2D antiscatter grid and scatter sampling based CBCT pipeline for image guided radiation therapy

TL;DR

This study introduces a novel qCBCT pipeline combining 2D antiscatter grids, residual scatter measurement, and iterative reconstruction to significantly improve image quality and quantitative accuracy in CBCT for radiation therapy.

Contribution

The paper presents a comprehensive qCBCT pipeline that enhances image quality and quantitative accuracy, bridging the gap between CBCT and MDCT for radiation therapy applications.

Findings

HU errors reduced by 10-40 HU with corrections

qCBCT shows lower HU inaccuracy compared to clinical CBCT

Contrast-to-noise ratio improved by 25% with iterative reconstruction

Abstract

Poor tissue visualization and quantitative accuracy in CBCT is a major barrier in expanding the role of CBCT imaging from target localization to quantitative treatment monitoring and plan adaptations in radiation therapy sessions. To further improve image quality in CBCT, 2D antiscatter grid based scatter rejection was combined with a raw data processing pipeline and iterative image reconstruction. The culmination of these steps was referred as quantitative CBCT, qCBCT. qCBCT data processing steps include 2D antiscatter grid implementation, measurement based residual scatter, image lag, and beam hardening correction for offset detector geometry CBCT with a bow tie filter. Images were reconstructed with iterative image reconstruction to reduce image noise. To evaluate image quality, qCBCT acquisitions were performed using a variety of phantoms to investigate the effect of object size and its composition on image quality. qCBCT image quality was benchmarked against clinical CBCT and MDCT images. Addition of image lag and beam hardening correction to scatter suppression reduced HU degradation in qCBCT by 10 HU and 40 HU, respectively. When compared to gold standard MDCT, mean HU errors in qCBCT and clinical CBCT were 10 HU and 27 HU, respectively. HU inaccuracy due to change in phantom size was 22 HU and 85 HU in qCBCT and clinical CBCT images, respectively. With iterative reconstruction, contrast to noise ratio improved by a factor of 1.25 when compared to clinical CBCT protocols. Robust artifact and noise suppression in qCBCT images can reduce the image quality gap between CBCT and MDCT, improving the promise of qCBCT in fulfilling the tasks that demand high quantitative accuracy, such as CBCT based dose calculations and treatment response assessment in image guided radiation therapy.