MR Fingerprinting based Quantitative Imaging and Analysis Platform (MRF-QIA) for brain tumors.

基于 MR 指纹的脑肿瘤定量成像和分析平台 (MRF-QIA)。

• 批准号: 10593584
• 负责人: Chaitra Badve
• 金额: $ 61.82万
• 依托单位: CASE WESTERN RESERVE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2027-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10593584
• 关键词: 3-Dimensional; Address; Aminolevulinic Acid; Binding; Biopsy; Brain Neoplasms; Brain scan; Clinical; Clinical Protocols; Clinical Research; Clinical Trials; Collaborations; Comprehensive Cancer Center; Computer software; Consumption; Country; Development; Diagnosis; Drops; Ensure; Environment; Excision; Fingerprint; Future; Glioblastoma; Glioma; Goals; Healthcare; Image; Image Analysis; Imaging Device; Imaging Techniques; Infiltration; Institution; Licensing; MRI Scans; Machine Learning; Magnetic Resonance Imaging; Malignant Neoplasms; Malignant neoplasm of brain; Maps; Marketing; Measurement; Measures; Multi-Institutional Clinical Trial; Neurosurgeon; Newly Diagnosed; Operative Surgical Procedures; Pathologic; Pathology; Patient Care; Patient-Focused Outcomes; Patients; Performance; Physicians; Physiological; Property; Radiation Dose Unit; Radiation Oncologist; Radiation therapy; Radiogenomics; Radiology Specialty; Recurrence; Relaxation; Reproducibility; Research; Sampling; Scanning; Site; Standardization; System; Techniques; Testing; Therapy trial; Time; Tissue Sample; Tissues; Translating; Travel; Validation; Variant; Vendor; brain tumor imaging; cancer imaging; clinical application; clinical care; clinical imaging; clinical implementation; clinical research site; clinical translation; computerized data processing; digital; experience; imaging biomarker; imaging platform; improved; improved outcome; industry partner; interest; neurosurgery; next generation; novel; outcome prediction; performance site; personalized medicine; predictive modeling; predictive tools; prospective; quantitative imaging; radiologist; radiomics; reconstruction; recruit; synergism; targeted treatment; tool; treatment planning; trial comparing; trial planning; tumor; volunteer

Abstract The clinical utility of MR images is largely as a qualitative tool without in-built standardization, which requires subjective interpretation and time-consuming analysis. Importantly, these qualitative MRI approaches have demonstrated poor tissue characterization, and poor center-to- center reproducibility, greatly limiting their use in clinical trials. Availability of a robust quantitative imaging tool with high tissue discriminability can directly impact clinical care by offering actionable information to end-user clinicians. As an example, availability of accurate tumor infiltration maps in Glioblastomas, a highly aggressive brain tumor, can pave the way for novel multisite clinical trials in personalized radiation therapy and neurosurgery for improved outcomes. None of the current MRI techniques offer this capability in an accurate and reproducible manner. MRF is a quantitative imaging scan that can address the limitations of qualitative MRI by providing reproducible and physiologically meaningful measurements of tissue properties. We have also shown that utilizing the underlying physical/physiological bounds of the quantitative MRF values improves the reproducibility of the image analysis techniques. Integration of MRF and advanced quantitative analytics could fundamentally address the well-recognized low-reproducibility in qualitative MRI approaches and allow broad clinical translation. In this proposal, we have established an academic-industrial partnership among MRF developers (CWRU), image analysis and AI experts (UPenn), Brain tumor imaging experts (UHCMC), and leading healthcare company (Siemens) to ensure successful clinical translation of the MRF-QIA into the clinical workflow. We will achieve our goal with the following aims: Aim 1: Establish a high throughput MRF scan and assess multisite performance for FDA approval; Aim 2: Fully integrate the MRF-QIA image analytics software into the clinical system for brain tumor analysis; Aim 3: Clinical validation of the MRF-QIA application for infiltration prediction in Glioblastoma patients. This project will add new capabilities to the clinical flow directly impacting the end-user experience and patient care: 1) FDA approval of MRF product scan will allow any Siemens clinical site to add it to their routine patient scans. 2) The MRF-QIA software will be distributed globally through Siemens Global Digital Market and will be available for broad clinical and multisite research applications. 3) The specialized application for GB infiltration prediction will lead to new clinical trials for planning targeted biopsy, extended resections, and personalized radiotherapy by neurosurgeons and neuro-oncologists to eventually provide targeted treatment plans for GB patients.

摘要 MR图像的临床应用主要是作为没有内置标准化的定性工具， 这需要主观解释和耗时的分析。重要的是这些 定性MRI方法已经证明了较差的组织定征，以及较差的中心- 中心的可重复性，极大地限制了它们在临床试验中的使用。可靠的定量 具有高组织辨别力的成像工具可以通过提供可操作的 最终用户临床医生的信息。例如，准确的肿瘤浸润图的可用性 胶质母细胞瘤是一种高度侵袭性的脑肿瘤， 在个性化放射治疗和神经外科手术中进行试验，以改善结果。概无 目前的MRI技术以精确和可再现的方式提供这种能力。MRF是一个 定量成像扫描可以通过提供 组织特性的可再现的和生理上有意义的测量。我们还 表明利用定量MRF值的潜在物理/生理界限 提高了图像分析技术的再现性。MRF与高级 定量分析可以从根本上解决公认的低重现性， 定性MRI方法，并允许广泛的临床翻译。在本提案中，我们有 在MRF开发人员（CWRU）之间建立了学术-工业合作伙伴关系，图像分析 人工智能专家（UPenn），脑肿瘤成像专家（UHCMC）和领先的医疗保健公司 （Siemens），以确保将MRF-QIA成功临床转化为临床工作流程。我们 将通过以下目标实现我们的目标：目标1：建立高通量MRF扫描， 评估FDA批准的多站点性能;目标2：完全集成MRF-QIA图像 目的3：将分析软件应用于脑肿瘤分析的临床系统;目的3： MRF-QIA应用于胶质母细胞瘤患者的浸润预测。该项目将新增 直接影响最终用户体验和患者护理的临床流程能力：1）FDA MRF产品扫描的批准将允许任何西门子临床研究中心将其添加到其常规患者中 扫描。2)MRF-QIA软件将通过西门子全球数字公司在全球范围内分销 市场，并将可用于广泛的临床和多站点研究应用。3)的 GB浸润预测的专门应用将导致新的临床试验计划 神经外科医生的靶向活检、扩大切除和个性化放疗， 神经肿瘤学家最终为GB患者提供有针对性的治疗计划。