Full-stack automation for reliable and reproducible MRS of brain cancer

全栈自动化实现可靠且可重复的脑癌 MRS

• 批准号: 10632895
• 负责人: MALGORZATA MARJANSKA
• 金额: $ 20.24万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-08 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10632895
• 关键词: 19q; 3-Dimensional; Adoption; Adult; Anatomy; Automation; Back; Body part; Brain; Brain Neoplasms; Calibration; Choline; Chromosomal Loss; Clinical; Clinical Research; Communities; Computer software; Cystathionine; Data; Data Analyses; Exhibits; Glioma; Isocitrate Dehydrogenase; Judgment; Lesion; Magnetic Resonance Imaging; Magnetic Resonance Spectroscopy; Malignant Neoplasms; Malignant neoplasm of brain; Manuals; Measurement; Measures; Metabolic; Methods; Molecular; Monitor; Morbidity - disease rate; Morphologic artifacts; Multi-Institutional Clinical Trial; Mutate; Mutation; Normal tissue morphology; Observational Study; Pathologic; Patient Care; Patients; Performance; Phenotype; Phospholipid Metabolism; Physiologic pulse; Positioning Attribute; Procedures; Protocols documentation; Reporting; Reproducibility; Research; Research Project Grants; Scanning; Site; Source; Spatial Distribution; Spectrum Analysis; System; Technical Expertise; Techniques; Testing; Time; Training; Treatment outcome; Tumor Subtype; Vendor; Water; Work; arm; automated analysis; body system; cancer therapy; clinical practice; clinical research site; convolutional neural network; data acquisition; drug discovery; image guided; imaging modality; improved; in vivo; individualized medicine; interest; magnetic resonance spectroscopic imaging; mortality; open source; open source tool; prevent; programs; prospective; skills; spectroscopic data; tool; translational barrier; transmission process; treatment response; tumor; tumor progression

PROJECT SUMMARY/ABSTRACT This project proposes to develop methods for automated, real-time, single-voxel magnetic resonance spectroscopy (MRS) in brain tumors, integrate these methods with a clinical MRI system, evaluate their performance, and make them available as open-source tools to the research community. MRS can provide metabolic information noninvasively for assessment of tumor phenotype and therapeutic response. Single- voxel MRS methods provide the best quality and most reliable data, but require the scanner operator to have a high skill level and expertise to produce good quality results. The need for this expert involvement in both acquisition and analysis remains a critical barrier to the translation of MRS methods to clinical research sites without spectroscopy experts and to clinical practice. The first part of this project is to develop a method for 3D voxel placement using image guidance, integrate this method with a clinical MR system, and evaluate its performance. In the second part, we will automate our advanced MRS methods. In the third part, we will create real-time, automatic quantification tool specific to the obtained MRS data that will provide clinically interpretable results. In the final part, we will assess the performance of automated methods in prospective in vivo study. Successful completion of this project will improve data robustness and quality, eliminating the need for the expert interaction at the time of the scan and enabling adoption of MRS in multi-site clinical trials and clinical practice.

项目总结/摘要 该项目提出开发自动化、实时、单体素磁共振的方法 磁共振波谱（MRS）在脑肿瘤中的应用，将这些方法与临床MRI系统相结合，评估其 性能，并将其作为开源工具提供给研究界。MRS可以提供 代谢信息，用于评估肿瘤表型和治疗反应。单身- 体素MRS方法提供最佳质量和最可靠的数据，但要求扫描仪操作员具有 高技能水平和专业知识，以产生高质量的结果。这两方面都需要专家参与， 采集和分析仍然是将MRS方法转化为临床研究场所的关键障碍 没有光谱学专家和临床实践。该项目的第一部分是开发一种方法， 使用图像引导的体素放置，将该方法与临床MR系统相结合，并评估其 性能在第二部分中，我们将自动化我们先进的MRS方法。在第三部分中，我们将创建 针对所获得的MRS数据的实时自动量化工具，将提供临床可解释的 结果在最后一部分中，我们将评估自动化方法在前瞻性体内研究中的性能。 该项目的成功完成将提高数据的可靠性和质量，消除对 扫描时的专家互动，并使MRS能够在多中心临床试验和临床试验中采用 实践