# A multi-channel reconstruction toolkit for computed tomography

> **NIH NIH RF1** · DUKE UNIVERSITY · 2022 · $226,310

## Abstract

Abstract
According to the FDA, the recent clinical availability of photon counting detector X-ray CT (PCCT) marks the
“first major imaging device advancement for computed tomography in nearly a decade” because it provides
fundamental improvements in terms of image noise, spatial resolution, material discrimination, and low contrast
detectability. Yet this new era of clinical PCCT follows years of preclinical research at our Quantitative Imaging
and Analysis Lab at Duke University and our preliminary research projects using the NAEOTOM Alpha clinical
PCCT scanner from Siemens and installed at Duke. To support these research projects and under the Aims of
our funded NIA grant (1RF1AG070149; Cardiac photon counting CT and its application in studying interactions
between Alzheimer's and heart disease), we are developing a GPU-based Multi-Channel Reconstruction (MCR)
Toolkit for CT (channels refer to dynamic and/or multi-energy images of 3D anatomical structures). In addition
to being built from the ground up to handle the unique challenges of multi-channel reconstruction, our Toolkit
includes several unique features not in other open-source packages: support for translational research using the
same code on both clinical and preclinical data, robust reference algorithms for performing iterative
reconstruction of low-dose multi-channel CT data, and automatic data adaptation of reconstruction parameters
to reduce manual parameter tuning. The objectives of this supplement are (1) to make the MCR Toolkit codebase
more robust and extensible for CT experts and (2) to provide a graphical user interface and reference
reconstruction protocols for those using CT to conduct other research studies. We will accomplish these
objectives through three distinct aims. In Aim 1 we propose to restructure our Toolkit codebase from its current
C code to object oriented code written in C++, providing native support for popular file formats, eliminating
redundant code, reducing opportunities for user errors, and allowing reconstruction pipelines to be saved and
shared. Aim 2 focuses on non-expert users, producing a graphical user interface for running reconstruction
pipelines on acquired data and uploading image data and metadata to an XNAT database. Finally, Aim 3 focuses
on community awareness, accessibility, and documentation for the MCR Toolkit, ensuring the Toolkit is available
and easy to use beyond the scope of this supplement and the parent grant. Successful completion of this work
will transform the MCR Toolkit from a collection of tools useful to experts in the field to a platform for open science
among CT, basic science, and clinical researchers. Widespread adoption of the Toolkit is expected thanks to the
unique benefits it provides for photon counting and dynamic CT which are not currently provided by open-source
software alternatives.

## Key facts

- **NIH application ID:** 10605585
- **Project number:** 3RF1AG070149-01S1
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** CRISTIAN T BADEA
- **Activity code:** RF1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $226,310
- **Award type:** 3
- **Project period:** 2021-02-15 → 2024-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10605585

## Citation

> US National Institutes of Health, RePORTER application 10605585, A multi-channel reconstruction toolkit for computed tomography (3RF1AG070149-01S1). Retrieved via AI Analytics 2026-06-01 from https://api.ai-analytics.org/grant/nih/10605585. Licensed CC0.

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