How high is the inter-observer reproducibility in the LIRADS reporting system?

PURPOSE: To investigate the reproducibility of LIRADS v2014 and contribute to its widespread use in clinical practice. MATERIAL AND METHODS: This retrospective, single-centre study was conducted between January 2010 and October 2015. A total of 132 patients who had dynamic magnetic resonance imaging (MRI)/computed tomography (CT) images in the Picture Archiving and Communication Systems (PACS) with liver nodule were included in the study, 37 of whom had histopathology results. Five radiologists who participated in the study, interpreted liver nodules independently on different PACS stations according to the LIRADS reporting system and its main parameters. RESULTS: We determined that level of inter-observer agreement in the LR-1, LR-5, and LR-5V categories was higher than in the LR-2, LR-3, and LR-4 categories (κ = 0.522, 0.442, and 0.600 in the LR-1, LR-5, and LR-5V categories, respectively; κ = 0.082, 0.298, and 0.143 in the LR-2, LR-3, and LR-4 categories, respectively). The parameter that we observed to have the highest level of inter-observer agreement was venous thrombus (κ = 0.600). CONCLUSIONS: Our study showed that LIRADS achieves an acceptable inter-observer reproducibility in terms of clinical practice although it is insufficient at intermediate risk levels. We think that the prevalence of its use will be further increased with training related to the subject and the assignment of numerical values that express the probability of malignancy for each category and including the ancillary features in the algorithm according to clearer rules.

Systematic Parameterization, Storage, and Representation of Volumetric DICOM Data.

Tomographic medical imaging systems produce hundreds to thousands of slices, enabling three-dimensional (3D) analysis. Radiologists process these images through various tools and techniques in order to generate 3D renderings for various applications, such as surgical planning, medical education, and volumetric measurements. To save and store these visualizations, current systems use snapshots or video exporting, which prevents further optimizations and requires the storage of significant additional data. The Grayscale Softcopy Presentation State extension of the Digital Imaging and Communications in Medicine (DICOM) standard resolves this issue for two-dimensional (2D) data by introducing an extensive set of parameters, namely 2D Presentation States (2DPR), that describe how an image should be displayed. 2DPR allows storing these parameters instead of storing parameter applied images, which cause unnecessary duplication of the image data. Since there is currently no corresponding extension for 3D data, in this study, a DICOM-compliant object called 3D presentation states (3DPR) is proposed for the parameterization and storage of 3D medical volumes. To accomplish this, the 3D medical visualization process is divided into four tasks, namely pre-processing, segmentation, post-processing, and rendering. The important parameters of each task are determined. Special focus is given to the compression of segmented data, parameterization of the rendering process, and DICOM-compliant implementation of the 3DPR object. The use of 3DPR was tested in a radiology department on three clinical cases, which require multiple segmentations and visualizations during the workflow of radiologists. The results show that 3DPR can effectively simplify the workload of physicians by directly regenerating 3D renderings without repeating intermediate tasks, increase efficiency by preserving all user interactions, and provide efficient storage as well as transfer of visualized data.

Anatomic variations of the intrahepatic bile ducts: analysis of magnetic resonance cholangiopancreatography in 1011 consecutive patients.

OBJECTIVES: The aim of the study was to evaluate the frequency of anatomic variations of the hepatic duct bifurcation using magnetic resonance cholangiopancreatography (MRCP). METHODS: A total of 1,160 consecutive patients, referred to our department for MRCP due to suspected pancreatobiliary disease or before liver transplantation, were reviewed retrospectively. A total of 149 patients with less than optimal results due to imaging limitations or secondary differentiations of bile duct anatomy were excluded from the study. The final study population was composed of 1,011 cases. RESULTS: Of the 1,160 patients, 149 were excluded from the analysis. Typical biliary anatomy was observed in 79.4% of cases, but female potential living liver donors more frequently presented an anatomic variation. Typical anatomy was present in 75.7% of the females and 85.3% of the males (p < 0.05). Out of the remaining 1,011 patients, 208 (20.57%) were diagnosed with different levels of various anatomic variations of the intra- and extrahepatic biliary ducts. Of the 208 cases with diagnosed variations, 204 (98.07%) and 4 (1.92%) turned out to have 1 and 2 different variations, respectively. The trifurcation variant was observed in 81 cases (8.01%), while 73 subjects (7.23%) had an aberrant right biliary duct draining into the common hepatic duct. A right dorsocaudal branch draining into the left hepatic duct was present in 42 cases (4.15%). Four cases (0.4%) had 2 different variations and 8 (0.8%) had uncommon anatomic variations. CONCLUSIONS: Typical intrahepatic biliary anatomy is present in about 80% of the inhabitants of the Aegean region of Turkey, but anatomic variants seem to be more frequent in females as compared to males. Trifurcation was the most common anatomic variation in our study population. The presence of an aberrant right hepatic duct emptying into the common hepatic duct was the second most common observation amongst our findings.

Voxel level dense prediction of acute stroke territory in DWI using deep learning segmentation models and image enhancement strategies.

PURPOSE: To build a stroke territory classifier model in DWI by designing the problem as a multiclass segmentation task by defining each stroke territory as distinct segmentation targets and leveraging the guidance of voxel wise dense predictions. MATERIALS AND METHODS: Retrospective analysis of DWI images of 218 consecutive acute anterior or posterior ischemic stroke patients examined between January 2017 to April 2020 in a single center was carried out. Each stroke area was defined as distinct segmentation target with different class labels. U-Net based network was trained followed by majority voting of the voxel wise predictions of the model to transform them into patient level stroke territory classes. Effects of bias field correction and registration to a common space were explored. RESULTS: Of the 218 patients included in this study, 141 (65%) were anterior stroke, and 77 were posterior stroke (35%) whereas 117 (53%) were male and 101 (47%) were female. The model built with original images reached 0.77 accuracy, while the model built with N4 bias corrected images reached 0.80 and the model built with images which were N4 bias corrected and then registered into a common space reached 0.83 accuracy values. CONCLUSION: Voxel wise dense prediction coupled with bias field correction to eliminate artificial signal increase and registration to a common space help models for better performance than using original images. Knowing the properties of target domain while designing deep learning models is important for the overall success of these models.

Artificial intelligence in diagnostic ultrasonography.

Artificial intelligence (AI) continues to change paradigms in the field of medicine with new applications that are applicable to daily life. The field of ultrasonography, which has been developing since the 1950s and continues to be one of the most powerful tools in the field of diagnosis, is also the subject of AI studies, despite its unique problems. It is predicted that many operations, such as appropriate diagnostic tool selection, use of the most relevant parameters, improvement of low-quality images, automatic lesion detection and diagnosis from the image, and classification of pathologies, will be performed using AI tools in the near future. Especially with the use of convolutional neural networks, successful results can be obtained for lesion detection, segmentation, and classification from images. In this review, relevant developments are summarized based on the literature, and examples of the tools used in the field are presented.

A snapshot of teleradiology practice in Turkey: the results of a survey among radiologists.

PURPOSE: This study featured a survey that offers a snapshot of various teleradiology practices in Turkey, a Group of Twenty country that has undertaken a major transformation of its health care system during the last two decades and is currently the world leader in terms of the combined number of per capita magnetic resonance imaging and computed tomography examinations performed (which represent the bulk of teleradiology services worldwide). METHODS: The study data was collected from 4736 Turkish Society of Radiology (TSR) members via an electronic platform in the web environment through a questionnaire consisting of 24 questions. The survey was conducted in a 3-month time window (March-May 2021). Statistical tools were used for the analysis of the quantitative data. RESULTS: Responses from 156 members of the TSR comprised the study data, revealing that teleradiology is used for various applications in Turkey. Almost half of the participants (49%) performed teleradiology only in the private sector. Half of the respondents (51%) stated that they reported images at home for multiple centers. Moreover, 38% of the participants had been reporting more than 50 examinations per day, and 74% of the respondents earned less than 0.50 Euro per examination they reported. The overall satisfaction with teleradiology among the teleradiologists was, on average, 4.7 out of 10 points. CONCLUSION: The results are both promising for the future (i.e., concerning the propensity for adopting new technology) and alarming for the current state of affairs (i.e., insufficient radiologist reimbursement and lack of licensing and accreditation of teleradiology service providers). Periodic surveys performed in countries with different health care systems concerning financial, technical, and medicolegal aspects might reveal an up-to-date landscape of teleradiology practices worldwide and help guide local and regional decision-makers.

Evaluation of abdominal computed tomography findings in patients with COVID-19: a multicenter study

PURPOSE: To evaluate the frequency of abdominal computed tomography (CT) findings in patients with coronavirus disease-2019 (COVID-19) and interrogate the relationship between abdominal CT findings and patient demographic features, clinical findings, and laboratory test results as well as the CT atherosclerosis score in the abdominal aorta. METHODS: This study was designed as a multicenter retrospective study. The abdominal CT findings of 1.181 patients with positive abdominal symptoms from 26 tertiary medical centers with a positive polymerase chain-reaction test for severe acute respiratory syndrome coronavirus 2 were reviewed. The frequency of ischemic and non-ischemic CT findings as well as the association between CT findings, clinical features, and abdominal aortic calcific atherosclerosis score (AA-CAS) were recorded. RESULTS: Ischemic and non-ischemic abdominal CT findings were detected in 240 (20.3%) and 328 (27.7%) patients, respectively. In 147 patients (12.4%), intra-abdominal malignancy was present. The most frequent ischemic abdominal CT findings were bowel wall thickening (n = 120; 10.2%) and perivascular infiltration (n = 40; 3.4%). As for non-ischemic findings, colitis (n = 91; 7.7%) and small bowel inflammation (n = 73; 6.2%) constituted the most frequent disease processes. The duration of hospital stay was found to be higher in patients with abdominal CT findings than in patients without any positive findings (13.8 ± 13 vs. 10.4 ± 12.8 days, P < 0.001). The frequency of abdominal CT findings was significantly higher in patients who did not survive the infection than in patients who were discharged after recovery (41.7% vs. 27.4%, P < 0.001). Increased AA-CAS was found to be associated with a higher risk of ischemic conditions in abdominal CT examinations. CONCLUSION: Abdominal symptoms in patients with COVID-19 are usually associated with positive CT findings. The presence of ischemic findings on CT correlates with poor COVID-19 outcomes. A high AA-CAS is associated with abdominal ischemic findings in patients with COVID-19.

How to perform an excellent radiology board examination: a web-based checklist.

BACKGROUND: Board exams are now considered as means of quality procedures that aim to keep the professional knowledge and skills of the physicians at the highest level. In addition, for an assessment to be scientifically valid, it has to be done within defined standards. Although there are different sources in this field, there is a need for a resource that details the steps required for the examinations to be performed perfectly, brings descriptions of the reasons for the procedure and associates the steps with assessment standards. Experts with national and international experience both in radiology and medical education contributed to the preparation of this checklist. RESULTS: The guide includes 174 elements to consider before, after the exam order and examination. From the perspective of assessment standards, it has been observed that the steps to be considered before the exam have a greater impact on the validity and reliability of the exam. The standard in which the questions are most associated was validity with 117 (67.24%) questions. CONCLUSIONS: We think that our guide, which will be accessible in the web environment, will be useful to the teams with a development goal or just start the exam, the candidates who will take the exam and the examiners.

TSR guidelines for the practice of teleradiology: 2021 update.

This update of Turkish Society of Radiology's (TSR) guidelines for the practice of teleradiology is intended to provide a reference framework for all parties involved in delivering imaging services away from the immediate vicinity of the patient. It includes relevant definitions and general principles, features organizational modes and qualifications of the practicing parties, lists technical issues, and addresses such management and legal aspects as archiving and documentation, security and privacy, reliability, responsibilities, quality inspection and improvement, reimbursement and accountability.

CHAOS Challenge - combined (CT-MR) healthy abdominal organ segmentation.

Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) introduced new state-of-the-art segmentation systems. Despite outperforming the overall accuracy of existing systems, the effects of DL model properties and parameters on the performance are hard to interpret. This makes comparative analysis a necessary tool towards interpretable studies and systems. Moreover, the performance of DL for emerging learning approaches such as cross-modality and multi-modal semantic segmentation tasks has been rarely discussed. In order to expand the knowledge on these topics, the CHAOS - Combined (CT-MR) Healthy Abdominal Organ Segmentation challenge was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI), 2019, in Venice, Italy. Abdominal organ segmentation from routine acquisitions plays an important role in several clinical applications, such as pre-surgical planning or morphological and volumetric follow-ups for various diseases. These applications require a certain level of performance on a diverse set of metrics such as maximum symmetric surface distance (MSSD) to determine surgical error-margin or overlap errors for tracking size and shape differences. Previous abdomen related challenges are mainly focused on tumor/lesion detection and/or classification with a single modality. Conversely, CHAOS provides both abdominal CT and MR data from healthy subjects for single and multiple abdominal organ segmentation. Five different but complementary tasks were designed to analyze the capabilities of participating approaches from multiple perspectives. The results were investigated thoroughly, compared with manual annotations and interactive methods. The analysis shows that the performance of DL models for single modality (CT / MR) can show reliable volumetric analysis performance (DICE: 0.98 ± 0.00 / 0.95 ± 0.01), but the best MSSD performance remains limited (21.89 ± 13.94 / 20.85 ± 10.63 mm). The performances of participating models decrease dramatically for cross-modality tasks both for the liver (DICE: 0.88 ± 0.15 MSSD: 36.33 ± 21.97 mm). Despite contrary examples on different applications, multi-tasking DL models designed to segment all organs are observed to perform worse compared to organ-specific ones (performance drop around 5%). Nevertheless, some of the successful models show better performance with their multi-organ versions. We conclude that the exploration of those pros and cons in both single vs multi-organ and cross-modality segmentations is poised to have an impact on further research for developing effective algorithms that would support real-world clinical applications. Finally, having more than 1500 participants and receiving more than 550 submissions, another important contribution of this study is the analysis on shortcomings of challenge organizations such as the effects of multiple submissions and peeking phenomenon.

In vivo proton magnetic resonance spectroscopic examination of benzodiazepine action in humans.

In an examination of the effect of benzodiazepines on brain chemistry, 44 healthy controls underwent a short echo-time proton magnetic resonance spectroscopy ((1)H MRS) session after induced sedation with intravenous midazolam (0.03mg/kg) plus fentanyl (2µg/kg). The regions of interest were the anterior cingulate cortex, right basal ganglia, right frontal lobe, and right hippocampus. Twenty-five of these subjects underwent the second (1)H MRS session while awake. The measured (1)H MRS metabolites included N-acetyl-aspartate, creatine-containing compounds (PCr+Cr), choline-containing compounds, myo-inositol, and glutamate plus glutamine, which were quantified both as absolute values and metabolite/PCr+Cr ratios. The results were analyzed using independent group t tests and repeated measures analysis of variance (ANOVA, with alpha values set at 0.025 to minimize the risk of false-positive findings arising from multiple comparisons. No significant difference between subjects under midazolam plus fentanyl induced sedation and awake could be detected with unpaired analyses. Paired comparisons by ANOVA with repeated measures found that neither drug (midazolam plus fentanyl) nor the drug by time (interval between two scan times) interaction had a significant effect on the quantified metabolites. These findings encourage utilization of benzodiazepine-induced brief sedation during in vivo (1)H MRS experiments of the brain, and may help with elucidation of state-dependent neurochemical alterations during the course of bipolar and schizoaffective disorders.

Reversible 3D compression of segmented medical volumes: usability analysis for teleradiology and storage.

BACKGROUND: DICOM standard does not have modules that provide the possibilities of two-dimensional Presentation States to three-dimensional (3D). Once the final 3D rendering is obtained, only video/image exporting or snapshots can be used. To increase the utility of 3D Presentation States in clinical practice and teleradiology, the storing and transferring the segmentation results, obtained after tedious procedures, can be very effective. PURPOSE: To propose a strategy for preserving interaction and mobility of visualizations for teleradiology by storing and transferring only binary segmented data, which is effectively compressed by modern adaptive and context-based reversible methods. MATERIAL AND METHODS: A diverse set of segmented data, which include four abdominal organs (liver, spleen, right, and left kidneys) from 20 T1-DUAL and 20 T2-SPIR MRI, liver from 20 CT, and abdominal aorta with aneurysms (AAA) from 19 computed tomography-angiography datasets, are collected. Each organ is segmented manually by expert physicians, and binary volumes are created. The well-established reversible binary compression methods PNG, JPEG-LS, JPEG-XR, CCITT-G4, LZW, JBIG2, and ZIP are applied to medical datasets. Recently proposed context-based (3D-RLE) and adaptive (ABIC) algorithms are also employed. The performance assessment has been presented in terms of the compression ratio that is a universal compression metric. RESULTS: Reversible compression of binary volumes results with substantial decreases in file size such as 254 to 2.14 MB for CT-AAA, 56.7 to 0.3 MB for CT-liver. Moreover, compared to the performance of well-established methods (i.e., mean 76.14%), CR is observed to be increased significantly for all segmented organs from both CT and MRI datasets when ABIC (95.49%) and 3D-RLE (94.98%) are utilized. The hypothesis is that morphological coherence of scanning procedure and adaptation between the segmented organs, that is, bi-level images, contributes to compression performance. Although the performance of well-established techniques is satisfactory, the sensitivity of ABIC to modality type and the advantage of 3D-RLE when the spatial coherence between the adjacent slices are high results with up to 10 times more CR performance. CONCLUSION: Adaptive and context-based compression strategies allow effective storage and transfer of segmented binary data, which can be used to re-produce visualizations for better teleradiology practices preserving all interaction mechanisms.

Comparison of radiological scoring systems, clinical scores, neutrophil-lymphocyte ratio and serum C-reactive protein level for severity and mortality in acute pancreatitis.

Comparison of radiological scoring systems, clinical scores, neutrophil-lymphocyte ratio and serum C-reactive protein level for severity and mortality in acute pancreatitis BACKGROUND/AIMS To compare radiological scoring systems, clinical scores, serum C-reactive protein (CRP) levels and the neutrophil-lymphocyte ratio (NLR) for predicting the severity and mortality of acute pancreatitis (AP). MATERIALS AND METHODS Demographic, clinical, and radiographic data from 80 patients with AP were retrospectively evaluated. The harmless acute pancreatitis score (HAPS), systemic inflammatory response syndrome (SIRS), bedside index for severity in acute pancreatitis (BISAP), Ranson score, Balthazar score, modified computed tomography severity index (CTSI), extrapancreatic inflammation on computed tomography (EPIC) score and renal rim grade were recorded. The prognostic performance of radiological and clinical scoring systems, NLR at admission, and serum CRP levels at 48 hours were compared for severity and mortality according to the revised Atlanta Criteria. The data were evaluated by calculating the receiver operator characteristic (ROC) curves and area under the ROC (AUROC). RESULTS Out of 80 patients, 19 (23.8%) had severe AP, and 9 (11.3%) died. The AUROC for the BISAP score was 0.836 (95%CI: 0.735-0.937), with the highest value for severity. With a cut-off of BISAP ≥2, sensitivity and specificity were 68.4% and 78.7%, respectively. The AUROC for NLR was 0.915 (95%CI: 0.790-1), with the highest value for mortality. With a cut-off of NLR >11.91, sensitivity and specificity were 76.5% and 94.1%, respectively. Of all the radiological scoring systems, the EPIC score had the highest AUROC, i.e., 0.773 (95%CI: 0.645-0.900) for severity and 0.851 (95%CI: 0.718-0.983) for mortality, with a cut-off value ≥6. CONCLUSION The BISAP score and NLR might be preferred as early determinants of severity and mortality in AP. The EPIC score might be suggested from the current radiological scoring systems.

Comparison of semi-automatic and deep learning-based automatic methods for liver segmentation in living liver transplant donors.

PURPOSE: To compare the accuracy and repeatability of emerging machine learning based (i.e. deep) automatic segmentation algorithms with those of well-established semi-automatic (interactive) methods for determining liver volume in living liver transplant donors at computerized tomography (CT) imaging. METHODS: A total of 12 (6 semi-, 6 full-automatic) methods are evaluated. The semi-automatic segmentation algorithms are based on both traditional iterative models including watershed, fast marching, region growing, active contours and modern techniques including robust statistical segmenter and super-pixels. These methods entail some sort of interaction mechanism such as placing initialization seeds on images or determining a parameter range. The automatic methods are based on deep learning and they include three framework templates (DeepMedic, NiftyNet and U-Net) the first two of which are applied with default parameter sets and the last two involve adapted novel model designs. For 20 living donors (6 training and 12 test datasets), a group of imaging scientists and radiologists created ground truths by performing manual segmentations on contrast material-enhanced CT images. Each segmentation is evaluated using five metrics (i.e. volume overlap and relative volume errors, average/RMS/maximum symmetrical surface distances). The results are mapped to a scoring system and a final grade is calculated by taking their average. Accuracy and repeatability were evaluated using slice by slice comparisons and volumetric analysis. Diversity and complementarity are observed through heatmaps. Majority voting and Simultaneous Truth and Performance Level Estimation (STAPLE) algorithms are utilized to obtain the fusion of the individual results. RESULTS: The top four methods are determined to be automatic deep models having 79.63, 79.46 and 77.15 and 74.50 scores. Intra-user score is determined as 95.14. Overall, deep automatic segmentation outperformed interactive techniques on all metrics. The mean volume of liver of ground truth is found to be 1409.93 mL ± 271.28 mL, while it is calculated as 1342.21 mL ± 231.24 mL using automatic and 1201.26 mL ± 258.13 mL using interactive methods, showing higher accuracy and less variation on behalf of automatic methods. The qualitative analysis of segmentation results showed significant diversity and complementarity enabling the idea of using ensembles to obtain superior results. The fusion of automatic methods reached 83.87 with majority voting and 86.20 using STAPLE that are only slightly less than fusion of all methods that achieved 86.70 (majority voting) and 88.74 (STAPLE). CONCLUSION: Use of the new deep learning based automatic segmentation algorithms substantially increases the accuracy and repeatability for segmentation and volumetric measurements of liver. Fusion of automatic methods based on ensemble approaches exhibits best results almost without any additional time cost due to potential parallel execution of multiple models.

Patient oriented and robust automatic liver segmentation for pre-evaluation of liver transplantation.

Identifying liver region from abdominal computed tomography-angiography (CTA) data sets is one of the essential steps in evaluation of transplantation donors prior to the hepatic surgery. However, due to gray level similarity of adjacent organs, injection of contrast media and partial volume effects; robust segmentation of the liver is a very difficult task. Moreover, high variations in liver margins, different image characteristics with different CT scanners and atypical liver shapes make the segmentation process even harder. In this paper, we propose a three stage (i.e. pre-processing, classification, post-processing); automatic liver segmentation algorithm that adapts its parameters according to each patient by learning the data set characteristics in parallel to segmentation process to address all the challenging aspects mentioned above. The efficiency in terms of the time requirement and the overall segmentation performance is achieved by introducing a novel modular classification system consisting of a K-Means based simple classification system and an MLP based complex one which are combined with a data-dependent and automated switching mechanism that decides to apply one of them. Proposed approach also makes the design of the overall classification system fully unsupervised that depends on the given CTA series only without requiring any given training set of CTA series. The segmentation results are evaluated by using area error rate and volume calculations and the success rate is calculated as 94.91% over a data set of diverse CTA series of 20 patients according to the evaluation of the expert radiologist. The results show that, the proposed algorithm gives better results especially for atypical liver shapes and low contrast studies where several algorithms fail.

Contribution of diffusion-weighted MRI to the differential diagnosis of hepatic masses.

PURPOSE: To evaluate the diagnostic contribution of diffusion-weighted magnetic resonance imaging (MRI) using apparent diffusion coefficient (ADC) values to the characterization of hepatic masses and differentiation of benign and malignant lesions. MATERIALS AND METHODS: The study included 30 patients that underwent upper abdominal MRI examinations because of hepatic masses that were found to be > or =1 cm in size with conventional sequences, and were additionally evaluated with diffusion-weighted MRI. Diffusion-weighted images and ADC maps in the axial plane were obtained using a 1.5 Tesla MRI device, single shot echo-planar spin echo sequences on 3 axes (x, y, z), and diffusion sensitive gradients with 2 different b values (b = 0 and b = 1000 s/mm (2)). Mean ADC measurements were calculated among the 30 cases involving 41 hepatic masses. RESULTS: Of the 41 hepatic masses, 24 were benign and 17 were malignant. Benign lesions included 6 cysts, 14 hemangiomas, 2 abscesses, and 2 hydatid cysts. Malignant masses included 8 metastases, 4 hepatocellular carcinomas, 4 cholangiocellular carcinomas, and 1 gall bladder adenocarcinoma. The highest ADC values were for cysts and hemangiomas. The mean ADC value of benign lesions was 2.57 +/- 0.26 x 10(-3)mm(2)/s, whereas malignant lesions had a mean ADC value of 0.86 +/- 0.11 x 10(-3)mm(2)/s. The mean ADC value of benign lesions was significantly higher than that of malignant lesions (P < 0.01). CONCLUSION: Diffusion-weighted MRI with quantitative ADC measurements can be useful in the differentiation of benign and malignant liver lesions.

The role of SPIO-enhanced MRI in the detection of malignant liver lesions.

The aim of this study was to evaluate the efficacy of superparamagnetic iron oxide (SPIO)-enhanced magnetic resonance imaging (MRI) in the detection of malignant liver tumors. MRI, using fast spin-echo T(2)-weighted and gradient-echo T(1)-weighted imagings before and after SPIO infusion, was performed in 32 patients with known or suspected hepatic lesions. Statistical analysis was performed using lesion-by-lesion analysis. SPIO-enhanced T(2)-weighted MRI showed results comparable to those of unenhanced T(2)-weighted MRI in the detection of focal liver lesions.

Publication rates of scientific presentations in Turkish national radiology congresses.

PURPOSE: To investigate the publication rates of scientific presentations in Turkish radiology congresses. MATERIALS AND METHODS: The abstracts of presentations at Turkish national radiology congresses between 1995-2002 were reviewed. According to the congress abstract booklets, types of the presentation (oral or poster presentation), as well as body system-based categorization were used. The presentations were searched for publication in Medline(R) indexed journals using the PubMed server. The rates of publication of the abstracts, the journals in which the presentations have been published and the time between the presentation and publication were investigated. RESULTS: An overall number of 521 publications were found originating from 4,413 presentations (11.81%). The publication numbers of 2,116 scientific research, 1,995 case report and 302 educational exhibit type presentations were 261 (12.33%), 249 (12.49%) and 11 (3.64%), respectively. For oral presentations the publication ratio was found as 15.38% (116/754) and for posters it was 11.06% (405/3,659). The number of presentations and the number of publications were found to increase by years, however the ratio of publication to presentation remained the same. Mean time to publication was calculated as 24.4 months. In frequency of publication, Tanisal ve Girisimsel Radyoloji, European Radiology, European Journal of Radiology were the first three publication media of the presentations. CONCLUSION: The publication rates of abstracts presented at Turkish national radiology congresses are lower than the previously reported publication rates in radiology and other specialties.

Automated measurement of intima-media thickness of carotid arteries in ultrasonography by computer software.

PURPOSE: Intima-media thickness (IMT) has been proposed to be a morphological criterion of atherosclerosis. The purpose of this study was to investigate the interobserver variability of manual and also of computer software measurements of IMT. MATERIALS AND METHODS: High-resolution common carotid artery (CCA) images of 88 patients that have been obtained by a linear broadband L5-12 MHz transducer and archived in PACS were retrospectively evaluated. Two separate investigators, who were unaware of the former results, evaluated the same images by using computer software that had a dedicated tool for automatic measurement of IMT. The results of the investigators were compared. RESULTS: According to the two investigators who have performed manual measurements, mean values of IMT of right CCA were 0.6396 mm and 0.6356 mm; of the left CCA were 0.6662 mm and 0.6575 mm, respectively. The interobserver variability of measurements revealed the mean IMT as 0.6071 mm and 0.6048 mm for the right, 0.6216 mm and 0.6227 mm for the left CCA. Manual measurements of both investigators were found to be higher than the automatic measurements and the differences were statistically significant. Interobserver correlation of manual measurements was between 0.80-0.88 and of the automated measurements was between 0.93-0.98. CONCLUSION: Manual measurements reveal higher values than the automated measurements of IMT. The interobserver correlation of automated measurements is higher than manual measurements. The use of dedicated software may be proposed to reduce the measurement errors.

How Turkish radiology residents access information related to their profession in this social media and smartphone era.

PURPOSE: To evaluate the frequency of mobile technology and social media usage among radiology residents and their access to professional information. MATERIALS AND METHODS: A questionnaire consisting of 24 questions prepared using Google Drive was sent via e-mail to 550 radiology residents throughout the country. Of the 176 participating residents, 74 completed the survey via the internet, and 102 completed it at three different national radiology meetings. Response rates and its relationship with responses given to different questions were assessed. RESULTS: Hundred two male and 74 female residents participated in the survey. 141 (81.3%) residents thought that they had appropriate internet access in their department. The number of residents using a smartphone was 153 (86.9%). The android operating system (70, 45.8%) was the preferred operating system of respondants. Only 24 (15.7%) of the smartphone users thought that there were enough radiology related applications. "Radiology assistant" (18.9%), "Radiopedia" (7.8%) and "Radiographics" (7.8%) were the most utilized applications. Of the smartphone users, 87(56.9%) stated that they used cell phones in order to find radiological information, and the most used web pages were Google (165, 93.8%), Radiopaedia.org (129, 73.3%), Radiologyassistant.nl (135, 76.7%), and Pubmed (114, 64.8%). Social media usages were as follows: None (10, 5.7%), Facebook (139, 79%), Twitter (55, 31.3%), Google + (51, 29%) and YouTube (44, 25%). CONCLUSION: While smartphone usage rates among the residents were high, the use of radiology specific applications was not common. Social media usage was very common among residents.