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Proceedings Paper

Disease quantification on PET/CT images without object delineation
Author(s): Yubing Tong; Jayaram K. Udupa; Dewey Odhner; Caiyun Wu; Danielle Fitzpatrick; Nicole Winchell; Stephen J. Schuster; Drew A. Torigian
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Paper Abstract

The derivation of quantitative information from images to make quantitative radiology (QR) clinically practical continues to face a major image analysis hurdle because of image segmentation challenges. This paper presents a novel approach to disease quantification (DQ) via positron emission tomography/computed tomography (PET/CT) images that explores how to decouple DQ methods from explicit dependence on object segmentation through the use of only object recognition results to quantify disease burden. The concept of an object-dependent disease map is introduced to express disease severity without performing explicit delineation and partial volume correction of either objects or lesions. The parameters of the disease map are estimated from a set of training image data sets. The idea is illustrated on 20 lung lesions and 20 liver lesions derived from 18F-2-fluoro-2-deoxy-D-glucose (FDG)-PET/CT scans of patients with various types of cancers and also on 20 NEMA PET/CT phantom data sets. Our preliminary results show that, on phantom data sets, “disease burden” can be estimated to within 2% of known absolute true activity. Notwithstanding the difficulty in establishing true quantification on patient PET images, our results achieve 8% deviation from “true” estimates, with slightly larger deviations for small and diffuse lesions where establishing ground truth becomes really questionable, and smaller deviations for larger lesions where ground truth set up becomes more reliable. We are currently exploring extensions of the approach to include fully automated body-wide DQ, extensions to just CT or magnetic resonance imaging (MRI) alone, to PET/CT performed with radiotracers other than FDG, and other functional forms of disease maps.

Paper Details

Date Published: 13 March 2017
PDF: 6 pages
Proc. SPIE 10137, Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging, 101370V (13 March 2017); doi: 10.1117/12.2254907
Show Author Affiliations
Yubing Tong, Univ. of Pennsylvania (United States)
Jayaram K. Udupa, Univ. of Pennsylvania (United States)
Dewey Odhner, Univ. of Pennsylvania (United States)
Caiyun Wu, Univ. of Pennsylvania (United States)
Danielle Fitzpatrick, Abramson Cancer Ctr., Perelman Ctr. for Advanced Medicine, Univ. of Pennsylvania (United States)
Nicole Winchell, Abramson Cancer Ctr., Perelman Ctr. for Advanced Medicine, Univ. of Pennsylvania (United States)
Stephen J. Schuster, Abramson Cancer Ctr., Perelman Ctr. for Advanced Medicine, Univ. of Pennsylvania (United States)
Drew A. Torigian, Univ. of Pennsylvania (United States)


Published in SPIE Proceedings Vol. 10137:
Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging
Andrzej Krol; Barjor Gimi, Editor(s)

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