# Deep Learning Reconstruction for Improved TOF PET Using Histo-Image Partitioning

> **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2021 · $620,138

## Abstract

Project Summary
 Clinical and research applications of the PET imaging are rapidly expanding from ever improving diagnostic
and treatment assessment applications to guidance of personalized treatments, ultra-low dose imaging, and
even interventional imaging procedures. Supporting these developments, reconstruction tools that are able to
reliably handle both typical and (ultra-)low count situations, imperfect data, and data from specialized imaging
geometries, with fast (near real-time) reconstruction performance are of crucial importance. The overall goal of
this project is to develop and investigate robust and efficacious Deep Learning (DL) reconstruction approaches
addressing these needs. A unique and innovative feature of the proposed approaches (compared to alternative
DL applications) is the utilization of list-mode data histogrammed into a very efficient histo-image format. TOF
data partitioned into the histo-image format are characterized by strong local properties, thus perfectly fitting
convolutional neural network formalism and making DL training and reconstruction directly from realistic clinical
data (in size and character) highly feasible and practical.
 The clinical utility of PET systems has significantly improved over the years thanks to advances in
instrumentation, data corrections, and reconstruction approaches. Nevertheless, full utilization of their potential
through robust and fast quantitative reconstruction remains a challenge especially for the cases of very low count
data, such as in low-count temporal (motion and dynamic) frames, delayed studies, longitudinal low-dose
studies, and studies using new isotopes with long half-life and low positron fraction rates (e.g. in 89Zr-labeled
CAR-T cell imaging), as well as in specialized PET systems with partial angular coverage, for which exact,
artifact-free, reconstruction does not exist. These are the situations for which the developed DL approaches
promise great potential due to the demonstrated success of the DL networks to be trained for imperfect and very
low count data without reliance on accurate data models. Furthermore, pre-trained networks can provide ultra-
fast, near real-time, performance in practical use.
 Specific Aim 1 will develop tools for DL PET reconstruction using histo-image partitioning along with
procedures for training of the proposed DL approaches, including novel approaches advancing the state-of-the-
art of DL reconstruction directly from acquired PET data. Specific Aim 2 is directed towards study and evaluation
of the performance of the investigated DL approaches for whole-body and long axial FOV scanner data for the
wide range of counts from applications such as typical FDG, low dose, delayed, low activity isotope scans, and
ultra-short frames in motion correction and dynamic studies. Specific Aim 3 will develop and apply motion
correction protocols involving the proposed DL reconstruction tools and test and study their efficacy for clinically
realis...

## Key facts

- **NIH application ID:** 10276952
- **Project number:** 1R01EB031806-01
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** SAMUEL MATEJ
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $620,138
- **Award type:** 1
- **Project period:** 2021-07-01 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10276952

## Citation

> US National Institutes of Health, RePORTER application 10276952, Deep Learning Reconstruction for Improved TOF PET Using Histo-Image Partitioning (1R01EB031806-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10276952. Licensed CC0.

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