Compressive Sensing Applications to Biomedical Engineering

压缩传感在生物医学工程中的应用

• 批准号: RGPIN-2014-04462
• 负责人: Ward, Rabab
• 金额: $ 3.72万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=566537
• 关键词: Compressive; Sensing; Applications; Biomedical; Engineering

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) .

压缩感知（CS）技术已成功应用于MRI。虽然MRI仍然是CS研究的一个有趣的场所，但其他生物医学应用也可以从CS理论中受益。本提案探讨了CS在MRI、x射线计算机断层扫描和高效脑电图信号传输等新研究问题中的应用。这些问题都很新颖，但都属于计算机科学的广泛范畴。这些成果将有利于生物医学工程界，并加强整个CS的研究。动态MRI实时重建涉及每秒多帧的“快速”处理。现有的重建技术是离线的，只对可以在死后完成的分析任务有用，比如医学诊断和神经学研究。还有其他主要应用需要实时重建-图像引导手术和跟踪/监测应用。实时动态MRI重建是一个难题，迄今为止得到的关注有限。有必要解决这一问题，并开发高效、稳健和准确的实时重建技术。在美国，减少动态x射线CT的电离辐射每年导致3万例癌症，约1.5万人死于癌症。CT不安全。这个问题在动态CT中更加严重，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，以解决减少CT电离辐射的迫切需要。无线体域网络（WBAN）的节能脑电图传输：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；对他人的依赖最小。与此同时，需要监测他们的健康状况。从脑电图信号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，脑电图信号将在受试者的位置被获取，然后传输到医疗保健单位进行监测和分析。由于WBAN应用的电池寿命有限，我们将设计节能的采样和传输协议。MRI、x射线CT和脑电图的问题有一个共同的目标——如何从减少的测量中重建潜在的信号。因此，每个问题都将首先在CS框架中重新构建，并开发其解决方案的方法。目前有两种方法解决实时动态MRI重建问题。第一种方法使用像卡尔曼滤波这样的动态系统模型，这种模型的计算效率和内存效率都不高。另一个使用CS。这样可以产生更准确的结果，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-修正框架中。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对预测估计进行细化。MRI离线动态重建研究较多，而CT离线动态重建研究较少。我们将利用我们在动态核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。这个矩阵在变换域中是稀疏的，并且是低秩的。新的Casorati矩阵模型将使我们能够利用变换域稀疏性和它的低秩结构来获得电离辐射较小的结果。无线广域网上的脑电图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来减少需要传输的样本数量-用于电力通信通信理论专家尝试编码技术来提高传输效率，而传感器网络研究人员使用新颖的交换技术来节省能源。我们希望从整体上（端到端）解决这个问题。