Computational Model-based Statistical Methods in Biomedicine

生物医学中基于计算模型的统计方法

• 批准号: 1312424
• 负责人: Erkki Somersalo
• 金额: $ 25.4万
• 依托单位: Case Western Reserve University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-15 至 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1312424&HistoricalAwards=false
• 关键词: Computational; Model; based; Statistical; Methods

This project concerns the mathematical modeling and analysis of biomedical applications in which the objective is to retrieve pertinent information of the structure or functioning of a biological system from indirect and minimally invasive measurements. The applications include electroencephalography (EEG) and magnetoencephalography (MEG), electrical impedance tomography (EIT), electric neurography (ENG), and dynamical PET imaging. Characteristic for all these problems is the high complexity of the mathematical model describing the system, significant level of noise in the signals, and severe ill-posedness of the inverse problem of recovering the information of interest from the data. Well-planned model reduction methods help to simplify the model, but at the same time a significant modeling error is introduced, as the simplified model can no longer capture all the features of the data. The aim in the project is to develop computational statistical methods to overcome these problems. Unknown quantities are modeled as random variables, making it possible to analyze in statistical terms the model reduction errors. In the MEG/EEG application, stochastic modeling is used to analyze and filter out the complex noise due to normal brain activity that easily masks the signal coming from an abnormal activity such as the onset of focal epileptic seizure. Well-planned prior models for the unknown quantities help to reduce the ill-posedness of the inverse problems, and lead to efficient numerical methods for both estimating the unknowns of interest as well as to quantify the uncertainty in the estimate. Novel time-dependent filtering methods are investigated to deal with noisy signals.The mathematical and computational methodology aims at improving the performance of different diagnostic processes: In the impedance tomography application, the goal is to be able to discern benign and malignant lesions seen in a mammography image without the need of breast biopsy, by injecting weak electric currents through contact electrodes in the breast and measuring the corresponding electric voltages, and by further computing the electric response of the tissue of interest. It is known that cancer tissue is characterized by an abnormal electric response. The main target in EEG and MEG research is to help localizing epileptic foci in the brain by measuring the electric and magnetic fields outside the patient's head. This information helps greatly the brain surgery planning for patients with severe epilepsy that does not respond to medication. Dynamic PET imaging is used in the studies of brain functioning, e.g., under severe liver conditions that change the ammonium level in the blood. Electric neurography aims at reading the electric signals inside a peripheral nerve in a minimally invasive manner using contact microelectrodes. This data can be used to give a patient control of a prosthetic robotic arm mounted on an amputated limb, as if the arm would be a real arm responding to neuronal commands. Another exciting application being investigated is the possibility to control chronic pain.

该项目涉及生物医学应用的数学建模和分析，其目标是从间接和微创测量中检索生物系统结构或功能的相关信息。应用包括脑电图（EEG）和脑磁图（MEG），电阻抗断层扫描（EIT），电神经描记术（ENG）和动态PET成像。所有这些问题的特征是描述系统的数学模型的高度复杂性、信号中的显著噪声水平以及从数据恢复感兴趣的信息的逆问题的严重不适定性。精心规划的模型简化方法有助于简化模型，但同时也会引入显著的建模误差，因为简化的模型不再能捕捉数据的所有特征。该项目的目的是开发计算统计方法来克服这些问题。未知量被建模为随机变量，使得可以在统计方面分析模型简化误差。在MEG/EEG应用中，随机建模用于分析和过滤掉由于正常大脑活动而导致的复杂噪声，该噪声容易掩盖来自异常活动（例如局灶性癫痫发作）的信号。精心策划的先验模型的未知量有助于减少不适定性的反问题，并导致有效的数值方法来估计的未知数的兴趣，以及量化的不确定性估计。研究了处理噪声信号的新型时变滤波方法。数学和计算方法旨在提高不同诊断过程的性能：在阻抗断层成像应用中，目标是能够辨别乳房X线摄影图像中看到的良性和恶性病变，而不需要乳房活检，通过在乳房中通过接触电极注入弱电流并测量相应的电压，以及通过进一步计算感兴趣组织的电响应。已知癌组织的特征在于异常电响应。 EEG和MEG研究的主要目标是通过测量患者头部外的电场和磁场来帮助定位大脑中的癫痫灶。这些信息极大地帮助了对药物无反应的严重癫痫患者的脑外科手术计划。动态PET成像用于研究大脑功能，例如，在严重的肝脏条件下，改变血液中的铵水平。神经电描记术旨在使用接触微电极以微创方式阅读周围神经内的电信号。该数据可用于给予患者对安装在截肢肢体上的假肢机器人手臂的控制，就好像该手臂是响应神经元命令的真实的手臂一样。另一个正在研究的令人兴奋的应用是控制慢性疼痛的可能性。