Single-photon emission computed tomography (SPECT) imaging has become a standard component of modern cardiology. In SPECT research (as in medical imaging generally), it has become widely accepted that advances in imaging hardware and algorithms should be guided by so-called task-based evaluation criteria, i.e., measures that reflect how the imaging technique will impact clinical decision making. In general, a human observer study is the gold standard for measuring task-based criteria;however, the expense and complexity of such studies precludes their routine use. Therefore, numerical observers-algorithms that emulate human observer performance-are now widely used as surrogates for human observers. In SPECT, one particular numerical observer, known as the channelized Hotelling observer (CHO), has come to dominate the field. The CHO is a detection algorithm that is used to approximate the human observer's performance in detecting lesions;in the case of cardiac SPECT, the lesions of interest are perfusion defects. An imaging system or algorithm can be judged by the ability of the CHO to accurately detect defects based on the images produced. SPECT researchers now rely heavily (and sometimes exclusively) on numerical observers such as the CHO, not only to validate their final results, but also as a figure of merit that guides optimization of hardware or algorithms. Because of the central role it has come to play, the CHO and its extensions have become a major research topic in their own right. In the proposed project, our goal will be to create a suite of numerical observers that will shed light on a much wider set of clinical tasks than the CHO, and we will pursue an approach that we hypothesize will be more accurate than the CHO. Therefore, the proposed research is significant because it will yield an evaluation methodology that could potentially be used very widely by the research community, underpinning the development of imaging hardware and software. We will develop a software package for image quality assessment using the proposed NO approach and distribute it freely to the research community. As a by-product of the research, the proposed project will also yield a thorough task-based evaluation of major image reconstruction algorithms, and will answer the question of which sorts of data (on the spectrum from simple phantoms to clinical data) are a sufficient foundation for numerical observers to perform as desired. The research will also yield the basis for a potential computer aided diagnostic system for cardiology.
The specific aims of the research will be as follows: 1) Create a comprehensive set of imaging data and human observer scores;2) Develop a suite of numerical observers based on our novel learning-based approach, as well as more-conventional statistical decision theory principles;3) Compare and evaluate the numerical observers;and 4) Develop and disseminate by-products of the research program.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Project (R01)
Project #
5R01HL091017-02
Application #
7585774
Study Section
Biomedical Imaging Technology Study Section (BMIT)
Program Officer
Larkin, Jennie E
Project Start
2008-03-15
Project End
2013-02-28
Budget Start
2009-03-01
Budget End
2010-02-28
Support Year
2
Fiscal Year
2009
Total Cost
$411,032
Indirect Cost
Name
Illinois Institute of Technology
Department
Engineering (All Types)
Type
Schools of Engineering
DUNS #
042084434
City
Chicago
State
IL
Country
United States
Zip Code
60616
Sinha, L; Fogarty, M; Zhou, W et al. (2018) Design and characterization of a dead-time regime enhanced early photon projection imaging system. Rev Sci Instrum 89:043707
Ba, Alexandre; Abbey, Craig K; Baek, Jongduk et al. (2018) Inter-laboratory comparison of channelized hotelling observer computation. Med Phys 45:3019-3030
Roux, Brianna M; Akar, Banu; Zhou, Wei et al. (2018) Preformed Vascular Networks Survive and Enhance Vascularization in Critical Sized Cranial Defects. Tissue Eng Part A 24:1603-1615
Massanes, Francesc; Brankov, Jovan G (2016) Full receiver operating characteristic curve estimation using two alternative forced choice studies. J Med Imaging (Bellingham) 3:011010
Sinha, Lagnojita; Brankov, Jovan G; Tichauer, Kenneth M (2016) Enhanced detection of early photons in time-domain optical imaging by running in the ""dead-time"" regime. Opt Lett 41:3225-8
de Sisternes, Luis; Brankov, Jovan G; Zysk, Adam M et al. (2015) A computational model to generate simulated three-dimensional breast masses. Med Phys 42:1098-118
Majidi, Keivan; Li, Jun; Muehleman, Carol et al. (2014) Noise and analyzer-crystal angular position analysis for analyzer-based phase-contrast imaging. Phys Med Biol 59:1877-97
Marin, Thibault; Kalayeh, Mahdi M; Parages, Felipe M et al. (2014) Numerical Surrogates for Human Observers in Myocardial Motion Evaluation From SPECT Images. IEEE Trans Med Imaging 33:38-47
Majidi, Keivan; Wernick, Miles N; Li, Jun et al. (2014) Limited-angle tomography for analyzer-based phase-contrast x-ray imaging. Phys Med Biol 59:3483-500
Kalayeh, Mahdi M; Marin, Thibault; Brankov, Jovan G (2013) Generalization Evaluation of Machine Learning Numerical Observers for Image Quality Assessment. IEEE Trans Nucl Sci 60:1609-1618

Showing the most recent 10 out of 19 publications