MRI Resource for Physiologic, Metabolic and Anatomic Biomarkers

生理、代谢和解剖生物标志物的 MRI 资源

• 批准号: 10614604
• 负责人: Hanzhang Lu
• 金额: $ 121.72万
• 依托单位: HUGO W. MOSER RES INST KENNEDY KRIEGER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10614604
• 关键词: Academia; Address; Aging; Anatomy; Anemia; Area; Bayesian Method; Biological Markers; Biometry; Biopsy; Brain Diseases; Calibration; Cerebrovascular Disorders; Clinic; Clinical; Clinical Trials; Complex; Computer software; Consensus; Contrast Media; Country; Critical Pathways; Data; Data Analyses; Data Set; Decision Making; Dementia; Detection; Development; Diagnosis; Diagnostic; Disease; Engineering; Ensure; Evaluation; Failure; Fingerprint; Foundations; Future; Goals; Hospitals; Image; In Situ; Industry; Injections; Injury; Ischemia; Joints; Lesion; Libraries; Machine Learning; Magnetic Resonance; Magnetic Resonance Imaging; Magnetic Resonance Spectroscopy; Manufacturer; Measures; Medical; Medical Technology; Metabolic; Methods; Monitor; Pain; Patient Care; Patients; Pharmaceutical Preparations; Pharmacodynamics; Physiologic pulse; Physiological; Process; Prognosis; Property; Public Health Schools; Qualifying; Radiology Specialty; Readiness; Reporting; Reproducibility; Research; Research Personnel; Resources; Sampling; Schools; Science; Services; Site; Speed; Standardization; Techniques; Technology; Terminology; Testing; Therapeutic; Tissues; Training; Translating; Translational Research; Traumatic Brain Injury; United States National Institutes of Health; Universities; Validation; Visualization; Vocabulary; Water; White Matter Hyperintensity; addiction; candidate marker; candidate validation; clinical care; clinical outcome assessment; clinical practice; computerized data processing; data acquisition; data reduction; deep learning; design; drug development; glymphatic function; imaging biomarker; imaging facilities; imaging science; improved; innovation; investigator training; magnetic resonance imaging biomarker; medical schools; multimodality; multiscale data; novel; outcome prediction; precision medicine; product development; prognostic; public-private partnership; quantitative imaging; response biomarker; success; tool; working group

SUMMARY The importance of biomarkers in clinical practice and regulatory science is difficult to overstate. The ultimate goal for clinical care of the future is to determine the most appropriate therapy for each patient’s unique version of a particular disease. This concept of precision medicine has been identified as a national priority in the USA, leading to a need for biomarkers that can provide objective and reproducible information. In addition, regulatory science for the approval of new qualified drugs and medical technologies has an urgent need for qualified biomarkers to report on the success or failure of these drugs and technologies. Imaging biomarkers, providing in situ “biopsies”, have the potential to provide such specific information and, ultimately, to improve routine clinical care and speed up development of new treatments. The overall goal of this Resource therefore is to develop novel noninvasive MRI candidate biomarkers that can ultimately be used for: (i) personalized assessment of patients for diagnosis, prognosis, and treatment monitoring; (ii) monitoring of the development of new medical technologies and drugs, i.e. for better guiding of clinical trials. The significance of our proposed developments lies in providing the fundamental design and initial testing (i.e. not clinical trials itself) of new MRI candidate biomarkers with the potential to provide surrogate quantitative imaging endpoints that are as close as possible to clinical endpoints. As such, this Resource will focus on designing, calibrating and standardizing new magnetic resonance (MR) technologies to provide reliable measures across sessions, scanners, and raters. This technology will then be disseminated for larger scale patient studies to allow clinical validation. The MRI Resource for Physiologic, Metabolic and Anatomic Biomarkers is an interdepartmental and interdisciplinary consortium combining facilities and expertise of the F.M. Kirby Research Center at Kennedy Krieger Institute (KKI), the Department of Radiology at Johns Hopkins University (JHU) School of Medicine, the Center for Imaging Science at the JHU Whiting School of Engineering, and the Department of Biostatistics at the JHU Bloomberg School of Public Health. We propose 4 TRD projects, 3 on MR acquisition approaches and one bringing them together with advanced multi-scale data analysis methods that include machine learning. To assure a proper choice of technologies, we will interact closely with a group of clinical and research experts in a push-pull relationship through collaborative projects (CPs) that focus on brain diseases, disorders, and injuries that have a need for new quantitative MR technology to better and noninvasively assess them. These include anemia/ischemia and related white matter hyperintensity lesions (CPs 1,5), aging, cerebrovascular disease, and dementia (CPs 2,7,10), traumatic brain injury (CP4), glymphatic function (CP6), pain (CP8) and addiction (CP9). As an initial testbed for our methods, we also have a group of service projects (SPs) to which we will provide both data acquisition methods and data analysis software. Finally, we will train investigators in their use and disseminate methods nationwide and to MRI manufacturers for even broader application.

总结 生物标志物在临床实践和监管科学中的重要性很难被夸大。最终 未来临床护理的目标是为每个患者的独特版本确定最合适的治疗方法 一种特殊的疾病。这种精准医疗的概念已被确定为美国的国家优先事项， 导致需要能够提供客观和可再现信息的生物标志物。此外，监管 科学批准新的合格药物和医疗技术，迫切需要合格的 生物标志物来报告这些药物和技术的成功或失败。成像生物标志物，提供 原位“活组织检查”有可能提供这种特定的信息，并最终改善常规检查。 加快临床护理和新疗法的开发。因此，本资源的总体目标是 开发新的非侵入性MRI候选生物标志物，其最终可用于：（i）个性化 评估患者的诊断、预后和治疗监测;（ii）监测发展 新的医疗技术和药物，即更好地指导临床试验。我们建议的重要性 发展在于提供新MRI的基本设计和初始测试（即，不是临床试验本身） 候选生物标志物有可能提供替代定量成像终点， 尽可能达到临床终点。因此，本资源将侧重于设计、校准和标准化 新的磁共振（MR）技术，提供跨会话、扫描仪和 评分员然后，这项技术将被推广用于更大规模的患者研究，以进行临床验证。 生理、代谢和解剖生物标志物的MRI资源是一个跨部门的， 跨学科的财团结合设施和专业知识的F.M.肯尼迪的柯比研究中心 约翰霍普金斯大学（JHU）医学院放射科克里格研究所（KKI）， JHU Whiting工程学院的成像科学中心和JHU Whiting工程学院的生物统计系 JHU Bloomberg公共卫生学院我们提出了4个TRD项目，3个关于MR采集方法， 将它们与包括机器学习在内的先进多尺度数据分析方法结合在一起。到 为了确保技术的正确选择，我们将与一组临床和研究专家密切合作， 通过合作项目（CP）建立推拉关系，重点关注脑部疾病、障碍和 这些损伤需要新的定量MR技术来更好地进行非侵入性评估。这些 包括贫血/局部缺血和相关白色高信号病变（CP 1，5）、衰老、脑血管 疾病和痴呆（CP 2、7、10）、创伤性脑损伤（CP 4）、胶质淋巴功能（CP 6）、疼痛（CP 8）和 成瘾（CP 9）。作为我们方法的初始测试平台，我们还有一组服务项目（SP）， 我们将提供数据采集方法和数据分析软件。最后，我们将培训调查人员， 他们的使用和传播方法在全国范围内和MRI制造商甚至更广泛的应用。

项目成果

Toward accurate cerebral blood flow estimation in mice after accounting for anesthesia.

• DOI: 10.3389/fphys.2023.1169622
• 发表时间: 2023
• 期刊: Frontiers in physiology
• 影响因子: 4

A Unified Framework on Generalizability of Clinical Prediction Models.

• DOI: 10.3389/frai.2022.872720
• 发表时间: 2022
• 期刊: FRONTIERS IN ARTIFICIAL INTELLIGENCE
• 影响因子: 4
• 作者: Wan, Bohua;Caffo, Brian;Vedula, S. Swaroop
• 通讯作者: Vedula, S. Swaroop

Association of inferior division MCA stroke location with populations with atrial fibrillation incidence.

MCA 下分区卒中位置与心房颤动发病率人群的关联。

• DOI: 10.1016/j.heliyon.2023.e15287
• 发表时间: 2023
• 期刊: Heliyon
• 影响因子: 4
• 作者: Kim,Ganghyun;Vitti,Emilia;Stockbridge,MelissaD;Saver,JeffreyL;Hillis,ArgyeE;Faria,AndreiaV
• 通讯作者: Faria,AndreiaV

NIfTI-MRS: A standard data format for magnetic resonance spectroscopy.

• DOI: 10.1002/mrm.29418
• 发表时间: 2022-12
• 期刊: Magnetic resonance in medicine
• 影响因子: 3.3
• 作者: Clarke WT;Bell TK;Emir UE;Mikkelsen M;Oeltzschner G;Shamaei A;Soher BJ;Wilson M
• 通讯作者: Wilson M

Magnetic resonance spectroscopic imaging of downfield proton resonances in the human brain at 3 T.

• DOI: 10.1002/mrm.29142
• 发表时间: 2022-04
• 期刊: Magnetic resonance in medicine
• 影响因子: 3.3
• 作者: Považan M;Schär M;Gillen J;Barker PB
• 通讯作者: Barker PB