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Background Cases of external hemorrhage are difficult to recognize on postmortem computed tomography (PMCT). Purpose To investigate the effects of blood loss on CT attenuation of the spleen, liver, kidneys, and lungs on PMCT and to assess the relationship between blood loss and organ weight. Methods A total of 125 cases with blood loss were sex- and age-matched to 125 control cases without blood loss. Individual organ attenuation was measured on transverse CT images. Organ weights of the liver, spleen, kidneys, and lung were extracted from the autopsy protocols. Results Organ weight was significantly lower in cases with blood loss (lung 30%, spleen 28%, kidneys 14%, liver 18%) than in controls. CT attenuation of the lungs was significantly lower (30%) in cases with blood loss than in controls. CT attenuation of the spleen and kidneys did not significantly differ between cases and controls. CT attenuation of the liver was significantly higher (25%) in cases with blood loss than in controls. Conclusion Blood loss decreases organ weight and CT attenuation of the lungs but appears to have no significant effect on CT attenuation of the spleen and kidneys. The increased liver attenuation in cases with blood loss compared to controls was an unexpected finding and remains challenging to explain. One probable interpretation refers to different levels of hepatic glycogen; however, further work is warranted to substantiate this hypothesis.

Objective This study is based on the hypothesis that occult cartilaginous fractures of the thyroid cartilage (TC) may result in asymmetry of the thyroid cartilage framework. Justification of this hypothesis was provided by an estimation of the prevalence of TC asymmetry based on a consecutive postmortem study group. The findings were then compared to three cases of nonfatal manual strangulation and one case of survival of blunt trauma to the neck. Methodology TC images from a consecutive two-year sample from the institutional postmortem computed tomography database (N= 1187) were assessed. Cases with acute TC injury were excluded. TC asymmetry, signs of prior TC trauma, sex and age, were retrieved. Results TC asymmetry was present in 72 cases (6.2%, 67 males). In 40 cases (3.5%), old TC fractures could be identified. The derived odds ratios were 7.48 for male sex and 1.02 for (each year of) age (p Conclusion TC asymmetry is associated with male sex and older age and is typically located on the left side. TC asymmetry in young and female cases is rare. TC asymmetry in young and/or female cases may result from occult cartilaginous fractures, as cases 1 and 2 indicated. Further research is needed to validate this hypothesis.

Background: Postmortem radiology often encounters foreign bodies in the stomach: food. Rarely, this intragastric content exhibits a high radiodensity, which can be confused with intragastric pills. We hypothesized that the radiodensity of this gastric content provided information about the interval between last meal and death. Methodology: The institutional database was searched for cases with high radiodense gastric contents seen on imaging. The postmortem interval was computed, and the interval between last meal and death was estimated. In addition, a simulation of postmortem gastric digestion was conducted. Samples of pasta, rice, lentils, chicken meat and broccoli were added to a gastric juice simulate. Computed tomography scans of the samples were repeatedly conducted for 4 h. Results: In total, 11 cases were included in the study. The gastric content was carbohydrates (rice, pasta, lentils) in six cases and meat in five cases. In both the cases and the experiment, maximum radiodensity of carbohydrates (>150 HU) was higher than of meat (100–150 HU) and vegetables (~50 HU, only experimental). Intragastric radiodensity did not correlate with the postmortem interval, and was stable in the experimental simulation apart from a decrease during the first 90 min for pasta and rice, which coincided with an increase of the radiodensity of the surrounding fluid. Conclusion: A differentiation of gastric content based on radiodensity and morphology is possible. All cases with high radiodensity had a short interval from last meal to death whenever computable. The radiodensity of intragastric food postmortem appears to be independent from the postmortem interval.

The assessment of danger to life (DTL) is the central question for medicolegal reports in cases of nonfatal strangulation. Petechiae (petechial hemorrhages on conjunctivae, mucosal surfaces and facial skin), and subjective symptoms, such as loss of consciousness, serve as indicators for the forensic assessment of DTL. However, only distinct petechiae are considered an objective finding for life-threatening strangulation. Since the presence of only a few petechiae does not necessarily indicate an asphyxial process and since petechiae may vanish within 1-3 days after the incident, further objective indicators are desired. Radiologic imaging has been considered a potential supplement for the forensic assessment of DTL. Computed tomography (CT) is the most commonly used radiologic modality for detecting the extent of injuries from blunt trauma to the neck and CT angiography allows the diagnosis of vascular injuries, such as carotid dissection. However, the focus of clinical radiology is the diagnosis of injuries that require medical treatment; thus, the applied examination methods and imaging protocols are not necessarily appropriate for forensic assessment in cases of strangulation. Furthermore, exposing a victim to radiation using CT may not be justifiable if the victim does not present severe symptoms. Consequently, noninvasive magnetic resonance imaging (MRI) has been considered for medicolegal examinations in cases of nonfatal strangulation. The aim of this article is to discuss the role and potential value of MRI for the assessment of DTL in cases of nonfatal strangulation. For this purpose, the results, conclusions and recommendations of individual studies were compared and assessed.

Several articles have described the use of postmortem computed tomography (CT) and postmortem magnetic resonance imaging (MRI) in forensic medicine. Although access to CT scanners and, particularly, access to MRI scanners, is still limited for several institutes, both modalities are being applied with increasing frequency in the forensic setting. Certainly, postmortem imaging can provide crucial information prior to autopsy, and this method has even been considered a replacement to autopsy in selected cases by some forensic institutes. However, the role of postmortem imaging has to be assessed individually according to various injury categories and causes of death. Therefore, this systematic review focuses on the role of postmortem CT and MRI in cases of hanging and ligature and manual strangulation. We assessed the most common and relevant findings on CT and MRI in cases of strangulation and compared the detectability of these findings among CT, MRI and autopsy. According to the available literature, mainly fractures of the hyoid bone or thyroid cartilage were investigated using postmortem CT. Compared to autopsy, CT demonstrated equivalent results concerning the detection of these fractures. A currently described "gas bubble sign" may even facilitate the detection of laryngeal fractures on CT. Regarding the detection of hemorrhages in the soft tissue of the neck, postmortem MRI is more suitable for the detection of this "vital sign" in strangulation. Compared to autopsy, postmortem MRI is almost equally accurate for the detection of hemorrhages in the neck. Another "vital sign", gas within the soft tissue in hanging, which is hardly detectable by conventional autopsy, can be clearly depicted by CT and MRI. The number of cases of manual and ligature strangulation that were investigated by means of postmortem CT and MRI is much smaller than the number of cases of hanging that were investigated by CT and MRI. Likewise, judicial hanging and the hangman's fracture on postmortem imaging were described in only a few cases. Based on the results of this systematic review, we discuss the additional value of CT and MRI in fatal strangulation compared to autopsy, and we reflect on where the literature is currently lacking.

We report a fetal loss following maternal ketoacidosis in a case of cryptic pregnancy. Biochemical analysis of peripheral blood revealed highly elevated levels of beta-hydroxybutyrate (BHB) in the mother (9.2 mmol/l) and the fetus (4.2 mmol/l). Fetal ketoacidosis with hypoxic-ischemic brain damage was determined to be the cause of death. 1H-MRS of the right cerebral hemisphere presented with distinctive resonances of BHB and acetone. Acetoacetate and glucose were not detected. Due to reported chronic abuse of ethanol and a period of fasting, alcoholic ketoacidosis was concluded to be the cause of the metabolic disorder. 1H-MRS is a viable examination for the postmortem detection of ketone bodies and may be a key supplement to noninvasive fetal autopsy for the diagnosis of ketoacidosis.

Objectives Postmortem computed tomography (PMCT) usually includes the generation of great amounts of imaging data, and is often reviewed by forensic pathologists. To allow a more resource-efficient diagnosis, deep neural networks may act as a pre-scanning tool in postmortem radiology. In this study, a deep neural network to classify cases depending on the presence skull fractures on curved maximum intensity projections (CMIP). Methods Calvarial CMIPs of each 75 cases with and without documented skull fractures were retrospectively generated from our database. Then, half of the data were randomly assigned to either training or validation. In supervised training, fractures were manually marked. During validation, each image received a gradual score between 0 and 1 predicting the likelihood of showing one or more fractures. Results With a total number of 100 networks trained, the average area under the Receiver Operating Characteristic curve (AUC) was 0.895. The best performing network had an AUC of 0.965. At a classification threshold of 0.79, the network classified fracture cases correctly with a sensitivity of 91.4% and a specificity of 87.5%. Conclusion Classification based on the existence of skull fractures on CMIPs with deep learning is feasible. For the purpose of pre-scanning PMCT data, a classification threshold of 0.75 with a sensitivity of 100% can be applied. A higher number of images of validated skull fractures available will increase the performance of the network. In the future, Deep learning might enable a more resource-efficient assessment in postmortem radiology.

Due to slowing or even inhibition of postmortem processes, freezing may make an estimation of the time-since-death very difficult. This is also true in previously frozen and subsequently thawed bodies. Knowledge of prior freezing is important, as it may lead to a different assessment of the time since death. Twelve pig heads were frozen at -20 °C, and 6 heads were either kept at room temperature (approximately 20 °C) or in a cooling cell (approximately 5 °C). The frozen brains and cadavers were thawed at either room temperature or in a cooling cell. All specimens underwent repeated CT and MRI scanning until the brains were sampled for histological examination. Two radiologists assessed the images and two pathologists reviewed the histological slides with regard to thawing artifacts and putrefaction. All raters were blinded regarding whether the samples had been frozen, for how long and how they had been thawed. Imaging revealed distinct, tiny bubble-like artifacts only in previously frozen specimens. Histology also revealed artifacts only seen in such cases, namely very distinct, columnar bubbles in the cerebral cortex. All raters successfully identified previously unfrozen brains (100% specificity) and nearly all previously frozen brains. Our results suggest that initial post-mortem imaging can be of enormous importance in everyday forensic practice by identifying possible cases of previous freezing - cases that would therefore warrant closer scrutiny and thus raise caution regarding the time of death.

Radiologic forensic identification is usually performed by comparing antemortem and postmortem radiographs. While computed tomography (CT) has become a valuable addition to radiologic identification, magnetic resonance (MR) imaging has only rarely been used for this purpose. In our case, identification was accomplished using fused MR- and CT images in a survivor of a gunshot injury to the head. This case supports and highlights the possibility to perform intermodality radiologic identification comparing preexisting MR imaging to subsequently aquired CT data in living (or deceased) humans as long as manual modifications of windowing, color and contrast enable differentiation of the two modalities in the fused image.

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.