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Semi-Supervised Deep Expectation-Maximization for Low-Dose Pet-Ct
Journal
Proceedings - International Symposium on Biomedical Imaging
ISSN
19457928
Date Issued
2022-01-01
Author(s)
Sharma, Vatsala
Khurana, Ansh
Yenamandra, Sriram
Awate, Suyash P.
Abstract
Reducing the dose of ionizing radiation underlying combined imaging with positron emission tomography (PET) and computed tomography (CT) typically leads to reduced image quality. We propose a novel variational deep-neural-network (DNN) framework for image quality enhancement of low-dose PET-CT images, relying on Monte-Carlo expectation maximization. Unlike existing DNN-based training that pairs low-dose PET-CT images with their corresponding high-dose versions, we propose a semi-supervised learning framework that enables learning using a small number of high-dose images. We propose a robust and uncertainty-aware loss motivated by a heavy-tailed generalized-Gaussian distribution on the residuals between the DNN output and the PET-CT data, aiding our semi-supervised learning scheme. Results on publicly available data show the benefits of our framework, quantitatively and qualitatively, over existing methods.
Volume
2022-March
Subjects