Works (3)
Updated: January 8th, 2024 11:25
2018 journal article
3D Auto-Context-Based Locality Adaptive Multi-Modality GANs for PET Synthesis
IEEE TRANSACTIONS ON MEDICAL IMAGING, 38(6), 1328–1339.
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
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)
open access via www.ncbi.nlm.nih.gov (repository)
UN Sustainable Development Goal Categories
3. Good Health and Well-being
(Web of Science)
Source: Web Of Science
Added: April 20, 2020
2018 article
Locality Adaptive Multi-modality GANs for High-Quality PET Image Synthesis
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2018, PT I, Vol. 11070, pp. 329–337.
MeSH headings : Algorithms; Electrons; Magnetic Resonance Imaging / methods; Multimodal Imaging / methods; Neural Networks, Computer; Positron-Emission Tomography / methods; Reproducibility of Results; Sensitivity and Specificity
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)
UN Sustainable Development Goal Categories
3. Good Health and Well-being
(Web of Science)
Source: Web Of Science
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.
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
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)
UN Sustainable Development Goal Categories
3. Good Health and Well-being
(Web of Science)
Sources: Web Of Science, Crossref
Added: August 6, 2018
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