Radiation Measurements without Posing the Inversion Problem

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The characteristics of older people suicides by sex and age subgroups

PubMed: 32492558As the older population steadily grows, a corresponding increase in elderly suicides is also expected. In addition, due to differences in the physical and psychosocial characteristics of this age group, the characteristics of elderly suicides are predicted to be different to those of other groups. In this study, we aimed to report the characteristics of suicides 60 years and older according to sex and age subgroups. We retrospectively reviewed the autopsy reports of individuals aged 60 and older who committed suicide in Turkey during the 10-year period between 2005 and 2014. Their age, sex, cause of death, and year, month, season, place, and method of suicide were analyzed. Comparisons were made based on sex, age subgroup, demographic variables, and descriptive characteristics of the suicides. Of 17,942 forensic autopsies, 525 were elderly suicides. Of these, 77.3% were men and the mean age was 71.26 ± 8.16 (range, 60–94) years. There were statistically significant differences in suicide method according to sex (p < 0.001, X = 43.984) and age subgroups (p = 0.001, X = 51.457). For both sexes, hanging was the most common suicide method (59.4%) and the majority of suicides occurred at home (73.1%). The suicides occurred more frequently in the 65–74 age subgroup, in the summer, and in the months of June and July. Identifying the characteristics of elderly suicides, especially by sex and age subgroups, may be beneficial for suicide risk assessment and the development of prediction and prevention programs. © 2020This study was performed with the permission of Istanbul Forensic Medicine Institute, Turkey

Integrated software for the analysis of brain PET/SPECT studies with Partial Volume Effect Correction

We present software for integrated analysis of brain PET studies and coregistered segmented MRI that couples a module for automated placement of regions of interest (ROI) with 4 alternative methods for partial-volume-effect correction (PVEc). The accuracy and precision of these methods have been measured using 4 simulated (18)F-FDG PET studies with increasing degrees of atrophy. METHODS: The software allows the application of a set of labels, defined a priori in the Talairach space, to segmented and coregistered MRI. Resulting ROIs are then transferred onto the PET study, and corresponding values are corrected according to the 4 PVEc techniques under investigation, providing corresponding corrected values. To evaluate the PVEc techniques, the software was applied to 4 simulated (18)F-FDG PET studies, introducing increasingly larger experimental errors, including errors in coregistration (0- to 6-pixel misregistration), segmentation (-13.7% to 14.1% gray matter [GM] volume change) and resolution estimate errors (-16.9% to 26.8% full-width-at-half-maximum mismatch). RESULTS: Even in the absence of segmentation and coregistration errors, uncorrected PET values showed -37.6% GM underestimation and 91.7% WM overestimation. Voxel-based correction only for the loss of GM activity as a result of spill-out onto extraparenchymal tissues left a residual underestimation of GM values (-21.2%). Application of the method that took into account both spill-in and spill-out effects between any possible pair of ROIs (R-PVEc) and of the voxel-based method that corrects also for the WM activity derived from R-PVEC (mMG-PVEc) provided an accuracy above 96%. The coefficient of variation of the GM ROIs, a measure of the imprecision of the GM concentration estimates, was 8.5% for uncorrected PET data and decreased with PVEc, reaching 6.0% for mMG-PVEc. Coregistration errors appeared to be the major determinant of the imprecision. CONCLUSION: Coupling of automated ROI placement and PVEc provides a tool for integrated analysis of brain PET/MRI data, which allows a recovery of true GM ROI values, with a high degree of accuracy when R-PVEc or mMG-PVEc is used. Among the 4 tested PVEc methods, R-PVEc showed the greatest accuracy and is suitable when corrected images are not specifically needed. Otherwise, if corrected images are desired, the mMG-PVEc method appears the most adequate, showing a similar accuracy