A review of partial volume correction techniques for emission tomography and their applications in neurology, cardiology and oncology
K Erlandsson, I Buvat, PH Pretorius… - Physics in Medicine …, 2012 - iopscience.iop.org
Accurate quantification in PET and SPECT requires correction for a number of physical
factors, such as photon attenuation, Compton scattering and random coincidences (in PET) …
factors, such as photon attenuation, Compton scattering and random coincidences (in PET) …
Deep learning-based image reconstruction and post-processing methods in positron emission tomography for low-dose imaging and resolution enhancement
Image processing plays a crucial role in maximising diagnostic quality of positron emission
tomography (PET) images. Recently, deep learning methods developed across many fields …
tomography (PET) images. Recently, deep learning methods developed across many fields …
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 …
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 …
Iterative PET image reconstruction using convolutional neural network representation
PET image reconstruction is challenging due to the ill-poseness of the inverse problem and
limited number of detected photons. Recently, the deep neural networks have been widely …
limited number of detected photons. Recently, the deep neural networks have been widely …
PET image reconstruction using kernel method
Image reconstruction from low-count positron emission tomography (PET) projection data is
challenging because the inverse problem is ill-posed. Prior information can be used to …
challenging because the inverse problem is ill-posed. Prior information can be used to …
Model-based deep learning PET image reconstruction using forward–backward splitting expectation–maximization
A Mehranian, AJ Reader - IEEE transactions on radiation and …, 2020 - ieeexplore.ieee.org
We propose a forward-backward splitting algorithm to integrate deep learning into maximum-
a-posteriori (MAP) positron emission tomography (PET) image reconstruction. The MAP …
a-posteriori (MAP) positron emission tomography (PET) image reconstruction. The MAP …
Joint reconstruction of PET-MRI by exploiting structural similarity
Recent advances in technology have enabled the combination of positron emission
tomography (PET) with magnetic resonance imaging (MRI). These PET-MRI scanners …
tomography (PET) with magnetic resonance imaging (MRI). These PET-MRI scanners …
Joint MR-PET reconstruction using a multi-channel image regularizer
While current state of the art MR-PET scanners enable simultaneous MR and PET
measurements, the acquired data sets are still usually reconstructed separately. We propose …
measurements, the acquired data sets are still usually reconstructed separately. We propose …
Multicontrast MRI reconstruction with structure-guided total variation
MJ Ehrhardt, MM Betcke - SIAM Journal on Imaging Sciences, 2016 - SIAM
Magnetic resonance imaging (MRI) is a versatile imaging technique that allows different
contrasts depending on the acquisition parameters. Many clinical imaging studies acquire …
contrasts depending on the acquisition parameters. Many clinical imaging studies acquire …