Improved detection of cholesterol gallstones using quasi-material decomposition images generated from single-energy computed tomography images via deep learning.

In this study, we developed a method for generating quasi-material decomposition (quasi-MD) images from single-energy computed tomography (SECT) images using a deep convolutional neural network (DCNN). Our aim was to improve the detection of cholesterol gallstones and to determine the clinical utility of quasi-MD images. Four thousand pairs of virtual monochromatic images (70 keV) and MD images (fat/water) of the same section, obtained via dual-energy computed tomography (DECT), were used to train the DCNN.

[Measurement of residual effect in the amorphous selenium flat panel detector system].

The purpose of this study was to evaluate the residual effect generated by the amorphous selenium flat panel detector system (a-Se FPD). A residual effect occurs as a result of the addition of delayed electrons by previous X-ray irradiation joining the signal and change in detector sensitivity caused by hole-electron recombination or trapped electrons in a-Se. To evaluate the effect of previous radiation exposure, we irradiated a-Se FPD that were half-shielded by a 3 mm thick lead plate.