In this work, we integrated a commercially-available fully-spectroscopic pixelated cadmium telluride (CdTe) detector system as a two-dimensional (2D) array detector into our existing benchtop conebeam x-ray fluorescence computed tomography (XFCT) system. After integrating this detector, known as High-Energy X-ray Imaging Technology (HEXITEC), we performed quantitative imaging of gold nanoparticle (GNP) distribution in a small animal-sized phantom using our benchtop XFCT system. Owing to the upgraded detector component within our benchtop XFCT system, we were able to conduct this phantom imaging in an unprecedented manner by volumetric XFCT scans followed by XFCT image reconstruction in 3D. The current results showed that adoption of HEXITEC, in conjunction with a custommade parallel-hole collimator, drastically reduced the XFCT scan time/dose. Compared with the previous work performed with our original benchtop XFCT system adopting a single crystal CdTe detector, the currently observed reduction was up to a factor of 5, while achieving comparable GNP detection limit under similar experimental conditions. Overall, we demonstrated, for the first time to the best our knowledge, the feasibility of benchtop XFCT imaging of small animal-sized objects containing biologically relevant GNP concentrations (on the order of 0.1 mg Au/cm 3 or 100 parts-per-million/ppm), with the scan time (on the order of 1 minute)/x-ray dose (on the order of 10 cGy) that are likely meeting the minimum requirements for routine preclinical imaging applications.

Transmission electron microscopy (TEM) imaging can be used for detection/localization of gold nanoparticles (GNPs) within tumor cells. However, quantitative analysis of GNP-containing cellular TEM images typically relies on conventional/thresholding-based methods, which are manual, time-consuming, and prone to human errors. In this study, therefore, deep learning (DL)–based methods were developed for fully automated detection of GNPs from cellular TEM images. Several models of “you only look once (YOLO)” v5 were implemented, with a few adjustments to enhance the model’s performance by applying the transfer learning approach, adjusting the size of the input image, and choosing the best optimization algorithm. Seventy-eight original (12,040 augmented) TEM images of GNP-laden tumor cells were used for model implementation and validation. A maximum F1 score (harmonic mean of the precision and recall) of 0.982 was achieved by the best-trained models, while mean average precision was 0.989 and 0.843 at 0.50 and 0.50–0.95 intersection over union threshold, respectively. These results suggested the developed DL-based approach was capable of precisely estimating the number/position of internalized GNPs from cellular TEM images. A novel DL-based TEM image analysis tool from this study will benefit research/development efforts on GNP-based cancer therapeutics, for example, by enabling the modeling of GNP-laden tumor cells using nanometer-resolution TEM images.