# Improving quantitative accuracy and tissue visualization in CBCT guided radiation therapy

> **NIH NIH R01** · UNIVERSITY OF COLORADO DENVER · 2020 · $460,915

## Abstract

PROJECT SUMMARY
 Even though cone beam computed tomography (CBCT) is the most commonly used volumetric image
guidance modality, its role has been severely limited in the context of treatment monitoring and patient-specific
treatment modifications in radiation therapy. Due to CBCT’s poor image quality, clinicians cannot clearly
visualize soft tissues to assess anatomical changes, thus affecting their clinical decision-making. Moreover,
tools for treatment monitoring, such as deformable registration and dose calculation, do not function robustly
with today’s CBCT images due to the lack of CT number accuracy.
 Scattered radiation remains to be the fundamental problem in improving CBCT image quality. Thus, in
this project, we propose the two-dimensional antiscatter grid (2D Grid) as a novel device to address the scatter
problem and achieve high-quality CBCT images that are suitable for treatment monitoring. Our device has
fundamentally different architecture and fabrication than existing antiscatter grids for CBCT. Due to its
optimized grid structure, our 2D Grid provides both higher primary transmission and better scatter rejection
performance than today’s state-of-the-art antiscatter grids. Due to its favorable primary transmission and
scatter rejection performance, our 2D Grid improves the contrast-to-noise ratio and CT number accuracy to
levels not achievable with existing antiscatter grids.
 We hypothesize that our 2D Grid will provide significantly better soft tissue visualization and CT number
accuracy, and deformable registration algorithms are expected to perform significantly better. To test our
hypotheses, we will develop and optimize data processing methods for 2D Grid implementation in CBCT (Aim
1). Subsequently, we will fabricate 2D Grid prototypes and evaluate their performance in clinical CBCT
systems for photon and proton therapy (Aim 2). Following phantom based evaluations, we will conduct a
prospective clinical trial to evaluate the clinical utility of improved image quality (Aim 3). We will perform
observer studies to quantify the improvement in soft tissue visualization with respect to existing clinical CBCT
and gold-standard Helical CT, assess the improvement in accuracy of deformable image registration
algorithms, and evaluate the improvement in consistency of image intensity and texture features.
 While our application is focused on radiation therapy, the 2D Grid can play a key role in other CBCT
applications, such as interventional radiology, extremity imaging, and intraoperative imaging. Due to its
improved low-contrast visualization performance, our 2D Grid may also allow reduction of the imaging dose in
CBCT.

## Key facts

- **NIH application ID:** 10052292
- **Project number:** 1R01CA245270-01A1
- **Recipient organization:** UNIVERSITY OF COLORADO DENVER
- **Principal Investigator:** Cem Altunbas
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $460,915
- **Award type:** 1
- **Project period:** 2020-09-15 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10052292

## Citation

> US National Institutes of Health, RePORTER application 10052292, Improving quantitative accuracy and tissue visualization in CBCT guided radiation therapy (1R01CA245270-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10052292. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*