Objective Assessment Methods for List Mode Imaging System
type: CreativeWork
description: It has been advocated that to properly quantify the imagequality of a medical imaging system, both the task and observer have to be well defined. The objective quality of the image depends on how well the specified observer can extract the desired information to perform the task. Classification or detection tasks are often considered in practice. Two commonly inspected observers are the Hotelling Observer and the ideal observer. The Hotelling observer is optimal among all linear observers in the sense of signal-to-noise ratio, while the performance of the ideal observer sets an upper bound for all other observers by employing the complete statistics of the imaging system and object. Both observers have been extensively studied for traditional medical images that take the form of binned data. Here in this work, we focus on the list mode data, where instead of binning the incident locations, energies, or other formats of attributes, the attributes of each incident are recorded to form a list. This data format has the potential to extract more information from the system and support better performance for clinical tasks. However, the current commonly seen observer constructs can not be applied directly to the list mode data given the unique format. For the Hotelling observer, the conventional implementation operating on binned data normally involves the inversion of covariance matrices and estimation of the difference in means of two vectors. With list mode data, the situation is salvageable by using the attribute list to construct a Poisson point process in attribute space, where instead of the inversion of matrices, the inversion of operators is needed. An example of computing the HOHotelling Observer test statistic on list mode data is provided in Chapter 4. The observer performance is measured on a signal-known-exactly and background-knownstatistically task and compared to the corresponding approximation by use of supervised learning methods on binned data, where a…
abstract: It has been advocated that to properly quantify the imagequality of a medical imaging system, both the task and observer have to be well defined. The objective quality of the image depends on how well the specified observer can extract the desired information to perform the task. Classification or detection tasks are often considered in practice. Two commonly inspected observers are the Hotelling Observer and the ideal observer. The Hotelling observer is optimal among all linear observers in the sense of signal-to-noise ratio, while the performance of the ideal observer sets an upper bound for all other observers by employing the complete statistics of the imaging system and object. Both observers have been extensively studied for traditional medical images that take the form of binned data. Here in this work, we focus on the list mode data, where instead of binning the incident locations, energies, or other formats of attributes, the attributes of each incident are recorded to form a list. This data format has the potential to extract more information from the system and support better performance for clinical tasks. However, the current commonly seen observer constructs can not be applied directly to the list mode data given the unique format. For the Hotelling observer, the conventional implementation operating on binned data normally involves the inversion of covariance matrices and estimation of the difference in means of two vectors. With list mode data, the situation is salvageable by using the attribute list to construct a Poisson point process in attribute space, where instead of the inversion of matrices, the inversion of operators is needed. An example of computing the HOHotelling Observer test statistic on list mode data is provided in Chapter 4. The observer performance is measured on a signal-known-exactly and background-knownstatistically task and compared to the corresponding approximation by use of supervised learning methods on binned data, where a…
author: Li, Dan
language: en
keywords: CNN, Hotelling Observer, Ideal Observer, Image quality assessment, List mode data, SLNN
license: http://rightsstatements.org/vocab/InC/1.0/
date published: 2022
date modified: 2026-07-24T00:57:22Z
provider: University of Arizona
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: The University of Arizona.

PropertyValue
property ID: Handle
value: http://hdl.handle.net/10150/667684