[Accuracy of Classification of Cerebral Blood Flow Reduction Patterns Using Statistical Analysis Images Generated with Simulated SPECT Datasets via Deep Learning].

Purpose: The aim of this study was to evaluate the classification accuracy of specific blood flow reduction patterns in clinical images by deep learning using simulation data.

Methods: We obtained Z-score maps for 100 cases each of simulated Alzheimer's disease (AD), simulated dementia with Lewy bodies (DLB), and simulated normal cognition (NC) by performing statistical analysis of the simulation data that provided defects and healthy patient data. The clinical images were determined by reference to radiological reports, and Z-score maps of AD (n=33), DLB (n=20), and NC (n=28) were used.

Fluctuation relations for adiabatic pumping.

We derive an extended fluctuation relation for an open system coupled with two reservoirs under adiabatic one-cycle modulation. We confirm that the geometrical phase caused by the Berry-Sinitsyn-Nemenman curvature in the parameter space generates non-Gaussian fluctuations. This non-Gaussianity is enhanced for the instantaneous fluctuation relation when the bias between the two reservoirs disappears.

Nonadiabatic Control of Geometric Pumping.

We study nonadiabatic effects of geometric pumping. With arbitrary choices of periodic control parameters, we go beyond the adiabatic approximation to obtain the exact pumping current. We find that a geometrical interpretation for the nontrivial part of the current is possible even in the nonadiabatic regime.