# Emission Computed Tomography and Parallel Computing

> **NIH NIH R01** · UNIVERSITY OF ARIZONA · 2020 · $614,399

## Abstract

PROJECT SUMMARY
This grant deals with Emission Computed Tomography (ECT), defined broadly as three-
dimensional imaging of molecules or cells that have been labeled so that they emit light, high-
energy photons or charged particles without significant alteration of their biological function.
The labeling can use radionuclides or light-emitting molecules, so the emissions can be
nuclear decay products, including electrons, positrons and high-energy photons, or visible or
near-infrared photons. The main application of ECT, and the focus of this grant, is molecular
imaging in clinical medicine and biomedical research. The basic premise of the proposed
research is that common theoretical and computational challenges recur in all forms of ECT.
 Five Specific Aims are proposed. Aim 1 will provide dedicated parallel computing systems
and associated algorithms optimized for image science as applied to ECT. The systems will
combine field-programmable gate arrays (FPGAs) with a cluster of graphics processing units
(GPUs) and fast interconnects.
 Aim 2 is on imaging the radiance, a function that describes any radiation field in terms of six
variables: 3 spatial coordinates, 2 variables specifying direction of flux and an energy or
wavelength. We give particular attention to photon-processing detectors, which use advanced
statistical methods to estimate as accurately as possible some subset of the six radiance
variables for each detected photon or particle. Tools will be developed for analyzing all steps
in the imaging chain in terms of radiance.
 Aim 3 deals with a critical but often neglected issue in imaging: null functions, which are
components of an object that make no contribution to the image data. We will develop
methods to compute these invisible components and determine how they influence the ability
to extract information from ECT images.
 Aim 4 relates objects of interest in molecular imaging to the underlying physiology of the
patient or animal subject being imaged. New mathematical tools, never before used in biology
or medicine, will be applied to the analysis and optimization of ECT systems.
 Aim 5 will develop task-based measures of image quality, which are crucial to any rigorous
science of imaging. We will develop the theory and computational tools needed to assess
image quality of ECT systems in terms of therapeutic efficacy as well as diagnostic efficacy,
and we will develop algorithms to search efficiently for configurations of ECT systems that are
optimal in terms of task performance.

## Key facts

- **NIH application ID:** 9898371
- **Project number:** 5R01EB000803-28
- **Recipient organization:** UNIVERSITY OF ARIZONA
- **Principal Investigator:** Luca Caucci
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $614,399
- **Award type:** 5
- **Project period:** 1990-04-01 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9898371

## Citation

> US National Institutes of Health, RePORTER application 9898371, Emission Computed Tomography and Parallel Computing (5R01EB000803-28). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9898371. Licensed CC0.

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