Task-based data-acquisition optimization for sparse image reconstruction systems

Yujia Chen, Yang Lou, Matthew A Kupinski, Mark A. Anastasio

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Conventional wisdom dictates that imaging hardware should be optimized by use of an ideal observer (IO) that exploits full statistical knowledge of the class of objects to be imaged, without consideration of the reconstruction method to be employed. However, accurate and tractable models of the complete object statistics are often difficult to determine in practice. Moreover, in imaging systems that employ compressive sensing concepts, imaging hardware and (sparse) image reconstruction are innately coupled technologies. We have previously proposed a sparsity-driven ideal observer (SDIO) that can be employed to optimize hardware by use of a stochastic object model that describes object sparsity. The SDIO and sparse reconstruction method can therefore be "matched" in the sense that they both utilize the same statistical information regarding the class of objects to be imaged. To efficiently compute SDIO performance, the posterior distribution is estimated by use of computational tools developed recently for variational Bayesian inference. Subsequently, the SDIO test statistic can be computed semi-analytically. The advantages of employing the SDIO instead of a Hotelling observer are systematically demonstrated in case studies in which magnetic resonance imaging (MRI) data acquisition schemes are optimized for signal detection tasks.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2017
Subtitle of host publicationImage Perception, Observer Performance, and Technology Assessment
PublisherSPIE
Volume10136
ISBN (Electronic)9781510607170
DOIs
Publication statusPublished - 2017
EventMedical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment - Orlando, United States
Duration: Feb 12 2017Feb 13 2017

Other

OtherMedical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment
CountryUnited States
CityOrlando
Period2/12/172/13/17

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Keywords

  • Imaging system optimization
  • Numerial observers

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

Cite this

Chen, Y., Lou, Y., Kupinski, M. A., & Anastasio, M. A. (2017). Task-based data-acquisition optimization for sparse image reconstruction systems. In Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment (Vol. 10136). [101360Z] SPIE. https://doi.org/10.1117/12.2255536