CAREER: Ultrahigh-Resolution Magnetic Resonance Spectroscopic Imaging for Label-Free Molecular Imaging of the Brain
CAREER: Ultrahigh-Resolution Magnetic Resonance Spectroscopic Imaging for Label-Free Molecular Imaging of the Brain
批准号:
1944249
负责人:
Fan Lam
金额:
$51.73万
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2020
资助国家:
美国
项目状态:
未结题
起止时间:
2020-02-01 至 2025-01-31
中文摘要
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英文摘要
Understanding how the brain works, including how to effectively treat brain disorders, is one of the most exciting scientific frontiers. Neuroimaging has significantly advanced this frontier by allowing noninvasive mapping of the brain’s anatomy and activity in unprecedented detail. However, new tools are needed to visualize molecular-level information in a living brain. Current methods are limited by several fundamental challenges, including the need of radioactive tracers or contrast agents, limited molecule recognition, noisy signals, long imaging time, and poor spatial resolution. The overall goal of this CAREER project is to develop a new generation of imaging technologies to address these challenges and enable label-free molecular imaging of the brain at unprecedented resolutions in space and time using magnetic resonance spectroscopic imaging (MRSI). Success of the research planned will significantly advance the field of molecular neuroimaging and enable new capabilities by simultaneously mapping many physiologically important molecules in the brain. These capabilities could revolutionize diagnosis and management of neurological diseases and mental disorders. The educational activities will integrate the research with curriculum innovation at the intersections of imaging science, machine learning and computational science and engineering. Activities include development of a Research Experiences for Undergraduates (REU) program to create unique training and professional development opportunities in biomedical imaging for underrepresented minorities in engineering and a new high-school student research internship program centered on neuroimaging.The investigator’s long-term research career goal is to develop a new generation of imaging technologies to enable label-free mapping of molecular profiles in the brain at unprecedented spatiotemporal resolutions and explore the potential of these technologies for studying brain functions and diseases at the molecular level. Towards this goal, building on the investigator’s expertise and previous contributions on fast MRI and MRSI, the goal of this CAREER project is to develop an innovative imaging framework to model, acquire and process MRSI data and enable new MRSI based molecular imaging capabilities. MRSI is a potentially powerful imaging modality that allows for simultaneous mapping of many physiologically important molecules in the human body without the need of radioactive tracers and contrast agents. However, to date, the development of MRSI still remains at its infancy because of its low signal-to-noise ratio (SNR), slow speed, poor spatial resolution, and susceptibility to system imperfection. The Research Plan is organized under five objectives: (1) Discovery of accurate and efficient low-dimensional models for high-dimensional MRSI signals by integrating biological priors, physics-based modeling and machine learning, to reduce the dimensionality of the imaging problem and enable better tradeoffs in speed, resolution and SNR; (2) Development of unconventional ultrafast data acquisition and data processing strategies that exploit the reduced dimensionality to achieve fast MRSI of the whole brain at the resolution level of functional MRI; (3) Development of novel mathematical formulations and efficient algorithms that work synergistically with the new models and acquisitions for optimal spatiospectral processing; (4) Integration of the new modeling, acquisition and processing methods to enable whole-brain mapping of metabolites and neurotransmitters and their biophysical properties, such as relaxation and diffusion parameters, for molecule-specific microstructural imaging of the brain and (5) Introduction of new dimensions into the proposed MRSI framework to map molecule dependent biophysical properties in 3D. These synergistic developments will transform MRSI from a slow, low-resolution modality to a powerful, high-resolution in vivo molecular neuroimaging tool and open up tremendous opportunities in studying brain biochemistry, microstructure and their connections to functions and disease processes.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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Rapid MRSI of the Brain on 7T Using Subspace-Based Processing
使用基于子空间的处理在 7T 上进行大脑的快速 MRSI
DOI:
--
发表时间:
2021
期刊:
Proceedings of the International Society for Magnetic Resonance in Medicine Scientific Meeting and Exhibition
影响因子:
--
作者:
[Lam, Fan, Hetherington, Hoby, Pan, Jullie]
通讯作者:
Pan, Jullie
Fast volumetric diffusion-weighted MRSI: improved acquisition and data processing
快速体积扩散加权 MRSI:改进的采集和数据处理
DOI:
--
发表时间:
2022
期刊:
Proceedings of the International Society for Magnetic Resonance in Medicine Scientific Meeting and Exhibition
影响因子:
--
作者:
[Z. Wang, F. Lam]
通讯作者:
Z. Wang, F. Lam
DOI:
10.1109/tbme.2024.3358223
发表时间:
2024-06-01
期刊:
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
影响因子:
4.6
作者:
[Zhao,Ruiyang, Peng,Xi, Lam,Fan]
通讯作者:
Lam,Fan
High-SNR J-Resolved MRSI by jointly learning nonlinear representation and projection
通过联合学习非线性表示和投影实现高信噪比 J 分辨 MRSI
DOI:
--
发表时间:
2022
期刊:
Proceedings of the International Society for Magnetic Resonance in Medicine Scientific Meeting and Exhibition
影响因子:
--
作者:
[Y. Li, Z. Wang]
通讯作者:
Y. Li, Z. Wang
SNR-Enhancing Reconstruction for Multi-TE MRSI Using a Learned Nonlinear Low-Dimensional Model
使用学习非线性低维模型增强多 TE MRSI 的 SNR 重建
DOI:
--
发表时间:
2021
期刊:
Proceedings of the International Society for Magnetic Resonance in Medicine Scientific Meeting and Exhibition
影响因子:
--
作者:
[Li, Yahang, Wang, Zepeng, Lam, Fan]
通讯作者:
Lam, Fan
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