Clinical CT Scan Research Articles

Surgical treatment of gallstone ileus caused by cholecystoduodenal fistula: A case report

Introduction: Gallstone ileus is a rare complication of cholelithiasis caused by bilioenteric fistula. Beside clinical examination CT scan is the considered radiological diagnostic procedure. Presentation of case: 68-year old male patient presented with epigastralgia, nausea and emesis. Abdomen was distended and diffusely painful to palpation with peritoneal irritation. Auscultation showed raised peristaltic sounds. CT scan showed aerobilia, distension of stomach, duodenum and proximal jejunum and inside it a 3.5 cm calcified round foreign body. Exploratory laparoscopy showed jejunum obstruction and chronic inflammation of gallbladder. Lifting the small bowel through small laparotomy was performed for gallstone removing. After recovery a delayed cholecystectomy and fistula closure followed. Hospital stays were short and complications-free. Conclusion: It is necessary to solve both, ileus and fistula, to prevent new bowel obstruction. The 2-step surgical procedure is a save treatment option, extraction of gallstone from bowel first, followed by delayed cholecystectomy and fistula closure.

Coexistence of Trigonocephaly and Sylvian Arachnoid Cysts: A Coincidence?

Introduction: The association between trigonocephaly and Sylvian fissure arachnoid cysts (ACs) has been occasionally reported in the literature. However, the real incidence of this association and its clinical relevance remain unknown. Methods: The authors collected and retrospectively reviewed all clinical charts and CT scans of patients surgically treated for trigonocephaly at the Pediatric Neurosurgical Department of Fondazione Policlinico Universitario “Agostino Gemelli” IRCCS from January 2014 to June 2023. Results: During the study period, 136 patients with trigonocephaly underwent surgery. Analysis of the clinical charts revealed that in 39.7% of the cases (54/136), preoperative CT scan depicted the presence of a Sylvian fissure AC. Of these, AC was bilateral in 23 cases and unilateral in the remaining 31. All unilateral ACs were on the left side. The ACs were classified as Galassi grade I in 52 cases (96.3%) and Galassi grade II in 2 cases (3.7%). Interestingly, in 1 case we reported a Galassi grade I AC enlargement during follow-up, thereby necessitating surgical fenestration. Conclusion: ACs and trigonocephaly are well-known conditions for pediatric neurosurgeons; however, their association is poorly defined. Despite the lack of reports on the incidence and clinical significance of this association, it is worth knowing that radiological follow-up is essential in monitoring AC evolution.

Use of Quantitative CT Imaging to Identify Bronchial Thermoplasty Responders

Bronchial thermoplasty (BT) is a treatment for patients with poorly controlled, severe asthma. However, predictors of treatment response to BT are defined poorly. Do baseline radiographic and clinical characteristics exist that predict response to BT? We conducted a longitudinal prospective cohort study of participants with severe asthma receiving BT across eight academic medical centers. Participants received three separate BT treatments and were monitored at 3-month intervals for 1 year after BT. Similar to prior studies, a positive response to BT was defined as either improvement in Asthma Control Test results of≥ 3 or Asthma Quality of Life Questionnaire of≥ 0.5. Regression analyses were used to evaluate the association between pretreatment clinical and quantitative CT scan measures with subsequent BT response. From 2006 through 2017, 88 participants received BT, with 70 participants (79.5%) identified as responders by Asthma Control Test or Asthma Quality of Life Questionnaire criteria. Responders were less likely to undergo an asthma-related ICU admission in the prior year (3%vs25%; P= .01). On baseline quantitative CT imaging, BT responders showed less air trapping percentage (OR, 0.90; 95%CI, 0.82-0.99; P= .03), a greater Jacobian determinant (OR, 1.49; 95%CI, 1.05-2.11), greater SD of the Jacobian determinant (OR, 1.84; 95%CI, 1.04-3.26), and greater anisotropic deformation index (OR, 3.06; 95%CI, 1.06-8.86). To our knowledge, this is the largest study to evaluate baseline quantitative CT imaging and clinical characteristics associated with BT response. Our results show that preservation of normal lung expansion, indicated by less air trapping, a greater magnitude of isotropic expansion, and greater within-lung spatial variation on quantitative CT imaging, were predictors of future BT response. ClinicalTrials.gov; No.: NCT01185275; URL: www. gov.

Spatial Transcriptomics Resolve an Emphysema-specific Lymphoid Follicle B Cell Signature in COPD.

Within chronic obstructive pulmonary disease (COPD), emphysema is characterized by a significant yet partially understood B cell immune component. To characterize the transcriptomic signatures from lymphoid follicles (LFs) in ever-smokers without COPD and COPD patients with varying degrees of emphysema. Lung sections from 40 COPD patients and ever-smokers were used for LF proteomic and transcriptomic spatial profiling. Formalin and OCT-fixed lung samples obtained from biopsies or lung explants, were assessed for LF presence. Emphysema measurements were obtained from clinical chest CT scans. High confidence transcriptional (HCT) target intersection analyses were conducted to resolve emphysema-induced transcriptional networks. Overall, 115 LFs from ever-smokers and GOLD 1-2 and GOLD 3-4 patients were analyzed. No LFs were found in never-smokers. Differential gene expression analysis revealed significantly increased expression of LF assembly and B cell markers genes in subjects with severe emphysema. HCT analysis revealed activation of abnormal B cell activity signature in LFs (q-value: 2.56E-111). LFs from GOLD 1-2 COPD patients with emphysema showed significantly increased expression of genes associated with antigen presentation, inflammation, and B cell activation and proliferation. LFs from GOLD 1-2 COPD patients without emphysema showed an anti-inflammatory profile. The extent of centrilobular emphysema was significantly associated with genes involved in B cell maturation and antibody production. Protein-RNA network analysis showed that LFs in emphysema have a unique signature skewed towards chronic B cell activation. An off-targeted B cell activation within LFs is associated with autoimmune-mediated emphysema pathogenesis.

Reliability of the global cortical atrophy visual rating scale applied to computed tomography versus magnetic resonance imaging scans in acute stroke

IntroductionResearch using magnetic resonance imaging (MRI) suggests regional cerebral atrophy measures (e.g., frontal lobe, temporal lobe) may predict post-stroke outcomes. Clinical CT scans have excellent potential for use in research but it is unclear whether regional atrophy measures from CT are reliable compared to MRI reference standards.MethodsWe used the Global Cortical Atrophy (GCA) scale to investigate reliability of atrophy measures on CT versus MRI scans from stroke patients originally recruited to the Oxford Cognitive Screening programme. Two raters provided standardised visual ratings at two timepoints. Weighted Kappa statistics assessed the reliability of regional atrophy scores. Spearman’s correlation and a two-way repeated measures ANOVA assessed the reliability of the total score.ResultsOn clinically acquired neuroimaging from 98 stroke patients (mean/SD age = 70.97/11.99, 42 female, 84 ischaemic stroke), regional GCA scores on CT versus MRI showed fair to almost perfect intra-rater agreement (κ = .50–.87), substantial to almost perfect intra-rater agreement on CT (κ = .67–.88), and moderate to almost perfect intra-rater reliability on MRI (κ = .50–.89). Regional GCA scores showed mostly moderate to substantial inter-rater reliability on both CT and MRI (κ = .43–.69), except the temporal horns and parieto-occipital region. There was a strong correlation between total GCA scores on CT and MRI (r (96) = .87–.88, p < .001).ConclusionsThese results support the use of cerebral atrophy measures from CT in clinical research, as visual ratings showed generally good agreement between CT and MRI, between raters, and between timepoints.

Development of a Scoring Method Based on a Chest CT Scan to Determine the Outcomes of COVID-19 Patients.

Introduction As COVID-19 shifts from pandemic urgency to endemic management, healthcare systems are faced with the evolving challenge of providing optimized care and adept resource allocation in this evolving landscape of the disease. However, the timely management and accurate assessment of disease severity remains a cornerstone of effective treatment. This study presents a pioneering scoring system, based on the primary chest CT scan findings, to predict patient outcomes and to equip clinicians with a tool that can expedite decision-making. Method A retrospective cohort study was conducted involving 406 confirmed COVID-19 cases referred to two of our hospitals in Tehran, between February and April 2020. Radiographic and CT scan data were sourced from the imaging archive system and evaluated by a certified radiologist. We devised distinct severity scores for CT findings, demographic factors, and clinical indicators. These were synthesized into a comprehensive severity score to forecast critical patient outcomes, such as mortality, ICU admission, intubation, or extended hospitalization. Of the total cases, 161 (39.7%) were classified as severe, while 245 (60%) fell into the low or moderate severity category. Results The mean score of demographic, CT scan, and clinical characteristics was significantly higher for those in the severe COVID-19 than the non-severe group. The cutoff score for predicting the outcomes in COVID-19 patients for demographic, clinical, and chest CT scan factors was 2.5, 9.5, and 8.5, respectively. Multivariate analysis indicated that each unit increase in these scores elevated the odds of fatal outcomes by 24%, 2.8%, and 12%, respectively. Then, using the comprehensive severity score, which is the sum of the above scores, we further predicted the disease severity. Conclusion The findings suggest that our innovative scoring system, based on initial chest CT scan findings, serves as a robust predictor of COVID-19 outcomes.

Association of Neuroimaging Markers on Clinical CT Scans With Domain-Specific Cognitive Impairment in the Early and Later Post-Stroke Stages.

Post-stroke cognitive impairment (PSCI) is associated with neuroimaging markers, including cortical atrophy and white matter lesions (WMLs), on clinically acquired CT neuroimaging. To investigate the association between cortical atrophy/WMLs and PSCI in specific cognitive domains in the acute/subacute and chronic stages after stroke, in order to provide clarity on the relationship between these neuroimaging markers and the temporal evolution of PSCI. We visually assessed cortical atrophy using the Global Cortical Atrophy (GCA) scale and WMLs using the Fazekas scale. Oxford Cognitive Screen (OCS) or Birmingham Cognitive Screen (BCoS) assessed PSCI at two timepoints (acute/subacute and chronic) in six domains (language, memory, number processing, executive function, attention, praxis). We binarised domain-specific performance as impaired/unimpaired using normative cut-offs. Multivariable linear and logistic regression analyses evaluated associations between GCA/Fazekas scores with acute/subacute/chronic global and domain-specific PSCI, and ANCOVAs examined whether these scores were significantly different in patients with recovered versus persistent PSCI. Age, sex, education, NIHSS, lesion volume, and recurrent stroke were covariates in these analyses. Among 411 stroke patients (Mdn/IQR age= 76.16/16.64; 193 female; 346 ischaemic stroke; 107 recurrent stroke), GCA and Fazekas scores were not associated with global cognitive impairment in the acute/subacute stage after stroke, but GCA score was associated with chronic global PSCI (B = 0.01, p < .001, 95% CI [0.00 0.01]). In domain-specific analyses, GCA score was associated with chronic impairment in the memory (B = 0.06, p < .001, 95% CI [0.03 0.10]) and attention (B = 0.05, p = .003, 95% CI [0.02 0.09]) domains, and patients with persistent PSCI these domains showed significantly higher GCA scores than patients who had recovered (Memory: F(1,157) = 6.63, p = .01, η 2 G = .04; Attention: F(1,268) = 10.66, p = .001, η 2 G = .04). The present study highlights the potential impact of cortical atrophy on the cognitive recovery process after stroke and demonstrates the prognostic utility of CT neuroimaging for post-stroke cognitive outcomes. Clinical neuroimaging could help identify patients at long-term risk of PSCI during acute hospitalisation.

A Statistical Shape Model Pipeline to Enable the Creation of Synthetic 3D Liver Data

Abstract The application of machine learning approaches in medical technology is gaining more and more attention. Due to the high restrictions for collecting intraoperative patient data, synthetic data is increasingly used to support the training of artificial neural networks. We present a pipeline to create a statistical shape model (SSM) using 28 segmented clinical liver CT scans. Our pipeline consists of four steps: data preprocessing, rigid alignment, template morphing, and statistical modeling. We compared two different template morphing approaches: Laplace-Beltrami-regularized projection (LBRP) and nonrigid iterative closest points translational (N-ICP-T) and evaluated both morphing approaches and their corresponding shape model performance using six metrics. LBRP achieved a smaller mean vertex-to-nearest-neighbor distances (2.486±0.897 mm) than N-ICP-T (5.559±2.413 mm). Generalization and specificity errors for LBRP were consistently lower than those of N-ICP-T. The first principal components of the SSM showed realistic anatomical variations. The performance of the SSM was comparable to a state-of-the-art model.

Two-Stage Structure-Focused Contrastive Learning for Automatic Identification and Localization of Complex Pelvic Fractures.

Pelvic fracture is a severe trauma with a high rate of morbidity and mortality. Accurate and automatic diagnosis and surgical planning of pelvic fracture require effective identification and localization of the fracture zones. This is a challenging task due to the complexity of pelvic fractures, which often exhibit multiple fragments and sites, large fragment size differences, and irregular morphology. We have developed a novel two-stage method for the automatic identification and localization of complex pelvic fractures. Our method is unique in that it allows to combine the symmetry properties of the pelvic anatomy and capture the symmetric feature differences caused by the fracture on both the left and right sides, thereby overcoming the limitations of existing methods which consider only image or geometric features. It implements supervised contrastive learning with a novel Siamese deep neural network, which consists of two weight-shared branches with a structural attention mechanism, to minimize the confusion of local complex structures of the pelvic bones with the fracture zones. A structure-focused attention (SFA) module is designed to capture the spatial structural features and enhances the recognition ability of fracture zones. Comprehensive experiments on 103 clinical CT scans from the publicly available dataset CTPelvic1K show that our method achieves a mean accuracy and sensitivity of 0.92 and 0.93, which are superior to those reported with three SOTA contrastive learning methods and five advanced classification networks, demonstrating the effectiveness of identifying and localizing various types of complex pelvic fractures from clinical CT images.

Clinical and radiological characteristics of pediatric COVID-19 before and after the Omicron outbreak: a multi-center study.

The emergence of the Omicron variant has seen changes in the clinical and radiological presentations of COVID-19 in pediatric patients. We sought to compare these features between patients infected in the early phase of the pandemic and those during the Omicron outbreak. A retrospective study was conducted on 68 pediatric COVID-19 patients, of which 31 were infected with the original SARS-CoV-2 strain (original group) and 37 with the Omicron variant (Omicron group). Clinical symptoms and chest CT scans were examined to assess clinical characteristics, and the extent and severity of lung involvement. Pediatric COVID-19 patients predominantly had normal or mild chest CT findings. The Omicron group demonstrated a significantly reduced CT severity score than the original group. Ground-glass opacities were the prevalent radiological findings in both sets. The Omicron group presented with fewer symptoms, had milder clinical manifestations, and recovered faster than the original group. The clinical and radiological characteristics of pediatric COVID-19 patients have evolved with the advent of the Omicron variant. For children displaying severe symptoms warranting CT examinations, it is crucial to weigh the implications of ionizing radiation and employ customized scanning protocols and protective measures. This research offers insights into the shifting disease spectrum, aiding in the effective diagnosis and treatment of pediatric COVID-19 patients.

Work overload and diagnostic errors in radiology

PurposeTo determine the association between workload and diagnostic errors on clinical CT scans. MethodThis retrospective study was performed at a tertiary care center and covered the period from January 2020 to March 2023. All clinical CT scans that contained an addendum describing a perceptual error (i.e. failure to detect an important abnormality) in the original report that was issued on office days between 7.30 a.m. and 18.00 p.m., were included. The workload of the involved radiologist on the day of the diagnostic error was calculated in terms of relative value units, and normalized for the known average daily production of each individual radiologist (workloadnormalized). A workloadnormalized of less than 100% indicates relative work underload, while a workloadnormalized of > 100% indicates relative work overload in terms of reported examinations on an individual radiologist’s basis. ResultsA total of 49 diagnostic errors were included. Top-five locations of diagnostic errors were lung (n = 8), bone (n = 8), lymph nodes (n = 5), peritoneum (n = 5), and liver (n = 4). Workloadnormalized on the days the diagnostic errors were made was on average 121% (95% confidence interval: 106% to 136%), which was significantly higher than 100% (P = 0.008). There was no significant upward monotonic trend in diagnostic errors over the course of the day (Mann-Kendall tau of 0.005, P = 1.000), and there were no other notable temporal trends either. ConclusionsRadiologists appear to have a relative work overload when they make a diagnostic error on CT. Diagnostic errors occurred throughout the entire day, without any increase towards the end of the day.

Ultra-high-resolution spectral silicon-based photon-counting detector CT for coronary CT angiography: Initial results in a dynamic phantom

BackgroundRecent improvements in CT detector technology have led to smaller detector pixels resolving frequencies beyond 20 lp/cm and enabled ultra-high-resolution CT. Silicon-based photon-counting detector (PCD) CT is one such technology that promises improved spatial and spectral resolution. However, when the detector pixel sizes are reduced, the impact of cardiac motion on CT images becomes more pronounced. Here, we investigated the effects cardiac motion on the image quality of a clinical prototype Si-PCD scanner in a dynamic heart phantom. MethodsA series of 3D-printed vessels were created to simulate coronary arteries with diameter in the 1–3.5 ​mm range. Four coronary stents were set inside the d ​= ​3.5 ​mm vessels and all vessels were filled with contrast agents and were placed inside a dynamic cardiac phantom. The phantom was scanned in motion (60 bpm) and at rest on a prototype clinical Si-PCD CT scanner in 8-bin spectral UHR mode. Virtual monoenergetic images (VMI) were generated at 70 ​keV and CT number accuracy and effective spatial resolution (blooming) of rest and motion VMIs were compared. ResultsLinear regression analysis of CT numbers showed excellent agreement (r ​> ​0.99) between rest and motion. We did not observe a significant difference (p ​> ​0.48) in estimating free lumen diameters. Differences in in-stent lumen diameter and stent strut thickness were non-significant with maximum mean difference of approximately 70 ​μm. ConclusionWe found no significant degradation in CT number accuracy or spatial resolution due to cardiac motion. The results demonstrate the potential of spectral UHR coronary CT angiography enabled by Si-PCD.

Deep learning-based ultrasound transducer induced CT metal artifact reduction using generative adversarial networks for ultrasound-guided cardiac radioablation

In US-guided cardiac radioablation, a possible workflow includes simultaneous US and planning CT acquisitions, which can result in US transducer-induced metal artifacts on the planning CT scans. To reduce the impact of these artifacts, a metal artifact reduction (MAR) algorithm has been developed based on a deep learning Generative Adversarial Network called Cycle-MAR, and compared with iMAR (Siemens), O-MAR (Philips) and MDT (ReVision Radiology), and CCS-MAR (Combined Clustered Scan-based MAR). Cycle-MAR was trained with a supervised learning scheme using sets of paired clinical CT scans with and without simulated artifacts. It was then evaluated on CT scans with real artifacts of an anthropomorphic phantom, and on sets of clinical CT scans with simulated artifacts which were not used for Cycle-MAR training. Image quality metrics and HU value-based analysis were used to evaluate the performance of Cycle-MAR compared to the other algorithms. The proposed Cycle-MAR network effectively reduces the negative impact of the metal artifacts. For example, the calculated HU value improvement percentage for the cardiac structures in the clinical CT scans was 59.58%, 62.22%, and 72.84% after MDT, CCS-MAR, and Cycle-MAR application, respectively. The application of MAR algorithms reduces the impact of US transducer-induced metal artifacts on CT scans. In comparison to iMAR, O-MAR, MDT, and CCS-MAR, the application of developed Cycle-MAR network on CT scans performs better in reducing these metal artifacts.

Quantitative analysis of small coronary artery calcium detectability with an accurate simulation and validation on a clinical CT scanner.

The absence of coronary artery calcium (CAC) measured via CT is associated with very favorable prognosis, and current guidelines recommend low-density lipoprotein cholesterol (LDL-c) lowering therapy for individuals with any CAC. This motivates early detection of small granules of CAC; however, calcium scan sensitivity for detecting very low levels of calcium has not been quantified. In this work, the size limit of detectability of small calcium hydroxyapatite (CaHA) granules with clinical CAC scanning was assessed using validated simulations. CT projections of digital 3D mathematical phantoms containing small CaHA granules were simulated analytically; images were reconstructed using a filter designed to reproduce the point spread function of a specific commercial scanner, and a relationship of HU number versus diameter was derived. These simulation results were validated with experimental measurements of HU versus diameter from phantoms containing small granules of CaHA on a GE Revolution CT scanner in the clinic; ground truth measurements of the CaHA granule diameters were obtained using a Zeiss Xradia 510 Versa high-resolution 3D micro-CT imaging system. Using experimental measurements on the clinical CT scanner, detectability was quantified with a detectability index (d') using a non-prewhitened matched filter. The effect of changes to reconstruction slice thickness and reconstruction kernel on granule detectability was evaluated. Under typical clinical calcium scanning and reconstruction conditions, the minimum detectable diameter of a simulated spherical calcium granule with a clinically relevant CaHA density was 0.76mm. The minimum detectable volume was 2.4 times smaller on images reconstructed at a slice thickness of 0.625mm compared to 2.5mm. The detectability index d' increased by a factor of 1.7 when images were reconstructed with 0.625mm slices compared to 2.5mm slices. d' did not change when images were reconstructed with the high-resolution BONE filter compared to the less sharp STANDARD resolution filter on the GE Revolution CT. We have quantified detectability versus size of small calcium granules at the resolution limit of a widely available clinical CT scanner. Detectability increased significantly with reduced slice thickness and did not change with a sharper reconstruction kernel. The simulation can be used to calculate the trade-off between dose and CAC detectability.

Automatic electrode scalar location assessment after cochlear implantation using a novel imaging software

As of today, image-based assessment of cochlear implant electrode array location is not part of the clinical routine. Low resolution and contrast of computer tomography (CT) imaging, as well as electrode array artefacts, prevent visibility of intracochlear structures and result in low accuracy in determining location of the electrode array. Further, trauma assessment based on clinical-CT images requires a uniform image-based trauma scaling. Goal of this study was to evaluate the accuracy of a novel imaging software to detect electrode scalar location. Six cadaveric temporal bones were implanted with Advanced Bionics SlimJ and Mid-Scala electrode arrays. Clinical-CT scans were taken pre- and postoperatively. In addition, micro-CTs were taken post-operatively for validation. The electrode scalar location rating done by the software was compared to the rating of two experienced otosurgeons and the micro-CT images. A 3-step electrode scalar location grading scale (0 = electrode in scala tympani, 1 = interaction of electrode with basilar membrane/osseous spiral lamina, 2 = translocation of electrode into scala vestibuli) was introduced for the assessment. The software showed a high sensitivity of 100% and a specificity of 98.7% for rating the electrode location. The correlation between rating methods was strong (kappa > 0.890). The software gives a fast and reliable method of evaluating electrode scalar location for cone beam CT scans. The introduced electrode location grading scale was adapted for assessing clinical CT images.

Incidental vertebral fracture prediction using neuronal network-based automatic spine segmentation and volumetric bone mineral density extraction from routine clinical CT scans.

To investigate vertebral osteoporotic fracture (VF) prediction by automatically extracted trabecular volumetric bone mineral density (vBMD) from routine CT, and to compare the model with fracture prevalence-based prediction models. This single-center retrospective study included patients who underwent two thoraco-abdominal CT scans during clinical routine with an average inter-scan interval of 21.7 ± 13.1 months (range 5-52 months). Automatic spine segmentation and vBMD extraction was performed by a convolutional neural network framework (anduin.bonescreen.de). Mean vBMD was calculated for levels T5-8, T9-12, and L1-5. VFs were identified by an expert in spine imaging. Odds ratios (ORs) for prevalent and incident VFs were calculated for vBMD (per standard deviation decrease) at each level, for baseline VF prevalence (yes/no), and for baseline VF count (n) using logistic regression models, adjusted for age and sex. Models were compared using Akaike's and Bayesian information criteria (AIC & BIC). 420 patients (mean age, 63 years ± 9, 276 males) were included in this study. 40 (25 female) had prevalent and 24 (13 female) had incident VFs. Individuals with lower vBMD at any spine level had higher odds for VFs (L1-5, prevalent VF: OR,95%-CI,p: 2.2, 1.4-3.5,p=0.001; incident VF: 3.5, 1.8-6.9,p<0.001). In contrast, VF status (2.15, 0.72-6.43,p=0.170) and count (1.38, 0.89-2.12,p=0.147) performed worse in incident VF prediction. Information criteria revealed best fit for vBMD-based models (AIC vBMD=165.2; VF status=181.0; count=180.7). VF prediction based on automatically extracted vBMD from routine clinical MDCT outperforms prediction models based on VF status and count. These findings underline the importance of opportunistic quantitative osteoporosis screening in clinical routine MDCT data.

Cranio-facial bones evaluation based on clinical CT data for sex determination in Northwest Indian population

The skull and pelvis have been the first choice of bones for determination of unknown human remains. The goal of the present study was to derive discriminant function equations by using clinical CT scan data of cranio-facial bones for sex determination in Northwest Indian population. This study was conducted at Department of Radiology, by collecting the retrospective data of CT scan of 217 samples. In the data, 106 were males and 111 were females in the age group between 20 and 80 years. The total number of parameters under investigation were 10. All the selected variables were sexually dimorphic and showed significant values. 91.7% of original grouped cases were correctly classified to their sex category. The TEM, rTEM and R were under the acceptable limits. The univariate, multivariate and stepwise discriminant function analysis recorded an accuracy of 88.9%, 91.7% and 93.6% respectively. Multivariate direct discriminant function analysis stepwise method yielded the highest level of accuracy in differentiating males and females. All the variables reflected statistically significant difference between males and females (p less than 0.001). The best single parameter with highest level of sexual dimorphic trait was length of cranial base. This study aims to provide sex assessment using clinical data of CT scan in Northwest Indian population by incorporating the BIOFB cranio-facial parameter. The morphometric measurements taken on CT scan images can be utilized by forensic experts in identification process.

Photon-counting CT for diagnosis of acute pulmonary embolism: potential for contrast medium and radiation dose reduction

ObjectiveTo evaluate the image quality of an ultra-low contrast medium and radiation dose CT pulmonary angiography (CTPA) protocol for the diagnosis of acute pulmonary embolism using a clinical photon-counting detector (PCD) CT system and compare its performance to a dual-energy-(DE)-CTPA protocol on a conventional energy-integrating detector (EID) CT system.MethodsSixty-four patients either underwent CTPA with the novel scan protocol on the PCD-CT scanner (32 patients, 25 mL, CTDIvol 2.5 mGy·cm) or conventional DE-CTPA on a third-generation dual-source EID-CT (32 patients, 50 mL, CTDIvol 5.1 mGy·cm). Pulmonary artery CT attenuation, signal-to-noise ratio, and contrast-to-noise-ratio were assessed as objective criteria of image quality, while subjective ratings of four radiologists were compared at 60 keV using virtual monoenergetic imaging and polychromatic standard reconstructions. Interrater reliability was determined by means of the intraclass correlation coefficient (ICC). Effective dose was compared between patient cohorts.ResultsSubjective image quality was deemed superior by all four reviewers for 60-keV PCD scans (excellent or good ratings in 93.8% of PCD vs. 84.4% of 60 keV EID scans, ICC = 0.72). No examinations on either system were considered “non-diagnostic.” Objective image quality parameters were significantly higher in the EID group (mostly p < 0.001), both in the polychromatic reconstructions and at 60 keV. The ED (1.4 vs. 3.3 mSv) was significantly lower in the PCD cohort (p < 0.001).ConclusionsPCD-CTPA allows for considerable reduction of contrast medium and radiation dose in the diagnosis of acute pulmonary embolism, while maintaining good to excellent image quality compared to conventional EID-CTPA.Clinical relevance statementClinical PCD-CT allows for spectral assessment of pulmonary vasculature with high scan speed, which is beneficial in patients with suspected pulmonary embolism, frequently presenting with dyspnea. Simultaneously PCD-CT enables substantial reduction of contrast medium and radiation dose.Key Points• The clinical photon-counting detector CT scanner used in this study allows for high-pitch multi-energy acquisitions.• Photon-counting computed tomography allows for considerable reduction of contrast medium and radiation dose in the diagnosis of acute pulmonary embolism.• Subjective image quality was rated best for 60-keV photon-counting scans.

Normal human craniofacial growth and development from 0 to 4 years

Knowledge of human craniofacial growth (increase in size) and development (change in shape) is important in the clinical treatment of a range of conditions that affects it. This study uses an extensive collection of clinical CT scans to investigate craniofacial growth and development over the first 48 months of life, detail how the cranium changes in form (size and shape) in each sex and how these changes are associated with the growth and development of various soft tissues such as the brain, eyes and tongue and the expansion of the nasal cavity. This is achieved through multivariate analyses of cranial form based on 3D landmarks and semi-landmarks and by analyses of linear dimensions, and cranial volumes. The results highlight accelerations and decelerations in cranial form changes throughout early childhood. They show that from 0 to 12 months, the cranium undergoes greater changes in form than from 12 to 48 months. However, in terms of the development of overall cranial shape, there is no significant sexual dimorphism in the age range considered in this study. In consequence a single model of human craniofacial growth and development is presented for future studies to examine the physio-mechanical interactions of the craniofacial growth.

Crowd-Sourced Deep Learning for Intracranial Hemorrhage Identification: Wisdom of Crowds or Laissez-Faire.

Researchers and clinical radiology practices are increasingly faced with the task of selecting the most accurate artificial intelligence tools from an ever-expanding range. In this study, we sought to test the utility of ensemble learning for determining the best combination from 70 models trained to identify intracranial hemorrhage. Furthermore, we investigated whether ensemble deployment is preferred to use of the single best model. It was hypothesized that any individual model in the ensemble would be outperformed by the ensemble. In this retrospective study, de-identified clinical head CT scans from 134 patients were included. Every section was annotated with "no-intracranial hemorrhage" or "intracranial hemorrhage," and 70 convolutional neural networks were used for their identification. Four ensemble learning methods were researched, and their accuracies as well as receiver operating characteristic curves and the corresponding areas under the curve were compared with those of individual convolutional neural networks. The areas under the curve were compared for a statistical difference using a generalized U-statistic. The individual convolutional neural networks had an average test accuracy of 67.8% (range, 59.4%-76.0%). Three ensemble learning methods outperformed this average test accuracy, but only one achieved an accuracy above the 95th percentile of the individual convolutional neural network accuracy distribution. Only 1 ensemble learning method achieved a similar area under the curve as the single best convolutional neural network (Δarea under the curve = 0.03; 95% CI, -0.01-0.06; P = .17). None of the ensemble learning methods outperformed the accuracy of the single best convolutional neural network, at least in the context of intracranial hemorrhage detection.