Multiresolution-fractal modeling for brain tumor detection

Old Dominion University, Norfolk VA

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Abstract

DESCRIPTION (provided by applicant): The PI's long-term research goal is to develop a fully functional automated robust CAD tool for accurate pediatric brain tumor volume segmentation and tracking over time. Note the current practice in brain tumor volume segmentation involves manual tracing and segmentation of suspected tumor areas in multimodality MRI which is time consuming, labor intensive, and may be imprecise. In an effort to reduce cognitive sequelae, contemporary protocols employ risk-adapted therapy in which risk stratification is based on volume of residual tumor after surgical resection and presence of metastatic disease at diagnosis. Therefore, further improvement in cancer treatment outcome in children is unlikely to be achieved without improved knowledge of tumor volume and classification among other factors. In addition, such automated volume computation and tracking tool would be of value as an adjunct marker in following up patients with brain tumors. This will, in turn, help the physicians to make important patient management decisions about surgery planning, critical radiation treatment planning modifications, treatment field modifications, localized control, sites of metastatic disease and post therapy response evaluation. However, development of such automated and precise tumor volume segmentation CAD tool requires solution to a few challenges such as detection of hard-to-detect brain tumor (small residual after surgery, poorly enhanced, multi foci and irregularly shaped) and abnormalities (edema, necrosis, and larger resection cavity due to surgery) detection and classification. This project aims at development, testing, and evaluation of innovative techniques and tools that will assist feature-based detection, segmentation and classification of brain tumor and a few specific abnormalities.} The specific aims of this project are: 1) Spline-multiresolution wavelet-fractal feature extraction;2) MR sequence-dependant feature fusion and tumor/abnormality size and volume determination for improved detection;3) Optimized feature fusion for improved tumor, tissue and abnormality classification;and 4) Algorithm testing and validation. {If successful, our method will allow for the automatic computation of brain tumors and abnormalities with improved accuracy, which can provide a rapid, objective, reproducible, and easily reported assessment of the disease. The results obtained from this project will have immediate impact in pediatric neuroradiology practice by providing an accurate, objective, and consistent way to evaluate and interpret brain tumors and associated abnormalities. PUBLIC HEALTH RELEVANCE: This project aims at development, testing, and evaluation of novel feature-based algorithms for robust, accurate and reproducible brain tumor and other abnormalities detection and classification. Such identification and classification will then be used to obtain precise segmentation of hard-to-detect brain tumors and abnormalities. We define hard-to-detect brain tumor as lesions that are small (residual after surgery), poorly enhanced, multi foci and irregularly shaped and abnormalities as edema, necrosis, and larger resection cavity due to surgery respectively. The algorithms capable of reliably and accurately computing segmented tumor volume would be of value as an adjunct marker in following up patients with brain tumors. Such a tumor volume quantification method would also have direct application in pre-clinical surgery planning and therapy trials leading to novel treatment strategies and devices. The results obtained from this project will have immediate impact in neuroradiology practice by providing an accurate, objective, and consistent way to evaluate and interpret brain tumors.