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XFCT is a novel method for the early cancer detection. Increasing concentration of contrast agents and incident X-rays’ energy were used to improve detecting accuracy, which greatly increased the prevalence of contrast-induced nephropathy. Therefore, this research explores the adaptive contrast agents and uses Geant4 to simulate the imaging conditions of Pt, Bi, Gd, Ru, and Au for searching the lowest detectable concentration based on the fast multi-pinhole collimated XFCT (fmpc-XFCT) imaging system and low incident energy. Several imaging parameters including pinhole radius (0.7, 0.8, and 1 mm) were adjusted, and the optimized EM-TV algorithm was used to reconstruct XFCT images. It is found that Bi element is superior to other metal elements in terms of the contrast-to-noise ratio (CNR) and fluorescence efficiency, and the lowest concentration that can be detected is 0.12% with optimal parameters.

This book review summarizes the content of "Scholarly publication trajectories of early-career scholars: Insider perspectives," edited by Pejman Habibie and Sally Burgess. The book presents personal accounts from early-career scholars about their experiences with scholarly publication. It is divided into three parts, focusing on networks and mentorships, scholarly identity construction, and ideological underpinnings and power relations in writing for publication. The review highlights the diverse perspectives and backgrounds of the contributors and emphasizes the strategies and lessons shared in the book, which will be valuable for graduate students, junior academics, mentors, and established scholars. [Extracted from the article]

In this work, we developed and validated two novel imaging geometries of benchtop multi-pinhole X-ray fluorescence computed tomography (XFCT) systems with Geant4 Toolkit. One of the Monte Carlo (MC) models utilized a fan beam source to illuminate a single slice of the object, a detector and a multi-pinhole collimator to image each slice's X-ray fluorescence (XRF). The other model consisted of a cone-beam X-ray source (designed as a 5 mm wide fan beam to reduce simulation time) to scan the whole object, two detectors and two multi-pinhole collimators to image the emissions. The phantom used in the simulations included four sections, each with three cone-shaped gold nanoparticle (GNP) inserts (5 mm in height, 3 mm in diameter across the top) with center-to-center distances of 4 mm, 4.5 mm and 4.86 mm. The GNPs concentration was 0.1 wt. %, 0.3 wt. %, 0.5 wt. % and 0.7 wt. %, respectively. The diameter of the multi-pinhole collimator was 1 mm. Performance was evaluated for pinhole-detector-distance (PDD) of 5 cm, 3.5 cm and 2.5 cm, and the results for different object layers and for single pinhole and multi-pinhole (9 pinholes) imaging were compared. The data showed that results worsened with decreasing GNPs insert diameters and with decreasing PDD (object-pinhole-distance was fixed). The multi-pinhole configurations performed better than a single pinhole. The detection limit for the first multi-pinhole operation was 0.21 wt. %; the second was 0.24 wt. %. Detection limits for the single pinhole were 0.32 wt. % and 0.35 wt. %, respectively. The first MC model could acquire 2D slice images of the object without rotation and the second MC model could image the 3D object efficiently. These two novel multi-pinhole systems could potentially provide a bioimaging modality for nanomedical applications. [ABSTRACT FROM AUTHOR]

For bench-top X-ray fluorescence computed tomography (XFCT), the X-ray tube source will bring extreme Compton background noise, resulting in a low signal-to-noise ratio and low contrast detection limit. In this paper, a noise2noise denoising algorithm based on the UNet deep learning network is proposed. The network can use noise image learning to convert the noise image into a clean image. Two sets of phantoms (high concentration Gd phantom and low concentration Bi phantom) are used for scanning to simulate the imaging process under different noise levels and generate the required data set. Additionally, the data set is generated by Geant4 simulation. In the training process, the L1 loss function is used for its good convergence. The image quality is evaluated according to CNR and pixel profile, which shows that our algorithm is better than BM3D, both visually and quantitatively. [ABSTRACT FROM AUTHOR]