Medical radiological procedures: which information would be chosen for the report?

Aims and objectives The aim of this study was to properly define the information regarding patient exposure to Ionizing Radiations in the radiological report, according to the European Directive 2013/59/EURATOM (EU 2013/59 art.58(b)). For this purpose, we evaluated the results from other Member States EU 2013/59 transpositions and from Guidelines recommendation published by International Organizations involved in diagnostic radiology. A practical way for implementing art.58 is also traced. Materials and methods Dosimetric quantities, such as exposure, absorbed dose and effective dose which may be included in radiological report, were first analyzed; then, in order to define international state of art of Member States EU 2013/59 transposition, a Web research using French, English, Spanish and German key words was performed. Results EU 2013/59 transposition for 5 Member States was reported. Especially regarding art.58, a European project reports that few European countries (11 of 28) have identified the dose metrics to be used in radiological report. Scientific organizations supporting clinical radiologists and medical physicists have published Guidelines reporting parameters useful to quantify the radiation output and to assess patient dose. Conclusions Our research revealed that there is not a shared interpretation of patient exposure information to be included in radiological report. Nevertheless, according to scientific community, authors believe that the exposure is the most appropriate information that could be included in radiological report. Alternatively, but with more expensiveness, a risk index based on effective dose could be used. Moreover, the systematic exposure information recorded could be useful for dose estimates of population from medical exposure

An hippocampal segmentation tool within an open cloud infrastructure

This study presents a fully automated algorithm for the segmentation of the hippocampus in structural Magnetic Resonance Imaging (MRI) and its deployment as a service on an open cloud infrastructure. Optimal atlases strategies for multi-atlas learning are combined with a voxel-wise classification approach. The method efficiency is optimized as training atlases are previously registered to a data driven template, accordingly for each test MRI scan only a registration is needed. The selected optimal atlases are used to train dedicated random forest classifiers whose labels are fused by majority voting. The method performances were tested on a set of 100 MRI scans provided by the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Leave-oneout results (Dice = 0.910 ± 0.004) show the presented method compares well with other state-of-the-art techniques and a benchmark segmentation tool as FreeSurfer. The proposed strategy significantly improves a standard multi-atlas approach (p < .001)

The new lens dose limit: implication for occupational radiation protection

The aim of this article was to explore the implications of the new Euratom dose limit for occupational radiation protection in the context of medical occupational radiation exposures. The European Directive 2013/59/Euratom takes into account the new recommendations on reduction in the dose limit for the lens of the eye for planned occupational exposures released in 2012 by the International Commission on Radiological Protection (ICRP 118)

Feature Selection Based on Machine Learning in MRIs for Hippocampal Segmentation

Neurodegenerative diseases are frequently associated with structural changes in the brain. Magnetic resonance imaging (MRI) scans can show these variations and therefore can be used as a supportive feature for a number of neurodegenerative diseases. The hippocampus has been known to be a biomarker for Alzheimer disease and other neurological and psychiatric diseases. However, it requires accurate, robust, and reproducible delineation of hippocampal structures. Fully automatic methods are usually the voxel based approach; for each voxel a number of local features were calculated. In this paper, we compared four different techniques for feature selection from a set of 315 features extracted for each voxel: (i) filter method based on the Kolmogorov-Smirnov test; two wrapper methods, respectively, (ii) sequential forward selection and (iii) sequential backward elimination; and (iv) embedded method based on the Random Forest Classifier on a set of 10 T1-weighted brain MRIs and tested on an independent set of 25 subjects. The resulting segmentations were compared with manual reference labelling. By using only 23 feature for each voxel (sequential backward elimination) we obtained comparable state-of-the-art performances with respect to the standard tool FreeSurfer

Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease.

Identifying accurate biomarkers of cognitive decline is essential for advancing early diagnosis and prevention therapies in Alzheimer's disease. The Alzheimer's disease DREAM Challenge was designed as a computational crowdsourced project to benchmark the current state-of-the-art in predicting cognitive outcomes in Alzheimer's disease based on high dimensional, publicly available genetic and structural imaging data. This meta-analysis failed to identify a meaningful predictor developed from either data modality, suggesting that alternate approaches should be considered for prediction of cognitive performance

Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease

Identifying accurate biomarkers of cognitive decline is essential for advancing early diagnosis and prevention therapies in Alzheimer's disease. The Alzheimer's disease DREAM Challenge was designed as a computational crowdsourced project to benchmark the current state-of-the-art in predicting cognitive outcomes in Alzheimer's disease based on high dimensional, publicly available genetic and structural imaging data. This meta-analysis failed to identify a meaningful predictor developed from either data modality, suggesting that alternate approaches should be considered for prediction of cognitive performance