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.

摘要 生物标记物在临床实践和调控科学中的重要性怎么强调都不为过。终极的 未来临床护理的目标是为每个患者确定最合适的治疗方案 一种特定的疾病。精准医学的这一概念在美国已被确定为国家优先事项， 这导致了对能够提供客观和可重现信息的生物标志物的需求。此外，监管部门 科学用于审批合格新药和医疗技术迫切需要合格 生物标志物，报告这些药物和技术的成功或失败。成像生物标记物，提供 原位“活组织检查”有可能提供这种特定信息，并最终改进常规 临床护理，加快开发新疗法。因此，此资源的总体目标是 开发新的非侵入性MRI候选生物标记物，最终可用于：(I)个性化 对患者进行诊断、预后和治疗监测的评估；(2)监测病情发展 新的医疗技术和药物，即更好地指导临床试验。我们提议的意义 发展在于提供新核磁共振的基本设计和初步测试(即不是临床试验本身) 有可能提供接近的替代定量成像终点的候选生物标记物 尽可能多地转移到临床终点。因此，本资源将侧重于设计、校准和标准化 新的磁共振(MR)技术可跨会话、扫描仪和 评分员。这项技术随后将被传播到更大规模的患者研究中，以便进行临床验证。 生理、代谢和解剖生物标记物的核磁共振资源是一个跨部门和 结合肯尼迪大学F.M.柯比研究中心的设施和专业知识的跨学科联盟 Krieger Institute(KKI)，约翰霍普金斯大学(JHU)医学院放射科， JHU怀廷工程学院成像科学中心和生物统计系 JHU布隆伯格公共卫生学院。我们提出了4个TRD项目，3个关于MR采购方法和 一种是将它们与包括机器学习在内的先进的多尺度数据分析方法结合在一起。至 确保选择适当的技术，我们将与一组临床和研究专家密切互动， 通过协作项目(CP)建立推拉关系，重点关注脑部疾病、紊乱和 需要新的定量磁共振技术来更好和非侵入性地评估损伤。这些 包括贫血/缺血和相关脑白质高信号损害(CP1，5)，衰老，脑血管 疾病、痴呆症(CPS2、7、10)、创伤性脑损伤(CP4)、淋巴功能(CP6)、疼痛(CP8)和 成瘾(CP9)。作为我们方法的初始试验台，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，并向核磁共振制造商更广泛的应用。

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