AIMC Topic: Positron-Emission Tomography

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Augmented deep learning model for improved quantitative accuracy of MR-based PET attenuation correction in PSMA PET-MRI prostate imaging.

European journal of nuclear medicine and molecular imaging
PURPOSE: Estimation of accurate attenuation maps for whole-body positron emission tomography (PET) imaging in simultaneous PET-MRI systems is a challenging problem as it affects the quantitative nature of the modality. In this study, we aimed to impr...

AI approach of cycle-consistent generative adversarial networks to synthesize PET images to train computer-aided diagnosis algorithm for dementia.

Annals of nuclear medicine
OBJECTIVE: An artificial intelligence (AI)-based algorithm typically requires a considerable amount of training data; however, few training images are available for dementia with Lewy bodies and frontotemporal lobar degeneration. Therefore, this stud...

Multi-Modality Medical Image Fusion Using Convolutional Neural Network and Contrast Pyramid.

Sensors (Basel, Switzerland)
Medical image fusion techniques can fuse medical images from different morphologies to make the medical diagnosis more reliable and accurate, which play an increasingly important role in many clinical applications. To obtain a fused image with high v...

Predicting sporadic Alzheimer's disease progression via inherited Alzheimer's disease-informed machine-learning.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: Developing cross-validated multi-biomarker models for the prediction of the rate of cognitive decline in Alzheimer's disease (AD) is a critical yet unmet clinical challenge.

PET image super-resolution using generative adversarial networks.

Neural networks : the official journal of the International Neural Network Society
The intrinsically low spatial resolution of positron emission tomography (PET) leads to image quality degradation and inaccurate image-based quantitation. Recently developed supervised super-resolution (SR) approaches are of great relevance to PET bu...

Research of Multimodal Medical Image Fusion Based on Parameter-Adaptive Pulse-Coupled Neural Network and Convolutional Sparse Representation.

Computational and mathematical methods in medicine
Visual effects of medical image have a great impact on clinical assistant diagnosis. At present, medical image fusion has become a powerful means of clinical application. The traditional medical image fusion methods have the problem of poor fusion re...

Machine learning derived input-function in a dynamic F-FDG PET study of mice.

Biomedical physics & engineering express
Tracer kinetic modelling, based on dynamic F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is used to quantify glucose metabolism in humans and animals. Knowledge of the arterial input-function (AIF) is required for such measurements. O...

The clinical feasibility of deep learning-based classification of amyloid PET images in visually equivocal cases.

European journal of nuclear medicine and molecular imaging
PURPOSE: Although most deep learning (DL) studies have reported excellent classification accuracy, these studies usually target typical Alzheimer's disease (AD) and normal cognition (NC) for which conventional visual assessment performs well. A clini...