# Robust Visualization in CT Colonography

> **NIH NIH R43** · KENTUCKY IMAGING TECHNOLOGIES, LLC · 2020 · $299,916

## Abstract

Computed-Tomography Colonoscopy (CTC) refers to visualization of the colon surface (lumen) by radiologists
following an abdominal CT scan of prepped patients. Removal of colonic polyps is performed by a minimally-
invasive procedure known as Optical Colonoscopy (OC). Through a sequence of image analysis steps, a three-
dimensional (3D) representation of the colon can be constructed, which is then visualized by radiologists, using
a virtual camera, in order to examine the colon surface for abnormalities and polyps. A proper synchronization
between CTC for early detection of polyps and OC for their removal is the most effective and economical
approach to prevent colon cancer, which has over 95% rate of recovery with early detection. With improved
efficacy and safety of the procedure, debate regarding the acceptance of CTC as a clinical and reimbursable
procedure for early detection of colon cancer has evolved in its favor. However, the limitations of current CTC
visualization methods in terms of distortions and large false positive rates, coupled with the fact that only a very
small segment of the population undergoes OC or CT screening, means that only about 4 out of 10 colorectal
cancers are found at an early stage. Reduction of false positive and proper synchronization of the CTC-OC cycle
hold promise for combating colorectal cancer.
This Phase 1 project introduces a novel CTC visualization which is entirely model-based, enabling quantifying
the performance with respect to the topology of the colon and the types and size of colonic polyps. The approach
uses a virtual camera rig arranged in a specific pattern within the colon’s inner surface (lumen) and will be
denoted by “Fly-In” and would be abbreviated by FI. This rig provides visualizes the lumen and presents it as a
sheet (filet) without distortion. This arrangement of virtual cameras is adaptable to topological variations in the
3D tubular shape (e.g., constrictions and expansions, bends and deformations) and the textural content of the
lumen. Thus FI transforms the 3D tubular object into a sheet/filet without distortions.
Specific Aims: This Phase 1 will have three main Specific Aims: 1) Implementation of the FI method. A
theoretical framework for the image formation in FI will be established to relate the lumen details to the number
of the virtual cameras in the rig to be used; focus would be on detecting and classification of all polyps above 5
mm in size. 2) Developing an automatic polyp methods for the method, using curvature and planar texture
measures as well, in order to detect various type of polyps. 3) Evaluate the FI method on synthetic and
retrospective scans of up to 50 CTC patients and comparing its performance against traditional CTC methods
such as Fly-Through (FT) and Fly-Over (FO) and Colon Flattening (Filet approach).
With successful execution of Phase 1, the investigators will move into clinical testing using a multi-center study,
and will pursue NIH Phase II funding and...

## Key facts

- **NIH application ID:** 10009211
- **Project number:** 1R43CA250750-01
- **Recipient organization:** KENTUCKY IMAGING TECHNOLOGIES, LLC
- **Principal Investigator:** Aly A Farag
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $299,916
- **Award type:** 1
- **Project period:** 2020-04-15 → 2021-09-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10009211

## Citation

> US National Institutes of Health, RePORTER application 10009211, Robust Visualization in CT Colonography (1R43CA250750-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10009211. Licensed CC0.

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