Body-wide anatomy recognition in PET/CT images

Huiqian Wang; Jayaram K. Udupa; Dewey Odhner; Yubing Tong; Liming Zhao; Drew A. Torigian M.D.

doi:10.1117/12.2082718

18 March 2015 Body-wide anatomy recognition in PET/CT images

Huiqian Wang, Jayaram K. Udupa, Dewey Odhner, Yubing Tong, Liming Zhao, Drew A. Torigian M.D.

Proceedings Volume 9415, Medical Imaging 2015: Image-Guided Procedures, Robotic Interventions, and Modeling; 941518 (2015) https://doi.org/10.1117/12.2082718
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

Abstract

With the rapid growth of positron emission tomography/computed tomography (PET/CT)-based medical applications, body-wide anatomy recognition on whole-body PET/CT images becomes crucial for quantifying body-wide disease burden. This, however, is a challenging problem and seldom studied due to unclear anatomy reference frame and low spatial resolution of PET images as well as low contrast and spatial resolution of the associated low-dose CT images. We previously developed an automatic anatomy recognition (AAR) system [15] whose applicability was demonstrated on diagnostic computed tomography (CT) and magnetic resonance (MR) images in different body regions on 35 objects. The aim of the present work is to investigate strategies for adapting the previous AAR system to low-dose CT and PET images toward automated body-wide disease quantification. Our adaptation of the previous AAR methodology to PET/CT images in this paper focuses on 16 objects in three body regions – thorax, abdomen, and pelvis – and consists of the following steps: collecting whole-body PET/CT images from existing patient image databases, delineating all objects in these images, modifying the previous hierarchical models built from diagnostic CT images to account for differences in appearance in low-dose CT and PET images, automatically locating objects in these images following object hierarchy, and evaluating performance. Our preliminary evaluations indicate that the performance of the AAR approach on low-dose CT images achieves object localization accuracy within about 2 voxels, which is comparable to the accuracies achieved on diagnostic contrast-enhanced CT images. Object recognition on low-dose CT images from PET/CT examinations without requiring diagnostic contrast-enhanced CT seems feasible.

Citation Download Citation

Huiqian Wang, Jayaram K. Udupa, Dewey Odhner, Yubing Tong, Liming Zhao, and Drew A. Torigian M.D. "Body-wide anatomy recognition in PET/CT images", Proc. SPIE 9415, Medical Imaging 2015: Image-Guided Procedures, Robotic Interventions, and Modeling, 941518 (18 March 2015); https://doi.org/10.1117/12.2082718

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