Bone quality assessment in patients with healing mandibular fracture sites: a computed tomography investigation.

The objective of this retrospective study was to assess the bone quality of healing mandibular fracture sites by measuring the Hounsfield units (HU) on computed tomographic (CT) images obtained presurgery and postsurgery in patients treated with rigid internal fixation (RIF). The HU values of healing fracture sites were compared to those of corresponding nonfractured (control) sites on the opposing side and cervical vertebrae sites in the same patients. In total, 31 patients with 45 mandibular fractures treated with RIF underwent presurgical and postsurgical CT examinations. The scans performed after surgery (1, 3, 6, 12, or 18 months) were taken only when there was a need for radiographic evaluation due to a complaint of discomfort from the patient or when the surgeon needed to verify the postsurgical outcome, and each patient underwent only a single postsurgical CT. At the presurgical CT examination, the HU values were lower in the fracture sites than in the control sites. At 3 months postsurgery, the HU values in the fracture sites had increased as the mandibular bone healed. At 6 months postsurgery, the HU values in the fracture sites were higher than those of the control sites. At 12 and 18 months postsurgery, the HU values of both sites were similar. The HU values of the cervical vertebrae remained constant with time. These results suggest that, in patients who have been treated with RIF for mandibular bone fracture, HU values measured by CT vary across time, expressing the physiologic bone healing process.

Simultaneous Viability Assessment and Invasive Coronary Angiography Using a Therapeutic CT System in Chronic Myocardial Infarction Patients.

PURPOSE: In a preclinical study using a swine myocardial infarction (MI) model, a delayed enhancement (DE)-multi-detector computed tomography (MDCT) scan was performed using a hybrid system alongside diagnostic invasive coronary angiography (ICA) without the additional use of a contrast agent, and demonstrated an excellent correlation in the infarct area compared with histopathologic specimens. In the present investigation, we evaluated the feasibility and diagnostic accuracy of a myocardial viability assessment by DE-MDCT using a hybrid system comprising ICA and MDCT alongside diagnostic ICA without the additional use of a contrast agent. MATERIALS AND METHODS: We prospectively enrolled 13 patients (median age: 67 years) with a previous MI (>6 months) scheduled to undergo ICA. All patients underwent cardiac magnetic resonance (CMR) imaging before diagnostic ICA. MDCT viability scans were performed concurrently with diagnostic ICA without the use of additional contrast. The total myocardial scar volume per patient and average transmurality per myocardial segment measured by DE-MDCT were compared with those from DE-CMR. RESULTS: The DE volume measured by MDCT showed an excellent correlation with the volume measured by CMR (r=0.986, p<0.0001). The transmurality per segment by MDCT was well-correlated with CMR (r=0.900, p<0.0001); the diagnostic performance of MDCT in differentiating non-viable from viable myocardium using a 50% transmurality criterion was good with a sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 87.5%, 99.5%, 87.5%, 99.5%, and 99.1%, respectively. CONCLUSION: The feasibility of the DE-MDCT viability assessment acquired simultaneously with conventional ICA was proven in patients with chronic MI using DE-CMR as the reference standard.

Longitudinal assessment of interstitial lung abnormalities on CT in patients with COPD using artificial intelligence-based segmentation: a prospective observational study.

BACKGROUND: Interstitial lung abnormalities (ILAs) on CT may affect the clinical outcomes in patients with chronic obstructive pulmonary disease (COPD), but their quantification remains unestablished. This study examined whether artificial intelligence (AI)-based segmentation could be applied to identify ILAs using two COPD cohorts. METHODS: ILAs were diagnosed visually based on the Fleischner Society definition. Using an AI-based method, ground-glass opacities, reticulations, and honeycombing were segmented, and their volumes were summed to obtain the percentage ratio of interstitial lung disease-associated volume to total lung volume (ILDvol%). The optimal ILDvol% threshold for ILA detection was determined in cross-sectional data of the discovery and validation cohorts. The 5-year longitudinal changes in ILDvol% were calculated in discovery cohort patients who underwent baseline and follow-up CT scans. RESULTS: ILAs were found in 32 (14%) and 15 (10%) patients with COPD in the discovery (n = 234) and validation (n = 153) cohorts, respectively. ILDvol% was higher in patients with ILAs than in those without ILA in both cohorts. The optimal ILDvol% threshold in the discovery cohort was 1.203%, and good sensitivity and specificity (93.3% and 76.3%) were confirmed in the validation cohort. 124 patients took follow-up CT scan during 5 ± 1 years. 8 out of 124 patients (7%) developed ILAs. In a multivariable model, an increase in ILDvol% was associated with ILA development after adjusting for age, sex, BMI, and smoking exposure. CONCLUSION: AI-based CT quantification of ILDvol% may be a reproducible method for identifying and monitoring ILAs in patients with COPD.

Reducing radiation exposure and costs: CT body scout views with an enhanced protocol versus conventional radiography after shunt surgery.

OBJECTIVE: Ventriculoperitoneal shunt implantation has become standard treatment for cerebrospinal fluid diversion, besides endoscopic third ventriculostomy for certain indications. Postoperative X-ray radiography series of skull, chest and abdomen combined with cranial CT are obtained routinely in many institutions to document the shunt position and valve settings in adult patients. Measures to reduce postoperative radiation exposure are needed, however, there is only limited experience with such efforts. Here, we aim to compare routine postoperative cranial CT plus conventional radiography series (retrospective arm) with cranial CT and body scout views only (prospective arm) concerning both diagnostic quality and radiation exposure. PATIENTS AND METHODS: After introduction of an enhanced CT imaging protocol, routine skull and abdomen radiography was no longer obtained after VP shunt surgery. The image studies of 25 patients with routine postoperative cranial CT and conventional radiography (retrospective arm of study) were then compared to 25 patients with postoperative cranial CT and CT body scout views (prospective arm of study). Patient demographics such as age, sex and primary diagnosis were collected. The image quality of conventional radiographic images and computed tomography scout views images were independently analyzed by one neurosurgeon and one neuroradiologist. RESULTS: There were no differences in quality assessments according to three different factors determined by two independent investigators for both groups. There was a statistically significant difference, however, between the conventional radiography series group and the CT body scout view imaging group with regard to radiation exposure. The effective dose estimation calculation yielded a difference of 0.05 mSv (two-tailed t-test, p = 0.044) in favor of CT body scout view imaging. Furthermore, the new enhanced protocol resulted in a reduction of cost and the use of human resources. CONCLUSION: CT body scout view imaging provides sufficient imaging quality to determine shunt positioning and valve settings. With regard to radiation exposure and costs, we suggest that conventional postoperative shunt series may be abandoned.

CT Cadaveric dataset for Radiomics features stability assessment in lumbar vertebrae.

Radiomics features (RFs) studies have showed limitations in the reproducibility of RFs in different acquisition settings. To date, reproducibility studies using CT images mainly rely on phantoms, due to the harness of patient exposure to X-rays. The provided CadAIver dataset has the aims of evaluating how CT scanner parameters effect radiomics features on cadaveric donor. The dataset comprises 112 unique CT acquisitions of a cadaveric truck acquired on 3 different CT scanners varying KV, mA, field-of-view, and reconstruction kernel settings. Technical validation of the CadAIver dataset comprises a comprehensive univariate and multivariate GLM approach to assess stability of each RFs extracted from lumbar vertebrae. The complete dataset is publicly available to be applied for future research in the RFs field, and could foster the creation of a collaborative open CT image database to increase the sample size, the range of available scanners, and the available body districts.

Quantum-enhanced hybrid feature engineering in thoracic CT image analysis for state-of-the-art nodule classification: an advanced lung cancer assessment.

The intricate nature of lung cancer treatment poses considerable challenges upon diagnosis. Early detection plays a pivotal role in mitigating its escalating global mortality rates. Consequently, there are pressing demands for robust and dependable early detection and diagnostic systems. However, the technological limitations and complexity of the disease make it challenging to implement an efficient lung cancer screening system. AI-based CT image analysis techniques are showing significant contributions to the development of computer-assisted detection (CAD) systems for lung cancer screening. Various existing research groups are working on implementing CT image analysis systems for assessing and classifying lung cancer. However, the complexity of different structures inside the CT image is high and comprehension of significant information inherited by them is more complex even after applying advanced feature extraction and feature selection techniques. Traditional and classical feature selection techniques may struggle to capture complex interdependencies between features. They may get stuck in local optima and sometimes require additional exploration strategies. Traditional techniques may also struggle with combinatorial optimization problems when applied to a prominent feature space. This paper proposed a methodology to overcome the existing challenges by applying feature extraction using Vision Transformer (FexViT) and Feature selection using the Quantum Computing based Quadratic unconstrained binary optimization (QC-FSelQUBO) technique. This algorithm shows better performance when compared with other existing techniques. The proposed methodology showed better performance as compared to other existing techniques when evaluated by applying necessary output measures, such as accuracy, Area under roc (receiver operating characteristics) curve, precision, sensitivity, and specificity, obtained as 94.28%, 99.10%, 96.17%, 90.16% and 97.46%. The further advancement of CAD systems is essential to meet the demand for more reliable detection and diagnosis of cancer, which can be addressed by leading the proposed quantum computation and growing AI-based technology ahead.

Assessment of fetal radiation exposure in pregnant women undergoing computed tomography and rotational angiography examinations for pelvic trauma.

This study aimed to assess fetal radiation exposure in pregnant women undergoing computed tomography (CT) and rotational angiography (RA) examinations for the diagnosis of pelvic trauma. In addition, this study aimed to compare the dose distributions between the two examinations. Surface and average fetal doses were estimated during CT and RA examinations using a pregnant phantom model and real-time dosemeters. The pregnant model phantom was constructed using an anthropomorphic phantom, and a custom-made abdominal phantom was used to simulate pregnancy. The total average fetal dose received by pregnant women from both CT scans (plain, arterial and equilibrium phases) and a single RA examination was ~60 mGy. Because unnecessary repetition of radiographic examinations, such as CT or conventional 2D angiography can increase the radiation risk, the irradiation range should be limited, if necessary, to reduce overall radiation exposure.

Characterizing imaging radiation risk in a population of 8918 patients with recurrent imaging for a better effective dose.

An updated extension of effective dose was recently introduced, namely relative effective dose ( E r ), incorporating age and sex factors. In this study we extended E r application to a population of about 9000 patients who underwent multiple CT imaging exams, and we compared it with other commonly used radiation protection metrics in terms of their correlation with radiation risk. Using Monte Carlo methods, E r , dose-length-product based effective dose ( E DLP ), organ-dose based effective dose ( E OD ), and organ-dose based risk index ( RI ) were calculated for each patient. Each metric's dependency to RI was assessed in terms of its sensitivity and specificity. E r showed the best sensitivity, specificity, and agreement with RI (R2 = 0.97); while E DLP yielded the lowest specificity and, along with E OD , the lowest sensitivity. Compared to other metrics, E r provided a closer representation of patient and group risk also incorporating age and sex factors within the established framework of effective dose.

Using the gamma-index analysis for inter-fractional comparison of in-beam PET images for head-and-neck treatment monitoring in proton therapy: A Monte Carlo simulation study.

GOAL: In-beam Positron Emission Tomography (PET) is a technique for in-vivo non-invasive treatment monitoring for proton therapy. To detect anatomical changes in patients with PET, various analysis methods exist, but their clinical interpretation is problematic. The goal of this work is to investigate whether the gamma-index analysis, widely used for dose comparisons, is an appropriate tool for comparing in-beam PET distributions. Focusing on a head-and-neck patient, we investigate whether the gamma-index map and the passing rate are sensitive to progressive anatomical changes. METHODS/MATERIALS: We simulated a treatment course of a proton therapy patient using FLUKA Monte Carlo simulations. Gradual emptying of the sinonasal cavity was modeled through a series of artificially modified CT scans. The in-beam PET activity distributions from three fields were evaluated, simulating a planar dual head geometry. We applied the 3D-gamma evaluation method to compare the PET images with a reference image without changes. Various tolerance criteria and parameters were tested, and results were compared to the CT-scans. RESULTS: Based on 210 MC simulations we identified appropriate parameters for the gamma-index analysis. Tolerance values of 3 mm/3% and 2 mm/2% were suited for comparison of simulated in-beam PET distributions. The gamma passing rate decreased with increasing volume change for all fields. CONCLUSION: The gamma-index analysis was found to be a useful tool for comparing simulated in-beam PET images, sensitive to sinonasal cavity emptying. Monitoring the gamma passing rate behavior over the treatment course is useful to detect anatomical changes occurring during the treatment course.

Assessment of temporal resolution and detectability of moving objects in CT: A task-based image quality study.

The metrics used for assessing image quality in computed tomography (CT) do not integrate the influence of temporal resolution. A shortcoming in the assessment of image quality for imaging protocols where motion blur can therefore occur. We developed a method to calculate the temporal resolution of standard CT protocols and introduced a specific spatiotemporal formulation of the non-prewhitening with eye filter (NPWE) model observer to assess the detectability of moving objects as a function of their speed. We scanned a cubic water phantom with a plexiglass cylindrical insert (120 HU) using a large panel of acquisition parameters (rotation times, pitch factors and collimation widths) on two systems (GE Revolution Apex and Siemens SOMATOM Force) to determine the in-plane task-based transfer functions (TTF) and noise power spectra (NPS). The phantom set in a uniform rectilinear motion in the transverse plane allowed the temporal modulation transfer function (MTF) calculation. The temporal MTF appropriately compared the temporal resolution of the various acquisition protocols. The longitudinal TTF was measured using a thin tungsten wire. The detectability index showed the advantage of applying high rotation speed, wide collimations and high pitch for object detection in the presence of motion. No counterpart to the increase in these three parameters was found in the in-plane and longitudinal image quality.

Precision Errors and Monitoring Time Interval in Pediatric Muscle Imaging and Neuromuscular Performance Assessment.

OBJECTIVES: To determine precision errors and monitoring time intervals in imaged muscle properties and neuromuscular performance, and to explore growth-related factors associated with precision errors in children. METHODS: We included 35 children (mean age 10.5yrs) in the precision study cohort and 40 children (10.7yrs) in the follow-up study cohort. We assessed forearm and lower leg muscle properties (area, density) with peripheral quantitative computed tomography. We measured neuromuscular performance via maximal pushup, grip force, countermovement and standing long jump force, power, and impulse along with long jump length. We calculated precision errors (root-mean-squared coefficient of variation) from the precision cohort and monitoring time intervals using annual changes from the follow-up cohort. We explored associations between precision errors (coefficient of variation) and maturity, time interval (between repeated measures), and anthropometric changes using Spearman's rank correlation (p<0.05). RESULTS: Muscle measures exhibited precision errors of 1.3-14%. Monitoring time intervals were 1-2.6yrs, except muscle density (>43yrs). We identified only one association between precision errors and maturity (maximal pushup force: rho=-0.349; p=0.046). CONCLUSIONS: Imaging muscle properties and neuromuscular performance measures had precision errors of 1-14% and appeared suitable for follow-up on ~2yr scales (except muscle density). Maximal pushup force appeared more repeatable in mature children.

Healthcare facilities management: A novel data-driven model for predictive maintenance of computed tomography equipment.

BACKGROUND: The breakdown of healthcare facilities is a huge challenge for hospitals. Medical images obtained by Computed Tomography (CT) provide information about the patients' physical conditions and play a critical role in diagnosis of disease. To deliver high-quality medical images on time, it is essential to minimize the occurrence frequencies of anomalies and failures of the equipment. METHODS: We extracted the real-time CT equipment status time series data such as oil temperature, of three equipment, between May 19, 2020, and May 19, 2021. Tube arcing is treated as the classification label. We propose a dictionary-based data-driven model SAX-HCBOP, where the two methods, Histogram-based Information Gain Binning (HIGB) and Coefficient improved Bag of Pattern (CoBOP), are implemented to transform the data into the bag-of-words paradigm. We compare our model to the existing predictive maintenance models based on statistical and time series classification algorithms. RESULTS: The results show that the Accuracy, Recall, Precision and F1-score of the proposed model achieve 0.904, 0.747, 0.417, 0.535, respectively. The oil temperature is identified as the most important feature. The proposed model is superior to other models in predicting CT equipment anomalies. In addition, experiments on the public dataset also demonstrate the effectiveness of the proposed model. CONCLUSIONS: The two proposed methods can improve the performance of the dictionary-based time series classification methods in predictive maintenance. In addition, based on the proposed real-time anomaly prediction system, the model assists hospitals in making accurate healthcare facilities maintenance decisions.

Revisiting the lung cancer screening eligibility criteria to promote equity for Black individuals.

BACKGROUND: Early detection using low-dose computed tomography reduces lung-cancer-specific mortality by 20% among high-risk individuals. Blacks are less likely than Whites to meet lung cancer screening (LCS) criteria under both the former and the updated United States Preventive Services Task Force (USPSTF) guidelines. The purpose of this study was to assess racial disparities in LCS eligibility and to propose tailored eligibility criteria for Blacks to enable equitable screening rate between Whites and Blacks. METHODS: Data for this study were obtained from the Behavioral Risk Factor Surveillance System (2017-2021). 101,552 subjects were included in the final analysis. By employing a systematic approach, we sought cut-off points at which Blacks were equally likely as Whites to be eligible for LCS. We evaluated the minimum age and smoking pack-years for Blacks while we retained the 2021 USPSTF criteria for Whites. The final decision was based on the minimum Wald's Chi-square statistics. RESULTS: The model we employed identified cut-off points at which Blacks were equally likely as Whites to be eligible for LCS. Retaining the 2021 USPSTF criteria for Whites, the model discovered a new pair of points for Blacks by reducing the minimum age to 43 years and decreasing the cumulative number of cigarettes smoked to 15 pack-years. Based on these cut-off points, we created tailored criteria for Blacks. Under the tailored criteria, Blacks (OR: 1.00; 95 %CI: 0.88-1.14) had the same odds of eligibility for LCS as Whites. The odds of eligibility for LCS by sex under the tailored criteria did not differ significantly for Black men (OR: 1.02; 95 %CI: 0.85-1.24) and Black women (OR: 0.95; 95 %CI: 0.81-1.12) compared to their respective White counterparts. CONCLUSIONS: These tailored criteria for Blacks eliminate the disparities between Blacks and Whites in LCS eligibility. Future studies should test the sensitivity and specificity of these tailored criteria.

Multi-instance learning based lung nodule system for assessment of CT quality after small-field-of-view reconstruction.

Small-field-of-view reconstruction CT images (sFOV-CT) increase the pixel density across airway structures and reduce partial volume effects. Multi-instance learning (MIL) is proposed as a weakly supervised machine learning method, which can automatically assess the image quality. The aim of this study was to evaluate the disparities between conventional CT (c-CT) and sFOV-CT images using a lung nodule system based on MIL and assessments from radiologists. 112 patients who underwent chest CT were retrospectively enrolled in this study between July 2021 to March 2022. After undergoing c-CT examinations, sFOV-CT images with small-field-of-view were reconstructed. Two radiologists analyzed all c-CT and sFOV-CT images, including features such as location, nodule type, size, CT values, and shape signs. Then, an MIL-based lung nodule system objectively analyzed the c-CT (c-MIL) and sFOV-CT (sFOV-MIL) to explore their differences. The signal-to-noise ratio of lungs (SNR-lung) and contrast-to-noise ratio of nodules (CNR-nodule) were calculated to evaluate the quality of CT images from another perspective. The subjective evaluation by radiologists showed that feature of minimal CT value (p = 0.019) had statistical significance between c-CT and sFOV-CT. However, most features (all with p < 0.05), except for nodule type, location, volume, mean CT value, and vacuole sign (p = 0.056-1.000), had statistical differences between c-MIL and sFOV-MIL by MIL system. The SNR-lung between c-CT and sFOV-CT had no statistical significance, while the CNR-nodule showed statistical difference (p = 0.007), and the CNR of sFOV-CT was higher than that of c-CT. In detecting the difference between c-CT and sFOV-CT, features extracted by the MIL system had more statistical differences than those evaluated by radiologists. The image quality of those two CT images was different, and the CNR-nodule of sFOV-CT was higher than that of c-CT.

Imaging biomarkers in cardiac CT: moving beyond simple coronary anatomical assessment.

Cardiac computed tomography angiography (CCTA) is considered the standard non-invasive tool to rule-out obstructive coronary artery disease (CAD). Moreover, several imaging biomarkers have been developed on cardiac-CT imaging to assess global CAD severity and atherosclerotic burden, including coronary calcium scoring, the segment involvement score, segment stenosis score and the Leaman-score. Myocardial perfusion imaging enables the diagnosis of myocardial ischemia and microvascular damage, and the CT-based fractional flow reserve quantification allows to evaluate non-invasively hemodynamic impact of the coronary stenosis. The texture and density of the epicardial and perivascular adipose tissue, the hypodense plaque burden, the radiomic phenotyping of coronary plaques or the fat radiomic profile are novel CT imaging features emerging as biomarkers of inflammation and plaque instability, which may implement the risk stratification strategies. The ability to perform myocardial tissue characterization by extracellular volume fraction and radiomic features appears promising in predicting arrhythmogenic risk and cardiovascular events. New imaging biomarkers are expanding the potential of cardiac CT for phenotyping the individual profile of CAD involvement and opening new frontiers for the practice of more personalized medicine.

A systematic assessment and optimization of photon-counting CT for lung density quantifications.

BACKGROUND: Photon-counting computed tomography (PCCT) has recently emerged into clinical use; however, its optimum imaging protocols and added benefits remains unknown in terms of providing more accurate lung density quantification compared to energy-integrating computed tomography (EICT) scanners. PURPOSE: To systematically assess the performance of a clinical PCCT scanner for lung density quantifications and compare it against EICT. METHODS: This cross-sectional study involved a retrospective analysis of subjects scanned (August-December 2021) using a clinical PCCT system. The influence of altering reconstruction parameters was studied (reconstruction kernel, pixel size, slice thickness). A virtual CT dataset of anthropomorphic virtual subjects was acquired to demonstrate the correspondence of findings to clinical dataset, and to perform systematic imaging experiments, not possible using human subjects. The virtual subjects were imaged using a validated, scanner-specific CT simulator of a PCCT and two EICT (defined as EICT A and B) scanners. The images were evaluated using mean absolute error (MAE) of lung and emphysema density against their corresponding ground truth. RESULTS: Clinical and virtual PCCT datasets showed similar trends, with sharper kernels and smaller voxel sizes increasing percentage of low-attenuation areas below -950 HU (LAA-950) by up to 15.7 ± 6.9% and 11.8 ± 5.5%, respectively. Under the conditions studied, higher doses, thinner slices, smaller pixel sizes, iterative reconstructions, and quantitative kernels with medium sharpness resulted in lower lung MAE values. While using these settings for PCCT, changes in the dose level (13 to 1.3 mGy), slice thickness (0.4 to 1.5 mm), pixel size (0.49 to 0.98 mm), reconstruction technique (70 keV-VMI to wFBP), and kernel (Qr48 to Qr60) increased lung MAE by 15.3 ± 2.0, 1.4 ± 0.6, 2.2 ± 0.3, 4.2 ± 0.8, and 9.1 ± 1.6 HU, respectively. At the optimum settings identified per scanner, PCCT images exhibited lower lung and emphysema MAE than those of EICT scanners (by 2.6 ± 1.0 and 9.6 ± 3.4 HU, compared to EICT A, and by 4.8 ± 0.8 and 7.4 ± 2.3 HU, compared to EICT B). The accuracy of lung density measurements was correlated with subjects' mean lung density (p < 0.05), measured by PCCT at optimum setting under the conditions studied. CONCLUSION: Photon-counting CT demonstrated superior performance in density quantifications, with its influences of imaging parameters in line with energy-integrating CT scanners. The technology offers improvement in lung quantifications, thus demonstrating potential toward more objective assessment of respiratory conditions.

Assessment of post-thrombectomy brain hemorrhage in acute ischemic stroke with dual-energy CT: how reliable is it in clinical practice?

PURPOSE: Acute ischemic stroke is currently among the main causes of mortality in Western countries. The current guidelines suggest different flowcharts of diagnostic work-up and treatment modalities, including endovascular thrombectomy. Immediately after intra-arterial recanalization, a brain CT scan is usually performed to assess for the presence of peri-procedural complications; in this setting, it is very hard, if possible, to differentiate blood from iodinated contrast material, which is normally present in ischemic tissue because of BBB disruption. Dual-energy CT may be used for this purpose, exploiting its ability to discriminate different materials. MATERIALS AND METHODS: We retrospectively studied 44 patients with acute ischemic stroke who were treated with endovascular recanalization at San Giovanni Bosco Hospital in Turin and were then scanned with DECT technology. Subsequent scan was used as standard, since iodine from contrast staining is usually reabsorbed in 24 h and blood persists longer. A χ2 test of independence was performed to examine the relationship between blood detected by DECT scan after the endovascular procedure and the presence of blood in the same areas on the following scans, with a significant result: χ2 (1, N = 37) = 10.7086, p = 0.0010. RESULTS: Patients with blood detected on DECT scans had a double chance of having hemorrhagic infarction in follow-up scans, (RR 2.02). The sensitivity and specificity of DECT were respectively 70% and 90%, with an overall diagnostic accuracy of 76% and a positive and negative predictive value, respectively, of 95% and 53%. CONCLUSION: Dual-energy CT scan after endovascular recanalization in ischemic stroke identifies early hemorrhagic infarction with excellent specificity and good overall diagnostic accuracy, representing a reliable diagnostic tool in everyday clinical practice.

Thin slice photon-counting CT coronary angiography compared to conventional CT: Objective image quality and clinical radiation dose assessment.

BACKGROUND: Photon-counting CT (PCCT) is the next-generation CT scanner that enables improved spatial resolution and spectral imaging. For full spectral processing, higher tube voltages compared to conventional CT are necessary to achieve the required spectral separation. This generated interest in the potential influence of thin slice high tube voltage PCCT on overall image quality and consequently on radiation dose. PURPOSE: This study first evaluated tube voltages and radiation doses applied in patients who underwent coronary CT angiography with PCCT and energy-integrating detector CT (EID-CT). Next, image quality of PCCT and EID-CT was objectively evaluated in a phantom study simulating different patient sizes at these tube voltages and radiation doses. METHODS: We conducted a retrospective analysis of clinical doses of patients scanned on a conventional and PCCT system. Average patient water equivalent diameters for different tube voltages were extracted from the dose reports for both EID-CT and PCCT. A conical phantom made of polyethylene with multiple diameters (26/31/36 cm) representing different patient sizes and containing an iodine insert was scanned with a EID-CT scanner using tube voltages and phantom diameters that match the patient scans and characteristics. Next, phantom scans were made with PCCT at a fixed tube voltage of 120 kV and with CTDIVOL values and phantom diameters identical to the EID-CT scans. Clinical image reconstructions at 0.6 mm slice thickness for conventional CT were compared to PCCT images with 0.4 mm slice thickness. Image quality was quantified using the detectability index (d'), which estimated the visibility of a 3 mm diameter contrast-enhanced coronary artery by considering noise, contrast, resolution, and human visual perception. Alongside d', noise, contrast and resolution were also individually assessed. In addition, the influence of various kernels (Bv40/Bv44/Bv48/Bv56), quantum iterative reconstruction strengths (QIR, 3/4) and monoenergetic levels (40/45/50/55 keV) for PCCT on d' was investigated. RESULTS: In this study, 143 patients were included: 47 were scanned on PCCT (120 kV) and the remaining on EID-CT (74 small-sized at 70 kV, 18 medium-sized at 80 kV and four large-sized at 90 kV). EID-CT showed 7%-17% higher d' than PCCT with Bv40 kernel and strength four for small/medium patients. Lower monoenergetic images (40 keV) helped mitigate the difference to 1%-6%. For large patients, PCCT's detectability was up to 31% higher than EID-CT. PCCT has thinner slices but similar noise levels for similar reconstruction parameters. The noise increased with lower keV levels in PCCT (≈30% increase), but higher QIR strengths reduced noise. PCCT's iodine contrast was stable across patient sizes, while EID-CT had 33% less contrast in large patients than in small-sized patients. CONCLUSION: At 120 kV, thin slice PCCT enables CCTA in phantom scans representing large patients without raising radiation dose or affecting vessel detectability. However, higher doses are needed for small and medium-sized patients to obtain a similar image quality as in EID-CT. The alternative of using lower mono-energetic levels requires further evaluation in clinical practice.

Ethnic differences in CT derived abdominal body composition measures: a comparative retrospect pilot study between European and Inuit study population.

Understanding ethnic variations in body composition is crucial for assessing health risks. Universal models may not suit all ethnicities, and there is limited data on the Inuit population. This study aimed to compare body composition between Inuit and European adults using computed tomography (CT) scans and to investigate the influence of demographics on these measurements. A retrospective analysis was conducted on 50 adults (29 Inuit and 21 European) who underwent standard trauma CT scans. Measurements focused on skeletal muscle index (SMI), various fat indices, and densities at the third lumbar vertebra level, analyzed using the Wilcoxon-Mann-Whitney test and multiple linear regression. Inuit women showed larger fat tissue indices and lower muscle and fat densities than European women. Differences in men were less pronouncehd, with only Intramuscular fat density being lower among Inuit men. Regression indicated that SMI was higher among men, and skeletal muscle density decreased with Inuit ethnicity and age, while visceral fat index was positively associated with age. This study suggests ethnic differences in body composition measures particularly among women, and indicates the need for Inuit-specific body composition models. It higlights the importance of further research into Inuit-specific body composition measurements for better health risk assessment.

Severity-stratification of interstitial lung disease by deep learning enabled assessment and quantification of lesion indicators from HRCT images.

BACKGROUND: Interstitial lung disease (ILD) represents a group of chronic heterogeneous diseases, and current clinical practice in assessment of ILD severity and progression mainly rely on the radiologist-based visual screening, which greatly restricts the accuracy of disease assessment due to the high inter- and intra-subjective observer variability. OBJECTIVE: To solve these problems, in this work, we propose a deep learning driven framework that can assess and quantify lesion indicators and outcome the prediction of severity of ILD. METHODS: In detail, we first present a convolutional neural network that can segment and quantify five types of lesions including HC, RO, GGO, CONS, and EMPH from HRCT of ILD patients, and then we conduct quantitative analysis to select the features related to ILD based on the segmented lesions and clinical data. Finally, a multivariate prediction model based on nomogram to predict the severity of ILD is established by combining multiple typical lesions. RESULTS: Experimental results showed that three lesions of HC, RO, and GGO could accurately predict ILD staging independently or combined with other HRCT features. Based on the HRCT, the used multivariate model can achieve the highest AUC value of 0.755 for HC, and the lowest AUC value of 0.701 for RO in stage I, and obtain the highest AUC value of 0.803 for HC, and the lowest AUC value of 0.733 for RO in stage II. Additionally, our ILD scoring model could achieve an average accuracy of 0.812 (0.736 - 0.888) in predicting the severity of ILD via cross-validation. CONCLUSIONS: In summary, our proposed method provides effective segmentation of ILD lesions by a comprehensive deep-learning approach and confirms its potential effectiveness in improving diagnostic accuracy for clinicians.