  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.
