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Deep learning for tomographic image reconstruction
Deep-learning-based tomographic imaging is an important application of artificial
intelligence and a new frontier of machine learning. Deep learning has been widely used in …
intelligence and a new frontier of machine learning. Deep learning has been widely used in …
Introduction to radiomics
ME Mayerhoefer, A Materka, G Langs… - Journal of Nuclear …, 2020 - jnm.snmjournals.org
Radiomics is a rapidly evolving field of research concerned with the extraction of quantitative
metrics—the so-called radiomic features—within medical images. Radiomic features capture …
metrics—the so-called radiomic features—within medical images. Radiomic features capture …
FISTA-Net: Learning a fast iterative shrinkage thresholding network for inverse problems in imaging
Inverse problems are essential to imaging applications. In this letter, we propose a model-
based deep learning network, named FISTA-Net, by combining the merits of interpretability …
based deep learning network, named FISTA-Net, by combining the merits of interpretability …
Image reconstruction is a new frontier of machine learning
Over past several years, machine learning, or more generally artificial intelligence, has
generated overwhelming research interest and attracted unprecedented public attention. As …
generated overwhelming research interest and attracted unprecedented public attention. As …
Deep learning for PET image reconstruction
This article reviews the use of a subdiscipline of artificial intelligence (AI), deep learning, for
the reconstruction of images in positron emission tomography (PET). Deep learning can be …
the reconstruction of images in positron emission tomography (PET). Deep learning can be …
Image reconstruction: From sparsity to data-adaptive methods and machine learning
S Ravishankar, JC Ye, JA Fessler - Proceedings of the IEEE, 2019 - ieeexplore.ieee.org
The field of medical image reconstruction has seen roughly four types of methods. The first
type tended to be analytical methods, such as filtered backprojection (FBP) for X-ray …
type tended to be analytical methods, such as filtered backprojection (FBP) for X-ray …
PET image denoising using unsupervised deep learning
Purpose Image quality of positron emission tomography (PET) is limited by various physical
degradation factors. Our study aims to perform PET image denoising by utilizing prior …
degradation factors. Our study aims to perform PET image denoising by utilizing prior …
DeepPET: A deep encoder–decoder network for directly solving the PET image reconstruction inverse problem
I Häggström, CR Schmidtlein, G Campanella… - Medical image …, 2019 - Elsevier
The purpose of this research was to implement a deep learning network to overcome two of
the major bottlenecks in improved image reconstruction for clinical positron emission …
the major bottlenecks in improved image reconstruction for clinical positron emission …
PET image reconstruction using deep image prior
Recently, deep neural networks have been widely and successfully applied in computer
vision tasks and have attracted growing interest in medical imaging. One barrier for the …
vision tasks and have attracted growing interest in medical imaging. One barrier for the …
Supervised learning with cyclegan for low-dose FDG PET image denoising
PET imaging involves radiotracer injections, raising concerns about the risk of radiation
exposure. To minimize the potential risk, one way is to reduce the injected tracer. However …
exposure. To minimize the potential risk, one way is to reduce the injected tracer. However …