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.

项目总结