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Incorporating Anatomical Side Information Into PET Reconstruction Using Nonlocal Regularization

Published: 01 October 2013 Publication History

Abstract

With the introduction of combined positron emission tomography (PET)/computed tomography (CT) or PET/magnetic resonance imaging (MRI) scanners, there is an increasing emphasis on reconstructing PET images with the aid of the anatomical side information obtained from X-ray CT or MRI scanners. In this paper, we propose a new approach to incorporating prior anatomical information into PET reconstruction using the nonlocal regularization method. The nonlocal regularizer developed for this application is designed to selectively consider the anatomical information only when it is reliable. As our proposed nonlocal regularization method does not directly use anatomical edges or boundaries which are often used in conventional methods, it is not only free from additional processes to extract anatomical boundaries or segmented regions, but also more robust to the signal mismatch problem that is caused by the indirect relationship between the PET image and the anatomical image. We perform simulations with digital phantoms. According to our experimental results, compared to the conventional method based on the traditional local regularization method, our nonlocal regularization method performs well even with the imperfect prior anatomical information or in the presence of signal mismatch between the PET image and the anatomical image.

Cited By

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  • (2017)Deep auto-context convolutional neural networks for standard-dose PET image estimation from low-dose PET/MRINeurocomputing10.1016/j.neucom.2017.06.048267:C(406-416)Online publication date: 6-Dec-2017
  • (2016)Multi-Level Canonical Correlation Analysis for Standard-Dose PET Image EstimationIEEE Transactions on Image Processing10.1109/TIP.2016.256707225:7(3303-3315)Online publication date: 1-Jul-2016

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cover image IEEE Transactions on Image Processing
IEEE Transactions on Image Processing  Volume 22, Issue 10
October 2013
409 pages

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IEEE Press

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Published: 01 October 2013

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  • (2017)Deep auto-context convolutional neural networks for standard-dose PET image estimation from low-dose PET/MRINeurocomputing10.1016/j.neucom.2017.06.048267:C(406-416)Online publication date: 6-Dec-2017
  • (2016)Multi-Level Canonical Correlation Analysis for Standard-Dose PET Image EstimationIEEE Transactions on Image Processing10.1109/TIP.2016.256707225:7(3303-3315)Online publication date: 1-Jul-2016

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