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Evaluation of Machine Log-Files/MC based Treatment Planning and Delivery QA as Compared to ArcCHECK QA.
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Evaluation of Machine Log-Files/MC based Treatment Planning and Delivery QA as Compared to ArcCHECK QA.

Med Phys. 2018 Apr 20;:

Authors: Stanhope CW, Drake DG, Liang J, Alber M, Söhn M, Habib C, Willcut V, Yan D

Abstract
PURPOSE: A treatment planning/delivery QA tool using linac log files (LF) and Monte Carlo (MC) dose calculation is investigated as a standalone alternative to phantom-based patient-specific QA (ArcCHECK (AC)).
METHODS: Delivering a variety of fields onto MapCHECK2 and ArcCHECK, diode sensitivity dependence on dose rate (in-field) and energy (primarily out-of-field) was quantified. AC and LF QAs were analyzed w.r.t. delivery complexity by delivering 12x12cm static fields/arcs comprised of varying numbers of abutting sub-fields onto ArcCHECK. 11 clinical dual-arc VMAT patients planned using Pinnacle's convolution-superposition (CS) were delivered on ArcCHECK and log file dose (LF-CS and LF-MC) calculated. To minimize calculation time, reduced LF-CS sampling (1/2/3/4° control point spacing) was investigated. Planned ('Plan') and LF-reconstructed CS and MC doses were compared with each other and AC measurement via statistical (mean ± StdDev(σ)) and gamma analyses to isolate dosimetric uncertainties and quantify the relative accuracies of AC QA and MC-based LF QA.
RESULTS: Calculation and ArcCHECK measurement differed by up to 1.5% in-field due to variation in dose rate and up to 5% out-of-field. For the experimental segment-varying plans, despite CS calculation deviating by as much as 13% from measurement, Plan-MC and LF-MC doses generally matched AC measurement within 3%. Utilizing 1º control point spacing, 2%/2mm LF-CS vs. AC pass rates (97%) were slightly lower than Plan-CS vs. AC pass rates (97.5%). Utilizing all log file samples, 2%/2mm LF-MC vs. AC pass rates (97.3%) were higher than Plan-MC vs. AC (96.5%). Phantom-dependent, calculation algorithm-dependent (MC vs. CS), and delivery error-dependent dose uncertainties were 0.8±1.2%, 0.2±1.1%, and 0.1±0.9% respectively.
CONCLUSION: Reconstructing every log file sample with no increase in computational cost, MC-based LF QA is faster and more accurate than CS-based LF QA. Offering similar dosimetric accuracy compared to AC measurement, MC-based log files can be used for treatment planning QA. This article is protected by copyright. All rights reserved.

PMID: 29676463 [PubMed - as supplied by publisher]




An Unsupervised Automatic Segmentation Algorithm for Breast Tissue Classification of Dedicated Breast Computed Tomography Images.
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An Unsupervised Automatic Segmentation Algorithm for Breast Tissue Classification of Dedicated Breast Computed Tomography Images.

Med Phys. 2018 Apr 19;:

Authors: Caballo M, Boone JM, Mann R, Sechopoulos I

Abstract
PURPOSE: To develop and evaluate a new automatic classification algorithm able to identify voxels containing skin, vasculature, adipose and fibroglandular tissue in dedicated breast CT images.
METHODS: The proposed algorithm combines intensity and region-based segmentation methods with energy minimizing splines and unsupervised data mining approaches for classifying and segmenting the different tissue types. Breast skin segmentation is achieved by a region growing method which uses constraints from the previously extracted skin centerline to add robustness to the model and to reduce the false positive rate. An energy minimizing active contour model is then used to classify adipose tissue voxels by including gradient flow and region-based features. Finally, blood vessels are separated from fibroglandular tissue by a k-means clustering algorithm based on automatically extracted shape-based features. To evaluate the accuracy of the algorithm, two sets of 15 different patient breast CT scans, each acquired with different breast CT systems and acquisition settings, were obtained. Three slices from each scan were manually segmented under the supervision of an experienced breast radiologist and considered the gold standard. Comparisons with manual segmentation were quantified using five similarity metrics: Dice Similarity Coefficient (DSC), sensitivity, conformity coefficient, and two Hausdorff distance measures. To evaluate the robustness to image noise, the segmentation was repeated after separately adding Gaussian noise with increasing standard deviation (in four steps, from 0.01 to 0.04) to an additional 15 slices from the first dataset. In addition, to evaluate vasculature classification, three different pre- and post-contrast injection patient breast CT images were classified and compared. Finally, DSC was also used for quantitative comparisons with previously proposed approaches for breast CT tissue classification using ten images from the first dataset.
RESULTS: The algorithm showed a high accuracy in classifying the different tissue types for both breast CT systems, with an average DSC of 95% and 90% for the first and second image dataset, respectively. Furthermore, it demonstrated to be robust to image noise with a robustness to image noise of 85%, 83%, 79%, and 71% for the images corrupted with the four increasing noise levels. Previous methods for breast tissues classification resulted, for the tested dataset, in an average global DSC of 87%, while our approach resulted in a global average DSC of 94.5%.
CONCLUSIONS: The proposed algorithm resulted in accurate and robust breast tissue classification, with no prior training or threshold setting. Potential applications include breast density quantification and tissue pattern characterization (both biomarkers of cancer development), simulation-based radiation dose analysis and patient data-based phantom design, which could be used for further breast imaging research. This article is protected by copyright. All rights reserved.

PMID: 29676025 [PubMed - as supplied by publisher]