Estimating task-based performance bounds for image reconstruction methods by use of learned-ideal observers

Kaiyan Li, Weimin Zhou, Hua Li, Mark A. Anastasio

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Medical imaging systems are commonly assessed and optimized by use of objective measures of image quality (IQ). The performance of the Ideal Observer (IO) acting on imaging measurements has long been advocated as a figure-of-merit to guide the optimization of imaging systems. For computed imaging systems, the performance of the IO acting on imaging measurements also sets an upper bound on task-performance that no image reconstruction method can transcend. As such, estimation of IO performance can provide valuable guidance when designing data-acquisition techniques by enabling the identification of designs that will not permit the reconstruction of diagnostically useful images for a specified task-no matter how advanced the reconstruction method is or plausible the reconstructed images appear. The need for such analyses is urgent because of the ubiquitous development of deep learning-based image reconstruction methods and the fact that they are often not assessed by use of objective image quality measures. However, until recently, estimation of the IO was generally intractable when clinically relevant objects and imaging conditions were assumed. In this work, for the first time, estimates of the IO acting on tomographic imaging measurements were computed with consideration of realistic object variability to establish task-based performance bounds for image reconstruction methods. This was accomplished by use of a recently developed learning-based procedure. Numerical studies that were inspired by breast x-ray computed tomography were conducted to validate and demonstrate the approach. The effectiveness of the approximation method was validated on raw measurements for a signal-known-exactly and background-known-exactly (SKE/BKE) binary signal detection task.

Original languageEnglish
Title of host publicationMedical Imaging 2023
Subtitle of host publicationImage Perception, Observer Performance, and Technology Assessment
EditorsClaudia R. Mello-Thoms, Yan Chen
PublisherSPIE
ISBN (Electronic)9781510660397
DOIs
StatePublished - 2023
EventMedical Imaging 2023: Image Perception, Observer Performance, and Technology Assessment - San Diego, United States
Duration: Feb 21 2023Feb 23 2023

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume12467
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2023: Image Perception, Observer Performance, and Technology Assessment
Country/TerritoryUnited States
CitySan Diego
Period02/21/2302/23/23

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