ArticlePhysics in medicine and biology2017
Locally linear constraint based optimization model for material decomposition.
Article in Physics in medicine and biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Multi-energy CT material decomposition using Bayesian deep convolutional neural network with explicit penalty of uncertainty and bias.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
- Deep learning-based virtual noncalcium imaging in multiple myeloma using dual-energy CT.Medical physics · 2022Article
- Image-domain Material Decomposition for Spectral CT using a Generalized Dictionary Learning.IEEE transactions on radiation and plasma medical sciences · 2021Article
- Deep-learning-based direct inversion for material decomposition.Medical physics · 2020Article
- Block matching frame based material reconstruction for spectral CT.Physics in medicine and biology · 2019Article
- Low-dose spectral CT reconstruction using image gradientApplied mathematical modelling · 2018Article
- Spatial-Spectral Cube Matching Frame for Spectral CT Reconstruction.Inverse problems · 2018Article
- Iterative spectral CT reconstruction based on low rank and average-image-incorporated BM3D.Physics in medicine and biology · 2018Article
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3 authors.
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Abstract
Dual spectral computed tomography (DSCT) has a superior material distinguishability than the conventional single spectral computed tomography (SSCT). However, the decomposition process is an illposed problem, which is sensitive to noise. Thus, the decomposed image quality is degraded, and the corresponding signal-to-noise ratio (SNR) is much lower than that of directly reconstructed image of SSCT. In this work, we establish a locally linear relationship between the decomposed results of DSCT and SSCT. Based on this constraint, we propose an optimization model for DSCT and develop an iterative method with image guided filtering. To further improve the image quality, we employ a preprocessing method based on the relative total variation regularization. Both numerical simulations and real experiments are performed, and the results confirm the effectiveness of our proposed approach.
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