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Positron-Emission Tomography

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Application of Deep Learning to Predict Standardized Uptake Value Ratio and Amyloid Status on F-Florbetapir PET Using ADNI Data.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Cortical amyloid quantification on PET by using the standardized uptake value ratio is valuable for research studies and clinical trials in Alzheimer disease. However, it is resource intensive, requiring co-registered MR imagi...

Deep learning-guided joint attenuation and scatter correction in multitracer neuroimaging studies.

Human brain mapping
PET attenuation correction (AC) on systems lacking CT/transmission scanning, such as dedicated brain PET scanners and hybrid PET/MRI, is challenging. Direct AC in image-space, wherein PET images corrected for attenuation and scatter are synthesized f...

Deep learning-guided estimation of attenuation correction factors from time-of-flight PET emission data.

Medical image analysis
PURPOSE: Attenuation correction (AC) is essential for quantitative PET imaging. In the absence of concurrent CT scanning, for instance on hybrid PET/MRI systems or dedicated brain PET scanners, an accurate approach for synthetic CT generation is high...

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...