Clinical CT Scan Research Articles

Comparing organ and effective dose of various CT localizer acquisition strategies: A Monte Carlo study.

CT examinations commonly start with the acquisition of one or two localizer radiographs (2D localizers). Recently, a manufacturer introduced the option to perform a heavily filtrated low-dose helical scan as a localizer acquisition. To compare the dose of one or two 2D localizer acquisitions to the dose of a 3D localizer acquisition, one cannot simply compare the CTDIs of the different acquisition techniques, because of the use of different geometries and spectra. To compare the organ and effective dose for various CT localizer acquisition techniques. A Geant4-based Monte Carlo simulation, replicating a clinical wide-area CT scanner was developed and validated. Various localizer acquisition strategies were simulated: Anterior-posterior (AP) alone, PA alone, combined AP+lateral (LAT), and PA+LAT 2D localizers, and an Ag-filtered 3D localizer acquisition. Validation was performed by measuring and simulating CTDI100 in both the periphery and the center of a CTDI phantom. The software was subsequently used to estimate organ and effective doses for localizers for chest, abdomen+pelvis, and the combined chest, abdomen, and pelvis exams. As representations of patients, eight ICRP computational phantoms (adult, 15-, 10-, and 5-year, both male and female) and five female and five male XCAT phantoms with various BMIs were used. The dose of the various strategies was compared to the current clinically-implemented AP+LAT localizers. CTDI100-measurements and simulations within the CTDI-phantom differs by a maximum of 8.1% and by an average of 0.9%. For chest, the average effective doses for AP, PA, AP+LAT, and the 3D localizer are 0.10, 0.07, 0.32, and 0.22 mSv, respectively. The organ dose to the breast varies the most across the various localizer strategies and is, on average, 0.17, 0.03, 0.44, and 0.33 mGy, in the same order. For abdomen, the average effective doses are 0.11, 0.07, 0.36, and 0.25 mSv for the AP, PA, AP+LAT and the 3D localizer, respectively. The organ dose to the stomach varies the most across the various localizers and is on average 0.14, 0.08, 0.58, and 0.30 mGy, in the same order. The PA-only localizer results in the lowest organ dose to the most radiosensitive organs and the lowest effective dose. For the chest exam, compared to AP+LAT, the PA+LAT results in a 7±2% effective dose reduction (mean±standard deviation), while the 3D localizer results in a 21±3% effective dose reduction. Using AP or PA only would result in 69±2% and 76±2% reduction, respectively. For the abdomen exam, also compared to AP+LAT, PA+LAT results in 6±2% effective dose reduction, while the 3D localizer results in a 20±5% reduction. Using AP or PA only would result in 69±5% and 76±4% reduction, respectively. Using a PA localizer results in a lower or equivalent organ dose in the most radiosensitive organs, and a lower effective dose compared to an AP localizer for both chest and abdomen+pelvis exams. Compared to a two-localizer strategy, the 3D localizer results in a lower effective dose in both the chest and abdomen+pelvis region.

Utility of the morphological scoring of costal cartilage ossification in age estimation of adult Egyptians using multidetector computed tomography

Abstract Age estimation of adults is a challenging procedure in forensic practice. Inspired by the previous work by Chinese scholars, we established population specific age estimation models from the osseous and calcified projections (OCP) of costal cartilages, using three-dimensional volume-rendering technique. A total of 168 clinical CT scans (2 mm slice thickness) were used to develop the sex-specific age prediction models from a sample of Egyptians, comprising 70 females and 98 males, with documented ages between 12 and 85 years. The sample was also used for validating the Chinese model. We reported the differences between the predictive accuracy of the Egyptian (population specific) and Chinese (non population specific) models. The most accurate age estimation model was stepwise linear regression with standard error of estimates (SEE) of 10.9 and 11.8 years in males and females, respectively. For the simple linear regression models, the most accurate formula included OCP of the right second costal cartilage in males and OCP of the left third costal cartilage in females with SEE of 11.2 and 12.2 years, respectively and mean absolute error (MAE) of 8.8 and 9.6 years, respectively. By comparison, the best accuracy rates produced by the Chinese vs. the Egyptian models in males and females within 5 years were 30.61% and 32.86% vs. 35.71% and 32.86%, respectively, whereas within 10 years, the accuracy rates increased up to 57.14% and 58.57% vs. 72.45% and 64.29%, respectively. Although the accuracy rates from the Chinese models were lower than those obtained from the Egyptian models, the MAE and least error values were comparable in both sexes. Notable accurate age estimation rates in the advanced age group ≥40 years were reached being 81.25% to 97.92% in males and 69.77% to 93.02% in females. OCP of the right first costal cartilage was the most accurate in cross-population application for males and females with MAE values of 10.7 and 11.03 years, respectively with balanced accuracy rates of age estimation using the 10-year interval and 40-year cutoff.

A Deep Learning-Based Framework for Predicting Intracerebral Hemorrhage Hematoma Expansion Using Head Non-contrast CT Scan

Rationale and ObjectivesHematoma expansion (HE) in intracerebral hemorrhage (ICH) is a critical factor affecting patient outcomes, yet effective clinical tools for predicting HE are currently lacking. We aim to develop a fully automated framework based on deep learning for predicting HE using only clinical non-contrast CT (NCCT) scans. Materials and MethodsA large retrospective dataset (n=2,484) was collected from 84 centers, while a prospective dataset (n=500) was obtained from 26 additional centers. Baseline NCCT scans and follow-up NCCT scans were conducted within 6 hours and 48 hours from symptom onset, respectively. HE was defined as a volume increase of more than 6 mL on the follow-up NCCT. The retrospective dataset was divided into a training set (n=1,876) and a validation set (n=608) by patient inclusion time. A two-stage framework was trained to predict HE, and its performance was evaluated on both the validation and prospective sets. Receiver operating characteristics area under the curve (AUC), sensitivity, and specificity were leveraged. ResultsOur two-stage framework achieved an AUC of 0.760 (95% CI 0.724-0.799) on the retrospective validation set and 0.806 (95% CI 0.750-0.859) on the prospective set, outperforming the commonly used BAT score, which had AUCs of 0.582 and 0.699, respectively. ConclusionOur framework can automatically and robustly identify ICH patients at high risk of HE using admission head NCCT scans, providing more accurate predictions than the BAT score.

Asynchronous calibration of a CT scanner for bone mineral density estimation: sources of error and correction.

The estimation of BMD with CT scans requires a calibration method, usually based on a phantom. In asynchronous calibration, the phantom is scanned separately from the patient. A standardized acquisition protocol must be used to avoid variations between patient and phantom. However, variations can still be induced, for example, by temporal fluctuations or patient characteristics. Based on the further use of 739 forensic and 111 clinical CT scans, this study uses the proximal femur BMD value ("total hip") to assess asynchronous calibration accuracy, using in-scan calibration as ground truth. It identifies the parameters affecting the calibration accuracy and quantifies their impact. For time interval and table height, the impact was measured by calibrating the CT scan twice (once using the phantom scan with closest acquisition parameters and once using a phantom scan with standard values) and comparing the calibration accuracy. For other parameters such as body weight, the impact was measured by computing a linear regression between parameter values and calibration accuracy. Finally, this study proposes correction methods to reduce the effect of these parameters and improve the calibration accuracy. The BMD error of the asynchronous calibration, using the phantom scan with the closest acquisition parameters, was -1.2 ± 1.7% for the forensic and - 1.6 ± 3.5% for the clinical dataset. Among the parameters studied, time interval and body weight were identified as the main sources of error for asynchronous calibration, followed by table height and reconstruction kernel. Based on these results, a correction method was proposed to improve the calibration accuracy.

Instance-level medical image classification for text-based retrieval in a medical data integration center

A medical data integration center integrates a large volume of medical images from clinical departments, including X-rays, CT scans, and MRI scans. Ideally, all images should be indexed appropriately with standard clinical terms. However, some images have incorrect or missing annotations, which creates challenges in searching and integrating data centrally. To address this issue, accurate and meaningful descriptors are needed for indexing fields, enabling users to efficiently search for desired images and integrate them with international standards.This paper aims to provide concise annotation for missing or incorrectly indexed fields, incorporating essential instance-level information such as radiology modalities (e.g., X-rays), anatomical regions (e.g., chest), and body orientations (e.g., lateral) using a Deep Learning classification model - ResNet50. To demonstrate the capabilities of our algorithm in generating annotations for indexing fields, we conducted three experiments using two open-source datasets, the ROCO dataset, and the IRMA dataset, along with a custom dataset featuring SNOMED CT labels. While the outcomes of these experiments are satisfactory (Precision of >75%) for less critical tasks and serve as a valuable testing ground for image retrieval, they also underscore the need for further exploration of potential challenges. This essay elaborates on the identified issues and presents well-founded recommendations for refining and advancing our proposed approach.

LONG-TERM FOLLOW-UP, REOPERATIONS, AND PATIENT-REPORTED OUTCOMES FOLLOWING REVISION SURGERY FOR FAILED TOTAL ANKLE ARTHROPLASTIES

IntroductionPrimary ankle arthroplasty (TAR) is increasingly used to treat end-stage ankle arthritis. Reported revision rates of TAR vary from 8.5% to 11.1% at 9 years. Revision surgery remains technically challenging with options ranging from simple joint debridement to tibio-talar-calcaneal fusion. The efficacy of these procedures remains unclear and there is no consensus on optimal revision options.MethodsA retrospective cohort study was performed of all patients undergoing surgery for a failed primary TAR at the Nuffield Orthopaedic Centre (2004–2021). TAR failure was determined by clinical assessment, serial radiographs and CT scans. Primary outcome measures included type and time of index surgery post TAR. Secondary outcomes included frequency of re-operations, post-operative complications, patient reported outcomes and union rate (for revision arthrodesis procedures).Results70 failed TARs in 69 patients (35M:34F, mean 65.7 years, s.d.=11.6) underwent re-operation a mean of 6.24 years (range 1–30) post primary. In total, 107 operations were performed including revision fusion (n=50), revision arthroplasty (n=14), bearing exchange (n=9) and joint clearance (n=9). The overall revision fusion union rate was 73.5% over a mean of 12.5 months (s.d.=7.6). 16/23 (69.6%) Tibio-Talo-Calcaneal and 9/12 (75%) ankle fusions (previous subtalar/triple fusion) using a hindfoot nail united over a mean 11.4 months (s.d.=6.0) and 15 months (s.d.=9.48) respectively. Only 64% of ankle fusions using screws alone united (mean=10.6 months, s.d.=8.14). The average post-operative MOXFQ score was 28.3 (s.d.=19.3). 73% said the operation improved their function and would recommend it to a friend/family member.ConclusionDespite low post-operative MOXFQ scores, over 70% of patients were satisfied with re-operation for a failed TAR. Over 26% of all TAR revision fusions fail to unite with the highest non-union rates observed post ankle arthrodesis with screws alone (36.4%).

Comparing quantitative image parameters between animal and clinical CT-scanners: a translational phantom study analysis.

This study compares phantom-based variability of extracted radiomics features from scans on a photon counting CT (PCCT) and an experimental animal PET/CT-scanner (Albira II) to investigate the potential of radiomics for translation from animal models to human scans. While oncological basic research in animal PET/CT has allowed an intrinsic comparison between PET and CT, but no 1:1 translation to a human CT scanner due to resolution and noise limitations, Radiomics as a statistical and thus scale-independent method can potentially close the critical gap. Two phantoms were scanned on a PCCT and animal PET/CT-scanner with different scan parameters and then the radiomics parameters were extracted. A Principal Component Analysis (PCA) was conducted. To overcome the limitation of a small dataset, a data augmentation technique was applied. A Ridge Classifier was trained and a Feature Importance- and Cluster analysis was performed. PCA and Cluster Analysis shows a clear differentiation between phantom types while emphasizing the comparability of both scanners. The Ridge Classifier exhibited a strong training performance with 93% accuracy, but faced challenges in generalization with a test accuracy of 62%. These results show that radiomics has great potential as a translational tool between animal models and human routine diagnostics, especially using the novel photon counting technique. This is another crucial step towards integration of radiomics analysis into clinical practice.

Computed tomographic age estimation from the iliac crest and ischial tuberosity in an Indian population using supervised machine learning approaches.

Within the pelvis the iliac crest and ischial tuberosity display delayed ossification and fusion, thus, presenting as reliable maturity indicators. Amongst the different iliac crest and ischial tuberosity age estimation methods, the modified Kreitner-Kellinghaus stages constitute one of the more promising methods. The present study was directed towards establishing the applicability of the modified Kreitner-Kellinghaus method using five supervised machine learning approaches. Clinical CT scans of consenting individuals were collected and scored using the modified Kreitner-Kellinghaus method for the iliac crest and ischial tuberosity, independently. Age was subsequently estimated using different machine learning models. Cumulative scores computed from both markers were additionally employed for age estimation using machine learning. For iliac crest age estimation, Random Forest and Gradient Boosting Regression furnished lowest mean absolute error (2.42 years) and root mean square error (3.06 years). For ischial tuberosity age estimation, Gradient Boosting Regression garnered the lowest computations of mean absolute error (2.60 years) and root mean square error (3.09 years). For cumulative score based age estimation, Support Vector Regression and Gradient Boosting Regression yielded lowest mean absolute error (2.48 years) and root mean square error (3.07 years). Obtained error computations indicate that the iliac crest is a more accurate age marker in comparison to the ischial tuberosity. Additionally, cumulative score-based approaches garnered similar/ marginally more precise results in comparison to the iliac crest with all five models. This marginal improvement is not sufficient to justify employing the relatively more complicated cumulative score-based approach for age estimation. Hence, whenever available, the iliac crest should be preferred over the ischial tuberosity/ cumulative score-based approaches for age estimation.

Super-resolution of clinical CT: Revealing microarchitecture in whole bone clinical CT image data

Osteoporotic fractures, prevalent in the elderly, pose a significant health and economic burden. Current methods for predicting fracture risk, primarily relying on bone mineral density, provide only modest accuracy. If better spatial resolution of trabecular bone in a clinical scan were available, a more complete assessment of fracture risk would be obtained using microarchitectural measures of bone (i.e. trabecular thickness, trabecular spacing, bone volume fraction, etc.). However, increased resolution comes at the cost of increased radiation or can only be applied at small volumes of distal skeletal locations. This study explores super-resolution (SR) technology to enhance clinical CT scans of proximal femurs and better reveal the trabecular microarchitecture of bone. Using a deep-learning-based (i.e. subset of artificial intelligence) SR approach, low-resolution clinical CT images were upscaled to higher resolution and compared to corresponding MicroCT-derived images. SR-derived 2-dimensional microarchitectural measurements, such as degree of anisotropy, bone volume fraction, trabecular spacing, and trabecular thickness were within 16 % error compared to MicroCT data, whereas connectivity density exhibited larger error (as high as 1094 %). SR-derived 3-dimensional microarchitectural metrics exhibited errors <18 %. This work showcases the potential of SR technology to enhance clinical bone imaging and holds promise for improving fracture risk assessments and osteoporosis detection. Further research, including larger datasets and refined techniques, can advance SR's clinical utility, enabling comprehensive microstructural assessment across whole bones, thereby improving fracture risk predictions and patient-specific treatment strategies.

Consistency of Monoenergetic Attenuation Measurements for a Clinical Dual-Source Photon-Counting Detector CT System Across Scanning Paradigms: A Phantom Study.

BACKGROUND. Use of virtual monoenergetic images (VMIs) from multienergy CT scans can mitigate inconsistencies in traditional attenuation measurements that result from variation in scan-related factors. Photon-counting detector (PCD) CT systems produce VMIs as standard image output under flexible scanning conditions. OBJECTIVE. The purpose of this article was to evaluate the consistency of monoenergetic attenuation measurements obtained from a clinical PCD CT scanner across a spectrum of scanning paradigms. METHODS. A phantom with 10 tissue-simulating inserts was imaged using a clinical dual-source PCD CT scanner. Nine scanning paradigms were obtained across combinations of tube voltages (90, 120, and 140 kVp) and image quality (IQ) levels (80, 145, and 180). Images were reconstructed at VMI levels of 50, 60, 70, and 80 keV. Consistency of attenuation measurements was assessed, using the 120 kVp with IQ level of 145 scanning paradigm as the reference scan. RESULTS. For all scanning paradigms, attenuation measurements showed intra-class correlation of 0.999 and higher with respect to the reference scan. Across inserts, mean bias relative to the reference scan ranged from -14.9 to 13.6 HU, -2.7 to 1.7 HU, and -3.9 to 3.8 HU at tube voltages of 90, 120, and 140 kVp, respectively; and from -14.9 to 13.6 HU, -6.4 to 3.8 HU, -3.7 to 1.4 HU, and -7.2 to 4.3 HU at VMI levels of 50, 60, 70, and 80 keV, respectively. Thus, mean bias did not exceed 5 HU for any insert at tube potentials of 120 kVp and 140 kVp, nor for any insert at a VMI level of 70 keV. At a VMI level of 50 keV and tube potential of 90 kVp, mean bias exceeded 5 HU for 14 of 30 possible combinations of inserts and scanning paradigms and exceeded 10 HU for four of 30 such combinations. At VMI levels of both 60 and 80 keV, mean bias exceeded 5 HU for only two combinations of inserts and scanning paradigms, all at a tube potential of 90 kVp. CONCLUSION. PCD CT generally provided consistent attenuation measurements across combinations of scanning paradigms and VMI levels. CLINICAL IMPACT. PCD CT may facilitate quantitative applications of CT data in clinical practice.

An automated technique for global noise level measurement in CT image with a conjunction of image gradient.

Automated assessment of noise level in clinical computed tomography (CT) images is a crucial technique for evaluating and ensuring the quality of these images. There are various factors that can impact CT image noise, such as statistical noise, electronic noise, structure noise, texture noise, artifact noise, etc. In this study, a method was developed to measure the global noise index (GNI) in clinical CT scans due to the fluctuation of x-ray quanta. Initially, a noise map is generated by sliding a 10 × 10 pixel for calculating Hounsfield unit (HU) standard deviation and the noise map is further combined with the gradient magnitude map. By employing Boolean operation, pixels with high gradients are excluded from the noise histogram generated with the noise map. By comparing the shape of the noise histogram from this method with Christianson's tissue-type global noise measurement algorithm, it was observed that the noise histogram computed in anthropomorphic phantoms had a similar shape with a close GNI value. In patient CT images, excluding the HU deviation due the structure change demonstrated to have consistent GNI values across the entire CT scan range with high heterogeneous tissue compared to the GNI values using Christianson's tissue-type method. The proposed GNI was evaluated in phantom scans and was found to be capable of comparing scan protocols between different scanners. The variation of GNI when using different reconstruction kernels in clinical CT images demonstrated a similar relationship between noise level and kernel sharpness as observed in uniform phantom: sharper kernel resulted in noisier images. This indicated that GNI was a suitable index for estimating the noise level in clinical CT images with either a smooth or grainy appearance. The study's results suggested that the algorithm can be effectively utilized to screen the noise level for a better CT image quality control.

Learned high-resolution cardiac CT imaging from ultra-high-resolution PCD-CT.

Coronary computed tomography angiography (cCTA) is a widely used non-invasive diagnostic exam for patients with coronary artery disease (CAD). However, most clinical CT scanners are limited in spatial resolution from use of energy-integrating detectors (EIDs). Radiological evaluation of CAD is challenging, as coronary arteries are small (3-4 mm diameter) and calcifications within them are highly attenuating, leading to blooming artifacts. As such, this is a task well suited for high spatial resolution. Recently, photon-counting-detector (PCD) CT became commercially available, allowing for ultra-high resolution (UHR) data acquisition. However, PCD-CTs are costly, restricting widespread accessibility. To address this problem, we propose a super resolution convolutional neural network (CNN): ILUMENATE (Improved LUMEN visualization through Artificial super-resoluTion imagEs), creating a high resolution (HR) image simulating UHR PCD-CT. The network was trained and validated using patches extracted from 8 patients with a modified U-Net architecture. Training input and labels consisted of UHR PCD-CT images reconstructed with a smooth kernel degrading resolution (LR input) and sharp kernel (HR label). The network learned the resolution difference and was tested on 5 unseen LR patients. We evaluated network performance quantitatively and qualitatively through visual inspection, line profiles to assess spatial resolution improvements, ROIs for CT number stability and noise assessment, structural similarity index (SSIM), and percent diameter luminal stenosis. Overall, ILUMENATE improved images quantitatively and qualitatively, creating sharper edges more closely resembling reconstructed HR reference images, maintained stable CT numbers with less than 4% difference, reduced noise by 28%, maintained structural similarity (average SSIM = 0.70), and reduced percent diameter stenosis with respect to input images. ILUMENATE demonstrates potential impact for CAD patient management, improving the quality of LR CT images bringing them closer to UHR PCD-CT images.

A dense and U-shaped transformer with dual-domain multi-loss function for sparse-view CT reconstruction.

CT image reconstruction from sparse-view projections is an important imaging configuration for low-dose CT, as it can reduce radiation dose. However, the CT images reconstructed from sparse-view projections by traditional analytic algorithms suffer from severe sparse artifacts. Therefore, it is of great value to develop advanced methods to suppress these artifacts. In this work, we aim to use a deep learning (DL)-based method to suppress sparse artifacts. Inspired by the good performance of DenseNet and Transformer architecture in computer vision tasks, we propose a Dense U-shaped Transformer (D-U-Transformer) to suppress sparse artifacts. This architecture exploits the advantages of densely connected convolutions in capturing local context and Transformer in modelling long-range dependencies, and applies channel attention to fusion features. Moreover, we design a dual-domain multi-loss function with learned weights for the optimization of the model to further improve image quality. Experimental results of our proposed D-U-Transformer yield performance improvements on the well-known Mayo Clinic LDCT dataset over several representative DL-based models in terms of artifact suppression and image feature preservation. Extensive internal ablation experiments demonstrate the effectiveness of the components in the proposed model for sparse-view computed tomography (SVCT) reconstruction. The proposed method can effectively suppress sparse artifacts and achieve high-precision SVCT reconstruction, thus promoting clinical CT scanning towards low-dose radiation and high-quality imaging. The findings of this work can be applied to denoising and artifact removal tasks in CT and other medical images.

Exploring the diagnostic value of ultrasound radiomics for neonatal respiratory distress syndrome

BackgroundNeonatal respiratory distress syndrome (NRDS) is a prevalent cause of respiratory failure and death among newborns, and prompt diagnosis is imperative. Historically, diagnosis of NRDS relied mostly on typical clinical manifestations, chest X-rays, and CT scans. However, recently, ultrasound has emerged as a valuable and preferred tool for aiding NRDS diagnosis. Nevertheless, evaluating lung ultrasound imagery necessitates rigorous training and may be subject to operator-dependent bias, limiting its widespread use. As a result, it is essential to investigate a new, reliable, and operator-independent diagnostic approach that does not require subjective factors or operator expertise. This article aims to explore the diagnostic potential of ultrasound-based radiomics in differentiating NRDS from other non-NRDS lung disease.MethodsA total of 150 neonatal lung disease cases were consecutively collected from the department of neonatal intensive care unit of the Quanzhou Maternity and Children’s Hospital, Fujian Province, from September 2021 to October 2022. Of these patients, 60 were diagnosed with NRDS, whereas 30 were diagnosed with neonatal pneumonia, meconium aspiration syndrome (MAS), and transient tachypnea (TTN). Two ultrasound images with characteristic manifestations of each lung disease were acquired and divided into training (n = 120) and validation cohorts (n = 30) based on the examination date using an 8:2 ratio. The imaging texture features were extracted using PyRadiomics and, after the screening, machine learning models such as random forest (RF), logistic regression (LR), K-nearest neighbors (KNN), support vector machine (SVM), and multilayer perceptron (MLP) were developed to construct an imaging-based diagnostic model. The diagnostic efficacy of each model was analyzed. Lastly, we randomly selected 282 lung ultrasound images and evaluated the diagnostic efficacy disparities between the optimal model and doctors across differing levels of expertise.ResultsTwenty-two imaging-based features with the highest weights were selected to construct a predictive model for neonatal respiratory distress syndrome. All models exhibited favorable diagnostic performances. Analysis of the Youden index demonstrated that the RF model had the highest score in both the training (0.99) and validation (0.90) cohorts. Additionally, the calibration curve indicated that the RF model had the best calibration (P = 0.98). When compared to the diagnostic performance of experienced and junior physicians, the RF model had an area under the curve (AUC) of 0.99; however, the values for experienced and junior physicians were 0.98 and 0.85, respectively. The difference in diagnostic efficacy between the RF model and experienced physicians was not statistically significant (P = 0.24), whereas that between the RF model and junior physicians was statistically significant (P < 0.0001).ConclusionThe RF model exhibited excellent diagnostic performance in the analysis of texture features based on ultrasound radiomics for diagnosing NRDS.

Effects of water and other filters on fracture resolution in industrial micro-CT scanning

Filtering is known to improve image quality of CT scans. Water immersion is one type of filtering that has been used for CT scanning dry (skeletonized) bones, and it has been suggested that this approach can increase measurement accuracy and improve fracture resolution. These tests have previously involved clinical CT scanners. Here we use an industrial micro-CT scanner and test whether water immersion and other x-ray filtering options increases fracture resolution in reconstructed scans of dry bones.Eleven dry non-human bones were CT scanned using the same acquisition parameters, while varying filter options. Bones were scanned (1) in an unfiltered “dry” air environment, (2) using metal filters at the x-ray source, and (3) with the bones immersed in water. A small subset of bones (N = 3) was also scanned using the same parameters except increasing the number of projections acquired from 500 to 1500. Reconstructed scans were evaluated by the authors, in part using a Likert scale comparing filtered with unfiltered scans, to assess fracture resolution (overall appearance and extent).Results showed that increasing the projections resulted in the greatest improvement in fracture resolution, followed by filtering at the x-ray source. Water immersion performed poorly overall, possibly due to movement artifacts that result from this type of scanning, in which the specimen rotates on a stage during the scan. When using this type of CT scanner, if increased fracture resolution is desired, water immersion is not recommended; increasing the number of projections or filtering at the x-ray source is suggested instead.

Development of A Micro-CT Scanner with Dual-Energy Option and Endovascular Contrast Agent Administration Protocol for Fetal and Neonatal Virtual Autopsy.

The rate of parental consent for fetal and perinatal autopsy is decreasing, whereas parents are more likely to agree to virtual autopsy by non-invasive imaging methods. Fetal and perinatal virtual autopsy needs high-resolution and good soft-tissue contrast for investigation of the cause of death and underlying trauma or pathology in fetuses and stillborn infants. This is offered by micro-computed tomography (CT), as opposed to the limited resolution provided by clinical CT scanners, and this is one of the most promising tools for non-invasive perinatal postmortem imaging. We developed and optimized a micro-CT scanner with a dual-energy imaging option. It is dedicated to post-mortem CT angiography and virtual autopsy of fetuses and stillborn infants in that the chamber can be cooled down to around 5 °C; this increases tissue rigidity and slows decomposition of the native specimen. This, together with the dedicated gantry-based architecture, attempts to reduce potential motion artifacts. The developed methodology is based on prior endovascular injection of a BaSO4-based contrast agent. We explain the design choices and considerations for this scanner prototype. We give details of the treatment of the optimization of the dual-energy and virtual mono-energetic imaging option that has been based on minimizing noise propagation and maximizing the contrast-to-noise ratio for vascular features. We demonstrate the scanner capabilities with proof-of-concept experiments on phantoms and stillborn piglets.

Tomo-PIV in a patient-specific model of human nasal cavities: a methodological approach

The human nose serves as the primary gateway for air entering the respiratory system and plays a vital role in breathing. Nasal breathing difficulties are a significant health concern, leading to substantial healthcare costs for patients. Understanding nasal airflow dynamics is crucial for comprehending respiratory mechanisms. This article presents a detailed study using tomo-Particle Image Velocimetry (PIV) to investigate nasal airflow dynamics while addressing its accuracy. Embedded in the OpenNose project, the work described aims to provide a validation basis for different numerical approaches to upper airway flow. The study includes the manufacturing of a transparent silicone model based on a clinical CT scan, refractive index matching to minimize optical distortions, and precise flow rate adjustments based on physiological breathing cycles. This method allows for spatial high-resolution investigations in different regions of interest within the nasopharynx during various phases of the breathing cycle. The results demonstrate the accuracy of the investigations, enabling detailed analysis of flow structures and gradients. This spatial high-resolution tomo-PIV approach provides valuable insights into the complex flow phenomena occurring during the physiological breathing cycle in the nasopharynx. The study’s findings contribute to advancements in non-free-of-sight experimental flow investigation of complex cavities under nearly realistic conditions. Furthermore, reliable and accurate experimental data is crucial for properly validating numerical approaches that compute this patient-specific flow for clinical purposes.

Development and tuning of models for accurate simulation of CT spatial resolution using CatSim.

Objective. We sought to systematically evaluate CatSim's ability to accurately simulate the spatial resolution produced by a typical 64-detector-row clinical CT scanner in the projection and image domains, over the range of clinically used x-ray techniques.Approach.Using a 64-detector-row clinical scanner, we scanned two phantoms designed to evaluate spatial resolution in the projection and image domains. These empirical scans were performed over the standard clinically used range of x-ray techniques (kV, and mA). We extracted projection data from the scanner, and we reconstructed images. For the CatSim simulations, we developed digital phantoms to represent the phantoms used in the empirical scans. We developed a new, realistic model for the x-ray source focal spot, and we empirically tuned a published model for the x-ray detector temporal response. We applied these phantoms and models to simulate scans equivalent to the empirical scans, and we reconstructed the simulated projections using the same methods used for the empirical scans. For the empirical and simulated scans, we qualitatively and quantitatively compared the projection-domain and image-domain point-spread functions (PSFs) as well as the image-domain modulation transfer functions. We reported four quantitative metrics and the percent error between the empirical and simulated results.Main Results.Qualitatively, the PSFs matched well in both the projection and image domains. Quantitatively, all four metrics generally agreed well, with most of the average errors substantially less than 5% for all x-ray techniques. Although the errors tended to increase with decreasing kV, we found that the CatSim simulations agreed with the empirical scans within limits required for the anticipated applications of CatSim.Significance.The new focal spot model and the new detector temporal response model are significant contributions to CatSim because they enabled achieving the desired level of agreement between empirical and simulated results. With these new models and this validation, CatSim users can be confident that the spatial resolution represented by simulations faithfully represents results that would be obtained by a real scanner, within reasonable, known limits. Furthermore, users of CatSim can vary parameters including but not limited to system geometry, focal spot size/shape and detector parameters, beyond the values available in physical scanners, and be confident in the results. Therefore, CatSim can be used to explore new hardware designs as well as new scanning and reconstruction methods, thus enabling acceleration of improved CT scan capabilities.

Evaluasi Nilai Diagnostic Reference Level (DRL) pada pemeriksaan CT Scan Thorax Kontras klinis tumor paru di RS X Jakarta

A contrast thorax CT scan is a CT scan procedure in the chest area (thorax) that uses a contrast agent to improve visualization of organs and structures in the chest. Lung tumors are the growth of abnormal lumps in lung tissue which can be benign or malignant. Awareness of the potential and increasing radiation dose in all CT scan examinations encourages radiographers to want and be able to minimize radiation exposure as the main goal in radiological examinations and also strive for radiation protection during examinations. CT scan radiation dose accounts for 70% of the total dose received from medical imaging. High or low doses of radiation received can increase the risk of cancer. The large dose of radiation received by a patient during a CT scan, therefore regulations are needed that will guarantee monitoring of the radiation dose received by the patient to ensure that the dose received is commensurate with medical purposes. The radiation dose to the patient needs to be optimized by applying the Diagnostic Reference Level (DRL) to optimize medical exposure protection for the patient. Dosage evaluation in clinical contrast chest CT scan examinations for lung tumors and the application of DRL in these examinations have never been carried out at RS X Jakarta. Objective: This study aims to determine the DRL value in clinical contrast thorax CT scan examination of lung tumors at RS CTDIvol and DLP values from clinical contrast Thorax CT Scan examination of lung tumors. The DRL value is calculated at Quartile 3 (75 percentile) using the descriptive frequency test and Wilcoxon test in the SPSS application, then compared with the latest IDRL value. Results: Quartile 3 value (75 percentile) from CTDIvol and DLP CT scan chest contrast clinical lung tumors, namely The CTDIvol value was 12.55 mGy and the DLP was 439 mGy.cm. Conclusion: The DRL value in the clinical contrast thorax CT scan for lung tumors at Hospital X Jakarta. is still below the recommended standard, which shows that the radiation output dose in this examination practice is within normal limits and is classified as safe.

Incorporating patient-specific hip orientation from weightbearing computed tomography affects discrete element analysis-computed regional joint contact mechanics in individuals treated with periacetabular osteotomy for hip dysplasia.

Computational models of the hip often omit patient-specific functional orientation when placing imaging-derived bony geometry into anatomic landmark-based coordinate systems for application of joint loading schemes. The purpose of this study was to determine if this omission meaningfully alters computed contact mechanics. Discrete element analysis models were created from non-weightbearing (NWB) clinical CT scans of 10 hip dysplasia patients (11 hips) and oriented in the International Society of Biomechanics (ISB) coordinate system (NWB-ISB). Three additional models were generated for each hip by adding patient-specific stance information obtained via weightbearing CT (WBCT) to each ISB-oriented model: (1) patient-specific sagittal tilt added (WBCT-sagittal), (2) coronal and axial rotation from optical motion capture added to (1; WBCT-combo), and (3) WBCT-derived axial, sagittal, and coronal rotation added to (1; WBCT-original). Identical gait cycle loading was applied to all models for a given hip, and computed contact stress and contact area were compared between model initialization techniques. Addition of sagittal tilt did not significantly change whole-joint peak (p = 0.922) or mean (p = 0.871) contact stress or contact area (p = 0.638). Inclusion of motion-captured coronal and axial rotation (WBCT-combo) decreased peak contact stress (p = 0.014) and slightly increased average contact area (p = 0.071) from WBCT-sagittal models. Including all WBCT-derived rotations (WBCT-original) further reduced computed peak contact stress (p = 0.001) and significantly increased contact area (p = 0.001). Variably significant differences (p = 0.001-1.0) in patient-specific acetabular subregion mechanics indicate the importance of functional orientation incorporation for modeling applications in which local contact mechanics are of interest.