Weili Lin

Works (3)

Updated: April 28th, 2025 10:34

2018 article

3D Auto-Context-Based Locality Adaptive Multi-Modality GANs for PET Synthesis

Wang, Y., Zhou, L., Yu, B., Wang, L., Zu, C., Lalush, D., … Shen, D. (2018, November 29). IEEE Transactions on Medical Imaging, Vol. 38, pp. 1328–1339.

By: Y. Wang *, L. Zhou *, B. Yu *, L. Wang *, C. Zu *, D. Lalush n , W. Lin*, Wu, J. Zhou *, D. Shen *

Contributors: Y. Wang *, L. Zhou *, B. Yu *, L. Wang *, C. Zu *, D. Lalush n , W. Lin*, X. Wu *, J. Zhou *, D. Shen *

author keywords: Image synthesis; positron emission topography (PET); generative adversarial networks (GANs); locality adaptive fusion; multi-modality
MeSH headings : Brain / diagnostic imaging; Databases, Factual; Deep Learning; Humans; Imaging, Three-Dimensional / methods; Magnetic Resonance Imaging / methods; Phantoms, Imaging; Positron-Emission Tomography / methods; Radiation Dosage
topics (OpenAlex): Advanced Image Processing Techniques; Generative Adversarial Networks and Image Synthesis; Image and Signal Denoising Methods
TL;DR: Experimental results show that the proposed 3D auto-context-based locality adaptive multi-modality generative adversarial networks model (LA-GANs) outperforms the traditional multi- modality fusion methods used in deep networks, as well as the state-of-the-art PET estimation approaches. (via Semantic Scholar)
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Sources: Web Of Science, ORCID, NC State University Libraries
Added: April 20, 2020

2018 article

Locality Adaptive Multi-modality GANs for High-Quality PET Image Synthesis

Wang, Y., Zhou, L., Wang, L., Yu, B., Zu, C., Lalush, D., … Shen, D. (2018, January 1). Lecture Notes in Computer Science, pp. 329–337.

By: Y. Wang *, L. Zhou *, L. Wang *, B. Yu *, C. Zu *, D. Lalush n , W. Lin*, Wu, J. Zhou *, D. Shen *

Contributors: Y. Wang *, L. Zhou *, L. Wang *, B. Yu *, C. Zu *, D. Lalush n , W. Lin*, X. Wu *, J. Zhou *, D. Shen *

MeSH headings : Algorithms; Electrons; Magnetic Resonance Imaging / methods; Multimodal Imaging / methods; Neural Networks, Computer; Positron-Emission Tomography / methods; Reproducibility of Results; Sensitivity and Specificity
topics (OpenAlex): Advanced Image Processing Techniques; Image and Signal Denoising Methods; Image Processing Techniques and Applications
TL;DR: A locality adaptive multi-modality generative adversarial networks model (LA-GANs) is proposed to synthesize the full-dose PET image from both the low-dose one and the accompanying T1-weighted MRI to incorporate anatomical information for better PET image synthesis. (via Semantic Scholar)
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Sources: Web Of Science, ORCID, NC State University Libraries
Added: August 19, 2019

2016 journal article

Predicting standard-dose PET image from low-dose PET and multimodal MR images using mapping-based sparse representation

Physics in Medicine and Biology, 61(2), 791–812.

By: Y. Wang *, P. Zhang *, L. An *, G. Ma*, J. Kang*, F. Shi *, X. Wu *, J. Zhou * ...

Contributors: Y. Wang *, P. Zhang *, L. An *, G. Ma*, J. Kang*, F. Shi *, X. Wu *, J. Zhou * ...

author keywords: positron emission tomography (PET); sparse representation; mapping-based sparse representation; incremental refinement; standard-dose PET prediction; multimodal MR images
MeSH headings : Brain / diagnostic imaging; Brain Mapping / methods; Humans; Image Processing, Computer-Assisted / methods; Magnetic Resonance Imaging / methods; Multimodal Imaging / methods; Positron-Emission Tomography / methods; Radiation Dosage
topics (OpenAlex): Medical Imaging Techniques and Applications; Radiomics and Machine Learning in Medical Imaging; Advanced MRI Techniques and Applications
TL;DR: Inspired by the success of patch-based sparse representation (SR) in super-resolution image reconstruction, a mapping-based SR (m-SR) framework is proposed for standard-dose PET image prediction and can outperform benchmark methods in both qualitative and quantitative measures. (via Semantic Scholar)
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Sources: Web Of Science, Crossref, ORCID, NC State University Libraries
Added: August 6, 2018

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