Compressive Sensing Applications to Biomedical Engineering
压缩传感在生物医学工程中的应用
基本信息
- 批准号:RGPIN-2014-04462
- 负责人:
- 金额:$ 3.72万
- 依托单位:
- 依托单位国家:加拿大
- 项目类别:Discovery Grants Program - Individual
- 财政年份:2018
- 资助国家:加拿大
- 起止时间:2018-01-01 至 2019-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Compressed Sensing (CS) has been successfully applied to MRI. While MRI remains an intersting venue for CS research, other biomedical applications could also benefit from CS theory. This proposal explores the application of CS to new research problems in MRI, X-Ray Computed Tomography and energy efficient EEG signal transmission. These problems are novel but all fall under the broad category of CS. The outcomes will benefit the biomedical engineering community as well as strengthen research in CS as a whole.*Dynamic MRI Reconstruction in real-time involves "fast" processing of many frames per second. Existing reconstruction techniques are offline and only useful for analytical tasks which can be done posthumously, like medical diagnosis and neurological studies. There are other major applications which require real-time reconstruction - image-guided surgery and tracking / monitoring applications. Real-time dynamic MRI reconstruction is a hard problem and has received limited focus so far. There is a need to address this problem and develop efficient, robust and accurate techniques for real-time reconstruction.*Reducing Ionizing Radiation for dynamic X-Ray CT results in 30,000 cases of cancer a year and about 15,000 deaths from cancer in the USA. CT is not safe .The problem is aggravated in dynamic CT, as the patient is subjected to more ionizing radiation compared to static scans. We will develop new techniques to address the dire need to reduce the ionizing radiation in CT.*Energy efficient EEG transmission for Wireless Body Area Network (WBAN) : In Canada 14.1% of the population is above the age of 65. They should be able to live with dignity; with minimum dependency on others. At the same time they need to be monitored for health conditions. From EEG signals it is possible to infer a variety of health problems. We envision a system where the EEG signal will be acquired at the subject's location and then transmitted to a healthcare unit for monitoring and analysis. Since the battery life is limited in WBAN applications we will design sampling and transmission protocols that are energy efficient.*The MRI, X-Ray CT and EEG problems are tied by a common goal - how to reconstruct the underlying signal from a reduced number of measurements. Thus each problem will be first recast in the CS framework and methodolgies for its solutions will be developed. *Real-time dynamic MRI reconstruction is presently solved via two approaches. The first uses dynamical system models like Kalman Filtering, which is not computationally or memory efficient. The other uses CS. This yields more accurate results but remains too slow for real-time performance. We propose to combine the two in a prediction-correction framework. In the prediction step a dynamical model will be used to estimate the frames; in the correction step the predicted estimate will be refined using CS. *Offline dynamic MRI reconstruction is well studied but for dynamic CT, only a handful of studies exist. We will leverage our expertise in dynamic MRI techniques to suit the needs for CT. We plan to model the dynamic CT frames as a Casorati matrix. This matrix is sparse in transform domain and will also be low-rank. The novel Casorati matrix model will enable us to exploit both transform domain sparsity as well as its low-rank structure to obtain results with lesser ionizing radiation.*The problem of EEG transmission over WBAN is not a mature topic. Signal processing researchers used CS to reduce the number of samples to be transmitted- for power communication Communication theory experts tried encoding techniques for efficient transmission whereas researchers in sensor networks used novel switching techniques to save energy. We want to address the problem as a whole (end-to-end) .
压缩传感(CS)已成功地应用于磁共振成像。虽然MRI仍然是CS研究的有趣场所,但其他生物医学应用也可以受益于CS理论。这项建议探索了CS在磁共振成像、X射线计算机断层扫描和能量高效脑电信号传输等新的研究问题上的应用。这些问题都是新奇的,但都属于CS的宽泛范畴。这一成果将使生物医学工程界受益,并作为一个整体加强对CS的研究。*实时动态核磁共振重建涉及每秒许多帧的“快速”处理。现有的重建技术是离线的,只适用于可以在死后完成的分析任务,如医学诊断和神经学研究。还有其他需要实时重建的主要应用--图像引导手术和跟踪/监控应用。MRI实时动态重建是一个难点问题,目前受到的关注有限。有必要解决这一问题,并开发高效、强大和准确的实时重建技术。*减少动态X射线CT的电离辐射在美国每年导致3万例癌症病例和大约1.5万人死于癌症。CT是不安全的。动态CT的问题更严重,因为与静态扫描相比,患者受到更多的电离辐射。我们将开发新技术,以满足减少CT中电离辐射的迫切需要。*无线体域网络(WBAN)的高能效脑电传输:在加拿大,14.1%的人口年龄在65岁以上。他们应该能够有尊严地生活,最大限度地减少对他人的依赖。同时,需要对他们的健康状况进行监测。从脑电信号中可以推断出各种健康问题。我们设想一个系统,在这个系统中,脑电信号将在受试者的位置被采集,然后传输到医疗单位进行监测和分析。由于电池寿命在WBAN应用中是有限的,我们将设计节能的采样和传输协议。*磁共振成像、X射线CT和EEG问题被一个共同的目标联系在一起--如何从减少的测量中重建潜在信号。因此,每个问题将首先在CS框架中重新塑造,并将制定其解决方案的方法。*实时动态核磁共振重建目前通过两种方法解决。第一种是使用像卡尔曼滤波这样的动态系统模型,这种模型在计算和内存方面都不是很有效。另一种是使用CS。这会产生更准确的结果,但对于实时性能来说仍然太慢。我们建议将两者结合在一个预测-校正框架中。在预测步骤中,将使用动态模型来估计帧;在校正步骤中,将使用CS来改进预测估计。*离线动态MRI重建得到了很好的研究,但对于动态CT,研究很少。我们将利用我们在动态磁共振技术方面的专业知识来满足CT的需求。我们计划将动态CT帧建模为Casorati矩阵。该矩阵在变换域中是稀疏的,也将是低秩阵。新的Casorati矩阵模型将使我们能够利用变换域的稀疏性以及其低阶结构来获得电离辐射较小的结果。*脑电信号在WBAN中的传输问题并不是一个成熟的课题。信号处理研究人员使用CS来减少要传输的样本数量--对于电力通信,通信理论专家尝试了编码技术来实现有效的传输,而传感器网络的研究人员则使用了新的开关技术来节省能源。我们希望作为一个整体(端到端)解决这个问题。
项目成果
期刊论文数量(0)
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会议论文数量(0)
专利数量(0)
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Ward, Rabab其他文献
Perceptual rate distortion optimization of 3D-HEVC using PSNR-HVS
- DOI:
10.1007/s11042-017-5486-z - 发表时间:
2018-09-01 - 期刊:
- 影响因子:3.6
- 作者:
Valizadeh, Sima;Nasiopoulos, Panos;Ward, Rabab - 通讯作者:
Ward, Rabab
Semi-supervised Stacked Label Consistent Autoencoder for Reconstruction and Analysis of Biomedical Signals
- DOI:
10.1109/tbme.2016.2631620 - 发表时间:
2017-09-01 - 期刊:
- 影响因子:4.6
- 作者:
Gogna, Anupriya;Majumdar, Angshul;Ward, Rabab - 通讯作者:
Ward, Rabab
Ethnic disparities in publicly-available pulse oximetry databases.
- DOI:
10.1038/s43856-022-00121-8 - 发表时间:
2022 - 期刊:
- 影响因子:0
- 作者:
Sinaki, Fatemeh Y;Ward, Rabab;Abbott, Derek;Allen, John;Fletcher, Richard Ribon;Menon, Carlo;Elgendi, Mohamed - 通讯作者:
Elgendi, Mohamed
Analysis: An optimal filter for short photoplethysmogram signals
- DOI:
10.1038/sdata.2018.76 - 发表时间:
2018-03-01 - 期刊:
- 影响因子:9.8
- 作者:
Liang, Yongbo;Elgendi, Mohamed;Ward, Rabab - 通讯作者:
Ward, Rabab
Reducing streak artifacts in computed tomography via sparse representation in coupled dictionaries
- DOI:
10.1118/1.4942376 - 发表时间:
2016-03-01 - 期刊:
- 影响因子:3.8
- 作者:
Karimi, Davood;Ward, Rabab - 通讯作者:
Ward, Rabab
Ward, Rabab的其他文献
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{{ truncateString('Ward, Rabab', 18)}}的其他基金
Deep Clustering for Image Data: Methods and Applications
图像数据的深度聚类:方法与应用
- 批准号:
RGPIN-2019-03981 - 财政年份:2022
- 资助金额:
$ 3.72万 - 项目类别:
Discovery Grants Program - Individual
Deep Clustering for Image Data: Methods and Applications
图像数据的深度聚类:方法与应用
- 批准号:
RGPIN-2019-03981 - 财政年份:2021
- 资助金额:
$ 3.72万 - 项目类别:
Discovery Grants Program - Individual
Deep Clustering for Image Data: Methods and Applications
图像数据的深度聚类:方法与应用
- 批准号:
RGPIN-2019-03981 - 财政年份:2020
- 资助金额:
$ 3.72万 - 项目类别:
Discovery Grants Program - Individual
Deep Clustering for Image Data: Methods and Applications
图像数据的深度聚类:方法与应用
- 批准号:
RGPIN-2019-03981 - 财政年份:2019
- 资助金额:
$ 3.72万 - 项目类别:
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Personalized progressive web application for eCommerce
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519934-2017 - 财政年份:2017
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$ 3.72万 - 项目类别:
Engage Grants Program
Compressive Sensing Applications to Biomedical Engineering
压缩传感在生物医学工程中的应用
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RGPIN-2014-04462 - 财政年份:2017
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$ 3.72万 - 项目类别:
Discovery Grants Program - Individual
Compressive Sensing Applications to Biomedical Engineering
压缩传感在生物医学工程中的应用
- 批准号:
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$ 3.72万 - 项目类别:
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503659-2016 - 财政年份:2016
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- 批准号:
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- 资助金额:
$ 3.72万 - 项目类别:
Discovery Grants Program - Individual
Compressive Sensing Applications to Biomedical Engineering
压缩传感在生物医学工程中的应用
- 批准号:
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$ 3.72万 - 项目类别:
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