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

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

A genetic algorithm for variable selection in logistic regression analysis of radiotherapy treatment outcomes: A genetic algorithm for radiotherapy outcome modeling

A given outcome of radiotherapy treatment can be modeled by analyzing its correlation with a combination of dosimetric, physiological, biological, and clinical factors, through a logistic regression fit of a large patient population. The quality of the fit is measured by the combination of the predictive power of this particular set of factors and the statistical significance of the individual factors in the model. We developed a genetic algorithm (GA), in which a small sample of all the possible combinations of variables are fitted to the patient data. New models are derived from the best models, through crossover and mutation operations, and are in turn fitted. The process is repeated until the sample converges to the combination of factors that best predicts the outcome. The GA was tested on a data set that investigated the incidence of lung injury in NSCLC patients treated with 3DCRT. The GA identified a model with two variables as the best predictor of radiation pneumonitis: the V30 (p=0.048) and the ongoing use of tobacco at the time of referral (p=0.074). This two-variable model was confirmed as the best model by analyzing all possible combinations of factors. In conclusion, genetic algorithms provide a reliable and fast way to select significant factors in logistic regression analysis of large clinical studies

Tolerance limits and methodologies for IMRT measurement‐based verification QA: Recommendations of AAPM Task Group No. 218

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143649/1/mp12810_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/143649/2/mp12810.pd

Simple Factors Associated With Radiation-Induced Lung Toxicity After Stereotactic Body Radiation Therapy of the Thorax: A Pooled Analysis of 88 Studies

To study the risk factors for radiation-induced lung toxicity (RILT) after stereotactic body radiotherapy (SBRT) of the thorax

Electron dose calculations using the Method of Moments

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134898/1/mp7920.pd