AIMC Topic: Positron-Emission Tomography

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Joint correction of attenuation and scatter in image space using deep convolutional neural networks for dedicated brain F-FDG PET.

Physics in medicine and biology
Dedicated brain positron emission tomography (PET) devices can provide higher-resolution images with much lower doses compared to conventional whole-body PET systems, which is important to support PET neuroimaging and particularly useful for the diag...

Machine learning based hierarchical classification of frontotemporal dementia and Alzheimer's disease.

NeuroImage. Clinical
BACKGROUND: In a clinical setting, an individual subject classification model rather than a group analysis would be more informative. Specifically, the subtlety of cortical atrophy in some frontotemporal dementia (FTD) patients and overlapping patter...

DeepPET: A deep encoder-decoder network for directly solving the PET image reconstruction inverse problem.

Medical image analysis
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 tomography (PET). These are the lack of an automated means for the optimiza...

Exploit fully automatic low-level segmented PET data for training high-level deep learning algorithms for the corresponding CT data.

PloS one
We present an approach for fully automatic urinary bladder segmentation in CT images with artificial neural networks in this study. Automatic medical image analysis has become an invaluable tool in the different treatment stages of diseases. Especial...

Scaled Subprofile Modeling and Convolutional Neural Networks for the Identification of Parkinson's Disease in 3D Nuclear Imaging Data.

International journal of neural systems
Over the last years convolutional neural networks (CNNs) have shown remarkable results in different image classification tasks, including medical imaging. One area that has been less explored with CNNs is Positron Emission Tomography (PET). Fluorodeo...

MRI-based attenuation correction for brain PET/MRI based on anatomic signature and machine learning.

Physics in medicine and biology
Deriving accurate attenuation maps for PET/MRI remains a challenging problem because MRI voxel intensities are not related to properties of photon attenuation and bone/air interfaces have similarly low signal. This work presents a learning-based meth...

PET Image Reconstruction Using Deep Image Prior.

IEEE transactions on medical imaging
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 application of deep neural networks to medical imaging is the need for large...

Ultra-Low-Dose F-Florbetaben Amyloid PET Imaging Using Deep Learning with Multi-Contrast MRI Inputs.

Radiology
Purpose To reduce radiotracer requirements for amyloid PET/MRI without sacrificing diagnostic quality by using deep learning methods. Materials and Methods Forty data sets from 39 patients (mean age ± standard deviation [SD], 67 years ± 8), including...

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

IEEE transactions on medical imaging
Positron emission tomography (PET) has been substantially used recently. To minimize the potential health risk caused by the tracer radiation inherent to PET scans, it is of great interest to synthesize the high-quality PET image from the low-dose on...