PFI-TT: Enabling More Scans per Machine through in Magnetic Resonance Imaging Data Processing

PFI-TT：通过磁共振成像数据处理实现每台机器的更多扫描

• 批准号: 2044599
• 负责人: Madhur Srivastava
• 金额: $ 24.96万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2023-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2044599&HistoricalAwards=false
• 关键词: PFI; TT; Enabling; More; Scans

The broader impact/commercial potential of this Partnerships for Innovation - Technology Translation (PFI-TT) project is to reduce Magnetic Resonance Imaging (MRI) scan-times and improve patient throughput. This techology will be beneficial for patients as well as healthcare providers. Shorter scan-times will improve patient comfort, especially for patients that are young, elderly, and/or claustrophobic. Improved throughput will increase accessibility, allow patients to receive MRI scans in a timely manner with less wait-time, and contribute to better healthcare outcomes. For healthcare providers and imaging facilities, shorter scans and better throughput will increase operational efficiency, revenue potential, and patient satisfaction. The technology can be leveraged to help accommodate rising healthcare demand from an aging population. The graduate student selected for technology development will obtain research experience and educational training to pursue entrepreneurship following the completion of the project. A team of 10 undergraduate students from underrepresented groups will also participate in the applied research and software development. The proposed project will provide a novel signal processing approach that inputs the poor quality (noisy) images obtained at short scan-times and outputs a noise-free image, similar to what would have been obtained after a long data acquisition time. In MRI, image quality is usually inversely proportional to scan-time. Longer scan-time yields better image quality and vice versa. The algorithm isolates noise from raw MRI data by distinguishing between their distinct characteristics: noise is random while raw MRI data contains patterns/features. There are two novel features of the approach: 1) ability to identify and separate noise; and 2) the application in denoising raw MRI data. Compared to conventional signal processing methods such as filtering methods, the team's proprietary wavelet-shrinkage-based denoising method can process low signal-to-noise ratio signals without the limitations of inadequate noise removal or signal distortion.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.

该创新-技术转化合作伙伴关系（PFI-TT）项目的更广泛影响/商业潜力是减少磁共振成像（MRI）扫描时间并提高患者吞吐量。 这项技术将有利于患者和医疗保健提供者。更短的扫描时间将改善患者的舒适度，特别是对于年轻、老年和/或幽闭恐怖症患者。提高吞吐量将增加可访问性，使患者能够及时接受MRI扫描，减少等待时间，并有助于改善医疗保健结果。对于医疗保健提供商和成像设施来说，更短的扫描时间和更高的吞吐量将提高运营效率、收入潜力和患者满意度。该技术可以用来帮助满足人口老龄化带来的不断增长的医疗保健需求。被选中进行技术开发的研究生将获得研究经验和教育培训，以便在项目完成后从事创业活动。来自代表性不足群体的10名本科生团队也将参与应用研究和软件开发。拟议的项目将提供一种新的信号处理方法，该方法输入在短扫描时间内获得的低质量（有噪声）图像，并输出无噪声图像，类似于长数据采集时间后获得的图像。在MRI中，图像质量通常与扫描时间成反比。扫描时间越长，图像质量越好，反之亦然。该算法通过区分原始MRI数据的不同特征来从原始MRI数据中隔离噪声：噪声是随机的，而原始MRI数据包含模式/特征。该方法有两个新的特点：1）识别和分离噪声的能力; 2）在去噪原始MRI数据中的应用。与传统的信号处理方法（如滤波方法）相比，该团队专有的基于小波收缩的去噪方法可以处理低信噪比信号，而不会受到噪声去除不足或信号失真的限制。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Hyperfine Decoupling of ESR Spectra Using Wavelet Transform

使用小波变换的 ESR 谱超精细解耦

• DOI: 10.3390/magnetochemistry8030032
• 发表时间: 2022
• 期刊: Magnetochemistry
• 影响因子: 2.7
• 作者: Roy, Aritro Sinha;Srivastava, Madhur
• 通讯作者: Srivastava, Madhur

Differentiating Unimodal and Multimodal Distributions in Pulsed Dipolar Spectroscopy Using Wavelet Transforms

• DOI: 10.1007/s00723-023-01616-w
• 发表时间: 2023-09-22
• 期刊: APPLIED MAGNETIC RESONANCE
• 影响因子: 1
• 作者: Sinha Roy，Aritro;Freed，Jack H.;Srivastava，Madhur
• 通讯作者: Srivastava，Madhur

Determining Decomposition Levels for Wavelet Denoising Using Sparsity Plot

• DOI: 10.1109/access.2021.3103497
• 发表时间: 2021-01-01
• 期刊: IEEE ACCESS
• 影响因子: 3.9
• 作者: Bekerman, William;Srivastava, Madhur
• 通讯作者: Srivastava, Madhur