33 research outputs found
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
Clinical and Dosimetric Impact of 2D kV Motion Monitoring and Intervention in Liver Stereotactic Body Radiation Therapy.
PURPOSE: Positional errors resulting from motion are a principal challenge across all disease sites in radiation therapy. This is particularly pertinent when treating lesions in the liver with stereotactic body radiation therapy (SBRT). To achieve dose escalation and margin reduction for liver SBRT, kV real-time imaging interventions may serve as a potential solution. In this study, we report results of a retrospective cohort of liver patients treated using real-time 2D kV-image guidance SBRT with emphasis on the impact of (1) clinical workflow, (2) treatment accuracy, and (3) tumor dose.
METHODS AND MATERIALS: Data from 33 patients treated with 41 courses of liver SBRT were analyzed. During treatment, planar kV images orthogonal to the treatment beam were acquired to determine treatment interventions, namely treatment pauses (ie, adequacy of gating thresholds) or treatment shifts. Patients were shifted if internal markers were \u3e3 mm, corresponding to the PTV margin used, from the expected reference condition. The frequency, duration, and nature of treatment interventions (ie, pause vs shift) were recorded, and the dosimetric impact associated with treatment shifts was estimated using a machine learning dosimetric model.
RESULTS: Of all fractions delivered, 39% required intervention, which took on average 1.9 ± 1.6 minutes and occurred more frequently in treatments lasting longer than 7 minutes. The median realignment shift was 5.7 mm in size, and the effect of these shifts on minimum tumor dose in simulated clinical scenarios ranged from 0% to 50% of prescription dose per fraction.
CONCLUSION: Real-time kV-based imaging interventions for liver SBRT minimally affect clinical workflow and dosimetrically benefit patients. This potential solution for addressing positional errors from motion addresses concerns about target accuracy and may enable safe dose escalation and margin reduction in the context of liver SBRT
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