Quantitative Steady-State and Dynamic Metabolic MRI for Evaluating Patients with Glioma

用于评估神经胶质瘤患者的定量稳态和动态代谢 MRI

• 批准号: 10588189
• 负责人: Susan M Chang
• 金额: $ 64.85万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-08 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10588189
• 关键词: ATRX gene; Address; Adoption; Adult; Algorithms; Area; Atlases; Biological; Biological Markers; Blood - brain barrier anatomy; Brain; Brain region; Carbon; Characteristics; Classification; Clinical; Clinical Trials; Clinical Trials Design; Combined Modality Therapy; Communities; Community Clinical Oncology Program; Complex; Computer software; Consensus Development; Data; Development; Digital Imaging and Communications in Medicine; Disease; Drug Delivery Systems; Edema; Eligibility Determination; Evaluation; Funding; Gadolinium; Glioma; Gliosis; Goals; Healthcare; Image; Imaging Device; Institution; Lesion; MGMT gene; Machine Learning; Magnetic Resonance Imaging; Malignant - descriptor; Measurement; Metabolic; Modeling; Molecular; Monitor; Outcome; Pathologic; Patient-Focused Outcomes; Patients; Performance; Primary Brain Neoplasms; Property; Protocols documentation; Protons; Radiology Specialty; Reproducibility; Research; Residual Neoplasm; Resolution; Scanning; Signal Transduction; Software Tools; Specificity; Standardization; Subgroup; Summary Reports; TP53 gene; Technology; Testing; Time; Tissues; Translating; Tumor Burden; Variant; Visual; Work; anatomic imaging; brain tumor imaging; clinical imaging; design; drug testing; feature extraction; follow-up; imaging approach; improved; industry partner; magnetic resonance imaging biomarker; metabolic imaging; metabolic profile; molecular marker; neuro-oncology; novel; open source; outcome prediction; personalized medicine; recruit; research clinical testing; response; serial imaging; standard of care; therapy outcome; tool; treatment effect; treatment planning; treatment response; treatment strategy; tumor; tumor heterogeneity; white matter

Project Summary The UCSF and GE team will address the limitations of current MR metabolic imaging approaches by developing a package of hardware and software tools to provide improved strategies for automatic prescription of imaging sequences to reduce operator differences, provide more extensive coverage of the lesion and enable improved serial comparisons. We will also simplify and streamline the processing and interpretation of the resulting data by using our open source, DICOM compatible software package, SIVIC. This will include features to facilitate the assessment of data from serial imaging examinations, as well as expanding the algorithms available to address the analysis of more complex types of metabolic imaging data. A further critical component of the proposal will be to refine the imaging and tissue acquisition workflow and integration of collected quantitative biomarkers in the clinical setting.

项目摘要 UCSF和GE团队将通过开发当前MR代谢成像方法的局限性 一套硬件和软件工具，可提供改进的策略以自动处方成像 减少操作员差异的序列，提供更广泛的病变覆盖范围并改善 串行比较。我们还将简化和简化所得数据的处理和解释 通过使用我们的开源DICOM兼容软件包Sivic。这将包括以方便的功能 评估来自串行成像检查的数据，以及扩展可用的算法 解决更复杂类型的代谢成像数据的分析。另一个重要组成部分 建议将是完善成像和组织采集工作流程以及收集的定量的整合 在临床环境中的生物标志物。