classification problem which obtained 47% weighted-average F1-score.

Modeling of microflow during viscoelastic polymer flooding in heterogenous reservoirs of Daqing Oilfield

Viscoelastic polymer flooding has been extensively used in oilfield development as an enhanced oil recovery method. Understanding the microflow mechanism is necessary to promote the polymer displacement effect and design the polymer flooding scheme. Previous studies have investigated polymer flooding mechanisms, such as a favorable mobility ratio and increasing sweep efficiency; however, the elasticity effect on displacement efficiency is still unclear, particularly in a heterogeneous reservoir. Class II reservoirs, which have an effective thickness between 1 and 4 m and a permeability greater than 100 mD, have recently become the target zones of polymer flooding in the Daqing Oilfield. Class II reservoirs are subdivided into type A and B oil layers. Compared with the type A oil layer, type B has a larger permeability contrast, more severe heterogeneity, and worse connectivity. There are considerable differences between types A and B in the actual development of polymer flooding as well as between type B oil layers of different oilfield blocks. Thus, it is necessary to investigate the factors that influence the micro-oil displacement mechanism. In this study, local and global micro-pore models are established based on a computed tomography scan slice of Class II reservoir cores, and mathematical models of the viscoelastic polymer and oil two-phase flow in porous media are established. The log conformation method is used, which can effectively enhance convergence, owing to a high relaxation time inducing high non-linear of equation. The volume-of-fluid method is used to track the interface between the two phases. The governing equations are solved using the OpenFOAM platform, which is open-source software written in C++. Then, the influences of pore structure and polymer elasticity on displacement characteristics are studied. The simulation results revealed that owing to the pore structure, the micro-oil displacement efficiencies of B_GI and B_PII, which belong to the type B oil layer, are lower than that of A_SIII, which belongs to type A, by 26.8% and 10.9%, respectively. The oil displacement efficiency of a commingled production comprising B_PII and B_GI is 4.0% and 15.6% lower than that of single-layer productions of B_PII and B_GI, respectively. The oil displacement efficiency increases by 6.45% when the relaxation time increases from 0.5 to 2 s and decreases when the relaxation time increases from 2 to 10 s. Therefore, by combining the micro-oil displacement efficiency and injectivity of a high-molecular-weight polymer, the optimum relaxation time is determined to be 2 s. The obtained results are significant for the design strata of a commingled production scheme and the optimization of polymer solutions in the type B oil layer of the Daqing Oilfield.

Pulmonary interstitial emphysema and complications: incidence and CT findings in COVID-19.

Background and Objectives:There are scant data available in the published literature providing chest computed tomography (CT) findings on pulmonary interstitial emphysema (PIE), complications and associated parenchymal abnormalities. We report the incidence of PIE and complications by chest CT in patients with COVID-19.Methods:We retrospective analyzed 897 chest CT scans performed with 64-slice CT scanners during the COVID-19 pandemic period from March 2020 to September 2021. Two radiologists and two physicians in training in diagnostic radiology, independently and in consensus, assessed PIE as air within the perilobular (low-attenuation area) and perivascular interstitium such as its complications, parenchymal anomalies and pleural effusion; in addition, the complications of PIE, parenchymal anomalies and pleural effusion were evaluated. Descriptive statistics were used to summarize the data, and the results were expressed as counts and percentages.Results:PIE was revealed in 25 out of 897 patients (2.8%) and associated with pneumomediastinum, subcutaneous emphysema, and pneumothorax in 25 (100%), 16 (64%), and 7 (28%), patients, respectively. Out of 25 patients, 24 patients had ground-glass opacity (GGO), 23 patients had crazy paving, 22 patients had consolidation and 2 patients had pleural effusion. Eighteen out of 25 patients had noninvasive ventilation before CT scan, initially treated with continuous positive airway pressure (positive end expiratory pressure [PEEP] of 10 cmH2O) and subsequently with bilevel positive airway pressure (range PEEP of 8–12 cmH2O). The remaining seven patients had invasive mechanical ventilation via orotracheal intubation (pressure plateau at approximately 25 cmH2O). Six out of 25 (24%) patients died.Conclusion:Chest CT allows the detection of complications associated with PIE and parenchyma abnormalities. The timely detection of PIE and minimal pneumomediastinum could aid the optimization of ventilation modalities and parameters based on patients clinical status therefore potentially reducing complications.

Impact of Emphysema Subtypes and Volume on Lung Ventilation and Gas Exchange Functions as Evidenced by Computed Tomography

Objective: to characterize the relationship between the subtype and volume of pulmonary emphysema on the indicators of lung ventilation and gas exchange functions. Material and methods. The data of radiation and functional studies were analyzed in 50 patients. The inclusion criteria were chronic obstructive pulmonary disease and emphysema, which had been diagnosed by computed tomography (CT) and confirmed by two radiologists; comprehensive pulmonary function studies, including spirometry and body plethysmography, were performed; diffusion capacity was measured using a single-breath method, involving inhalation of carbon monoxide, and a breath hold. Patients with primary pulmonary emphysema, any history of pulmonary surgery, and emphysema concurrent with other lung X-ray syndromes (consolidation, cavity) were excluded. CT was performed with a 1-mm thick slice and standard scanning parameters on Toshiba tomographs (Japan). Pulmonary function was tested using a MasterScreen Body Diffusion expert diagnostic unit (VIASYS Healthcare, Germany) in accordance with the criteria for correct pulmonary functional tests proposed by a joint group of experts from the American Thoracic Society and the European Respiratory Society. Volumetric analysis of emphysema was performed using the Lung Volume Analysis software package (Toshiba, Japan). In the study, there was a predominance of male patients (n = 42 (84%)), mainly in the 61-70 age group. Results. The isolated type of emphysema was rare: centrilobular and paraseptal emphysemas were seen in 3 (6%) and 2 (4%) patients, respectively. The mixed type of emphysema was detected in 90% of cases; 33 (66%) patients having a predominant centrilobular component constituted a large proportion. It was determined that as the volume of emphysema increased, the patency of the airways worsened, the static pulmonary volumes increased, the lungs were hyperinflated, pulmonary gas exchange worsened, the bronchial resistance slightly increased during calm breathing. No statistically significant results were found from the point of view of correlations between the volume of emphysema and other parameters of pulmonary function. Conclusion. An increase in the volume of emphysema deteriorates pulmonary function; the greatest contribution to the overall picture is made by the patients with a mixed type of emphysema with a predominance of the centrilobular component.

Radiation dose and image quality in abdominal CT: Phantom Study

The purpose of this study was to evaluate and compare the effect of acquisition parameters of the abdominal computed tomography protocol on image quality and dose quantities, including contrast-to-noise ratio (CNR), noise, spatial resolution (SR) as well as volumetric computed tomography dose index (CTDIv).&#x0D; The measurements have been done on a HITACHI 16-slice scanner using two phantoms (PMMA and Catphan 500), two tube voltages of 120KVp and 100KVp have been analyzed while varying the tube current settings from 155 to 300 (mAs) in order to measure the abdominal image quality quantities and dose (CTDIv).&#x0D; The scanning at 100KVp compared to 120KVp has resulted in a dose reduction up to 38% at mAs ranging from 100 to 300, a decrease in CNR of 28%, a slight decrease in spatial resolution accompanied with an increase in noise.&#x0D; This study has shown that despite the increase in noise at low tube voltages, it is possible to reduce the dose without affecting the quantities of spatial resolution and the contrast/noise ratio by lowering the tube voltage from 120KVp to 100KVp.&#x0D; Keywords: Abdominal CT; CTDIv; Image Quality; Phantom.

Computed tomography dose index and spatial resolution in abdominal ct: phantom study

This work aimed to evaluate the impact of tube current on spatial resolution and volumetric computed tomography dose index (CTDIvol) at abdominal CT examinations. The measurements have been performed on HITACHI CT 16-slice Scanner using twophantoms (PMMA and Catphan 500) for evaluating the CTDIvol and spatial resolution. two tube voltages of 120KVp and 100KVp have been used while varying the tube current settings from 155 to 300 (mAs). The scanning at 100KVp compared to 120KVp has resulted in a dose reduction up to 38% at tube currents ranging from 100 to 300 (mAs) with a slight decrease in spatial resolution. This work has shown that it is possible to reduce the dose without affecting the spatial resolution by lowering the tube voltage from 120KVp to 100KVp.Keywords: Abdominal CT; CTDIv; Image Quality; Phantom.

Towards subpercentage uncertainty proton stopping-power mapping via dual-energy CT: Direct experimental validation and uncertainty analysis of a statistical iterative image reconstruction method.

To assess the potential of a joint dual-energy computerized tomography (CT) reconstruction process (statistical image reconstruction method built on a basis vector model (JSIR-BVM)) implemented on a 16-slice commercial CT scanner to measure high spatial resolution stopping-power ratio (SPR) maps with uncertainties of less than 1%. JSIR-BVM was used to reconstruct images of effective electron density and mean excitation energy from dual-energy CT (DECT) sinograms for 10 high-purity samples of known density and atomic composition inserted into head and body phantoms. The measured DECT data consisted of 90 and 140 kVp axial sinograms serially acquired on a Philips Brilliance Big Bore CT scanner without beam-hardening corrections. The corresponding SPRs were subsequently measured directly via ion chamber measurements on a MEVION S250 superconducting synchrocyclotron and evaluated theoretically from the known sample compositions and densities. Deviations of JSIR-BVM SPR values from their theoretically calculated and directly measured ground-truth values were evaluated for our JSIR-BVM method and our implementation of the Hünemohr-Saito (H-S) DECT image-domain decomposition technique for SPR imaging. A thorough uncertainty analysis was then performed for five different scenarios (comparison of JSIR-BVM stopping-power ratio/stopping power (SPR/SP) to International Commission on Radiation Measurements and Units benchmarks; comparison of JSIR-BVM SPR to measured benchmarks; and uncertainties in JSIR-BVM SPR/SP maps for patients of unknown composition) per the Joint Committee for Guides in Metrology and the Guide to Expression of Uncertainty in Measurement, including the impact of uncertainties in measured photon spectra, sample composition and density, photon cross section and I-value models, and random measurement uncertainty. Estimated SPR uncertainty for three main tissue groups in patients of unknown composition and the weighted proportion of each tissue type for three proton treatment sites were then used to derive a composite range uncertainty for our method. Mean JSIR-BVM SPR estimates deviated by less than 1% from their theoretical and directly measured ground-truth values for most inserts and phantom geometries except for high-density Delrin and Teflon samples with SPR error relative to proton measurements of 1.1% and -1.0% (head phantom) and 1.1% and -1.1% (body phantom). The overall root-mean-square (RMS) deviations over all samples were 0.39% and 0.52% (head phantom) and 0.43% and 0.57% (body phantom) relative to theoretical and directly measured ground-truth SPRs, respectively. The corresponding RMS (maximum) errors for the image-domain decomposition method were 2.68% and 2.73% (4.68% and 4.99%) for the head phantom and 0.71% and 0.87% (1.37% and 1.66%) for the body phantom. Compared to H-S SPR maps, JSIR-BVM yielded 30% sharper and twofold sharper images for soft tissues and bone-like surrogates, respectively, while reducing noise by factors of 6 and 3, respectively. The uncertainty (coverage factor k=1) of the DECT-to-benchmark values comparison ranged from 0.5% to 1.5% and is dominated by scanning-beam photon-spectra uncertainties. An analysis of the SPR uncertainty for patients of unknown composition showed a JSIR-BVM uncertainty of 0.65%, 1.21%, and 0.77% for soft-, lung-, and bony-tissue groups which led to a composite range uncertainty of 0.6-0.9%. Observed JSIR-BVM SPR estimation errors were all less than 50% of the estimated k=1 total uncertainty of our benchmarking experiment, demonstrating that JSIR-BVM high spatial resolution, low-noise SPR mapping is feasible and is robust to variations in the geometry of the scanned object. In contrast, the much larger H-S SPR estimation errors are dominated by imaging noise and residual beam-hardening artifacts. While the uncertainties characteristic of our current JSIR-BVM implementation can be as large as 1.5%, achieving<1% total uncertainty is feasible by improving the accuracy of scanner-specific scatter-profile and photon-spectrum estimates. With its robustness to beam-hardening artifact, image noise, and variations in phantom size and geometry, JSIR-BVM has the potential to achieve high spatial-resolution SPR mapping with subpercentage accuracy and estimated uncertainty in the clinical setting.

Fractures of the neuro-cranium: sensitivity and specificity of post-mortem computed tomography compared with autopsy.

Post-mortem computed tomography (PMCT) is a routine tool in many forensic pathology departments as it is fast and non-destructive and allows less gruesome visualization than photographs, and the images are indefinitely storable. Several studies investigated congruence between PMCT and autopsy for skull fracture but registered only the presence or absence of fracture systems. The objective of this study was to determine location-specific sensitivity and specificity of PMCT for individual fracture lines in blunt force head trauma. Accurate 3D models based on PMCT data with all fracture lines visible are important for future studies on fractures, applying finite element analysis (FEA). We retrospectively sampled adult cases from 2013 to 2019 with skull fracture mentioned in the autopsy report. PMCT was on a Siemens 64-slice scanner and autopsy according to international guidelines. The location and direction of all fracture lines at autopsy and at de novo interpretation of scans were registered and compared. Ninety-nine cases with 4809 individual findings were included. Age ranged from 18 to 100years. The overall sensitivity was 0.58, and specificity was 0.91. For individual locations, sensitivity ranged from 0.24 to 0.85, and specificity ranged from 0.73 to 1.00. Intra-observer agreement was 0.74, and inter-observer agreement ranged from 0.43 to 0.58. In conclusion, PMCT is suited for detection of fracture systems, but not for detection of all individual fracture lines. Our results differed from the existing literature due to the methodological choices of registering individual fracture lines. Future studies utilising FEA must supplement PMCT with autopsy data.

Volumetric accuracy of different imaging modalities in acute intracerebral hemorrhage

BackgroundFollow-up imaging in intracerebral hemorrhage is not standardized and radiologists rely on different imaging modalities to determine hematoma growth. This study assesses the volumetric accuracy of different imaging modalities (MRI, CT angiography, postcontrast CT) to measure hematoma size.Methods28 patients with acute spontaneous intracerebral hemorrhage referred to a tertiary stroke center were retrospectively included between 2018 and 2019. Inclusion criteria were (1) spontaneous intracerebral hemorrhage (supra- or infratentorial), (2) noncontrast CT imaging performed on admission, (3) follow-up imaging (CT angiography, postcontrast CT, MRI), and (4) absence of hematoma expansion confirmed by a third cranial image within 6 days. Two independent raters manually measured hematoma volume by drawing a region of interest on axial slices of admission noncontrast CT scans as well as on follow-up imaging (CT angiography, postcontrast CT, MRI) using a semi-automated segmentation tool (Visage image viewer; version 7.1.10). Results were compared using Bland–Altman plots.ResultsMean admission hematoma volume was 18.79 ± 19.86 cc. All interrater and intrarater intraclass correlation coefficients were excellent (1; IQR 0.98–1.00). In comparison to hematoma volume on admission noncontrast CT volumetric measurements were most accurate in patients who received postcontrast CT (bias of − 2.47%, SD 4.67: n = 10), while CT angiography often underestimated hemorrhage volumes (bias of 31.91%, SD 45.54; n = 20). In MRI sequences intracerebral hemorrhage volumes were overestimated in T2* (bias of − 64.37%, SD 21.65; n = 10). FLAIR (bias of 6.05%, SD 35.45; n = 13) and DWI (bias of-14.6%, SD 31.93; n = 12) over- and underestimated hemorrhagic volumes.ConclusionsVolumetric measurements were most accurate in postcontrast CT while CT angiography and MRI sequences often substantially over- or underestimated hemorrhage volumes.

Stage Classification of Lung Cancer using the Comparative Analysis of the Machine Learning Techniques

Stage classification today is widely used in the fields of biological sciences and engineering, The idea of the stage classification is to perform a clinical analysis of the spread of the disease . Lung cancer which is termed as Lung Carcinoma is a highly dangerous lung tumour which is defined by the uncontrolled growth of cells in the tissues of the lung. This growth of cells leading to the tumour identifies the different stages of cancer. The tumours are identified based on the probability density function. The goal is to design models for the stage classification of the of cancer patients . The description of the extent of a tumour consists of three components: T for extent of the primary tumour, N for involvement of lymph nodes, and M for distant metastases. Each T, N, and M component is divided into several categories (eg, T1, T2). This study proposes to build a classification system that can identify the stage classification using the lung cancer dataset for better accuracy. An “IQ-OTHNCCD” lung cancer dataset of 1190 images representing CT scan slices of 110 cases is used in this research.