DRONE: Dual-Domain Residual-based Optimization NEtwork for Sparse-View CT Reconstruction

Deep learning has attracted rapidly increasing attention in the field of tomographic image reconstruction, especially for CT, MRI, PET/SPECT, ultrasound and optical imaging. Among various topics, sparse-view CT remains a challenge which targets a decent image reconstruction from very few projections...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:IEEE transactions on medical imaging Ročník 40; číslo 11; s. 3002 - 3014
Hlavní autori: Wu, Weiwen, Hu, Dianlin, Niu, Chuang, Yu, Hengyong, Vardhanabhuti, Varut, Wang, Ge
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.11.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Biomedical measurement
compressed sensing
Computed tomography
Computed tomography (CT)
Deep learning
Domains
Drones
Embedding
Generative adversarial networks
Image processing
Image Processing, Computer-Assisted
Image quality
Image reconstruction
Imaging
iterative reconstruction
Magnetic resonance imaging
Medical imaging
Modules
Optimization
Phantoms, Imaging
Positron emission
Reconstruction algorithms
Single photon emission computed tomography
sparse-view CT reconstruction
Tomography
Tomography, X-Ray Computed
ISSN:0278-0062, 1558-254X, 1558-254X
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Deep learning has attracted rapidly increasing attention in the field of tomographic image reconstruction, especially for CT, MRI, PET/SPECT, ultrasound and optical imaging. Among various topics, sparse-view CT remains a challenge which targets a decent image reconstruction from very few projections. To address this challenge, in this article we propose a Dual-domain Residual-based Optimization NEtwork (DRONE). DRONE consists of three modules respectively for embedding, refinement, and awareness. In the embedding module, a sparse sinogram is first extended. Then, sparse-view artifacts are effectively suppressed in the image domain. After that, the refinement module recovers image details in the residual data and image domains synergistically. Finally, the results from the embedding and refinement modules in the data and image domains are regularized for optimized image quality in the awareness module, which ensures the consistency between measurements and images with the kernel awareness of compressed sensing. The DRONE network is trained, validated, and tested on preclinical and clinical datasets, demonstrating its merits in edge preservation, feature recovery, and reconstruction accuracy.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:0278-0062
1558-254X
1558-254X
DOI:10.1109/TMI.2021.3078067