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.

抽象的 MR图像的临床实用性在很大程度上是一种定性工具，没有内置的标准化， 这需要主观的解释和耗时的分析。重要的是，这些 定性MRI方法表明组织表征不佳，并且中心对 - 中心可重现性，极大地限制了它们在临床试验中的使用。可用的可用定量 具有高组织可区分性的成像工具可以通过提供可行的方式直接影响临床护理 向最终用户临床医生提供信息。例如，准确的肿瘤浸润图的可用性 在高度侵略性脑肿瘤的胶质母细胞瘤中，可以为新型多站点临床铺平道路 个性化放射疗法和神经外科的试验，以改善结果。没有 当前的MRI技术以准确且可重复的方式提供了此能力。 MRF是一个 定量成像扫描可以通过提供定性MRI的局限性 可再现和生理上有意义的组织特性测量。我们也有 表明使用定量MRF值的基本物理/生理界限 提高图像分析技术的可重复性。 MRF和高级的整合 定量分析可以从根本上解决公认的低可重复性的问题 定性MRI方法并允许广泛的临床翻译。在这个建议中，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 和AI专家（UPENN），脑肿瘤成像专家（UHCMC）和领先的医疗保健公司 （西门子）确保将MRF-QIA成功临床翻译为临床工作流程。我们 将以以下目的实现我们的目标：目标1：建立高吞吐量MRF扫描和 评估多站点绩效以供FDA批准； AIM 2：完全整合MRF-QIA图像 分析软件进入临床系统进行脑肿瘤分析；目标3：临床验证 MRF-QIA在胶质母细胞瘤患者中浸润预测的应用。这个项目将添加新 临床流的能力直接影响最终用户体验和患者护理：1）FDA MRF产品扫描的批准将允许任何西门子的临床部位将其添加到常规患者中 扫描。 2）MRF-QIA软件将通过西门子全球数字在全球范围内分发 市场，将用于广泛的临床和多站点研究应用。 3） GB浸润预测的专业应用将导致计划的新临床试验 神经外科医生和 神经肿瘤学家最终为GB患者提供有针对性的治疗计划。