AHP-Net: adaptive-hyper-parameter deep learning based image reconstruction method for multilevel low-dose CT

Low-dose CT (LDCT) imaging is desirable in many clinical applications to reduce X-ray radiation dose to patients. Inspired by deep learning (DL), a recent promising direction of model-based iterative reconstruction (MBIR) methods for LDCT is via optimization-unrolling DL-regularized image reconstruc...

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Bibliographic Details
Published in:arXiv.org 2021-02
Main Authors: Ding, Qiaoqiao, Yuesong Nan, Gao, Hao, Ji, Hui
Format: Article
Language:English
Subjects:
Computed tomography
Deep learning
Filter banks
Image reconstruction
Iterative methods
Machine learning
Mathematical models
Medical imaging
Multilevel
Noise
Noise levels
Optimization
Parameters
Radiation dosage
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Summary:Low-dose CT (LDCT) imaging is desirable in many clinical applications to reduce X-ray radiation dose to patients. Inspired by deep learning (DL), a recent promising direction of model-based iterative reconstruction (MBIR) methods for LDCT is via optimization-unrolling DL-regularized image reconstruction, where pre-defined image prior is replaced by learnable data-adaptive prior. However, LDCT is clinically multilevel, since clinical scans have different noise levels that depend of scanning site, patient size, and clinical task. Therefore, this work aims to develop an adaptive-hyper-parameter DL-based image reconstruction method (AHP-Net) that can handle multilevel LDCT of different noise levels. AHP-Net unrolls a half-quadratic splitting scheme with learnable image prior built on framelet filter bank, and learns a network that automatically adjusts the hyper-parameters for various noise levels. As a result, AHP-Net provides a single universal training model that can handle multilevel LDCT. Extensive experimental evaluations using clinical scans suggest that AHP-Net outperformed conventional MBIR techniques and state-of-the-art deep-learning-based methods for multilevel LDCT of different noise levels.
ISSN:2331-8422