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Designing solid-state quantum sensors for brain imaging

Designing solid-state quantum sensors for brain imaging
设计用于脑成像的固态量子传感器
批准号:
568637-2021
负责人:
Simon, ChristophC
金额:
$3.62万
依托单位:
依托单位国家:
加拿大
项目类别:
Alliance Grants
财政年份:
2022
资助国家:
加拿大
项目状态:
已结题
起止时间:
2022-01-01 至 2023-12-31

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中文摘要
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英文摘要
Quantum physics is the foundation for many important biomedical imaging techniques such as magnetic resonance imaging (MRI) or positron emission tomography (PET). Magnetoencephalography (MEG) is a promising new non-invasive imaging technique based on measuring the tiny magnetic field outside the skull, which is already being used clinically, e.g. for distinguishing healthy from unhealthy tissue when planning brain surgeries. Currently MEG typically uses superconducting quantum interference devices (SQUIDs). These sensors require cryogenic cooling, resulting in the need for quite bulky scanners, where patients have to hold completely still, which is limiting for many potential applications. More recently new types of sensors based on atomic vapors are being developed. These so-called optically pumped magnetometers (OPMs) have the advantage that they can operate without cryogenic cooling and are beginning to be commercialized. One limitation of such OPMs is their relatively low spatial resolution, which is on the scale of several millimeters. Solid-state sensors based on nitrogen-vacancy (NV) centers in diamond also do not require cryogenic cooling, while promising much higher (micron-scale) spatial resolution. They are starting to be applied to biomedical imaging in research settings, but are in an earlier stage of development compared to OPMs. Our team consists of experts on NV center based quantum technology (Simon, Barclay) and diamond nanofabrication (Barclay), as well as neuroscience (Simon). Leveraging this expertise, we will design chip-based arrays of micro-cavity enhanced absorptive quantum sensors based on NV centers. Our design will be informed by a broad comparison with SQUID and OPM based approaches, on detailed theoretical modeling of our NV-based systems, as well as on initial experiments. Our goal is to bring the technology to the point where it can be the basis for applications to health-related funding programs such as AICE and generate commercial interest (from currently TRL 2 to TRL 4).
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