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Accurate and rapid diagnosis of COVID-19 using chest X-ray (CXR) plays an important role in large-scale screening and epidemic prevention. Unfortunately, identifying COVID-19 from the CXR images is challenging as its radiographic features have a variety of complex appearances, such as widespread ground-glass opacities and diffuse reticular-nodular opacities. To solve this problem, we propose an adaptive attention network (AANet), which can adaptively extract the characteristic radiographic findings of COVID-19 from the infected regions with various scales and appearances. It contains two main components: an adaptive deformable ResNet and an attention-based encoder. First, the adaptive deformable ResNet, which adaptively adjusts the receptive fields to learn feature representations according to the shape and scale of infected regions, is designed to handle the diversity of COVID-19 radiographic features. Then, the attention-based encoder is developed to model nonlocal interactions by self-attention mechanism, which learns rich context information to detect the lesion regions with complex shapes. Extensive experiments on several public datasets show that the proposed AANet outperforms state-of-the-art methods.

Abstract: To investigate computed tomography (CT) diagnostic reference levels for coronavirus disease 2019 (COVID-19) pneumonia by collecting radiation exposure parameters of the most performed chest CT examinations and emphasize the necessity of low-dose CT in COVID-19 and its significance in radioprotection.The survey collected RIS data from 2119 chest CT examinations for 550 COVID-19 patients performed in 92 hospitals from January 23, 2020 to May 1, 2020. Dose data such as volume computed tomography dose index, dose-length product, and effective dose (ED) were recorded and analyzed. The radiation dose levels in different hospitals have been compared, and average ED and cumulative ED have been studied.The median dose-length product, volume computed tomography dose index, and ED measurements were 325.2 mGy cm with a range of 6.79 to 1098 mGy cm, 9.68 mGy with a range of 0.62 to 33.80 mGy, and 4.55 mSv with a range of 0.11 to 15.37 mSv for COVID-19 CT scanning protocols in Chongqing, China. The distribution of all observed EDs of radiation received by per patient undergoing CT protocols during hospitalization yielded a median cumulative ED of 17.34 mSv (range, 2.05-53.39 mSv) in the detection and management of COVID-19 patients. The average number of CT scan times for each patient was 4.0 ± 2.0, and the average time interval between 2 CT scans was 7.0 ± 5.0 days. The average cumulative ED of chest CT examinations for COVID-19 patients in Chongqing, China greatly exceeded public limit and the annual dose limit of occupational exposure in a short period.For patients with known or suspected COVID-19, a chest CT should be performed on the principle of rapid-scan, low-dose, single-phase protocol instead of routine chest CT protocol to minimize radiation doses and motion artifacts., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc.)

Coronavirus disease (COVID-19) has been the main agenda of the whole world ever since it came into sight. X-ray imaging is a common and easily accessible tool that has great potential for COVID-19 diagnosis and prognosis. Deep learning techniques can generally provide state-of-the-art performance in many classification tasks when trained properly over large data sets. However, data scarcity can be a crucial obstacle when using them for COVID-19 detection. Alternative approaches such as representation-based classification [collaborative or sparse representation (SR)] might provide satisfactory performance with limited size data sets, but they generally fall short in performance or speed compared to the neural network (NN)-based methods. To address this deficiency, convolution support estimation network (CSEN) has recently been proposed as a bridge between representation-based and NN approaches by providing a noniterative real-time mapping from query sample to ideally SR coefficient support, which is critical information for class decision in representation-based techniques. The main premises of this study can be summarized as follows: 1) A benchmark X-ray data set, namely QaTa-Cov19, containing over 6200 X-ray images is created. The data set covering 462 X-ray images from COVID-19 patients along with three other classes; bacterial pneumonia, viral pneumonia, and normal. 2) The proposed CSEN-based classification scheme equipped with feature extraction from state-of-the-art deep NN solution for X-ray images, CheXNet, achieves over 98% sensitivity and over 95% specificity for COVID-19 recognition directly from raw X-ray images when the average performance of 5-fold cross validation over QaTa-Cov19 data set is calculated. 3) Having such an elegant COVID-19 assistive diagnosis performance, this study further provides evidence that COVID-19 induces a unique pattern in X-rays that can be discriminated with high accuracy.

Novel coronavirus pneumonia (NCP) has become a global pandemic disease, and computed tomography-based (CT) image analysis and recognition are one of the important tools for clinical diagnosis. In order to assist medical personnel to achieve an efficient and fast diagnosis of patients with new coronavirus pneumonia, this paper proposes an assisted diagnosis algorithm based on ensemble deep learning. The method combines the Stacked Generalization ensemble learning with the VGG16 deep learning to form a cascade classifier, and the information constituting the cascade classifier comes from multiple subsets of the training set, each of which is used to collect deviant information about the generalization behavior of the data set, such that this deviant information fills the cascade classifier. The algorithm was experimentally validated for classifying patients with novel coronavirus pneumonia, patients with common pneumonia (CP), and normal controls, and the algorithm achieved a prediction accuracy of 93.57%, sensitivity of 94.21%, specificity of 93.93%, precision of 89.40%, and F1-score of 91.74% for the three categories. The results show that the method proposed in this paper has good classification performance and can significantly improve the performance of deep neural networks for multicategory prediction tasks., Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this paper., (Copyright © 2021 Xiaoshuo Li et al.)

Objectives/hypothesis: The objective of this study was to classify anomalous facial nerve (FN) routes and to determine their association with inner ear malformations (IEMs)., Study Design: Retrospective cross sectional study., Methods: The computed tomography images of 519 patients (796 ears) with IEMs were retrospectively evaluated, and the abnormal routes of the FN were classified as: Meatal segment: type 1, normal internal auditory canal (IAC); type 2, narrow IAC; type 3, facial canal (FC) only; type 4: separate FC/duplicated IAC. Labyrinthine segment (LS): type 1, normal; type 2a/b/c, mild/moderate/severe anterior displacement; type 3, superior displacement; type 4: straight LS. Tympanic segment (TS): type 1, normal; type 2, superiorly displaced TS; type 3, TS at the oval window; type 4: TS inferior to the oval window; type 5: unclassified. Mastoid segment: type 1, normal facial recess (FR)/normal mastoid segment; type 2: narrow FR; type 3, unclassified., Results: In meatal segment classification, a narrow IAC was common in ears with cochlear hypoplasia (CH) (76.1%), and only FC was common in ears with severe IEMs (62.7%) such as Michel deformity, common cavity, and cochlear aplasia. Incomplete partition-III has its unique superiorly displaced LS (100%). CH-IV also has its unique mild anterosuperior displacement. Ears with a superiorly displaced TS usually (93.1%) had aplastic or hypoplastic semicircular canals. The FR is likely to be narrow in CH and severe IEMs., Conclusions: The FN route is affected in IEMs, which must be kept in mind when operating on ears with IEMs. Especially in CH cases, all segments of the FN can be abnormal., Level of Evidence: 4 Laryngoscope, 130:E696-E703, 2020., (© 2020 The American Laryngological, Rhinological and Otological Society, Inc.)

This study presents a systematic review of artificial intelligence (AI) techniques used in the detection and classification of coronavirus disease 2019 (COVID-19) medical images in terms of evaluation and benchmarking. Five reliable databases, namely, IEEE Xplore, Web of Science, PubMed, ScienceDirect and Scopus were used to obtain relevant studies of the given topic. Several filtering and scanning stages were performed according to the inclusion/exclusion criteria to screen the 36 studies obtained; however, only 11 studies met the criteria. Taxonomy was performed, and the 11 studies were classified on the basis of two categories, namely, review and research studies. Then, a deep analysis and critical review were performed to highlight the challenges and critical gaps outlined in the academic literature of the given subject. Results showed that no relevant study evaluated and benchmarked AI techniques utilised in classification tasks (i.e. binary, multi-class, multi-labelled and hierarchical classifications) of COVID-19 medical images. In case evaluation and benchmarking will be conducted, three future challenges will be encountered, namely, multiple evaluation criteria within each classification task, trade-off amongst criteria and importance of these criteria. According to the discussed future challenges, the process of evaluation and benchmarking AI techniques used in the classification of COVID-19 medical images considered multi-complex attribute problems. Thus, adopting multi-criteria decision analysis (MCDA) is an essential and effective approach to tackle the problem complexity. Moreover, this study proposes a detailed methodology for the evaluation and benchmarking of AI techniques used in all classification tasks of COVID-19 medical images as future directions; such methodology is presented on the basis of three sequential phases. Firstly, the identification procedure for the construction of four decision matrices, namely, binary, multi-class, multi-labelled and hierarchical, is presented on the basis of the intersection of evaluation criteria of each classification task and AI classification techniques. Secondly, the development of the MCDA approach for benchmarking AI classification techniques is provided on the basis of the integrated analytic hierarchy process and VlseKriterijumska Optimizacija I Kompromisno Resenje methods. Lastly, objective and subjective validation procedures are described to validate the proposed benchmarking solutions., (Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Objective: The aim of the study was to compare the relative predictive value of Marshall Classification System and Rotterdam scores on long-term rehabilitation outcomes. This study hypothesized that Rotterdam would outperform Marshall Classification System., Design: The study used an observational cohort design with a consecutive sample of 88 participants (25 females, mean age = 42.0 [SD = 21.3]) with moderate to severe traumatic brain injury who were admitted to trauma service with subsequent transfer to the rehabilitation unit between February 2009 and July 2011 and who had clearly readable computed tomography scans. Twenty-three participants did not return for the 9-mo postdischarge follow-up. Day-of-injury computed tomography images were scored using both Marshall Classification System and Rotterdam criteria by two independent raters, blind to outcomes. Functional outcomes were measured by length of stay in rehabilitation and the cognitive and motor subscales of the Functional Independence Measure at rehabilitation discharge and 9-mo postdischarge follow-up., Results: Neither Marshall Classification System nor Rotterdam scales as a whole significantly predicted Functional Independence Measure motor or cognitive outcomes at discharge or 9-mo follow-up. Both scales, however, predicted length of stay in rehabilitation. Specific Marshall scores (3 and 6) and Rotterdam scores (5 and 6) significantly predicted subacute outcomes such as Functional Independence Measure cognitive at discharge from rehabilitation and length of stay., Conclusions: Marshall Classification System and Rotterdam scales may have limited utility in predicting long-term functional outcome, but specific Marshall and Rotterdam scores, primarily linked to increased severity and intracranial pressure, may predict subacute outcomes.

Objectives: This paper attempts to create a new classification type of cochlear hypoplasia (CH)-type malformation taking into consideration of vestibular section and internal auditory canal (IAC)., Materials and Methods: Preoperative computed-tomography (CT) scans of cochlear implant (CI) candidates (N=31) from various clinics across the world with CH type malformation were taken for analysis. CT dataset were loaded into 3D-slicer freeware for three-dimensional (3D) segmentation of the inner-ear by capturing complete inner-ear structures from the entire dataset. Cochlear size in terms of diameter of available cochlear basal turn and length of cochlear lumen was measured from the dataset. In addition, structural connection between IAC and cochlear portions was scrutinized, which is highly relevant to the proposed CH classification in this study., Results: CH group-I has the normal presence of IAC leading to cochlear and vestibular portions, whereas CH group-II is like CH group-I but with some degree of disruption in vestibular portion. In CH group-III, a disconnection between IAC and the cochlear portion irrespective of other features. Within all these three CH groups, the basal turn diameter varied between 3.1 mm and 9.6 mm, and the corresponding cochlear lumen length varied between 3 mm and 21 mm for the CI electrode array placement., Conclusion: A new classification of CH mainly based on the IAC connecting the cochlear and vestibular portions is presented in this study. CI electrode array length could be selected based on the length of the cochlear lumen, which can be observed from the 3D image.

Purpose: Because of the widespread use of CT in the diagnosis of COVID 19, indeterminate presentations such as single, few or unilateral lesions amount to a considerable number. We aimed to develop a new classification and structured reporting system on CT imaging (COVID-19 S) that would facilitate the diagnosis of COVID-19 in the most accurate way., Methods: Our retrospective cohort included 803 patients with a chest CT scan upon suspicion of COVID 19. The patients' history, physical examination, CT findings, RT PCR, and other laboratory test results were reviewed, and a final diagnosis was made as COVID 19 or non-COVID 19. Chest CT scans were classified according to the COVID 19 S CT diagnosis criteria. Cohen's kappa analysis was used., Results: Final clinical diagnosis was COVID-19 in 98 patients (12%). According to the COVID-19 S CT diagnosis criteria, the number of patients in the normal, compatible with COVID 19, indeterminate and alternative diagnosis groups were 581 (72.3%), 97 (12.1%), 16 (2.0%) and 109 (13.6%). When the indeterminate group was combined with the group compatible with COVID 19, the sensitivity and specificity of COVID-19 S were 99.0% and 87.1%, with 85.8% positive predictive value (PPV) and 99.1% negative predictive value (NPV). When the indeterminate group was combined with the alternative diagnosis group, the sensitivity and specificity of COVID-19 S were 93.9% and 96.0%, with 94.8% PPV and 95.2% NPV., Conclusion: COVID-19 S CT classification system may meet the needs of radiologists in distinguishing COVID-19 from pneumonia of other etiologies and help optimize patient management and disease control in this pandemic by the use of structured reporting.

The purpose of this study was to determine the interobserver variability among providers of different specialties and levels of experience across five established computed tomography (CT) scoring systems for acute traumatic brain injury (TBI). One hundred cases were selected at random from a retrospective population of adult patients transported to our emergency department and subjected to a non-contrast head CT due to suspicion of TBI. Eight neuroradiologists and neurosurgeons in trainee (residents and fellows) and attending roles independently scored each non-contrast head CT scan on the Marshall, Rotterdam, Helsinki, Stockholm, and NeuroImaging Radiological Interpretation System (NIRIS) head CT scales. Interobserver variability of scale scores-overall and by specialty and level of training-was quantified using the intraclass correlation coefficient (ICC), and agreement with respect to National Institutes of Health Common Data Elements (NIH CDEs) was assessed using Cohen's kappa. All CT severity scoring systems showed high interobserver agreement as evidenced by high ICCs, ranging from 0.75-0.89. For all scoring systems, neuroradiologists (ICC range from 0.81-0.94) tended to have higher interobserver agreement than neurosurgeons (ICC range from 0.63-0.76). For all scoring systems, attendings (ICC range from 0.76-0.89) had similar interobserver agreement to trainees (ICC range from 0.73-0.89). Agreement with respect to NIH CDEs was high for ascertaining presence/absence of hemorrhage, skull fracture, and mass effect, with estimated kappa statistics of least 0.89. Acute TBI CT scoring systems demonstrate high interobserver agreement. These results provide scientific rigor for future use of these systems for the classification of acute TBI.

Introduction: In 2018, ARPANSA published updated national DRLs for adult CT, which were first published in 2012, and augmented the national DRL categories. This paper presents the updated national DRLs and describes the process by which they were produced., Methods: Examine patient survey data submitted to the Australian Radiation Protection and Nuclear Safety Agency (ARPANSA) National Diagnostic Reference Level Service (NDRLS). Determine the quartiles of the distributions of median survey dose metrics with categorisation by procedure type. Engage a liaison panel representing the radiology professions to review procedure categories and recommend revised national DRLs. The revised NDRL procedure categories are: head (non-contrast brain (trauma/headache)), cervical spine (Non-contrast (trauma)), soft-tissue neck (post-contrast (oncology)), chest (post-contrast (oncology)), abdomen-pelvis (post-contrast (oncology)), kidney-ureter-bladder (non-contrast (suspected renal colic)), chest-abdomen-pelvis (post-contrast (oncology)) and lumbar spine (non-contrast (degenerative pain))., Results: The existing six procedure categories were revised and refined. Updated Australian national diagnostic reference levels for adult CT were recommended and endorsed for eight procedure categories: head (52 mGy/880 mGycm), cervical spine (23 mGy/470 mGycm),soft-tissue neck (17 mGy/450 mGycm), chest (10 mGy/390 mGycm), abdomen-pelvis (13 mGy/600 mGycm), kidney-ureter-bladder (13 mGy/600 mGycm), chest-abdomen-pelvis (11 mGy/940 mGycm) and lumbar spine (26 mGy/670 mGycm). The updated national DRLs are between 12 and 26% lower than the previous DRLs for dose-length product and between 13 and 63% lower for volume computed tomography dose index., Conclusions: Australian national DRLs for adult CT have been reviewed and revised. The revised national DRLs are lower, better reflecting current practice among imaging facilities in Australia. The revised Australian national DRLs are similar to those in other developed countries., (© 2020 Commonwealth of Australia. Journal of Medical Radiation Sciences published by John Wiley & Sons Australia, Ltd on behalf of Australian Society of Medical Imaging and Radiation Therapy and New Zealand Institute of Medical Radiation Technology.)

Background: Nontraumatic headache is a frequent complaint in the emergency department (ED). Cranial computed tomography (CT) is a widely available test for the diagnostic work-up, despite the risk of exposure to ionizing radiation., Objectives: We sought to develop and evaluate a cranial CT request computerized decision support system (CDSS) for adults with their first presentation of unusual severe nontraumatic headache in the ED., Methods: Electronic database searches identified clinical decision and prediction rules and studies delineating risk factors in nontraumatic headache. A long list of risk factors extracted from these articles was reduced by a 30-member multidisciplinary expert panel (radiologists, emergency physicians, methodologists), using a 90% agreement threshold. This shortlist was used to develop the algorithm for the cranial CT request CDSS, which was implemented in March 2016. Impact evaluation compared CT scan frequency and diagnostic yield of pathologic findings before (March-August 2015) and after (March-August 2016) implementation., Results: From the 10 selected studies, 10 risk factors were shortlisted to activate a request for cranial CT. Before implementation, 377 cranial CTs were ordered (15.3% of 2469 CT scans) compared with 244 after (9.5% of 2561 CT scans; pre-post difference 5.74%; 95% confidence interval [CI] 3.92-7.56%; p < 0.001), corresponding to a 37.6% relative reduction in the test ordering rate (95% CI 25.7-49.5%; p < 0.001). Despite the reduction in cranial CT scans, we did not observe an increase in pathological findings after introducing the decision support system (70 cases before [18.5%] vs. 35 cases after [14.3%]; pre-post difference -4.0% [95% CI -10.0 to 1.6%]; p = 0.170)., Conclusion: In nontraumatic headache among adults seen in the ED, CDSS decreased the cranial CT request rate but the diagnostic yield did not improve., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Purpose: Deep learning methods (DLMs) have recently been proposed to generate pseudo-CT (pCT) for magnetic resonance imaging (MRI) based dose planning. This study aims to evaluate and compare DLMs (U-Net and generative adversarial network [GAN]) using various loss functions (L2, single-scale perceptual loss [PL], multiscale PL, weighted multiscale PL) and a patch-based method (PBM)., Methods and Materials: Thirty-nine patients received a volumetric modulated arc therapy for prostate cancer (78 Gy). T 2 -weighted MRIs were acquired in addition to planning CTs. The pCTs were generated from the MRIs using 7 configurations: 4 GANs (L2, single-scale PL, multiscale PL, weighted multiscale PL), 2 U-Net (L2 and single-scale PL), and the PBM. The imaging endpoints were mean absolute error and mean error, in Hounsfield units, between the reference CT (CT ref ) and the pCT. Dose uncertainties were quantified as mean absolute differences between the dose volume histograms (DVHs) calculated from the CT ref and pCT obtained by each method. Three-dimensional gamma indexes were analyzed., Results: Considering the image uncertainties in the whole pelvis, GAN L2 and U-Net L2 showed the lowest mean absolute error (≤34.4 Hounsfield units). The mean errors were not different than 0 (P ≤ .05). The PBM provided the highest uncertainties. Very few DVH points differed when comparing GAN L2 or U-Net L2 DVHs and CT ref DVHs (P ≤ .05). Their dose uncertainties were ≤0.6% for the prostate planning target Volume V 95% , ≤0.5% for the rectum V 70Gy , and ≤0.1% for the bladder V 50Gy . The PBM, U-Net PL, and GAN PL presented the highest systematic dose uncertainties. The gamma pass rates were >99% for all DLMs. The mean calculation time to generate 1 pCT was 15 s for the DLMs and 62 min for the PBM., Conclusions: Generating pCT for MRI dose planning with DLMs and PBM provided low-dose uncertainties. In particular, the GAN L2 and U-Net L2 provided the lowest dose uncertainties together with a low computation time., (Crown Copyright © 2019. Published by Elsevier Inc. All rights reserved.)

Objective: We describe and evaluate the mapping of computerized tomography (CT) terms from 40 hospitals participating in a health information exchange (HIE) to a standard terminology., Methods: Proprietary CT exam terms and corresponding exam frequency data were obtained from 40 participant HIE sites that transmitted radiology data to the HIE from January 2013 through October 2015. These terms were mapped to the Logical Observations Identifiers Names and Codes (LOINC®) terminology using the Regenstrief LOINC mapping assistant (RELMA) beginning in January 2016. Terms without initial LOINC match were submitted to LOINC as new term requests on an ongoing basis. After new LOINC terms were created, proprietary terms without an initial match were reviewed and mapped to these new LOINC terms where appropriate. Content type and token coverage were calculated for the LOINC version at the time of initial mapping (v2.54) and for the most recently released version at the time of our analysis (v2.63). Descriptive analysis was performed to assess for significant differences in content-dependent coverage between the 2 versions., Results: LOINC's content type and token coverages of HIE CT exam terms for version 2.54 were 83% and 95%, respectively. Two-hundred-fifteen new LOINC CT terms were created in the interval between the releases of version 2.54 and 2.63, and content type and token coverages, respectively, increased to 93% and 99% (P < .001)., Conclusion: LOINC's content type coverage of proprietary CT terms across 40 HIE sites was 83% but improved significantly to 93% following new term creation.

Background: Patients with an aneurysmal subarachnoid hemorrhage (aSAH) and World Federation of Neurosurgical Societies (WFNS) grade I on admission are generally considered to have a good clinical outcome., Objective: The objective of this study was to assess the actual clinical outcome of WFNS grade I aSAH patients, and to determine which factors are associated with unfavourable outcome., Methods: For this prospective cohort study, 132 consecutive patients (age 18 years or older) with a WFNS grade I aSAH admitted to our hospital between December 2011 and January 2016 were eligible. Clinical outcome was measured using the modified Rankin Scale (mRS) at 6-month follow-up. Unfavorable outcome was defined as an mRS score of 3-6. Univariable analyses were performed using logistic regression models., Results: Of 116 patients, only 5 patients (4%) had an mRS score of 0 and most (65%) had an mRS score of 2. Twenty-five patients (22%) had an unfavorable outcome. Nine (8%) patients died, of whom 4 died during admission. Factors associated with unfavorable outcome were age (per increasing decade: odds ratio [OR]. 1.78; 95% confidence interval [CI], 1.16-2.72), delayed cerebral ischemia (OR, 4.32; 95% CI, 1.63-11.44), pneumonia (OR, 10.75; 95% CI, 1.94-59.46) and meningitis (OR, 28.47; 95% CI, 1.42-571.15)., Conclusions: Despite their neurologically optimal clinical condition on admission, 1 in 5 patients with WFNS grade I aSAH has an unfavorable clinical outcome or is dead at 6-month follow-up. Additional multivariable analysis in larger patient cohorts is necessary to identify the extent to which preventable complications contribute to unfavorable outcomes in these patients., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Radiological reporting generates a large amount of free-text clinical narratives, a potentially valuable source of information for improving clinical care and supporting research. The use of automatic techniques to analyze such reports is necessary to make their content effectively available to radiologists in an aggregated form. In this paper we focus on the classification of chest computed tomography reports according to a classification schema proposed for this task by radiologists of the Italian hospital ASST Spedali Civili di Brescia. The proposed system is built exploiting a training data set containing reports annotated by radiologists. Each report is classified according to the schema developed by radiologists and textual evidences are marked in the report. The annotations are then used to train different machine learning based classifiers. We present in this paper a method based on a cascade of classifiers which make use of a set of syntactic and semantic features. The resulting system is a novel hierarchical classification system for the given task, that we have experimentally evaluated., (Copyright © 2018 Elsevier B.V. All rights reserved.)

This study aimed at elucidating the relationship between the number of computed tomography (CT) images, including data concerning the accuracy of models and contrast enhancement for classifying the images. We enrolled 1539 patients who underwent contrast or noncontrast CT imaging, followed by dividing the CT imaging dataset for creating classification models into 10 classes for brain, neck, chest, abdomen, and pelvis with contrast-enhanced and plain imaging. The number of images prepared in each class were 100, 500, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, and 10,000. Accordingly, the names of datasets were defined as 0.1K, 0.5K, 1K, 2K, 3K, 4K, 5K, 6K, 7K, 8K, 9K, and 10K, respectively. We subsequently created and evaluated the models and compared the convolutional neural network (CNN) architecture between AlexNet and GoogLeNet. The time required for training models of AlexNet was lesser than that for GoogLeNet. The best overall accuracy for the classification of 10 classes was 0.721 with the 10K dataset of GoogLeNet. Furthermore, the best overall accuracy for the classification of the slice position without contrast media was 0.862 with the 2K dataset of AlexNet.

Study Design: A computed tomography (CT) study of the morphology of the C1 vertebra., Objective: Is to determine the prevalence of ponticulus posticus (PP) by analyzing CT scans performed on a large, diverse population in the northeast United States. This study also proposes a CT-based classification system both to aid in unifying the description of PP, and to aid in future research., Summary of Background Data: The prevalence of PP varies from 5% to 68% in published studies. There may be geographic variation in the prevalence of PP. Our objective was to establish the prevalence of PP in the general population, and to develop a comprehensive classification system to describe PP., Methods: We evaluated cervical spine CT scans performed on patients in the emergency room of a level I trauma center over a 1-year period (January 1, 2014-December 31, 2014). The CT images were evaluated for the presence of a PP, and if present the following demographic data were collected: age, sex, race/ethnicity, and body mass index (BMI). We propose a novel classification system to standardize the description of PP identified on CT scan., Results: Two thousand, nine hundred and seventeen cervical spine CT scans were reviewed in this study. The prevalence of PP was 22.5%. Men had a higher prevalence of PP than women (53.5% male vs. 46.5% female P ≤ 0.01). When compared with the overall population, African-Americans were more likely to have a PP (P ≤ 0.01), while Caucasian patients were less likely (P ≤ 0.01). The novel classification consisted of a two letter designation for each patient, with the first letter denoting the right sided arch and the second letter the left sided arch. Each side of the arch described as an A, B, or C type. The A type had no presence of a PP, B type had in incomplete PP, and C type had a complete PP. The most common type of a PP was CC encompassing 25% of the patients. The presence of a PP was more common in the left sided arch than the right side (B and C type Left 89.2% vs. B and C type Right 84.7%, P = 0.02)., Conclusion: We found a 22.5% prevalence of PP in 2917 patients undergoing a cervical spine CT. This is the largest study to evaluating the prevalence of PP., Level of Evidence: 4.

Clinical outcome after traumatic diffuse axonal injury (DAI) is difficult to predict. In this study, three magnetic resonance imaging (MRI) sequences were used to quantify the anatomical distribution of lesions, to grade DAI according to the Adams grading system, and to evaluate the value of lesion localization in combination with clinical prognostic factors to improve outcome prediction. Thirty patients (mean 31.2 years ±14.3 standard deviation) with severe DAI (Glasgow Motor Score [GMS] <6) examined with MRI within 1 week post-injury were included. Diffusion-weighted (DW), T2*-weighted gradient echo and susceptibility-weighted (SWI) sequences were used. Extended Glasgow outcome score was assessed after 6 months. Number of DW lesions in the thalamus, basal ganglia, and internal capsule and number of SWI lesions in the mesencephalon correlated significantly with outcome in univariate analysis. Age, GMS at admission, GMS at discharge, and low proportion of good monitoring time with cerebral perfusion pressure <60 mm Hg correlated significantly with outcome in univariate analysis. Multivariate analysis revealed an independent relation with poor outcome for age (p = 0.005) and lesions in the mesencephalic region corresponding to substantia nigra and tegmentum on SWI (p = 0.008). We conclude that higher age and lesions in substantia nigra and mesencephalic tegmentum indicate poor long-term outcome in DAI. We propose an extended MRI classification system based on four stages (stage I-hemispheric lesions, stage II-corpus callosum lesions, stage III-brainstem lesions, and stage IV-substantia nigra or mesencephalic tegmentum lesions); all are subdivided by age (≥/<30 years)., Competing Interests: Author Disclosure Statement No competing financial interests exist.

The purpose of this study was to evaluate the accuracy of using a combination of International Classification of Diseases (ICD) diagnostic codes and imaging procedure codes for identifying deep vein thrombosis (DVT) and pulmonary embolism (PE) within administrative databases. Information from the Alberta Health (AH) inpatients and ambulatory care administrative databases in Alberta, Canada was obtained for subjects with a documented imaging study result performed at a large teaching hospital in Alberta to exclude venous thromboembolism (VTE) between 2000 and 2010. In 1361 randomly-selected patients, the proportion of patients correctly classified by AH administrative data, using both ICD diagnostic codes and procedure codes, was determined for DVT and PE using diagnoses documented in patient charts as the gold standard. Of the 1361 patients, 712 had suspected PE and 649 had suspected DVT. The sensitivities for identifying patients with PE or DVT using administrative data were 74.83% (95% confidence interval [CI]: 67.01-81.62) and 75.24% (95% CI: 65.86-83.14), respectively. The specificities for PE or DVT were 91.86% (95% CI: 89.29-93.98) and 95.77% (95% CI: 93.72-97.30), respectively. In conclusion, when coupled with relevant imaging codes, VTE diagnostic codes obtained from administrative data provide a relatively sensitive and very specific method to ascertain acute VTE., (© The Author(s) 2015.)

Objective: This paper presents an automated system for classifying the results of imaging examinations (CT, MRI, positron emission tomography) into reportable and non-reportable cancer cases. This system is part of an industrial-strength processing pipeline built to extract content from radiology reports for use in the Victorian Cancer Registry., Materials and Methods: In addition to traditional supervised learning methods such as conditional random fields and support vector machines, active learning (AL) approaches were investigated to optimize training production and further improve classification performance. The project involved two pilot sites in Victoria, Australia (Lake Imaging (Ballarat) and Peter MacCallum Cancer Centre (Melbourne)) and, in collaboration with the NSW Central Registry, one pilot site at Westmead Hospital (Sydney)., Results: The reportability classifier performance achieved 98.25% sensitivity and 96.14% specificity on the cancer registry's held-out test set. Up to 92% of training data needed for supervised machine learning can be saved by AL., Discussion: AL is a promising method for optimizing the supervised training production used in classification of radiology reports. When an AL strategy is applied during the data selection process, the cost of manual classification can be reduced significantly., Conclusions: The most important practical application of the reportability classifier is that it can dramatically reduce human effort in identifying relevant reports from the large imaging pool for further investigation of cancer. The classifier is built on a large real-world dataset and can achieve high performance in filtering relevant reports to support cancer registries., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.)

Background: Reliably abstracting outcomes from free-text electronic health records remains a challenge. While automated classification of free text has been a popular medical informatics topic, performance validation using real-world clinical data has been limited. The two main approaches are linguistic (natural language processing [NLP]) and statistical (machine learning). The authors have developed a hybrid system for abstracting computed tomography (CT) reports for specified outcomes., Objectives: The objective was to measure performance of a hybrid NLP and machine learning system for automated outcome classification of emergency department (ED) CT imaging reports. The hypothesis was that such a system is comparable to medical personnel doing the data abstraction., Methods: A secondary analysis was performed on a prior diagnostic imaging study on 3,710 blunt facial trauma victims. Staff radiologists dictated CT reports as free text, which were then deidentified. A trained data abstractor manually coded the reference standard outcome of acute orbital fracture, with a random subset double-coded for reliability. The data set was randomly split evenly into training and testing sets. Training patient reports were used as input to the Medical Language Extraction and Encoding (MedLEE) NLP tool to create structured output containing standardized medical terms and modifiers for certainty and temporal status. Findings were filtered for low certainty and past/future modifiers and then combined with the manual reference standard to generate decision tree classifiers using data mining tools Waikato Environment for Knowledge Analysis (WEKA) 3.7.5 and Salford Predictive Miner 6.6. Performance of decision tree classifiers was evaluated on the testing set with or without NLP processing., Results: The performance of machine learning alone was comparable to prior NLP studies (sensitivity = 0.92, specificity = 0.93, precision = 0.95, recall = 0.93, f-score = 0.94), and the combined use of NLP and machine learning showed further improvement (sensitivity = 0.93, specificity = 0.97, precision = 0.97, recall = 0.96, f-score = 0.97). This performance is similar to, or better than, that of medical personnel in previous studies., Conclusions: A hybrid NLP and machine learning automated classification system shows promise in coding free-text electronic clinical data., (© 2013 by the Society for Academic Emergency Medicine.)

Conclusion: The width of the bony cochlear nerve canal (BCNC) and the size of the cochlear nerve are reliable predictors of long-term speech perception abilities for children with auditory neuropathy spectrum disorder (ANSD) after cochlear implantation (CI). In addition, electrical stapedial reflex (ESR) and electrical compound action potential (ECAP) also have considerable value in predicting postoperative speech perception abilities in these children., Objectives: To assess whether speech perception abilities after CI in children with ANSD can be predicted from the results of radiologic studies and electrophysiologic tests., Methods: Fifteen children with ANSD underwent CI. The width of the BCNC and the size of the cochlear nerve were measured using preoperative CT and MRI. The results of early postoperative ESR, ECAP, and implant evoked electrical auditory brainstem response were reviewed. The latest speech perception test scores were also reviewed., Results: Radiologic findings of normal BCNC and normal cochlear nerve correlated with excellent speech perception abilities after CI. A narrow or obliterated BCNC and a deficient cochlear nerve correlated with poor speech perception abilities. Children with good speech perception abilities showed robust responses on ESR and ECAP, but there were no responses from any of the children with poor speech perception abilities.

A retrospective analysis was performed in patients undergoing prospective ECG-triggered coronary computed tomography (CT) angiography (CCTA) with the single-source 64-slice CT (SSCT), dual-source 64-slice CT (DSCT), dual-source 128-slice CT and 320-slice CT with the aim of comparing the radiation dose associated with different CT generations. A total of 164 patients undergoing prospective ECG-triggered CCTA with different types of CT scanners were studied with the mean effective doses estimated at 6.8 ± 3.2, 4.2 ± 1.9, 4.1±0.6 and 3.8 ± 1.4 mSv corresponding to the 128-slice DSCT, 64-slice DSCT, 64-slice SSCT and 320-slice CT scanners. In this study a positive relationship was found between the effective dose and the body mass index (BMI). A low radiation dose is achieved in prospective ECG-triggered CCTA, regardless of the CT scanner generation. BMI is identified as the major factor that has a direct impact on the effective dose associated with prospective ECG-triggered CCTA.

Objective: Cardiac CT allows the detection and quantification of coronary artery calcification (CAC). Electron-beam CT (EBCT) has been widely replaced by high-end CT generations in the assessment of CAC. The aim of this study was to compare the CAC scores derived from an EBCT with those from a dual-source CT (DSCT)., Methods: We retrospectively selected 92 patients (61 males; mean age, 60.7 ± 12 years) from our database, who underwent both EBCT and DSCT. CAC was assessed using the Agatston score by two independent readers (replicates: 1, 2; 3=mean of reading 1 and 2)., Results: EBCT scores were on average slightly higher than DSCT scores (281 ± 569 vs 241 ± 502; p<0.05). In regression analysis R(2)-values vary from 0.956 (1) to 0.966 (3). We calculated a correction factor as EBCT=(DSCT+1)(1.026)-1. When stratifying into CAC categories (0, 1-99, 100-399, 400-999 and ≥1000), 79 (86%) were correctly classified. From those with positive CAC scores, 7 out of 61 cases (11%, κ=0.81) were classified in different categories. Using the corrected DSCT CAC score, linear regression analysis for the comparison to the EBCT results were r=0.971 (p<0.001), with a mean difference of 6.4 ± 147.8. Five subjects (5.4%) were still classified in different categories (κ=0.84)., Conclusion: CAC obtained from DSCT is highly correlated with the EBCT measures. Using the calculated correction factor, agreement only marginally improved the clinical interpretation of results. Overall, for clinical purposes, face value use of DSCT-derived values appears as useful as EBCT for CAC scoring.

Effective capability to search biomedical articles based on visual properties of article images may significantly augment information retrieval in the future. In this paper, we present a new method to classify the window setting types of brain CT images. Windowing is a technique frequently used in the evaluation of CT scans, and is used to enhance contrast for the particular tissue or abnormality type being evaluated. In particular, it provides radiologists with an enhanced view of certain types of cranial abnormalities, such as the skull lesions and bone dysplasia which are usually examined using the " bone window" setting and illustrated in biomedical articles using "bone window images". Due to the inherent large variations of images among articles, it is important that the proposed method is robust. Our algorithm attained 90% accuracy in classifying images as bone window or non-bone window in a 210 image data set.

Objective: To evaluate the ability of systematized nomenclature of medicine clinical terms (SNOMED CT) to represent computed tomography procedures in computed tomography dictionaries used in the Canadian province of Newfoundland and Labrador., Methods: This study was conducted in two stages. In the first stage computed tomography dictionaries were collected and consolidated to one master list. The duplicated procedure names were deleted from the list. In the second stage the unique data items from the master list were matched with the SNOMED CT concepts. Sensitivity, specificity, and positive and negative predictive values of SNOMED CT were investigated., Results: After eliminating 680 duplicate procedures from the total of 833, the study sample consisted of 153 data items. For pre-coordination, SNOMED CT had sensitivity of 56% and for post-coordination SNOMED CT had sensitivity of 98%., Conclusion: Our results suggest that SNOMED CT is a valid nomenclature for representing computed tomography procedures., (Crown Copyright © 2011. Published by Elsevier Ireland Ltd. All rights reserved.)

Advances in multidetector computed tomography (CT) technology have revolutionized cardiovascular imaging in children. Faster acquisition times and high-quality reconstructions have enabled CT to become a valuable modality for the evaluation of cardiac and vascular pathology. Identification of congenital cardiovascular anomalies and their complications is important for patient management and treatment planning. CT offers an excellent technology in this patient population. The confident diagnosis of congenital cardiovascular lesions requires awareness of the appearances of these defects on CT, before and after surgery. In addition, understanding the techniques for performing CT in the pediatric population, including the risks, is essential for the radiologist.

Purpose: To investigate the diagnostic accuracy and limitations of 16-slice multidetector computed tomography (MDCT) in the detection of significant ( > 50%) stenosis of coronary artery bypass grafts (CABG) and native coronary arteries., Materials and Methods: One hundred two patients with 236 grafts were investigated by 16-slice MDCT. Native coronary arteries were also investigated. The image quality was assessed in terms of artifact, and the evaluable segments were screened for the presence of occlusion and significant ( > 50%) stenosis. MDCT results were compared with conventional coronary angiography., Results: The evaluability of MDCT was 90.4% for CABG and 71.2% for native coronary arteries. The most frequent causes of nonevaluable segments were motion artifact in venous grafts, metallic clip artifact in arterial grafts, and severe calcification in native coronary arteries. MDCT correctly diagnosed all of the 46 occluded grafts. The sensitivity, specificity, and the positive and negative predictive value of MDCT for the detection of significant CABG stenoses were 91.4%, 98.5%, 84.2%, and 99.2%, respectively. Including nonevaluable segments in the analysis, overall sensitivity was 84.2%. For the evaluation of native coronary arteries, MDCT had a sensitivity of 82.1% and a specificity of 75.3%, but evaluability was only 71.2%, resulting in overall sensitivity of 62.1%., Conclusion: Use of 16-slice MDCT angiography allows very accurate evaluation of CABG patency and has high diagnostic accuracy in detecting graft stenoses. But evaluation of native coronary artery stenosis is limited, particularly in patients with advanced coronary artery disease with severe calcification.

Purpose: We have developed a classification of high-resolution computed tomography (HRCT) images for screening, surveillance and epidemiological studies of respiratory diseases caused by occupational and environmental factors. The proposed classification consists of three parts: a guideline explaining the elements of the classification scheme, a reading sheet, and reference films to aid in assessing thin-section CT films. We assessed the reliability of the proposed classification system by blinded, independent trial reading., Materials and Methods: Seven independent radiologists and pulmonologists performed a trial reading to measure the reliability of the classification system using HRCT films from 27 pneumoconiosis patients and 7 normal controls., Results: The agreement was moderate to good for rounded opacities (weighted kappa=0.68 and 0.64), irregular opacities (0.59, 0.48), honeycombing (0.65, 0.47), emphysema (0.76, 0.62) and large opacities (0.48, 0.52). Ground glass opacities (0.16, 0.20) showed poor to fair agreement. Intra-reader agreement of each of the seven readers was moderate to good (mean: weighted kappa=0.52-0.80) for parenchymal findings, but the agreement was relatively low (mean weighted kappa=0.52) for ground glass opacities., Conclusion: The proposed classification is able to describe early dust-related fibrotic changes and provide a semi-quantitative description of the HRCT features of major fibrotic changes in the parenchyma and pleura. Reliability, as measured by inter-reader agreement, was satisfactory.

Introduction: To validate a preliminary version of a radiological lexicon (RadLex) against terms found in thoracic CT reports and to index report content in RadLex term categories., Material and Methods: Terms from a random sample of 200 thoracic CT reports were extracted using a text processor and matched against RadLex. Report content was manually indexed by two radiologists in consensus in term categories of Anatomic Location, Finding, Modifier, Relationship, Image Quality, and Uncertainty. Descriptive statistics were used and differences between age groups and report types were tested for significance using Kruskal-Wallis and Mann-Whitney Test (significance level <0.05)., Results: From 363 terms extracted, 304 (84%) were found and 59 (16%) were not found in RadLex. Report indexing showed a mean of 16.2 encoded items per report and 3.2 Finding per report. Term categories most frequently encoded were Modifier (1,030 of 3,244, 31.8%), Anatomic Location (813, 25.1%), Relationship (702, 21.6%) and Finding (638, 19.7%). Frequency of indexed items per report was higher in older age groups, but no significant difference was found between first study and follow up study reports. Frequency of distinct findings per report increased with patient age (p < 0.05)., Conclusion: RadLex already covers most terms present in thoracic CT reports based on a small sample analysis from one institution. Applications for report encoding need to be developed to validate the lexicon against a larger sample of reports and address the issue of automatic relationship encoding.

Objective: The objective of this study was to investigate the outcome and clinical implications of nonhypervascular hypoattenuating nodules observed on portal or equilibrium phase CT images of cirrhotic livers., Materials and Methods: One hundred one cirrhotic patients (male:female = 69:32) with hypoattenuating nodules observed on initial portal or equilibrium phase CT images were retrospectively evaluated by follow-up CT performed 6-66 months after the initial CT examination. Depending on the background nodularity, patients were separated into macronodular (n = 33, 288 nodules) and micronodular (n = 68, 346 nodules) cirrhotic groups. Each nodule was categorized as category I (enlarged) or category II (stable). Nodule categories were correlated with the initial lesion size and the pattern of background cirrhosis., Results: The frequency of category I nodules was higher in patients with micronodular cirrhosis (40%) than in those with macronodular cirrhosis (27%) (p = 0.001). Category I nodules were significantly larger than category II nodules in patients with micronodular cirrhosis (p < 0.001). The doubling times of category I nodules had no statistical difference between patients with micronodular or macronodular cirrhosis (p = 0.954). Of the category I nodules in patients with micronodular cirrhosis, 8.6% showed malignant changes., Conclusion: More careful attention should be paid to large nodules in patients with micronodular cirrhosis because of the potentially greater risk of malignancy, and small hypoattenuating nodules should be more often followed up in shorter intervals than large nodules.

Recent developments in cross-sectional imaging, particularly computerized tomography and magnetic resonance imaging have provided increasingly accurate noninvasive preoperative staging, especially for rectal cancer. Image-based TNM staging, by definition a pathological system, has entered both the literature and everyday usage with no universal agreement as to the exact terminology. Clarification of the current terminology and suggestions to reflect recent developments are outlined to facilitate multidisciplinary team decision processes and objective stratification for entry to, and monitoring of, future clinical trials in rectal cancer.

Computed tomography (CT) is the diagnostic standard in Emergency Department evaluation of suspected renal colic but delivers substantial radiation. We determined the frequency of CT scan in suspected renal colic, diagnosis and outcome, and cumulative CT scans per patient. A retrospective chart review with waiver of informed consent was conducted. A total of 356 patient encounters were reviewed from January to October 2003. Mean age was 39 years. Seventy-four percent included a CT scan, with 38% normal, 58% showing urolithiasis, and 1% showing emergent etiologies. Six percent of patients undergoing CT were admitted for urolithiasis, and 6% had a urologic procedure within 7 days. Sixteen percent of patients did not have a CT scan, and 79% underwent two or more CT scans. Emergency Department patients presenting with symptoms suggesting renal colic are likely to undergo CT on multiple occasions. Radiation exposures from repeated CT scans are substantial, and a clinical decision rule for this scenario is needed.

We present a case of ulnar artery aneurysm and dissection associated with a congenitally hypoplastic ipsilateral radial artery. We postulate that the aetiology is due to increased blood flow through the ulnar artery as a consequence of the radial artery anomaly, therefore making the ulnar artery more vulnerable to aneurysm formation and traumatic damage. This might represent a variant of the hypothenar hammer syndrome with associated ulnar artery dissection and recurrent distal embolisation manifesting as Raynaud's phenomenon. Forearm arterial injuries, treatment and the importance of upper limb arterial anatomical variations are also discussed. To our knowledge, this is the first reported case of its type diagnosed by multi-detector row computed tomography angiography.

The purpose of this study was to assess the role of multi-detector row CT urography (MDCTU), on a 16-row CT scanner in the evaluation of patients with painless hematuria, with emphasis placed in the detection of urothelial tumors. We retrospectively reviewed the MDCT urographies of 75 patients, referred for painless hematuria. The CT protocol included unenhanced images, obtained with a detector configuration of 16x1.5 mm and pitch of 1.2, nephrographic and excretory-phase images, obtained with a detector collimation of 16x0.75 mm and pitch of 1.2. Axial and coronal reformatted images were evaluated. Three-dimensional reformation of the excretory-phase images was performed using the volume-rendering technique. The standard of reference included clinical and imaging follow-up, cystoscopic, surgical and histologic findings. In 55 (73%) of 75 patients, the cause of hematuria was identified on MDCTU; the most common cause was urothelial cancer, including seven tumors with a diameter equal or smaller than 0.5 cm in diameter. Sixteen-row MDCTU provided satisfactory results in the investigation of patients with painless hematuria. The main advantage of the technique is its ability to detect uroepithelial malignancies.

To determine which cross-sectional imaging findings predict the short-term outcome of cervical facet joint blocks (FJB) and to evaluate the effect of combined intra-/periarticular versus periarticular injection on pain. Fifty facet joints in 37 patients were included in the study. Single, unilateral FJBs in 24 patients, and bilateral single level FJBs in 13 patients were performed, respectively. In all patients, pain relief was assessed using a visual analogue scale. All computed tomography (CT) examinations were blindly reviewed by two radiologists. Osteoarthritis was rated using the Kellgren classification. The presence of combined intra-/periarticular vs. sole periarticular injection of contrast was evaluated. Kellgren Grades 0 (n=23), 1 (n=5), 2 (n=3), 3 (n=9), and 4 (n=10) were found. Mean pain relief after injection was 35% (range: 0-100%). 40% of all injections were combined intra-/periarticular. There was neither a statistically significant difference between pain relief and combined intra-/periarticular versus sole periarticular injection (p=0.64) nor the grade of osteoarthritis (p=0.49). Pain relief after cervical FJBs does not correlate with morphologic alterations seen on CT. Periarticular FJBs are not less successful than combined intra-/periarticular FJBs.

The CT Image Library (CTIL) of the Lung Screening Study (LSS) network of the National Lung Screening Trial (NLST) consists of up to three annual screens using CT imaging from each of 17,308 participants with a significant history of smoking but no evidence of cancer at trial enrollment (Fall 2002-Spring 2004). Screens performed at numerous medical centers associated with 10 LSS-NLST screening centers are deidentified of protected health information and delivered to the CTIL via DVD, external hard disk, or Internet/Virtual Private Network transmission. The collection will be completed in late 2006. The CTIL is of potential interest to clinical researchers and software developers of nodule detection algorithms. Its attractiveness lies in its very specific, well-defined patient population, scanned via a common CT protocol, and in its collection of evenly spaced serial screens. In this work, we describe the technical details of the CTIL collection process from screening center retrieval through library storage.

The accuracy of coronary calcium scoring using 16-row MSCT comparing 1- and 3-mm slices was assessed. A thorax phantom with calcium cylinder inserts was scanned applying a non-enhanced retrospectively ECG-gated examination protocol: collimation 12 x 0.75 mm; 120 kV; 133 mAs(eff). Thirty-eight patients were examined using the same scan protocol. Image reconstruction was performed with an effective slice thickness of 3 and 1 mm. The volume score, calcium mass and Agatston score were determined. Image noise was measured in both studies. The volume score and calcium mass varied less than the Agatston score. The overall measured calcium mass compared to the actual calcium mass revealed a relative difference of +2.0% for 1-mm slices and -1.2% for 3-mm slices. Due to increased image noise in thinner slices in the patient study (26.1 HU), overall calcium scoring with a scoring threshold of 130 HU was not feasible. Interlesion comparison showed significantly higher scoring results for thinner slices (all P<0.001). A similar accuracy comparing calcium scoring results of 1- and 3-mm slices was shown in the phantom study; therefore, the potentially necessary increase of the patient's dose in order to achieve assessable 1-mm slices with an acceptable image-to-noise-ratio appears not to be justified.

Objective: To investigate the clinical importance of discography and CT after discography (CTD) in the lumbar disc diseases, and make a comparison between CTD and magnetic resonance imaging (MRI)., Methods: 265 patients with back and leg pain 177 males and 79 females, age 39 +/- 16, underwent discography and CT 2 hours later on 298 discs. All the patients' discs were classified into 7 types according to the findings by discography and CTD. Comprehensive MRI pictures were available in 237 of the 265 patients (with 257 discs); they accepted the MRI classification and were compared with the CT-discographic findings., Results: 245 patients underwent operation, among which 247 discs showed visual lesions during the procedure and 238 cases (96.4%) had their disc diseases diagnosed accurately through discography and CTD. Considering the positive screening rate for the discogenic abnormality, the difference was of statistical significance between the CTD and MRI groups (P < 0.01). MRI was considered to have limited value in the disc diseases with passive discographic finding., Conclusion: Discography is an efficient, sound and safe assistant diagnostic tool, which can get some special information about disc. The matched-group study shows that the MRI and CTD can not be re placed by each other. MRI may act as the screening tool for disc diseases, but is inferior to CTD, especially for the contiguous disc structure in the spinal fixation procedure.

In cone-beam geometry, image quality may be degraded or artifacts may occur if the cone angle is substantially wide. This is because a cone-beam scan along a circular orbit does not collect the complete set of data required to make an exact reconstruction of all volumetric data. To increase temporal resolution and thus image quality in cone-beam geometry, Silver proposed the new half-scan algorithm (NHS-FDK), which extends Parker's weighting function (HS-FDK) by utilizing a larger range up to 2pi. Here, we evaluated these algorithms for hepatic contrast-enhanced CT in cine scan mode using a 256-detector row CT. The full-scan (FS-FDK) images show uniform distribution of the image noise and CT-number uniformity. Image noise and CT-number uniformity with HS-FDK and NHS-FDK images follow the initial projection angle. HS-FDK images therefore have more changeable higher intensity (brighter) and a lower intensity (darker) areas than respective FS-FDK and NHS-FDK images. We concluded that, considering the trade-off between image quality and temporal resolution, the NHS-FDK algorithm is useful in volumetric cine imaging for the abdomen.

Objective: Worldwide demand has increased for the development of a computed tomography (CT) classification system that supplements the ILO classification of radiographs for pneumoconioses. The authors aimed to show preliminary reliability test results for selected referent films for the CT classification system developed through an international effort by researchers from seven countries., Methods: Reading trials by eight physicians who have considerable experience in pneumoconioses using a total of 114 lung zones consisting of 6 lung zones of 19 CT films of dust-exposed workers were performed to assess reliability of the classification system by weighted kappa. The results were also utilized for selecting reference films., Results: A good agreement was observed for both first and second reading trials for rounded opacities (weighted kappa=0.76, 0.74, first and second trial results, respectively), irregular opacities (0.60, 0.48), emphysema (0.56, 0.70) and honeycombing (0.72, 0.79). Ground glass opacities, on the other hand, showed moderate agreement (0.43, 0.38). Intra-reader agreements among eight readers were shown in the same table as the mean and standard deviation of weighted kappa statistics. The inter-reader agreement for pleural thickening was not as good as for parenchymal lesions., Discussion: The CT classification development may pioneer noble and sensitive medical screening for dust-exposed workers in selected settings. This system may be applied to radiographic borderline cases of profusion 0/1 and 1/0 by the ILO classification, in a setting that assures the occupational safety and health of workers exposed to some newly developed chemical compounds.

Background: Most studies of traumatic intraventricular hemorrhage (tIVH) contain fewer than 25 subjects and are retrospective in design, providing minimal information about the entity and its clinical significance., Methods: We prospectively enrolled trauma patients from 18 centers in North America in the National Emergency X-Radiography Utilization Study (NEXUS) II if they received an emergent head computed tomography (CT) scan, as determined by the managing physician. Clinical data were collected at the time of enrollment and CT reports were compiled at least 1 month later. We calculated prevalence and demographics of tIVH from the 18 sites, while outcome data were gathered from medical records of patients with tIVH who were seen at any of six sites that participated in the follow-up portion of the study. We considered patients who underwent a neurosurgical intervention or who had a "poor outcome" (Glasgow Outcome Scale score of 1 to 3, death, persistent vegetative state, or severe disability) to have suffered a "combined outcome.", Results: Prevalence of tIVH among all trauma patients who received a head CT was 118 in 8,374, or 1.41%. Among tIVH patients, 70% had a "poor outcome" and 76% had a "combined outcome." A poor outcome appeared to be associated with an abnormal presenting Glasgow Coma Scale score and involvement of the third or fourth ventricle, whereas age appeared to be unrelated. Patients with tIVH and no major associated injury on CT tended to do well; only one patient with isolated tIVH had a poor outcome., Conclusions: Traumatic IVH is rare and is associated with poor outcomes that seem to be the consequence of associated injuries. Isolated tIVH patients who are clinically well appear to have a functional outcome; we were unable to identify a case of isolated tIVH, combined with a normal neurologic examination, resulting in a poor or combined outcome.

The Oxfordshire Community Stroke Project (OCSP) classification provides a simple means of classifying the clinical syndromes associated with acute stroke. The validity of the classification can depend on many factors. Accuracy and time of the clinical and radiological examination are very important. It was used in the International Stroke Trial (IST). The study was conducted in 467 hospitals in 34 countries. Our aim was to assess how well the OCSP classification could predict infarct site and size on computed tomography (CT) scan when performed in a trial within 48 h after the onset of stroke and the clinical assessment was carried out by different doctors in different hospitals. We examined data on the patients randomized in the IST by the seven participating hospitals in Poland. Patients admitted to the hospital were examined by the doctor on duty. Eight aspects of the neurological deficit present just before randomization were recorded. The computer system in the randomization centre employed a validated algorithm to assign the patient to one of the four infarct syndromes: lacunar syndrome (LACS), partial anterior circulation syndrome (PACS), total anterior circulation syndrome (TACS) and posterior circulation syndrome (POCS). We assessed the localization and extent of the recent infarction on available CT scans and correlated these with the computer-assigned OCSP category. CT scans were available for 558/759 (74%) of the patients randomized in Poland. In 458 (82%) of cases, CT was carried out in the first 24 h after the onset of stroke. In 444 (80%) scans, a recent infarct was visible. These radiological lesions were appropriate to the clinical classification in 56% of patients with TACS, 73% with PACS, 61% with LACS and 59% with POCS. In Polish centres in IST, amongst the patients with infarction visible on CT, the OCSP subtype predicted the size and site of the infarct in about two-thirds of cases. These data suggest that, provided its limitations are taken into account, the classification can be usefully applied in multicentre clinical trials (or epidemiological studies) and to aspects of the routine clinical care of patients with acute stroke.

In summary, it is also important to remember the hidden rules: 1) Just because there is a code in the manual, it doesn't mean it can be billed to insurance, or that once billed, it will be reimbursed. 2) Just because a code was paid once, doesn't mean it will ever be paid again--or that you get to keep the money! 3) The healthcare provider is responsible for knowing all the rules, but then it is impossible to know all the rules! And not knowing all the rules can lead to fines, penalties or worse! New codes are added annually (quarterly for OPPS), definitions of existing codes are changed, and it is the responsibility of healthcare providers to keep abreast of all coding updates and changes. In addition, the federal regulations are constantly updated and changed, making compliant billing a moving target. All healthcare entities should focus on complete documentation, the adherence to authoritative coding guidance and the provision of detailed explanations and specialty education to the payor, as necessary.