Charged-particle emission tomography

Dual-isotope imaging method for Actinium-225 and Bismuth-213 using alpha imaging detector

Recently, 225Ac has received considerable attention for its use in targeted alpha therapy because it has a relatively long half-life and yields four more alpha-particles from the daughter nuclides. The performance evaluation should separately assess the distribution of 225Ac and 213Bi because daughter nuclides, including 213Bi, can cause renal toxicity, which may hinder the widespread use of 225Ac for targeted alpha therapy. In this study, we describe and validate a spectrum decomposition method for dual-isotope imaging of 225Ac and 213Bi using an alpha imaging detector. We implemented an experiment to demonstrate the feasibility of using the alpha imaging detector to obtain distribution images using therapeutic amounts of 225Ac. In addition, we designed and conducted a Monte Carlo simulation under realistic conditions based on the experimental results to evaluate the spectrum decomposition method for dual-isotope imaging. The alpha imaging detector exhibited a detection efficiency of 18.5% and an energy resolution of 13.4% at 5.5 MeV. In the simulation, the distributions of 225Ac and 213Bi were obtained in each region with a relative error of 5%. The results of this study confirmed the feasibility of the dual-isotope imaging method for discriminating alpha-emitters using small amounts of 225Ac.

Shift-and-add image processing incorporated with the unsharp masking method

Shift-and-add (SAA) is a simple image processing procedure. SAA was devised to reconstruct a diffraction-limited image from atmospherically degraded stellar images. Recently SAA has been applied to biological imaging. There are several variants of SAA. Here proposed is an SAA procedure incorporated with unsharp masking (USM). The SAA procedure proposed here encompasses an extended version of USM. The proposed SAA method retains the simplicity and easiness, and the basic features of SAA. The effectiveness of the proposed method is examined by restoring atmospherically degraded solar images. It is shown that the USM SAA reconstructed image exhibits high contrast and reveals fine structures blurred by atmospheric turbulence. It is also shown that the USM SAA performs better with a data frame selection scheme.

Stochastic models for objects and images in oncology and virology: application to PI3K-Akt-mTOR signaling and COVID-19 disease

Purpose: The goal of this research is to develop innovative methods of acquiring simultaneous multidimensional molecular images of several different physiological random processes (PRPs) that might all be active in a particular disease such as COVID-19. Approach: Our study is part of an ongoing effort at the University of Arizona to derive biologically accurate yet mathematically tractable models of the objects of interest in molecular imaging and of the images they produce. In both cases, the models are fully stochastic, in the sense that they provide ways to estimate any estimable property of the object or image. The mathematical tool we use for images is the characteristic function, which can be calculated if the multivariate probability density function for the image data is known. For objects, which are functions of continuous variables rather than discrete pixels or voxels, the characteristic function becomes infinite dimensional, and we refer to it as the characteristic functional. Results: Several innovative mathematical results are derived, in particular for simultaneous imaging of multiple PRPs. Then the application of these methods to cancers that disrupt the mammalian target of rapamycin signaling pathway and to COVID-19 are discussed qualitatively. One reason for choosing these two problems is that they both involve lipid rafts. Conclusions: We found that it was necessary to employ a new algorithm for energy estimation to do simultaneous single-photon emission computerized tomography imaging of a large number of different tracers. With this caveat, however, we expect to be able to acquire and analyze an unprecedented amount of molecular imaging data for an individual COVID patient.

Generating patient-specific virtual tumor populations with reaction-diffusion models and molecular imaging data

The use of mathematical tumor growth models coupled to noisy imaging data has been suggested as a possible component in the push towards precision medicine. We discuss the generation of population and patient-specific virtual populations in this context, providing in silico experiments to demonstrate how intra- and inter-patient heterogeneity can be estimated by applying rigorous statistical procedures to noisy molecular imaging data, and how the noise properties of such data can be analyzed to estimate uncertainties in predicted patient outcomes.

Is there a role for image science in the brave new world of artificial intelligence?

Knowledge of the principles of image science is essential to the successful application of artificial intelligence in medical imaging.

Towards continuous-to-continuous 3D imaging in the real world

Imaging systems are often modeled as continuous-to-discrete mappings that map the object (i.e. a function of continuous variables such as space, time, energy, wavelength, etc) to a finite set of measurements. When it comes to reconstruction, some discretized version of the object is almost always assumed, leading to a discrete-to-discrete representation of the imaging system. In this paper, we discuss a method for single-photon emission computed tomography (SPECT) imaging that avoids discrete representations of the object or the imaging system, thus allowing reconstruction on an arbitrarily fine set of points.

Physiological random processes in precision cancer therapy

Many different physiological processes affect the growth of malignant lesions and their response to therapy. Each of these processes is spatially and genetically heterogeneous; dynamically evolving in time; controlled by many other physiological processes, and intrinsically random and unpredictable. The objective of this paper is to show that all of these properties of cancer physiology can be treated in a unified, mathematically rigorous way via the theory of random processes. We treat each physiological process as a random function of position and time within a tumor, defining the joint statistics of such functions via the infinite-dimensional characteristic functional. The theory is illustrated by analyzing several models of drug delivery and response of a tumor to therapy. To apply the methodology to precision cancer therapy, we use maximum-likelihood estimation with Emission Computed Tomography (ECT) data to estimate unknown patient-specific physiological parameters, ultimately demonstrating how to predict the probability of tumor control for an individual patient undergoing a proposed therapeutic regimen.

Null functions in three-dimensional imaging of alpha and beta particles

Null functions of an imaging system are functions in the object space that give exactly zero data. Hence, they represent the intrinsic limitations of the imaging system. Null functions exist in all digital imaging systems, because these systems map continuous objects to discrete data. However, the emergence of detectors that measure continuous data, e.g. particle-processing (PP) detectors, has the potential to eliminate null functions. PP detectors process signals produced by each particle and estimate particle attributes, which include two position coordinates and three components of momentum, as continuous variables. We consider Charged-Particle Emission Tomography (CPET), which relies on data collected by a PP detector to reconstruct the 3D distribution of a radioisotope that emits alpha or beta particles, and show empirically that the null functions are significantly reduced for alpha particles if ≥3 attributes are measured or for beta particles with five attributes measured.