A High-Sensitivity Benchtop X-Ray Fluorescence Emission Tomography (XFET) System With a Full-Ring of X-Ray Imaging-Spectrometers and a Compound-Eye Collimation Aperture.

The advent of metal-based drugs and metal nanoparticles as therapeutic agents in anti-tumor treatment has motivated the advancement of X-ray fluorescence computed tomography (XFCT) techniques. An XFCT imaging modality can detect, quantify, and image the biodistribution of metal elements using the X-ray fluorescence signal emitted upon X-ray irradiation. However, the majority of XFCT imaging systems and instrumentation developed so far rely on a single or a small number of detectors. This work introduces the first full-ring benchtop X-ray fluorescence emission tomography (XFET) system equipped with 24 solid-state detectors arranged in a hexagonal geometry and a 96-pinhole compound-eye collimator. We experimentally demonstrate the system's sensitivity and its capability of multi-element detection and quantification by performing imaging studies on an animal-sized phantom. In our preliminary studies, the phantom was irradiated with a pencil beam of X-rays produced using a low-powered polychromatic X-ray source (90kVp and 60W max power). This investigation shows a significant enhancement in the detection limit of gadolinium to as low as 0.1 mg/mL concentration. The results also illustrate the unique capabilities of the XFET system to simultaneously determine the spatial distribution and accurately quantify the concentrations of multiple metal elements.

Simulation of a Compton-based detector for low-dose high-resolution time-of-flight positron emission tomography.

Two major challenges in time-of-flight positron emission tomography (TOF-PET) are low spatial resolution and high radioactive dose to the patient, both of which result from limitations in detection technology rather than fundamental physics. A new type of TOF-PET detector employing low-atomic number (low-Z) scintillation media and large-area, high-resolution photodetectors to record Compton scattering locations in the detector has been proposed as a promising alternative, but the minimum technical requirements for such a system have not yet been established. Here we present a simulation study evaluating the potential of a proposed low-Z detection medium, linear alkylbenzene (LAB) doped with a switchable molecular recorder, for next-generation TOF-PET detection. We developed a custom Monte Carlo simulation of full-body TOF-PET using the TOPAS Geant4 software package. By quantifying contributions and tradeoffs for energy, spatial, and timing resolution of the detector, we show that at reasonable combination of specifications, our likelihood-based identification of pairs of first interaction locations in the simulated detector identifies 87.1% of pairs with zero or negligible error, and correctly rejects 90% of all in-patient scatters. The same specifications give TOF-PET sensitivity of ~66.7% and PSF width 4.6 mm with clear contrast. A detector with these specifications provides a clear image of a brain phantom simulated at less than 1% of a standard radiotracer dose.

Phase Diverse Phase Retrieval for Microscopy: Comparison of Gaussian and Poisson Approaches.

Phase diversity is a widefield aberration correction method that uses multiple images to estimate the phase aberration at the pupil plane of an imaging system by solving an optimization problem. This estimated aberration can then be used to deconvolve the aberrated image or to reacquire it with aberration corrections applied to a deformable mirror. The optimization problem for aberration estimation has been formulated for both Gaussian and Poisson noise models but the Poisson model has never been studied in microscopy nor compared with the Gaussian model. Here, the Gaussian- and Poisson-based estimation algorithms are implemented and compared for widefield microscopy in simulation. The Poisson algorithm is found to match or outperform the Gaussian algorithm in a variety of situations, and converges in a similar or decreased amount of time. The Gaussian algorithm does perform better in low-light regimes when image noise is dominated by additive Gaussian noise. The Poisson algorithm is also found to be more robust to the effects of spatially variant aberration and phase noise. Finally, the relative advantages of re-acquisition with aberration correction and deconvolution with aberrated point spread functions are compared.