# Groundwork for a Synchrotron MicroCT Imaging Resource for Biology (SMIRB)

> **NIH NIH R24** · PENNSYLVANIA STATE UNIV HERSHEY MED CTR · 2021 · $657,323

## Abstract

Project Summary
Each major human disease is associated with a specific range of morphological changes to cells and tissues in
the micron scale. Normal and abnormal structure was discovered and is still characterized using histology - a
microscopic technique that depends on physical tissue slices. Presently, histology’s use in systems biology is
limited by its largely descriptive and two-dimensional nature. Making histology quantitative and three-dimensional
would be potentially transformational for research and diagnostics, but has been impractical. Accordingly, we
have now created a 3D form of histology by customizing X-ray microtomography (micro-CT) of fixed and stained,
millimeter-scale, whole organisms and tissue samples. We used fixed and metal-stained, whole zebrafish
because they contain a full range of tissues within the size range currently studied histologically. The result is
the first practical way to create virtual histology-like “sections” in any plane. Three-dimensional, complete
histological phenotyping has potential use in genetic and chemical screens, and in clinical and toxicological
tissue diagnostics. Here, we propose the next steps needed to enable high-throughput, quantitative, 3D
histological phenotyping of whole, millimeter-scale animals. The proposed work applies the principles of
chemistry, physics, and computer science to improve image resolution, throughput, and analytics, organized into
three specific aims. Specific Aim 1 will build on our developments in this project and further improve imaging
volume and resolution by upgrading imaging array, optics, and sub-pixel shifting, and to throughput by changes
in sample embedding, loading geometry and mechanics, helical CT scanning, scintillator material, and to data
sharing by improvements to the ViewTool infrastructure and user interface. Specific Aim 2 will yield reference
images to define the range of normal phenotypic variation and to obtain samples related to a range of potential
applications. Specific Aim 3 will apply the power of machine learning to segmentation, annotation, and analytics.
Together, this work will establish a practical foundation for large-scale genetic and chemical screens involving
mm-scale, whole organisms based on 3-dimensional, quantitative, histological phenotyping. The instrumentation
and analytics will be state-of-the-art in its combination of resolution, field-of-view, pancellularity, image quality,
analytical potential, throughput, sample stability, and reproducibility and largely usable with both tube and
synchrotron X-ray sources. The voxel resolution will be at least 0.5 μm across fields-of-view of up to 1 cm.
Representation of every cell type make the images suitable for cross-referencing across imaging modalities.
Potential applications will be explored, “wild-type” will begin to be defined, and training sets for automated
segmentation generated. The potential impact will encompass the missions of most NIH Institutes and Centers.
The...

## Key facts

- **NIH application ID:** 10222804
- **Project number:** 5R24OD018559-07
- **Recipient organization:** PENNSYLVANIA STATE UNIV HERSHEY MED CTR
- **Principal Investigator:** Keith Chi Cheng
- **Activity code:** R24 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $657,323
- **Award type:** 5
- **Project period:** 2015-08-15 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10222804

## Citation

> US National Institutes of Health, RePORTER application 10222804, Groundwork for a Synchrotron MicroCT Imaging Resource for Biology (SMIRB) (5R24OD018559-07). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10222804. Licensed CC0.

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