Developing informatics tools for optimized MRS for brain cancer research

开发信息学工具以优化脑癌研究的 MRS

基本信息

批准号：
10700005
负责人：
MALGORZATA MARJANSKA
金额：
$ 37.76万
依托单位：
UNIVERSITY OF MINNESOTA
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-07 至 2025-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10700005
关键词：
19q 3-Dimensional Adoption Adult Anatomy Automation Back Biological Markers Body part Brain Neoplasms Calibration Choline Chromosomal Loss Clinical Clinical Research Communities Computer software Cystathionine Data Data Analyses Data Set Exhibits Glioma Isocitrate Dehydrogenase Judgment Lesion Magnetic Resonance Imaging Magnetic Resonance Spectroscopy Malignant Neoplasms Malignant neoplasm of brain Measurement Measures Metabolic Methods Molecular Monitor Morphologic artifacts Multi-Institutional Clinical Trial Mutation Normal tissue morphology Pathologic Patient Care Patients Performance Phenotype Phospholipid Metabolism Physiologic pulse Positioning Attribute Procedures Research Research Personnel Research Project Grants Scanning Site Source Spectrum Analysis System Technical Expertise Techniques Time Training Vendor Water anticancer research arm automated analysis body system cancer therapy clinical practice clinical research site data acquisition deep neural network heuristics image guided improved informatics tool interest novel open source open source tool prevent skills tool translational barrier transmission process treatment response tumor tumor progression

项目摘要

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 distribute them 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 processing 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. We identified collaborators, the brain cancer researchers and clinicians, who will be early adopters and beta-testers of our tools. 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.

项目概要/摘要该项目建议开发自动化、实时、单体素磁共振的方法脑肿瘤中的光谱学（MRS），将这些方法与临床 MRI 系统相结合，评估它们的性能，并将它们作为开源工具分发给研究社区。 MRS可以提供代谢无创地评估肿瘤表型和治疗反应的信息。单体素MRS 方法可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，以产生良好的质量结果。需要专家参与这两项收购处理仍然是将 MRS 方法转化为临床研究场所的关键障碍光谱专家和临床实践。该项目的第一部分是开发 3D 体素方法使用图像引导进行放置，将该方法与临床 MR 系统集成，并评估其表现。在第二部分中，我们将自动化我们的高级 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数据结果。我们确定了合作者、脑癌研究人员和临床医生，他们将成为早期采用者和我们工具的 Beta 测试人员。该项目的成功完成将提高数据的稳健性和质量，扫描时无需专家互动，并可在多领域采用 MRS 现场临床试验和临床实践。