Multiresolution-fractal modeling for brain tumor detection

用于脑肿瘤检测的多分辨率分形模型

• 批准号: 7988732
• 负责人: Khan M Iftekharuddin
• 金额: $ 10.11万
• 依托单位: UNIVERSITY OF MEMPHIS
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-01 至 2011-10-15
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7988732
• 关键词: Algorithms; Area; Base of the Brain; Benign; Biological Neural Networks; Brain; Brain Neoplasms; Characteristics; Child; Childhood; Childhood Brain Neoplasm; Classification; Cognitive; Community Clinical Oncology Program; Complex; Computer software; Detection; Development; Devices; Diagnosis; Diagnostic; Diagnostic Imaging; Disease; Double-Blind Method; Drug Formulations; Drug usage; Edema; Evaluation; Excision; Family; Fractals; Future; Goals; Gold; Healthcare; Histocompatibility Testing; Hospitals; Image; Imagery; Knowledge; Lesion; Literature; MRI Scans; Machine Learning; Magnetic Resonance; Magnetic Resonance Imaging; Manuals; Maps; Measurement; Medical Imaging; Methods; Modeling; Modification; Morphologic artifacts; Motion; Movement; Necrosis; Noise; Operative Surgical Procedures; Pathology; Patients; Pediatric Hospitals; Performance; Philadelphia; Physicians; Play; Process; Property; Protocols documentation; Protons; Radiation; Relaxation; Reporting; Research; Research Project Grants; Residual Tumors; Residual state; Risk; Role; Rotation; Scientist; Sensitivity and Specificity; Shapes; Signal Transduction; Site; Skin; Slice; Solutions; Stratification; Structure; Study Section; Surface; Surgically-Created Resection Cavity; System; Techniques; Testing; Texture; Time; Tissues; Translations; Treatment outcome; Tumor Tissue; Tumor Volume; United States; Validation; Variant; Work; base; brain tissue; cancer therapy; clinically significant; density; design; direct application; dosimetry; evaluation/testing; experience; feeding; gray matter; image processing; image registration; imaging modality; improved; innovation; interest; malignant neurologic neoplasms; multimodality; neuro-oncology; novel; object recognition; pre-clinical; prospective; public health relevance; research clinical testing; research study; response; tool; treatment planning; treatment strategy; tumor; white matter

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.

描述（由申请人提供）：PI的长期研究目标是开发一种功能齐全的自动化稳健CAD工具，以精确的小儿脑肿瘤体积分段和随着时间的推移跟踪。请注意，当前的脑肿瘤体积分段的实践涉及多模式MRI中可疑肿瘤区域的手动跟踪和分割，这是耗时，劳动量的，并且可能不精确。 为了减少认知后遗症，当代方案采用了适应风险的治疗，其中风险分层基于手术切除后残留肿瘤的数量和诊断时发生转移性疾病的存在。因此，如果没有提高对肿瘤体积和其他因素的分类知识，则不太可能实现儿童癌症治疗结果的进一步改善。此外，这种自动体积计算和跟踪工具在跟随脑肿瘤患者的辅助标记中具有价值。反过来，这将帮助医生就手术计划，关键放射治疗计划修改，治疗现场修改，局部控制，转移性疾病的部位和治疗后反应评估做出重要的患者管理决策。 但是，这种自动化和精确的肿瘤体积分割CAD工具的开发需要解决一些挑战，例如检测难以检测的脑肿瘤（手术后的残留较小，增强不良，多焦点和不规则形状）和异常（水肿，水肿，坏死和由于手术而引起的更大切除腔）检测和分类。该项目旨在开发，测试和评估创新技术和工具，这些技术和工具将有助于基于功能的检测，分割和分类脑肿瘤以及一些特定的异常。 -Fractal特征提取； 2）MR序列依赖性特征融合以及肿瘤/异常大小和体积测定以改善检测； 3）优化的特征融合，可改善肿瘤，组织和异常分类； 4）算法测试和验证。 {如果成功的话，我们的方法将允许以提高精度对脑肿瘤和异常进行自动计算，这可以提供快速，客观，可重现且易于报道的疾病评估。从该项目获得的结果将通过提供准确，客观和一致的方法来评估和解释脑肿瘤以及相关异常，从而在小儿神经放射学实践中立即产生影响。 公共卫生相关性：该项目旨在开发，测试和评估新型基于特征的算法，以实现鲁棒，准确和可重现的脑肿瘤以及其他异常检测和分类。然后，这种鉴定和分类将用于获得难以检测的脑肿瘤和异常的精确分割。 我们将难以检测的脑肿瘤定义为小的病变（手术后残留），分别是由于手术而导致的水肿，坏死和较大的切除腔。能够可靠，准确地计算分段肿瘤体积的算法在跟随脑肿瘤患者时具有价值作为辅助标记。这种肿瘤体积定量方法还将在临床前手术计划和治疗试验中直接应用，从而导致新的治疗策略和设备。从该项目获得的结果将通过提供准确，客观和一致的评估和解释脑肿瘤的方法来立即对神经放射学实践产生影响。