Quantitative myelin water imaging is able to show demyelinating processes and, therefore, provides insight into the pathology of white matter diseases such as multiple sclerosis. So far, mapping of the myelin water fraction most often was performed using single‐slice multiecho spin‐echo sequences. Recently, a different approach using two‐dimensional multigradient‐echo pulse sequences was suggested. In this work, a solution to three‐dimensional in vivo myelin water fraction imaging is presented that applies multigradient‐echo pulse sequences and uses non‐negative least squares algorithms to analyze the multicomponent T*₂ decay. The suggested method offers not only whole brain coverage but also clinically practicable acquisition times. The obtained myelin water fraction values are low (6.9% for white matter) but are able to detect demyelination in multiple sclerosis lesions. However, the clinical application of the proposed method remains questionable, because further measurements that clarify the possibility of detecting ongoing processes in lesions are needed. Magn Reson Med, 2012.

During the last years, the detection of different causes of death based on postmortem imaging findings became more and more relevant. Especially postmortem computed tomography (PMCT) as a non-invasive, relatively cheap, and fast technique is progressively used as an important imaging tool for supporting autopsies. Additionally, previous works showed that deep learning applications yielded robust results for in vivo medical imaging interpretation. In this work, we propose a pipeline to identify fatal cerebral haemorrhage on three-dimensional PMCT data. We retrospectively selected 81 PMCT cases from the database of our institute, whereby 36 cases suffered from a fatal cerebral haemorrhage as confirmed by autopsy. The remaining 45 cases were considered as neurologically healthy. Based on these datasets, six machine learning classifiers (k-nearest neighbour, Gaussian naive Bayes, logistic regression, decision tree, linear discriminant analysis, and support vector machine) were executed and two deep learning models, namely a convolutional neural network (CNN) and a densely connected convolutional network (DenseNet), were trained. For all algorithms, 80% of the data was randomly selected for training and 20% for validation purposes and a five-fold cross-validation was executed. The best-performing classification algorithm for fatal cerebral haemorrhage was the artificial neural network CNN, which resulted in an accuracy of 0.94 for all folds. In the future, artificial neural network algorithms may be applied by forensic pathologists as a helpful computer-assisted diagnostics tool supporting PMCT-based evaluation of cause of death.

Objective: MRI temperature sensitivity presents a major issue in in situ post mortem MRI (PMMRI), as the tissue temperatures differ from living persons due to passive cooling of the deceased. This study aims at computing brain temperature effects on the MRI parameters to correct for temperature in PMMRI, laying the foundation for future projects on post mortem validation of in vivo MRI techniques. Materials and methods: Brain MRI parameters were assessed in vivo and in situ post mortem using a 3 T MRI scanner. Post mortem brain temperature was measured in situ transethmoidally. The temperature effect was computed by fitting a linear model to the MRI parameters and the corresponding brain temperature. Results: Linear positive temperature correlations were observed for T₁, T₂* and mean diffusivity in all tissue types. A significant negative correlation was observed for T₂ in white matter. Fractional anisotropy revealed significant correlations in all gray matter regions except for the thalamus. Discussion: The linear models will allow to correct for temperature in post mortem MRI. Comparing in vivo to post mortem conditions, the mean diffusivity, in contrast to T₁ and T₂, revealed additional effects besides temperature, such as cessation of perfusion and active diffusion.

Purpose: imaging of brain white matter is well known for being sensitive to the orientation of nerve fibers with respect to the B₀ field of the MRI scanner. The goal of this study was to evaluate whether and to which extent fiber orientation dependent differs between in vivo and post mortem in situ examinations, and to investigate the influence of varying temperatures and post mortem intervals (PMI).Methods: Post mortem in situ and in vivo MRI scans were conducted at 3T. was acquired with a multi‐echo gradient‐echo sequence, and the orientation of white matter fibers was computed using diffusion tensor imaging (DTI). Fitting of the measured fiber orientation dependent was performed using three different formulations of a previously proposed model.Results: increased with increasing fiber angle for in vivo and post mortem in situ examinations, whereby the orientation dependency was lower post mortem. The different formulations of the fiber orientation model resulted in an identical fit, but showed large variations of the estimated parameters. The higher order orientation dependent components significantly decreased with decreasing temperature, while the orientation independent components showed no significant correlation with either temperature or PMI.Conclusion: Although the mean diffusivity is strongly reduced post mortem, we could successfully estimate the fiber angle using DTI. Due to the strong correlation of the higher order orientation dependent components with temperature, the decreased fiber orientation dependency post mortem in situ might primarily be attributed to the lower brain temperature.

Diffusion-weighted magnetic resonance imaging (DWI) provides qualitative and quantitative information about the random motion of water molecules in biological tissues and is able to give functional insight into tissue architecture and pathological changes on a cellular level. This technique has the major advantages of not requiring the administration of contrast agents and not exposing the patient to ionizing radiation. Recent technological advances have led to the development of diffusion-weighted whole-body imaging with background body signal suppression (DWIBS) that allows screening of the whole body in 25<!-- --> <!-- -->minutes. DWI and DWIBS have both revealed great potential in the field of oncology and proved to be useful for detecting and characterizing tumors and evaluating treatment response. This article reviews the basic principles and experimental setup of DWI and DWIBS and illustrates its potential application to the assessment of extracranial tumors. In addition, current limitations and challenges of this promising imaging procedure are discussed.