Dynamic field monitoring during in vivo MRI
Dynamic field monitoring during in vivo MRI
批准号:
RTI-2023-00108
负责人:
Baron, Corey
金额:
$7.58万
依托单位国家:
加拿大
项目类别:
Research Tools and Instruments
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
中文摘要
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英文摘要
Magnetic resonance imaging (MRI) utilizes very strong magnetic fields to obtain images of tissue within the body. However, there are always small unwanted magnetic fields present that can cause errors in the images. Conventionally, these errors are reduced by using data sampling techniques that are less sensitive to the unwanted fields, but this strategy creates limits on how we can use the MRI. "Field probe monitoring" is a recently introduced technology that allows direct and accurate measurement of these fields. With knowledge of the fields, the errors can be completely removed, which lifts the restrictions we normally abide by to limit the errors from the fields. Accordingly, this enables advanced methods that can provide greatly improved MRI image quality and resolution. We recently acquired a field probe monitoring system for our 7 Tesla MRI, and have made numerous research contributions using it. However, we recently suffered a devastating setback to our research when the 7 Tesla MRI machine underwent a spontaneous "quench", which is a serious malfunction that may have caused permanent damage to the MRI. Luckily, the primary component of our field probe system - the "spectrometer", which collects the signals from field probes - can also be used with our 3 Tesla MRI. The set of field probes that actually detect the unwanted fields within the scanner can only work at the particular MRI field strength it was designed for, though, and we are thus requesting funding for the purchase of a set of field probes for our 3 Tesla MRI. This equipment will allow us to continue our fruitful line of research into field probe monitoring-based methods to improve MRI image quality and reliability. There are many advanced MRI approaches that have not yet been translated to clinical use because of concerns with image quality and reliability, and by solving these issues this work will enable more accurate diagnosis for patients and improved understanding of disease.
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