A Naive-Bayes model observer for detection and localization of perfusion defects in cardiac SPECT-MPI

Felipe M. Parages; J. Michael O’Connor; P. Hendrik Pretorius; Jovan G. Brankov

doi:10.1117/12.2044441

11 March 2014 A Naive-Bayes model observer for detection and localization of perfusion defects in cardiac SPECT-MPI

Felipe M. Parages, J. Michael O’Connor, P. Hendrik Pretorius, Jovan G. Brankov

Proceedings Volume 9037, Medical Imaging 2014: Image Perception, Observer Performance, and Technology Assessment; 90370N (2014) https://doi.org/10.1117/12.2044441
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

Model observers (MO) are widely used in medical imaging to act as surrogates of human observers in task-based image quality evaluation, frequently towards optimization of reconstruction algorithms. In SPECT myocardial perfusion imaging (MPI), a realistic task-based approach involves detection and localization of perfusion defects, as well as a subsequent assessment of defect severity. In this paper we explore a machine-learning MO based on Naive- Bayes classification (NB-MO). NB-MO uses a set of polar-map image features to predict lesion detection, localization and severity scores given by five human readers for a set of simulated 3D SPECT-MPI patients. The simulated dataset included lesions with different sizes, perfusion-reduction ratios, and locations. Simulated projections were reconstructed using two readily used methods namely: FBP and OSEM. For validation, a multireader multi-case (MRMC) analysis of alternative free-response ROC (AFROC) curve was performed for NB-MO and human observers. For comparison, we also report performances of a statistical Hotelling Observer applied on polar-map images. Results show excellent agreement between NB-MO and humans, as well as model’s good generalization between different reconstruction treatments.

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Felipe M. Parages, J. Michael O’Connor, P. Hendrik Pretorius, and Jovan G. Brankov "A Naive-Bayes model observer for detection and localization of perfusion defects in cardiac SPECT-MPI", Proc. SPIE 9037, Medical Imaging 2014: Image Perception, Observer Performance, and Technology Assessment, 90370N (11 March 2014); https://doi.org/10.1117/12.2044441

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KEYWORDS

Image segmentation

Single photon emission computed tomography

Medical imaging

Molybdenum

Image quality

Reconstruction algorithms

Optimization (mathematics)

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