Classification of rib fracture types from postmortem computed tomography images using deep learning.

Human or time resources can sometimes fall short in medical image diagnostics, and analyzing images in full detail can be a challenging task. With recent advances in artificial intelligence, an increasing number of systems have been developed to assist clinicians in their work. In this study, the objective was to train a model that can distinguish between various fracture types on different levels of hierarchical taxonomy and detect them on 2D-image representations of volumetric postmortem computed tomography (PMCT) data. We used a deep learning model based on the ResNet50 architecture that was pretrained on ImageNet data, and we used transfer learning to fine-tune it to our specific task. We trained our model to distinguish between "displaced," "nondisplaced," "ad latus," "ad longitudinem cum contractione," and "ad longitudinem cum distractione" fractures. Radiographs with no fractures were correctly predicted in 95-99% of cases. Nondisplaced fractures were correctly predicted in 80-86% of cases. Displaced fractures of the "ad latus" type were correctly predicted in 17-18% of cases. The other two displaced types of fractures, "ad longitudinem cum contractione" and "ad longitudinem cum distractione," were correctly predicted in 70-75% and 64-75% of cases, respectively. The model achieved the best performance when the level of hierarchical taxonomy was high, while it had more difficulties when the level of hierarchical taxonomy was lower. Overall, deep learning techniques constitute a reliable solution for forensic pathologists and medical practitioners seeking to reduce workload.

Towards a New Qualitative Screening Assay for Synthetic Cannabinoids Using Metabolomics and Machine Learning.

BACKGROUND: Synthetic cannabinoids (SCs) are steadily emerging on the drug market. To remain competitive in clinical or forensic toxicology, new screening strategies including high-resolution mass spectrometry (HRMS) are required. Machine learning algorithms can detect and learn chemical signatures in complex datasets and use them as a proxy to predict new samples. We propose a new screening tool based on a SC-specific change of the metabolome and a machine learning algorithm. METHODS: Authentic human urine samples (n = 474), positive or negative for SCs, were used. These samples were measured with an untargeted metabolomics liquid chromatography (LC)-quadrupole time-of-flight-HRMS method. Progenesis QI software was used to preprocess the raw data. Following feature engineering, a random forest (RF) model was optimized in R using a 10-fold cross-validation method and a training set (n = 369). The performance of the model was assessed with a test (n = 50) and a verification (n = 55) set. RESULTS: During RF optimization, 49 features, 200 trees, and 7 variables at each branching node were determined as most predictive. The optimized model accuracy, clinical sensitivity, clinical specificity, positive predictive value, and negative predictive value were 88.1%, 83.0%, 92.7%, 91.3%, and 85.6%, respectively. The test set was predicted with an accuracy of 88.0%, and the verification set provided evidence that the model was able to detect cannabinoid-specific changes in the metabolome. CONCLUSIONS: An RF approach combined with metabolomics enables a novel screening strategy for responding effectively to the challenge of new SCs. Biomarkers identified by this approach may also be integrated in routine screening methods.

RiFNet: Automated rib fracture detection in postmortem computed tomography.

Imaging techniques are widely used for medical diagnostics. In some cases, a lack of medical practitioners who can manually analyze the images can lead to a bottleneck. Consequently, we developed a custom-made convolutional neural network (RiFNet = Rib Fracture Network) that can detect rib fractures in postmortem computed tomography. In a retrospective cohort study, we retrieved PMCT data from 195 postmortem cases with rib fractures from July 2017 to April 2018 from our database. The computed tomography data were prepared using a plugin in the commercial imaging software Syngo.via whereby the rib cage was unfolded on a single-in-plane image reformation. Out of the 195 cases, a total of 585 images were extracted and divided into two groups labeled "with" and "without" fractures. These two groups were subsequently divided into training, validation, and test datasets to assess the performance of RiFNet. In addition, we explored the possibility of applying transfer learning techniques on our dataset by choosing two independent noncommercial off-the-shelf convolutional neural network architectures (ResNet50 V2 and Inception V3) and compared the performances of those two with RiFNet. When using pre-trained convolutional neural networks, we achieved an F1 score of 0.64 with Inception V3 and an F1 score of 0.61 with ResNet50 V2. We obtained an average F1 score of 0.91 ± 0.04 with RiFNet. RiFNet is efficient in detecting rib fractures on postmortem computed tomography. Transfer learning techniques are not necessarily well adapted to make classifications in postmortem computed tomography.

Evaluation of the mediastinal-thoracic volume ratio on postmortem computed tomography.

OBJECTIVES: The aim of this study was to measure the mediastinal-thoracic volume ratio (CTR_VOL) on PMCT as a more accurate version of traditional CTR, in order to assess the terminal positional relationship between the heart and lungs in the different causes of death with regard to age, gender, BMI, cardiomegaly, and lung expansion. MATERIALS: Two hundred fifty consecutive postmortem cases with pre-autopsy PMCT and full forensic autopsy were retrospectively evaluated. The lungs and the mediastinum were manually segmented on the PMCT data and the correspondent volumes were estimated in situ. CTR_VOL was calculated as the ratio of the mediastinal to the thoracic volume. The volume measurements were repeated by the same rater for the evaluation of the intrarater reliability. Age, gender, body weight and height, heart weight at autopsy, and cause of death were retrieved from the autopsy reports. Presence of lung expansion was radiologically evaluated in situ. RESULTS: CTR_VOL was positively associated with age and BMI but not with gender and was higher for cardiomegaly compared to normal hearts, lower for asphyxiation-related deaths compared to cardiac deaths and intoxications, and lower for cases with lung expansion. The intrarater reliability was excellent for the calculated volumes of both lungs and mediastinum. CONCLUSION: The results of the present study support CTR_VOL as a tool to assess the relationship between the heart and lungs in situ, which differs significantly between the studied cause of death categories.

Interpretable machine learning model to detect chemically adulterated urine samples analyzed by high resolution mass spectrometry.

OBJECTIVES: Urine sample manipulation including substitution, dilution, and chemical adulteration is a continuing challenge for workplace drug testing, abstinence control, and doping control laboratories. The simultaneous detection of sample manipulation and prohibited drugs within one single analytical measurement would be highly advantageous. Machine learning algorithms are able to learn from existing datasets and predict outcomes of new data, which are unknown to the model. METHODS: Authentic human urine samples were treated with pyridinium chlorochromate, potassium nitrite, hydrogen peroxide, iodine, sodium hypochlorite, and water as control. In total, 702 samples, measured with liquid chromatography coupled to quadrupole time-of-flight mass spectrometry, were used. After retention time alignment within Progenesis QI, an artificial neural network was trained with 500 samples, each featuring 33,448 values. The feature importance was analyzed with the local interpretable model-agnostic explanations approach. RESULTS: Following 10-fold cross-validation, the mean sensitivity, specificity, positive predictive value, and negative predictive value was 88.9, 92.0, 91.9, and 89.2%, respectively. A diverse test set (n=202) containing treated and untreated urine samples could be correctly classified with an accuracy of 95.4%. In addition, 14 important features and four potential biomarkers were extracted. CONCLUSIONS: With interpretable retention time aligned liquid chromatography high-resolution mass spectrometry data, a reliable machine learning model could be established that rapidly uncovers chemical urine manipulation. The incorporation of our model into routine clinical or forensic analysis allows simultaneous LC-MS analysis and sample integrity testing in one run, thus revolutionizing this field of drug testing.

An algorithm for automatically generating gas, bone and foreign body visualizations from postmortem computed tomography data.

Post mortem computed tomography (PMCT) can aid in localizing foreign bodies, bone fractures, and gas accumulations. The visualization of these findings play an important role in the communication of radiological findings. In this article, we present an algorithm for automated visualization of gas distributions on PMCT image data of the thorax and abdomen. The algorithm uses a combination of region growing segmentation and layering of different visualization methods to automatically generate overview images that depict radiopaque foreign bodies, bones and gas distributions in one image. The presented method was tested on 955 PMCT scans of the thorax and abdomen. The algorithm managed to generate useful images for all cases, visualizing foreign bodies as well as gas distribution. The most interesting cases are presented in this article. While this type of visualization cannot replace a real radiological analysis of the image data, it can provide a quick overview for briefings and image reports.

Postmortem imaging findings and cause of death determination compared with autopsy: a systematic review of diagnostic test accuracy and meta-analysis.

BACKGROUND: The aim of this study was to evaluate the sensitivity of postmortem computed tomography (PMCT), postmortem magnetic resonance imaging (PMMR) and PMCT angiography (PMCTA) compared with autopsy in cases of adult death investigations. METHODS: For this systematic review and meta-analysis, Embase, PubMed, Scopus, Web of Science and Medline were searched for eligible studies in October 2016; a follow-up literature search was conducted in March 2018. Studies referring to PMCT, PMCTA and/or PMMR of more than 3 cases with subsequent autopsy were included. Data were extracted from published texts in duplicate. The extracted outcomes were categorized as follows: soft tissue and organ findings, skeletal injuries, haemorrhages, abnormal gas accumulations and causes of death. The summary measure was sensitivity, if 3 or more studies were available. To combine studies, a random effects model was used. Variability and heterogeneity within the meta-analysis was assessed. RESULTS: Of 1053 studies, 66 were eligible, encompassing a total of 4213 individuals. For soft tissue and organ findings, there was a high pooled sensitivity with PMCTA (0.91, 95% CI 0.81-0.96), without evidence for between-study variability (Cochrane's Q test p = 0.331, I2 = 24.5%). The pooled sensitivity of PMCT+PMMR was very high in skeletal injuries (0.97, CI 0.87-0.99), without evidence for variability (p = 0.857, I2 = 0.0%). In detecting haemorrhages, the pooled sensitivity for PMCT+PMMR was the highest (0.88, 95% CI 0.35-0.99), with strong evidence of heterogeneity (p < 0.05, I2 > 50%). Pooled sensitivity for the correct cause of death was the highest for PMCTA with 0.79 (95% CI 0.52-0.93), again with evidence of heterogeneity (p = 0.062, I2 > 50%). CONCLUSION: Distinct postmortem imaging modalities can achieve high sensitivities for detecting various findings and causes of death. This knowledge should lead to a reasoned use of each modality. Both forensic evidence and in-hospital medical quality would be enhanced.

Communicating 3D data-interactive 3D PDF documents for expert reports and scientific publications in the field of forensic medicine.

INTRODUCTION: Modern forensic investigations increasingly revert to 3D imaging techniques, such as computed tomography, magnetic resonance imaging, and 3D surface imaging. Findings are therefore often based on 3D data sets; however, this information is commonly reported and communicated within 2D imagery. The use of interactive 3D PDFs is already established in the scientific community but has yet to be implemented in the field of forensic medicine. METHODS AND MATERIALS: Three example cases were chosen to serve as exemplary data for the most commonly applied imaging techniques in postmortem imaging. 3D surface models were created from postmortem magnetic resonance imaging (PMMR), postmortem computed tomography (PMCT), and 3D surface imaging data sets. RESULTS: PMMR revealed a space-occupying subdural hemorrhage that led to ipsilateral compression of the brain tissue of the right hemisphere. PMCT displayed a defect in the skull on the left side of the temporal bone. 3D surface imaging data displayed a patterned discoloration on the inside of the left forearm. DISCUSSION: Interactive 3D PDFs offer the possibility to communicate 3D information to the reader while maintaining all the benefits of a regular 2D PDF. With Adobe Acrobat, the reader can interactively navigate through 3D data sets and create sufficient depth cues to generate a realistic 3D perception of the data. CONCLUSION: The interactive 3D PDF is a useful extension of standard 2D PDFs and has the potential to communicate 3D data to the reader in a more complete, more comprehensible, and less subjective manner than 2D PDFs.

Clinical forensic height measurements on injured people using a multi camera device for 3D documentation.

Documenting the existence, size, position and shape of injuries is an important part of medical forensic examinations. In the photography of an injury, the documentation is limited to an approximation of size and position of the injury based on a ruler included in the image. The documentation of injuries can be improved with photogrammetry, which allows the creation of scaled 3D models of an injury that can be used to not only document and visualize the injury but also to match the injury with an injury-causing object. In this paper, the multicamera device "Botscan" was used to perform 3D whole-body documentation and measure the positions of injuries. A major advantage of 3D whole-body documentation compared to photography is that the former can be performed at a later stage of the investigation. This makes the whole-body 3D documentation of injuries an important tool for re-examination.

Potential use of deep learning techniques for postmortem imaging.

The use of postmortem computed tomography in forensic medicine, in addition to conventional autopsy, is now a standard procedure in several countries. However, the large number of cases, the large amount of data, and the lack of postmortem radiology experts have pushed researchers to develop solutions that are able to automate diagnosis by applying deep learning techniques to postmortem computed tomography images. While deep learning techniques require a good understanding of image analysis and mathematical optimization, the goal of this review was to provide to the community of postmortem radiology experts the key concepts needed to assess the potential of such techniques and how they could impact their work.

CT-based sex estimation on human femora using statistical shape modeling.

OBJECTIVES: Estimating the sex of decomposed corpses and skeletal remains of unknown individuals is one of the first steps in the identification process in forensic contexts. Although various studies have considered the femur for sex estimation, the focus has primarily been on a specific single or a handful of measurements rather than the entire shape of the bone. In this article, we use statistical shape modeling (SSM) for sex estimation. We hypothesize that the accuracy of sex estimation will be improved by using the entire shape. MATERIALS AND METHODS: For this study, we acquired a total of 61 femora from routine postmortem CT scans at the Institute for Forensic Medicine of the University of Zurich. The femora were extracted using segmentation technique. After building a SSM, we used the linear regression and nonlinear support vector machine technique for classification. RESULTS: Using linear logistic regression and only the first principal component of the SSM, 76% of the femora were correctly classified by sex. Using the first five principal components, this value could be increased to 80%. Using nonlinear support vector machines and the first 20 principal components increased the rate of correctly classified femora to 87%. DISCUSSION: Despite some limitations, the results obtained by using SSM for sex estimation in femur were promising and confirm the findings of other studies. Sex estimation accuracy, however, is not significantly improved over single or multiple linear measurements. Further research might improve the sex determination process in forensic anthropology by using SSM.

Applying virtual reality in forensics - a virtual scene walkthrough.

A major task of forensic investigations is the documentation and interpretation of evidence to reconstruct a forensically relevant incident. To accomplish this task, a scene is documented not only with photographs but also with 3D documentation technologies. The resulting 3D data are used for 3D visualization and to perform 3D reconstructions. In this article, we present an approach for using forensic 3D data in conjunction with virtual reality to perform scene walkthroughs in the context of witness or suspect interrogations. The aim is to provide a method for scene visits showing the original scene even years after the incident. These scene walkthroughs in VR can be reproduced and allow to see through the eyes of a witness by recording their behavior and actions. These recordings allow subsequent examinations and reconstruction to support the investigation and scene understanding and can be used as evidence in court.

Application of Cinematic Rendering in Clinical Routine CT Examination of Ankle Sprains.

OBJECTIVE: Cinematic rendering technique is used to generate almost photorealistic 3D reconstructions of volumetric data. The purpose of this study was to evaluate the feasibility of cinematically rendered reconstructions in routine CT examinations of ankle sprains. CONCLUSION: Cinematic rendering technique may be primarily used to deliver visual information to patients, physicians, and virtual anatomy classes. Postprocessing requires more time than traditional methods do, which can be a hindrance in clinical work.

Time-dependent postmortem redistribution of morphine and its metabolites in blood and alternative matrices-application of CT-guided biopsy sampling.

Interpretation of postmortem morphine concentrations in forensic toxicology provides several pitfalls such as missing information on tolerance, analyte stability, or postmortem redistribution (PMR). Recently, it had been shown that computed tomography (CT)-guided collection of biopsies using a robotic arm (virtobot) provides a valuable strategy for systematic studies on time-dependent PMR. Using this technique, time-dependent PMR of morphine and its metabolites was investigated in 12 cases. At admission to the institute (t1), femoral and heart blood (right ventricle) as well as biopsies from the right lung, the right kidney, liver, spleen, and muscle tissue were collected. At autopsy approximately 24 h later (t2), samples from the same body regions were collected again. Additionally, gastric contents, urine, brain tissue, and heart blood from the left ventricle was collected. Morphine, normorphine, hydromorphone, morphine-3-glucuronide, morphine-6-glucuronide, and morphine-sulfate were quantified with LC-MS/MS. In femoral blood, significant increase of morphine concentrations was observed, although ultimately not relevant for forensic interpretation. In the alternative matrices, increases as well as decreases were observed without a clear trend. The morphine metabolites did not exhibit relevant concentration changes. Investigation of underlying redistribution mechanisms indicated that concentration change (i.e., increase) of morphine in femoral blood rather resulted from diffusion processes than from release of morphine from its conjugates. Concentration changes in heart blood might have been caused by redistribution from lung tissue or gastric content. This study also proved that CT-guided collection of biopsies using a virtobot arm is an invaluable tool for future studies on PMR redistribution of other substance groups.

Forensic 3D Visualization of CT Data Using Cinematic Volume Rendering: A Preliminary Study.

OBJECTIVE: The 3D volume-rendering technique (VRT) is commonly used in forensic radiology. Its main function is to explain medical findings to state attorneys, judges, or police representatives. New visualization algorithms permit the generation of almost photorealistic volume renderings of CT datasets. The objective of this study is to present and compare a variety of radiologic findings to illustrate the differences between and the advantages and limitations of the current VRT and the physically based cinematic rendering technique (CRT). MATERIALS AND METHODS: Seventy volunteers were shown VRT and CRT reconstructions of 10 different cases. They were asked to mark the findings on the images and rate them in terms of realism and understandability. RESULTS: A total of 48 of the 70 questionnaires were returned and included in the analysis. On the basis of most of the findings presented, CRT appears to be equal or superior to VRT with respect to the realism and understandability of the visualized findings. Overall, in terms of realism, the difference between the techniques was statistically significant (p < 0.05). Most participants perceived the CRT findings to be more understandable than the VRT findings, but that difference was not statistically significant (p > 0.05). CONCLUSION: CRT, which is similar to conventional VRT, is not primarily intended for diagnostic radiologic image analysis, and therefore it should be used primarily as a tool to deliver visual information in the form of radiologic image reports. Using CRT for forensic visualization might have advantages over using VRT if conveying a high degree of visual realism is of importance. Most of the shortcomings of CRT have to do with the software being an early prototype.

Death by "Snow"! A Fatal Forensic Case of Cocaine Leakage in a "Drug Mule" on Postmortem Computed and Magnetic Resonance Tomography Compared With Autopsy.

This forensic case presents unique postmortem imaging of a "drug mule" with fatal intoxication due to cocaine leakage on postmortem computed and magnetic resonance (MR) tomography compared with autopsy.Imaging by postmortem computed and MR tomography was performed before autopsy, histology, and toxicology were commissioned. Forensic imaging revealed 91 hyperdense, uniformly shaped body packs with signs of leakage, which was confirmed by autopsy. Postmortem MR imaging displayed the rarely described hypointense appearance of the body packs in T1- and T2-weighted sequences. Toxicology stated the dosage of cocaine intoxication as lethal.This case provides an opportunity to image internal cocaine drug containers on postmortem computed and MR tomography. The cause of death could be determined based on imaging and the radiological morphology of these packs by both imaging methods.

VirtoScan - a mobile, low-cost photogrammetry setup for fast post-mortem 3D full-body documentations in x-ray computed tomography and autopsy suites.

Injuries such as bite marks or boot prints can leave distinct patterns on the body's surface and can be used for 3D reconstructions. Although various systems for 3D surface imaging have been introduced in the forensic field, most techniques are both cost-intensive and time-consuming. In this article, we present the VirtoScan, a mobile, multi-camera rig based on close-range photogrammetry. The system can be integrated into automated PMCT scanning procedures or used manually together with lifting carts, autopsy tables and examination couch. The VirtoScan is based on a moveable frame that carries 7 digital single-lens reflex cameras. A remote control is attached to each camera and allows the simultaneous triggering of the shutter release of all cameras. Data acquisition in combination with the PMCT scanning procedures took 3:34 min for the 3D surface documentation of one side of the body compared to 20:20 min of acquisition time when using our in-house standard. A surface model comparison between the high resolution output from our in-house standard and a high resolution model from the multi-camera rig showed a mean surface deviation of 0.36 mm for the whole body scan and 0.13 mm for a second comparison of a detailed section of the scan. The use of the multi-camera rig reduces the acquisition time for whole-body surface documentations in medico-legal examinations and provides a low-cost 3D surface scanning alternative for forensic investigations.

Automatic detection of hemorrhagic pericardial effusion on PMCT using deep learning - a feasibility study.

Post mortem computed tomography (PMCT) can be used as a triage tool to better identify cases with a possibly non-natural cause of death, especially when high caseloads make it impossible to perform autopsies on all cases. Substantial data can be generated by modern medical scanners, especially in a forensic setting where the entire body is documented at high resolution. A solution for the resulting issues could be the use of deep learning techniques for automatic analysis of radiological images. In this article, we wanted to test the feasibility of such methods for forensic imaging by hypothesizing that deep learning methods can detect and segment a hemopericardium in PMCT. For deep learning image analysis software, we used the ViDi Suite 2.0. We retrospectively selected 28 cases with, and 24 cases without, hemopericardium. Based on these data, we trained two separate deep learning networks. The first one classified images into hemopericardium/not hemopericardium, and the second one segmented the blood content. We randomly selected 50% of the data for training and 50% for validation. This process was repeated 20 times. The best performing classification network classified all cases of hemopericardium from the validation images correctly with only a few false positives. The best performing segmentation network would tend to underestimate the amount of blood in the pericardium, which is the case for most networks. This is the first study that shows that deep learning has potential for automated image analysis of radiological images in forensic medicine.

Accuracy of non-contrast PMCT for determining cause of death.

The aim of this study was (1) to compare levels of accuracy regarding the categorization of causes of death between non-contrast post-mortem computed tomography (PMCT) and the final forensic report as well as between autopsy and the final forensic report, and (2) to assess levels of confidence regarding the categorization of causes of death after non-contrast PMCT and after autopsy. This prospective study was conducted over a 5 month period during which 221 cases were admitted to our institute for forensic investigations. Whole-body PMCT and forensic autopsy were performed in every case. Of these, 101 cases were included in the final study population. Inclusion criteria were: (1) age > 18 years, (2) presence of at least one of the two principal investigators at the time of admission. One radiologist and one forensic pathologist independently read all PMCT datasets using a report template. Cause of death category and confidence levels were determined by consensus. Forensic autopsy was performed by two forensic pathologists; both unblinded to imaging results. Both post-imaging and post-autopsy cause of death categorization were compared against the final cause of death, as stated in the forensic expert report, which included findings from histology and/or toxicology. Accuracy of post-imaging cause of death categorization in reference to the final cause of death category was substantial (82%, 83/101 cases, Kappa 0.752). Accuracy of post-autopsy cause of death categorization in reference to the final cause of death category was near perfect (89%, 90/101 cases, Kappa 0.852). Post-imaging sensitivity and specificity regarding the categorization of causes of death were 82% and 97%, respectively. Post-autopsy sensitivity and specificity regarding the categorization of causes of death were 89% and 98%, respectively. There was a high consistency between the accuracy of post-imaging cause of death categorization and post-imaging levels of confidence. There was less consistency between accuracy of post-autopsy cause of death categorization and post-autopsy levels of confidence. In this study categorization of causes of death based on non-contrast enhanced PMCT alone, and on PMCT and macroscopic autopsy together, proved to be consistent with the final cause of death-category as determined based on all available information including PMCT, autopsy, and (if available) histology and/or toxicology in more than 82% and 89% of all cases, respectively. There was higher consistency between levels of confidence and accuracy of causes of death categorization was higher post-imaging than post-autopsy. These results underline the fact that the diagnostic potential of PMCT goes beyond the assessment of trauma cases.

Differentiation of hemopericardium due to ruptured myocardial infarction or aortic dissection on unenhanced postmortem computed tomography.

The aim of the study was to evaluate unenhanced postmortem computed tomography (PMCT) in cases of non-traumatic hemopericardium by establishing the sensitivity, specificity and accuracy of diagnostic criteria for the differentiation between aortic dissection and myocardial wall rupture due to infarction. Twenty six cases were identified as suitable for evaluation, of which ruptured aortic dissection could be identified as the underlying cause of hemopericardium in 50% of the cases, and myocardial wall rupture also in 50% of the cases. All cases underwent a PMCT and 24 of the cases also underwent one or more additional examinations: a subsequent autopsy, or a postmortem magnetic resonance (PMMR), or a PMCT angiography (PMCTA), or combinations of the above. Two radiologists evaluated the PMCT images and classified each case as "aortic dissection", "myocardial wall rupture" or "undetermined". Quantification of the pericardial blood was carried out using segmentation techniques. 17 of 26 cases were correctly identified, either as aortic dissections or myocardial ruptures, by both readers. 7 of 13 myocardial wall ruptures were identified by both readers, whereas both readers identified correctly 10 of 13 aortic dissection cases. Taking into account the responses of both readers, specificity was 100% for both causes of hemopericardium and sensitivity as well as accuracy was higher for aortic dissections than myocardial wall ruptures (72.7% and 87.5% vs 53.8% and 75% respectively). Pericardial blood volumes were constantly higher in the aortic dissection group, but a statistical significance of these differences could not be proven, since the small count of cases did not allow for statistical tests. This study showed that diagnostic criteria for the differentiation between ruptured aortic dissection and myocardial wall rupture due to infarction are highly specific and accurate.