AIMC Topic: Positron-Emission Tomography

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A cross-scanner and cross-tracer deep learning method for the recovery of standard-dose imaging quality from low-dose PET.

European journal of nuclear medicine and molecular imaging
PURPOSE: A critical bottleneck for the credibility of artificial intelligence (AI) is replicating the results in the diversity of clinical practice. We aimed to develop an AI that can be independently applied to recover high-quality imaging from low-...

Subtyping of mild cognitive impairment using a deep learning model based on brain atrophy patterns.

Cell reports. Medicine
Trajectories of cognitive decline vary considerably among individuals with mild cognitive impairment (MCI). To address this heterogeneity, subtyping approaches have been developed, with the objective of identifying more homogeneous subgroups. To date...

Comparison of deep learning-based emission-only attenuation correction methods for positron emission tomography.

European journal of nuclear medicine and molecular imaging
PURPOSE: This study aims to compare two approaches using only emission PET data and a convolution neural network (CNN) to correct the attenuation (μ) of the annihilation photons in PET.

Automatic Inter-Frame Patient Motion Correction for Dynamic Cardiac PET Using Deep Learning.

IEEE transactions on medical imaging
Patient motion during dynamic PET imaging can induce errors in myocardial blood flow (MBF) estimation. Motion correction for dynamic cardiac PET is challenging because the rapid tracer kinetics of 82Rb leads to substantial tracer distribution change ...

Deep learning-based automatic delineation of anal cancer gross tumour volume: a multimodality comparison of CT, PET and MRI.

Acta oncologica (Stockholm, Sweden)
BACKGROUND: Accurate target volume delineation is a prerequisite for high-precision radiotherapy. However, manual delineation is resource-demanding and prone to interobserver variation. An automatic delineation approach could potentially save time an...

Leveraging deep neural networks to improve numerical and perceptual image quality in low-dose preclinical PET imaging.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
The amount of radiotracer injected into laboratory animals is still the most daunting challenge facing translational PET studies. Since low-dose imaging is characterized by a higher level of noise, the quality of the reconstructed images leaves much ...

Performance evaluation in [18F]Florbetaben brain PET images classification using 3D Convolutional Neural Network.

PloS one
High accuracy has been reported in deep learning classification for amyloid brain scans, an important factor in Alzheimer's disease diagnosis. However, the possibility of overfitting should be considered, as this model is fitted with sample data. The...

Feasibility evaluation of PET scan-time reduction for diagnosing amyloid-β levels in Alzheimer's disease patients using a deep-learning-based denoising algorithm.

Computers in biology and medicine
PURPOSE: To shorten positron emission tomography (PET) scanning time in diagnosing amyloid-β levels thus increasing the workflow in centers involving Alzheimer's Disease (AD) patients.

Prediction of post-stroke cognitive impairment using brain FDG PET: deep learning-based approach.

European journal of nuclear medicine and molecular imaging
PURPOSE: Post-stroke cognitive impairment can affect up to one third of stroke survivors. Since cognitive function greatly contributes to patients' quality of life, an objective quantitative biomarker for early prediction of dementia after stroke is ...