Phantomless calibration of CT scans for hip fracture risk prediction in silico: Comparison with phantom-based calibration.

Finite element models built from quantitative computed tomography images rely on element-wise mapping of material properties starting from Hounsfield Units (HU), which can be converted into mineral densities upon calibration. While calibration is preferably carried out by scanning a phantom with known-density components, conducting phantom-based calibration may not always be possible. In such cases, a phantomless procedure, where the scanned subject's tissues are used as a phantom, is an interesting alternative. The aim of this study was to compare a phantom-based and a phantomless calibration method on 41 postmenopausal women. The proposed phantomless calibration utilized air, adipose, and muscle tissues, with reference equivalent mineral density values of -797, -95, and 38 mg/cm3, extracted from a previously performed phantom-based calibration. A 9-slice volume of interest (VOI) centred between the femoral head and knee rotation centres was chosen. Reference HU values for air, adipose, and muscle tissues were extracted by identifying HU distribution peaks within the VOI, and patient-specific calibration was performed using linear regression. Comparison of FE models calibrated with the two methods showed average relative differences of 1.99% for Young's modulus1.30% for tensile and 1.34% for compressive principal strains. Excellent correlations (R2 > 0.99) were identified for superficial maximum tensile and minimum compressive strains. Maximum normalised root mean square relative error (RMSRE) values settled at 4.02% for Young's modulus, 2.99% for tensile, and 3.22% for compressive principal strains, respectively. The good agreement found between the two methods supports the adoption of the proposed methodology when phantomless calibration is needed.

Recommendations for cardiovascular magnetic resonance and computed tomography in congenital heart disease: a consensus paper from the CMR/CCT Working Group of the Italian Society of Pediatric Cardiology and the Italian College of Cardiac Radiology endorsed by the Italian Society of Medical and Interventional Radiology (Part II).

Cardiovascular magnetic resonance (CMR) and computed tomography (CCT) are advanced imaging modalities that recently revolutionized the conventional diagnostic approach to congenital heart diseases (CHD), supporting echocardiography and often replacing cardiac catheterization. This is the second of two complementary documents, endorsed by experts from the Working Group of the Italian Society of Pediatric Cardiology and the Italian College of Cardiac Radiology of the Italian Society of Medical and Interventional Radiology, aimed at giving updated indications on the appropriate use of CMR and CCT in different clinical CHD settings, in both pediatrics and adults. In this article, support is also given to radiologists, pediatricians, cardiologists, and cardiac surgeons for indications and appropriateness criteria for CMR and CCT in the most referred CHD, following the proposed new criteria presented and discussed in the first document. This second document also examines the impact of devices and prostheses for CMR and CCT in CHD and additionally presents some indications for CMR and CCT exams when sedation or narcosis is needed.

CT-guided spatial normalization of nuclear hybrid imaging adapted to enlarged ventricles: Impact on striatal uptake quantification.

INTRODUCTION: Spatial normalization is a prerequisite step for the quantitative analysis of SPECT or PET brain images using volume-of-interest (VOI) template or voxel-based analysis. MRI-guided spatial normalization is the gold standard, but the wide use of PET/CT or SPECT/CT in routine clinical practice makes CT-guided spatial normalization a necessary alternative. Ventricular enlargement is observed with aging, and it hampers the spatial normalization of the lateral ventricles and striatal regions, limiting their analysis. The aim of the present study was to propose a robust spatial normalization method based on CT scans that takes into account features of the aging brain to reduce bias in the CT-guided striatal analysis of SPECT images. METHODS: We propose an enhanced CT-guided spatial normalization pipeline based on SPM12. Performance of the proposed pipeline was assessed on visually normal [123I]-FP-CIT SPECT/CT images. SPM12 default CT-guided spatial normalization was used as reference method. The metrics assessed were the overlap between the spatially normalized lateral ventricles and caudate/putamen VOIs, and the computation of caudate and putamen specific binding ratios (SBR). RESULTS: In total 231 subjects (mean age ± SD = 61.9 ± 15.5 years) were included in the statistical analysis. The mean overlap between the spatially normalized lateral ventricles of subjects and the caudate VOI and the mean SBR of caudate were respectively 38.40 % (± SD = 19.48 %) of the VOI and 1.77 (± 0.79) when performing SPM12 default spatial normalization. The mean overlap decreased to 9.13 % (± SD = 1.41 %, P < 0.001) of the VOI and the SBR of caudate increased to 2.38 (± 0.51, P < 0.0001) when performing the proposed pipeline. Spatially normalized lateral ventricles did not overlap with putamen VOI using either method. The mean putamen SBR value derived from the proposed spatial normalization (2.75 ± 0.54) was not significantly different from that derived from the default SPM12 spatial normalization (2.83 ± 0.52, P > 0.05). CONCLUSION: The automatic CT-guided spatial normalization used herein led to a less biased spatial normalization of SPECT images, hence an improved semi-quantitative analysis. The proposed pipeline could be implemented in clinical routine to perform a more robust SBR computation using hybrid imaging.

Project to improve the management of the head injury patients presenting to the emergency department.

INTRODUCTION: At Sandwell General Hospital, there was no risk stratification tool or pathway for head injury (HI) patients presenting to the emergency department (ED). This resulted in significant delays in the assessment of HI patients, compromising patient safety and quality of care. AIMS: To employ quality improvement methodology to design an effective adult HI pathway that: ensured >90% of high-risk HI patients being assessed by ED clinicians within 15 min of arrival, reduce CT turnaround times, and aiming to keep the final decision making <4 hours. METHODS: SWOT analysis was performed; driver diagrams were used to set out the aims and objectives. Plan-Do-Study-Act cycle was used to facilitate the change and monitor the outcomes. Process map was designed to identify the areas for improvement. A new HI pathway was introduced, imaging and transporting the patients was modified, and early decisions were made to meet the standards. RESULTS: Data were collected and monitored following the interventions. The new pathway improved the proportion of patients assessed by the ED doctors within 15 min from 31% to 63%. The average time to CT head scan was decreased from 69 min to 53 min. Average CT scan reporting time also improved from 98 min to 71 min. Overall, the average time to decision for admission or discharge decreased from 6 hours 48 min to 4 hours 24 min. CONCLUSIONS: Following implementation of the new HI pathway, an improvement in the patient safety and quality of care was noted. High-risk HI patients were picked up earlier, assessed quicker and had CT head scans performed sooner. Decision time for admission/discharge was improved. The HI pathway continues to be used and will be reviewed and re-audited between 3 and 6 months to ensure the sustained improvement.

An NHS Trust comparison of reporting radiographers and consultant radiologists ability to identify cancer on chest radiographs with confirmation through a Fast track CT thorax referral system.

INTRODUCTION: The National Institute for Health and Care Excellence (NICE) recommends that GPs initially refer patients with suspected lung cancer for a chest X-ray (CXR). The Radiology department has a 'fast track system' to identify those patients who may have lung cancer on CXR and are referred for a CT thorax with contrast to help determine a cancer diagnosis. This fast track system was put in place to ensure the NICE guidelines and NHS England's standards on a faster cancer diagnosis are being met. This audit studied the ability of radiologists and reporting radiographers to identify lung cancer on CXRs and the accuracy of the fast-track system. METHODS: 846 cases with lung alerts were analysed and 545 CXRs were audited. The CXRs were split into images reported by radiologists (168) and those reported by reporting radiographers (377). CT thorax results were collected through PACS and Cerner computer systems to identify if the 'fast track' system had yielded a "positive", "negative", or "other findings" result for lung cancer. RESULTS: 32.8% (179) of CXRs flagged for lung cancer were positive, 40.6% (221) were negative, and 26.6% (145) had other findings. Chi square statistical test showed no significant difference (p = 0.14) between the two reporting groups in their ability to identify lung cancer on CXRs. 27% (38) of CXRs flagged by radiologists and 35% (125) by reporting radiographers were positive for lung cancer. CONCLUSION: This clinical audit indicates, reporting radiographers and radiologists are not statistically significantly different regarding their ability to identify lung cancer on CXRs, when supported by the fast track system. The fast-track system had a 59.4 % accuracy rate, detected by the number of imaging of reports that identified a serious pathology. This concludes that the system is performing well, yet could still be improved. IMPLICATIONS FOR PRACTICE: This audit provides further evidence for the value of developing and deploying reporting radiographers for projection radiography reporting.

Updating national diagnostic reference levels for computed tomography in Greece: Challenges on patient protection optimisation.

The escalating use of Computed Tomography (CT) imaging necessitates establishment and periodic revision of Diagnostic Reference Levels (DRLs) to ensure patient protection optimization. This paper presents the outcomes of a national survey conducted from 2019 to 2022, focusing on revising DRLs for adult CT examinations. Dosimetric data from 127 scanners in 120 medical facilities, representing 25% of the country's CT scanners, were collected, emphasizing geographic distribution and technology representation. Τhe parameters used for DRLs were the CTDIvol and the DLP of a typical acquisition of the region of interest (scan DLP). In addition to the 7 CT examination for which the DRL values were revised, establishment of DRLs for neck, cervical spine, pelvic bones-hips, coronary artery calcium (Ca) score and cardiac computed tomography angiography (CCTA) examinations was performed. Revised DRLs exhibited a 15 % average decrease in CTDIvol and a 7 % average decrease in scan DLP from the initial DRLs. This reduction of dosimetric values is relatively low compared to other national studies. The findings revealed wide variations in dosimetric values and scan lengths among scanners, emphasizing the need for standardization and optimization. Incorporation of advanced technologies like Iterative Reconstruction (IR) showcased potential for further dose reduction, yet challenges in uniform implementation persist. The study underscores the importance of ongoing optimisation efforts, particularly in the context of increased CT utilization and evolving technology. The revised DRLs have been officially adopted in Greece, emphasizing the commitment to safe and effective CT practices.

Performance evaluation of computed tomography scanners in Indonesia: recommendation from a nation-wide study.

In this study, an evaluation of the compliance test data from 684 computed tomography (CT)-scanners in Indonesia for the 2019-22 test period was carried out. The study was aimed to describe the performance profile of CT-scanners in Indonesia and evaluate the testing protocol. A total of 87.8% of the CT-scanners unconditionally passed the tests, 8.8% passed the tests with conditions and 3.4% failed the tests. Of the devices conditionally passed the tests, the top two causes were water CT number accuracy (45.2%) and laser position accuracy (41.9%). Meanwhile, 75.0% of the failed devices were due to failing to meet the patient dose test criteria. The failure of the test for the water CT number accuracy parameter was caused by variations in the type of phantom used in the test, where several types of phantoms did not use water as material of the homogeneity module. Failures in laser position accuracy test were caused by the passing criteria that adjust to the minimum slice thickness, so that modern CT-scanner with small detector sizes and collimations tend not to pass. On the other hand, the failure on dose aspects was due to the frequent unavailability of baseline values for comparison. Of these top three failure causes, two of them, namely the CT number and dose test parameters, have been accommodated in the latest regulation (BAPETEN Regulation No. 2/2022) with a change in the evaluation method, while for the laser position accuracy test it is recommended to alter the passing criteria to an absolute value, namely 1 mm.

Normative reference data for intracranial volume in children: The results of CT volumetry.

BACKGROUND: Intracranial volume (ICV) is an important indicator of the development of the brain and skull in children. At present, there is a lack of ICV growth standards based on large infant and children samples. Our aim was to assess the normal range of the ICV variation in Russian children using a modern automatic system for constructing the endocranial cavity (Endex) and to provide growth standards of the ICV for clinical practice. METHODS: High-resolution head CT scans were obtained from 673 apparently healthy children (380 boys and 293 girls) aged 0-17 years and transformed into the ICV estimates using the Endex software. The open-source software RefCurv utilizing R and the GAMLSS add-on package with the LMS method was then used for the construction of smooth centile growth references for ICV according to age and sex. RESULTS: We demonstrated that the ICVs estimates calculated using the Endex software are perfectly comparable with those obtained by a conventional technique (i.e. seed feeling). Sex-specific pediatric growth charts for ICV were constructed. CONCLUSIONS: This study makes available for use in clinical practice ICV growth charts for the age from 0 to 17 based on a sample of 673 high-resolution CT images.

Improving acute stroke assessment in non-enhanced computed tomography: automated tool for early ischemic lesion volume detection.

BACKGROUND AND OBJECTIVES: ASPECTs is a widely used marker to identify early stroke signs on non-enhanced computed tomography (NECT), yet it presents interindividual variability and it may be hard to use for non-experts. We introduce an algorithm capable of automatically estimating the NECT volumetric extension of early acute ischemic changes in the 3D space. We compared the power of this marker with ASPECTs evaluated by experienced practitioner in predicting the clinical outcome. METHODS: We analyzed and processed neuroimaging data of 153 patients admitted with acute ischemic stroke. All patients underwent a NECT at admission and on follow-up. The developed algorithm identifies the early ischemic hypodense region based on an automatic comparison of the gray level in the images of the two hemispheres, assumed to be an approximate mirror image of each other in healthy patients. RESULTS: In the two standard axial slices used to estimate the ASPECTs, the regions identified by the algorithm overlap significantly with those identified by experienced practitioners. However, in many patients, the regions identified automatically extend significantly to other slices. In these cases, the volume marker provides supplementary and independent information. Indeed, the clinical outcome of patients with volume marker = 0 can be distinguished with higher statistical confidence than the outcome of patients with ASPECTs = 10. CONCLUSION: The volumetric extension and the location of acute ischemic region in the 3D-space, automatically identified by our algorithm, provide data that are mostly in agreement with the ASPECTs value estimated by expert practitioners, and in some cases complementary and independent.

Diagnostic reference levels in spinal CT: Jordanian assessments and global benchmarks.

BACKGROUND: To reduce radiation dose and subsequent risks, several legislative documents in different countries describe the need for Diagnostic Reference Levels (DRLs). Spinal radiography is a common and high-dose examination. Therefore, the aim of this work was to establish the DRL for Computed Tomography (CT) examinations of the spine in healthcare institutions across Jordan. METHODS: Data was retrieved from the picture archiving and communications system (PACS), which included the CT Dose Index (CTDI (vol) ) and Dose Length Product (DLP). The median radiation dose values of the dosimetric indices were calculated for each site. DRL values were defined as the 75th percentile distribution of the median CTDI (vol)  and DLP values. RESULTS: Data was collected from 659 CT examinations (316 cervical spine and 343 lumbar-sacral spine). Of the participants, 68% were males, and the patients' mean weight was 69.7 kg (minimum = 60; maximum = 80, SD = 8.9). The 75th percentile for the DLP of cervical and LS-spine CT scans in Jordan were 565.2 and 967.7 mGy.cm, respectively. CONCLUSIONS: This research demonstrates a wide range of variability in CTDI (vol)  and DLP values for spinal CT examinations; these variations were associated with the acquisition protocol and highlight the need to optimize radiation dose in spinal CT examinations.

Development of a Multimodal Machine Learning-Based Prognostication Model for Traumatic Brain Injury Using Clinical Data and Computed Tomography Scans: A CENTER-TBI and CINTER-TBI Study.

Computed tomography (CT) is an important imaging modality for guiding prognostication in patients with traumatic brain injury (TBI). However, because of the specialized expertise necessary, timely and dependable TBI prognostication based on CT imaging remains challenging. This study aimed to enhance the efficiency and reliability of TBI prognostication by employing machine learning (ML) techniques on CT images. A retrospective analysis was conducted on the Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) data set (n = 1016). An ML-driven binary classifier was developed to predict favorable or unfavorable outcomes at 6 months post-injury. The prognostic performance was assessed using the area under the curve (AUC) over fivefold cross-validation and compared with conventional models that depend on clinical variables and CT scoring systems. An external validation was performed using the Comparative Indian Neurotrauma Effectiveness Research in Traumatic Brain Injury (CINTER-TBI) data set (n = 348). The developed model achieved superior performance without the necessity for manual CT assessments (AUC = 0.846 [95% CI: 0.843-0.849]) compared with the model based on the clinical and laboratory variables (AUC = 0.817 [95% CI: 0.814-0.820]) and established CT scoring systems requiring manual interpretations (AUC = 0.829 [95% CI: 0.826-0.832] for Marshall and 0.838 [95% CI: 0.835-0.841] for International Mission for Prognosis and Analysis of Clinical Trials in TBI [IMPACT]). The external validation demonstrated the prognostic capacity of the developed model to be significantly better (AUC = 0.859 [95% CI: 0.857-0.862]) than the model using clinical variables (AUC = 0.809 [95% CI: 0.798-0.820]). This study established an ML-based model that provides efficient and reliable TBI prognosis based on CT scans, with potential implications for earlier intervention and improved patient outcomes.

Quantitative imaging predicts pancreatic fatty infiltration on routine CT examination.

PURPOSE: The purpose of this study was to assess the performance of quantitative computed tomography (CT) imaging for detecting pancreatic fatty infiltration, using the results of histopathological analysis as reference. MATERIALS AND METHODS: Sixty patients who underwent pancreatic surgery for a pancreatic tumor between 2016 and 2019 were retrospectively included. There were 33 women and 27 men with a mean age of 56 ± 12 (SD) years (age range: 18-79 years). Patients with dilatation of the main pancreatic duct, chronic pancreatitis, or preoperative treatment were excluded to prevent any bias in the radiological-pathological correlation. Pancreatic fatty infiltration was recorded at pathology. Pancreatic surface lobularity, pancreatic attenuation, visceral fat area, and subcutaneous fat area were derived from preoperative CT images. The performance for the prediction of fatty infiltration was assessed using area under receiver operating characteristic curve (AUC) and backward binary logistic regression analysis. Results were validated in a separate cohort of 34 patients (17 women; mean age, 50 ± 14 [SD] years; age range: 18-73). RESULTS: A total of 28/60 (47%) and 17/34 (50%) patients had pancreatic fatty infiltration in the derivation and validation cohorts, respectively. In the derivation cohort, patients with pancreatic fatty infiltration had a significantly higher PSL (P < 0.001) and a lower pancreatic attenuation on both precontrast and portal venous phase images (P = 0.011 and 0.003, respectively), and higher subcutaneous fat area and visceral fat area (P = 0.010 and 0.007, respectively). Multivariable analysis identified pancreatic surface lobularity > 7.6 and pancreatic attenuation on portal venous phase images < 83.5 Hounsfield units as independently associated with fatty infiltration. The combination of these variables resulted in an AUC of 0.85 (95% CI: 0.74-0.95) and 0.83 (95% CI: 0.67-0.99) in the derivation and validation cohorts, respectively. CONCLUSION: CT-based quantitative imaging accurately predicts pancreatic fatty infiltration.

CTformer: convolution-free Token2Token dilated vision transformer for low-dose CT denoising.

Objective. Low-dose computed tomography (LDCT) denoising is an important problem in CT research. Compared to the normal dose CT, LDCT images are subjected to severe noise and artifacts. Recently in many studies, vision transformers have shown superior feature representation ability over the convolutional neural networks (CNNs). However, unlike CNNs, the potential of vision transformers in LDCT denoising was little explored so far. Our paper aims to further explore the power of transformer for the LDCT denoising problem.Approach. In this paper, we propose a Convolution-free Token2Token Dilated Vision Transformer (CTformer) for LDCT denoising. The CTformer uses a more powerful token rearrangement to encompass local contextual information and thus avoids convolution. It also dilates and shifts feature maps to capture longer-range interaction. We interpret the CTformer by statically inspecting patterns of its internal attention maps and dynamically tracing the hierarchical attention flow with an explanatory graph. Furthermore, overlapped inference mechanism is employed to effectively eliminate the boundary artifacts that are common for encoder-decoder-based denoising models.Main results. Experimental results on Mayo dataset suggest that the CTformer outperforms the state-of-the-art denoising methods with a low computational overhead.Significance. The proposed model delivers excellent denoising performance on LDCT. Moreover, low computational cost and interpretability make the CTformer promising for clinical applications.

Computed Tomography-Based Radiomics Analysis for Prediction of Response to Neoadjuvant Chemotherapy in Breast Cancer Patients.

PURPOSE: Previous studies have pointed out that magnetic resonance- and fluorodeoxyglucose positron emission tomography-based radiomics had a high predictive value for the response of the neoadjuvant chemotherapy (NAC) in breast cancer by respectively characterizing tumor heterogeneity of the relaxation time and the glucose metabolism. However, it is unclear whether computed tomography (CT)-based radiomics based on density heterogeneity can predict the response of NAC. This study aimed to develop and validate a CT-based radiomics nomogram to predict the response of NAC in breast cancer. METHODS: A total of 162 breast cancer patients (110 in the training cohort and 52 in the validation cohort) who underwent CT scans before receiving NAC and had pathological response results were retrospectively enrolled. Grades 4 to 5 cases were classified as response to NAC. According to the Miller-Payne grading system, grades 1 to 3 cases were classified as nonresponse to NAC. Radiomics features were extracted, and the optimal radiomics features were obtained to construct a radiomics signature. Multivariate logistic regression was used to develop the clinical prediction model and the radiomics nomogram that incorporated clinical characteristics and radiomics score. We assessed the performance of different models, including calibration and clinical usefulness. RESULTS: Eight optimal radiomics features were obtained. Human epidermal growth factor receptor 2 status and molecular subtype showed statistical differences between the response group and the nonresponse group. The radiomics nomogram had more favorable predictive efficacy than the clinical prediction model (areas under the curve, 0.82 vs 0.70 in the training cohort; 0.79 vs 0.71 in the validation cohort). The Delong test showed that there are statistical differences between the clinical prediction model and the radiomics nomogram ( z = 2.811, P = 0.005 in the training cohort). The decision curve analysis showed that the radiomics nomogram had higher overall net benefit than the clinical prediction model. CONCLUSION: The radiomics nomogram based on CT radiomics signature and clinical characteristics has favorable predictive efficacy for the response of NAC in breast cancer.

Liver fat quantification in photon counting CT in head to head comparison with clinical MRI - First experience.

PURPOSE: To compare liver fat quantification between MRI and photon-counting CT (PCCT). METHOD: A cylindrical phantom with inserts containing six concentrations of oil (0, 10, 20, 30, 50 and 100%) and oil-iodine mixtures (0, 10, 20, 30 and 50% fat +3 mg/mL iodine) was imaged with a PCCT (NAEOTOM Alpha) and a 1.5 T MRI system (MR 450w, IDEAL-IQ sequence), using clinical parameters. An IRB-approved prospective clinical evaluation included 12 obese adult patients with known fatty liver disease (seven women, mean age: 61.5 ± 13 years, mean BMI: 30.3 ± 4.7 kg/m2). Patients underwent a same-day clinical MRI and PCCT of the abdomen. Liver fat fractions were calculated for four segments (I, II, IVa and VII) using in- and opposed-phase on MRI ((Meanin - Meanopp)/2*Meanin) and iodine-fat, tissue decomposition analysis in PCCT (Syngo.Via VB60A). CT and MRI Fat fractions were compared using two-sample t-tests with equal variance. Statistical analysis was performed using RStudio (Version1.4.1717). RESULTS: Phantom results showed no significant differences between the known fat fractions (P = 0.32) or iodine (P = 0.6) in comparison to PCCT-measured concentrations, and no statistically significant difference between known and MRI-measured fat fractions (P = 0.363). In patients, the mean fat signal fraction measured on MRI and PCCT was 13.1 ± 9.9% and 12.0 ± 9.0%, respectively, with an average difference of 1.1 ± 1.9% between the modalities (P = 0.138). CONCLUSION: First experience shows promising accuracy of liver fat fraction quantification for PCCT in obese patients. This method may improve opportunistic screening for CT in the future.

Location-Dependent Spatiotemporal Antialiasing in Photoacoustic Computed Tomography.

Photoacoustic computed tomography (PACT) images optical absorption contrast by detecting ultrasonic waves induced by optical energy deposition in materials such as biological tissues. An ultrasonic transducer array or its scanning equivalent is used to detect ultrasonic waves. The spatial distribution of the transducer elements must satisfy the spatial Nyquist criterion; otherwise, spatial aliasing occurs and causes artifacts in reconstructed images. The spatial Nyquist criterion poses different requirements on the transducer elements' distributions for different locations in the image domain, which has not been studied previously. In this research, we elaborate on the location dependency through spatiotemporal analysis and propose a location-dependent spatiotemporal antialiasing method. By applying this method to PACT in full-ring array geometry, we effectively mitigate aliasing artifacts with minimal effects on image resolution in both numerical simulations and in vivo experiments.

Computed Tomography Dose Level in Selected Five Principal Hospitals in Ethiopia.

Background: X-ray Computed Tomography dose levels have been varying among modalities and scanning body regions due to the absence of incessant routine follow-up. Thus, the study aimed to compute the dose index discrepancies in Ethiopia for the most recurring scan protocols (head, chest, abdomen, and pelvis). Methods: A purposive sampling method was employed to select the hospitals due to the rare existence of functional CT scanners in Ethiopia. From the selected hospitals, a total of 1,385 (249 heads, 804 chests, 132 abdomens, and 200 pelvis) were collected in terms of standard dose metric values in the period of December 2019-March 2020. Patients' DLP was computed into mean value using IBM SPSS Statistics 20 software. From the mean DLP, we can compute the effective dose. Results: Patients' dose level disparity was observed in this study though it is below the ICRP standard level for all body regions except for pelvis DLP (593.37 mGy-cm) at Black Lion. The dose level for the head and chest are computed within the recommended level at all hospitals. Effective doses for the pelvis at four hospitals (Teklehaimanot, Black Lion, ALERT, Paul's, and Ayder hospitals) were computed as 6.45, 8.90, 5.08, 6.54, and 6.84 mSv respectively, and the effective doses for abdomen at Ayder Hospital was obtained to be 8.90 mSv, which is above the recommended value. Conclusion: X-ray CT scanners are somewhat properly functioning although some sort of justification and optimization for pelvis and abdomen examinations are strongly recommended to implement as low as reasonably achievable principle.

Effective deep learning approaches for predicting COVID-19 outcomes from chest computed tomography volumes.

The rapid evolution of the novel coronavirus disease (COVID-19) pandemic has resulted in an urgent need for effective clinical tools to reduce transmission and manage severe illness. Numerous teams are quickly developing artificial intelligence approaches to these problems, including using deep learning to predict COVID-19 diagnosis and prognosis from chest computed tomography (CT) imaging data. In this work, we assess the value of aggregated chest CT data for COVID-19 prognosis compared to clinical metadata alone. We develop a novel patient-level algorithm to aggregate the chest CT volume into a 2D representation that can be easily integrated with clinical metadata to distinguish COVID-19 pneumonia from chest CT volumes from healthy participants and participants with other viral pneumonia. Furthermore, we present a multitask model for joint segmentation of different classes of pulmonary lesions present in COVID-19 infected lungs that can outperform individual segmentation models for each task. We directly compare this multitask segmentation approach to combining feature-agnostic volumetric CT classification feature maps with clinical metadata for predicting mortality. We show that the combination of features derived from the chest CT volumes improve the AUC performance to 0.80 from the 0.52 obtained by using patients' clinical data alone. These approaches enable the automated extraction of clinically relevant features from chest CT volumes for risk stratification of COVID-19 patients.

A Retrospectively Study: Diagnosis of Pathological Types of Malignant Lung Tumors by Dual-layer Detector Spectral Computed Tomography.

Object: By retrospectively analyzing the energy spectrum of squamous cell carcinoma, adenocarcinoma, small cell lung cancer (SCLC), and pulmonary metastases that underwent dual-layer detector spectral computed tomography (DLCT) 3-phase scan of the chest, we explored the value of a multiparameter energy spectrum in the assessment of pathological types of lung tumors. Methods: Cases of squamous cell carcinoma (n = 20), adenocarcinoma (n = 24), SCLC (n = 26), and metastases (n = 14) were collected. Then the largest cross-sectional area (LCA) of the lesion, computed tomography (CT) values in the plain scan phase, arterial and venous phases (HU, HUa, and HUv), iodine concentration, and effective atomic number in the arterial and venous phases (ICa, ICv, Zeff[a], and Zeff[v]) were measured and compared among the nonsmall cell lung cancer (NSCLC), SCLC and metastases, and other 3 groups of SCLC, squamous cell carcinoma, and adenocarcinoma. Results: Only the LCA is statistically different among SCLC, NSCLC, and metastases (P < .05). And the treated subgroup analysis did not show significant differences among the groups. However, the untreated subgroup analysis showed that there was a significant difference between NSCLC and metastases in LCA, SCLC and metastases in ICa, NSCLC and SCLC in HUv, NSCLC and SCLC in Zeff(v) (P < .05). Conclusion: The energy spectrum parameters of DLCT have a certain clinical value in distinguishing NSCLC from SCLC in the Zeff(v) and distinguishing SCLC from metastases in the ICa.

Application of Unenhanced Computed Tomography Texture Analysis to Differentiate Pancreatic Adenosquamous Carcinoma from Pancreatic Ductal Adenocarcinoma.

OBJECTIVE: The objective of this study was to investigate the application of unenhanced computed tomography (CT) texture analysis in differentiating pancreatic adenosquamous carcinoma (PASC) from pancreatic ductal adenocarcinoma (PDAC). METHODS: Preoperative CT images of 112 patients (31 with PASC, 81 with PDAC) were retrospectively reviewed. A total of 396 texture parameters were extracted from AnalysisKit software for further texture analysis. Texture features were selected for the differentiation of PASC and PDAC by the Mann-Whitney U test, univariate logistic regression analysis, and the minimum redundancy maximum relevance algorithm. Furthermore, receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of the texture feature-based model by the random forest (RF) method. Finally, the robustness and reproducibility of the predictive model were assessed by the 10-times leave-group-out cross-validation (LGOCV) method. RESULTS: In the present study, 10 texture features to differentiate PASC from PDAC were eventually retained for RF model construction after feature selection. The predictive model had a good classification performance in differentiating PASC from PDAC, with the following characteristics: sensitivity, 95.7%; specificity, 92.5%; accuracy, 94.3%; positive predictive value (PPV), 94.3%; negative predictive value (NPV), 94.3%; and area under the ROC curve (AUC), 0.98. Moreover, the predictive model was proved to be robust and reproducible using the 10-times LGOCV algorithm (sensitivity, 90.0%; specificity, 71.3%; accuracy, 76.8%; PPV, 59.0%; NPV, 95.2%; and AUC, 0.80). CONCLUSION: The unenhanced CT texture analysis has great potential for differentiating PASC from PDAC.