Summary - Summary Report Model-Based Estimation for Dynamic Cardiac Studies Using ECT Group Member Wan Huang Yiying Zhu Yue Hou I INTRODUCTION In

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Summary Report – Model-Based Estimation for Dynamic Cardiac Studies Using ECT Group Member: Wan Huang, Yiying Zhu, Yue Hou I.INTRODUCTION In this report, model-based estimation using Emission Computed Tomography (ECT) for dynamic cardiac study is discussed about, which has multitude of advantages over existing imaging techniques. Through imaging an organ of interest over time using ECT, one can observe the dynamic behaviour of the radiotracer, thus quantify the cardiac processes. The estimation process can be modelled as: Using measurements of plasma tracer concentration as input and tomographically measuring the organ tracer concentration as output, the object is to determine the compartmental parameters as the intermediary system. [1] Conventionally, human operators are needed to specify myocardial regions of interest (ROI) for each image and function of detected counts versus time index is calculated. Estimation algorithm is only used then to underlie the concentration change in each ROI from the count-time function. [1] Two factors obstruct accurate qualification of ROI when ECT is applied, which are limited system resolution and measurement noise, [two difficulties] where limited system resolution causes systematic error (called bias) because adjacent regions would contaminate each other thus the boundary is difficult to define. Existing researches are divided to two directions: Post-reconstruction correction and ROI-based estimator based on maximum likelihood (ML) criteria. [2] For ROI-based ML estimators, [2] shows statistically unbiased ROI concentration estimates can be generated if ROIs are specified exactly. He formulated parametric image model as following: (1) The formula allows estimation of compartmental parameters (X) directly from the projection data (Y) and system response matrix accounting for resolution (W). Both post-reconstruction correction and ROI-based estimators assume perfect ROI delineation from reconstructed ECT images [1], which is never true. Consequently, erroneous ROI specification leads to an extra source of error. The research paper is to establish a strategy that jointly estimating boundaries as well as compartmental parameters. II. OBSERVATION MODEL The author proposed an observation model to parameterize compartmental parameters, myocardial boundaries, left ventricular input function and background concentration. [1] Based on this, estimator is applied. The model has a more generalized form than [2] as: (2) Where Ψ is degradation factor that depends on boundary S, and C is ROI-based concentration vector that depends on compartmental parameter Θ. Two assumptions are made for simplification: 1. On short axis section, the object consists of three homogeneous regions: left ventricle, myocardium, and background. 2. The heart is stationary. A simple observation model can be described as:
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This note was uploaded on 01/10/2012 for the course EECS 551 taught by Professor Wakefield during the Spring '08 term at University of Michigan.

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Summary - Summary Report Model-Based Estimation for Dynamic Cardiac Studies Using ECT Group Member Wan Huang Yiying Zhu Yue Hou I INTRODUCTION In

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