Experimental measurement of radiation dose in a dedicated breast CT system

Radiation dose is an important performance indicator of a dedicated breast CT（DBCT）.In this paper,the method of putting thermoluminescent dosimeters（TLD） into a breast shaped PMMA phantom to study the dose distribution in breasts was improved by using smaller TLDs and a new half-ellipsoid PMMA phantom.Then the weighted CT dose index（CTDI_w） was introduced to average glandular assessment in DBCT for the first time and two measurement modes were proposed for different sizes of breasts.The dose deviations caused by using cylindrical phantoms were simulated using the Monte Carlo method and a set of correction factors were calculated.The results of the confirmatory measurement with a cylindrical phantom（11 cm/8 cm） show that CTDI_w gives a relatively conservative overestimate of the average glandular dose comparing to the results of Monte Carlo simulation and TLDs measurement.But with better practicability and stability,the CTDI_w is suitable for dose evaluations in daily clinical practice.Both of the TLDs and CTDI_w measurements demonstrate that the radiation dose of our DBCT system is lower than conventional two-view mammography.

Machine learning super-resolution of laboratory CT images in all-solid-state batteries using synchrotron radiation CT as training data

High-performance all-solid-state lithium-ion batteries require observation,control,and optimization of the electrode structure.X-ray computational tomography(CT)is an effective nondestructive method for observing the electrode structure in three dimensions.However,the limited availability of synchrotron radiation CT,which offers high-resolution imaging with a high signal-to-noise ratio,makes it difficult to conduct experiments and restricts the use of X-ray CT in battery development.Conversely,laboratory CT systems are widely available,but they use X-rays emitted from a metal target,resulting in lower image quality and resolution compared with synchrotron radiation CT.This study explores a method for achieving comparable resolution in laboratory CT images of all-solid-state batteries to that of synchrotron radiation CT.Our method involves using the synchrotron radiation CT images as training data for machine learning super-resolution.The results demonstrate that,by employing an appropriate machine learning algorithm and activation function,along with a sufficiently deep network,the image quality of laboratory CT becomes equivalent to that of synchrotron radiation CT.