Engineering topics

Altman, M

Publications and source records attributed to Altman, M.

4 records

DOE OSTI2015

SU-F-BRA-14: Optimization of Dosimetric Guidelines for Accelerated Partial Breast Irradiation (APBI) Using the Strut-Adjusted Volume Implant (SAVI)

Purpose: Treatment planning guidelines for accelerated partial breast irradiation (ABPI) using the strut-adjusted volume implant (SAVI) are inconsistent between the manufacturer and NSABP B-39/RTOG 0413 protocol. Furthermore neither set of guidelines accounts for different applicator sizes. The purpose of this work is to establish guidelines specific to the SAVI that are based on clinically achievable dose distributions. Methods: Sixty-two consecutive patients were implanted with a SAVI and prescribed to receive 34 Gy in 10 fractions twice daily using high dose-rate (HDR) Ir-192 brachytherapy. The target (PTV-EVAL) was defined per NSABP. The treatments were planned and evaluated using a combination of dosimetric planning goals provided by the NSABP, the manufacturer, and our prior clinical experience. Parameters evaluated included maximum doses to skin and ribs, and volumes of PTV-EVAL receiving 90%, 95%, 100%, 150%, and 200% of the prescription (V90, etc). All target parameters were evaluated for correlation with device size using the Pearson correlation coefficient. Revised dosimetric guidelines for target coverage and heterogeneity were determined from this population. Results: Revised guidelines for minimum target coverage (ideal in parentheses): V90≥95%(97%), V95≥90%(95%), V100≥88%(91%). The only dosimetric parameters that were significantly correlated (p<0.05) with device size were V150 and V200. Heterogeneity criteria were revised for the 6–1 Mini/6-1 applicators to V150≤30cc and V200≤15cc, and unchanged for the other sizes. Re-evaluation of patient plans showed 90% (56/62) met the revised minimum guidelines and 76% (47/62) met the ideal guidelines. All and 56/62 patients met our institutional guidelines for maximum skin and rib dose, respectively. Conclusions: We have optimized dosimetric guidelines for the SAVI applicators, and found that implementation of these revised guidelines for SAVI treatment planning yielded target coverage exceeding that required by existing guidelines while preserving heterogeneity constraints and minimizing dose to organs at risk.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2014

MO-G-9A-01: Imaging Refresher for Standard of Care Radiation Therapy

Imaging techniques and technology which were previously the domain of diagnostic medicine are becoming increasingly integrated and utilized in radiation therapy (RT) clinical practice. As such, there are a number of specific imaging topics that are highly applicable to modern radiation therapy physics. As imaging becomes more widely integrated into standard clinical radiation oncology practice, the impetus is on RT physicists to be informed and up-to-date on those imaging modalities relevant to the design and delivery of therapeutic radiation treatments. For example, knowing that, for a given situation, a fluid attenuated inversion recovery (FLAIR) image set is most likely what the physician would like to import and contour is helpful, but may not be sufficient to providing the best quality of care. Understanding the physics of how that pulse sequence works and why it is used could help assess its utility and determine if it is the optimal sequence for aiding in that specific clinical situation. It is thus important that clinical medical physicists be able to understand and explain the physics behind the imaging techniques used in all aspects of clinical radiation oncology practice. This session will provide the basic physics for a variety of imaging modalities for applications that are highly relevant to radiation oncology practice: computed tomography (CT) (including kV, MV, cone beam CT [CBCT], and 4DCT), positron emission tomography (PET)/CT, magnetic resonance imaging (MRI), and imaging specific to brachytherapy (including ultrasound and some brachytherapy specific topics in MR). For each unique modality, the image formation process will be reviewed, trade-offs between image quality and other factors (e.g. imaging time or radiation dose) will be clarified, and typically used cases for each modality will be introduced. The current and near-future uses of these modalities and techniques in radiation oncology clinical practice will also be discussed. Learning Objectives: To review the basic physical science principles of CT, PET, MR, and ultrasound imaging. To understand how the images are created, and present their specific role in patient management and treatment planning for therapeutic radiation (both external beam and brachytherapy). To discuss when and how each specific imaging modality is currently used in clinical practice, as well as how they may come to be used in the near future.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2014

TU-C-17A-03: An Integrated Contour Evaluation Software Tool Using Supervised Pattern Recognition for Radiotherapy

Purpose: Radiotherapy (RT) contours delineated either manually or semiautomatically require verification before clinical usage. Manual evaluation is very time consuming. A new integrated software tool using supervised pattern contour recognition was thus developed to facilitate this process. Methods: The contouring tool was developed using an object-oriented programming language C# and application programming interfaces, e.g. visualization toolkit (VTK). The C# language served as the tool design basis. The Accord.Net scientific computing libraries were utilized for the required statistical data processing and pattern recognition, while the VTK was used to build and render 3-D mesh models from critical RT structures in real-time and 360° visualization. Principal component analysis (PCA) was used for system self-updating geometry variations of normal structures based on physician-approved RT contours as a training dataset. The inhouse design of supervised PCA-based contour recognition method was used for automatically evaluating contour normality/abnormality. The function for reporting the contour evaluation results was implemented by using C# and Windows Form Designer. Results: The software input was RT simulation images and RT structures from commercial clinical treatment planning systems. Several abilities were demonstrated: automatic assessment of RT contours, file loading/saving of various modality medical images and RT contours, and generation/visualization of 3-D images and anatomical models. Moreover, it supported the 360° rendering of the RT structures in a multi-slice view, which allows physicians to visually check and edit abnormally contoured structures. Conclusion: This new software integrates the supervised learning framework with image processing and graphical visualization modules for RT contour verification. This tool has great potential for facilitating treatment planning with the assistance of an automatic contour evaluation module in avoiding unnecessary manual verification for physicians/dosimetrists. In addition, its nature as a compact and stand-alone tool allows for future extensibility to include additional functions for physicians’ clinical needs.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2014

TU-C-17A-04: BEST IN PHYSICS (THERAPY) - A Supervised Framework for Automatic Contour Assessment for Radiotherapy Planning of Head- Neck Cancer

Purpose: Precise contour delineation of tumor targets and critical structures from CT simulations is essential for accurate radiotherapy (RT) treatment planning. However, manual and automatic delineation processes can be error prone due to limitations in imaging techniques and individual anatomic variability. Tedious and laborious manual verification is hence needed. This study develops a general framework for automatically assessing RT contours for head-neck cancer patients using geometric attribute distribution models (GADMs). Methods: Geometric attributes (centroid and volume) were computed from physician-approved RT contours of 29 head-neck patients. Considering anatomical correlation between neighboring structures, the GADM for each attribute was trained to characterize intra- and interpatient structure variations using principal component analysis. Each trained GADM was scalable and deformable, but constrained by the principal attribute variations of the training contours. A new hierarchical model adaptation algorithm was utilized to assess the RT contour correctness for a given patient. Receiver operating characteristic (ROC) curves were employed to evaluate and tune system parameters for the training models. Results: Experiments utilizing training and non-training data sets with simulated contouring errors were conducted to validate the framework performance. Promising assessment results of contour normality/abnormality for the training contour-based data were achieved with excellent accuracy (0.99), precision (0.99), recall (0.83), and F-score (0.97), while corresponding values of 0.84, 0.96, 0.83, and 0.9 were achieved for the non-training data. Furthermore, the areas under the ROC curves were above 0.9, validating the accuracy of this test. Conclusion: The proposed framework can reliably identify contour normality/abnormality based upon intra- and inter-structure constraints derived from clinically-approved contours. It also allows physicians to analytically determine the system parameters to fit various clinic requirements (e.g. as-low-as-possible false positives). It has great potential for improving RT work flow. More geometric attributes and training sets will be investigated to improve framework performance in the future.

60 APPLIED LIFE SCIENCES↗