AIMC Topic: Positron-Emission Tomography
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Automatic Inter-Frame Patient Motion Correction for Dynamic Cardiac PET Using Deep Learning.
IEEE transactions on medical imaging
Nov 30, 2021
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)
Nov 16, 2021
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
Nov 7, 2021
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
Oct 20, 2021
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
Oct 4, 2021
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
Oct 2, 2021
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 ...
Deep learning based synthetic-CT generation in radiotherapy and PET: A review.
Medical physics
Sep 15, 2021
Recently,deep learning (DL)-based methods for the generation of synthetic computed tomography (sCT) have received significant research attention as an alternative to classical ones. We present here a systematic review of these methods by grouping the...
An [18F]FDG-PET/CT deep learning method for fully automated detection of pathological mediastinal lymph nodes in lung cancer patients.
European journal of nuclear medicine and molecular imaging
Sep 14, 2021
PURPOSE: The identification of pathological mediastinal lymph nodes is an important step in the staging of lung cancer, with the presence of metastases significantly affecting survival rates. Nodes are currently identified by a physician, but this pr...
MRI-guided attenuation correction in torso PET/MRI: Assessment of segmentation-, atlas-, and deep learning-based approaches in the presence of outliers.
Magnetic resonance in medicine
Sep 4, 2021
PURPOSE: We compare the performance of three commonly used MRI-guided attenuation correction approaches in torso PET/MRI, namely segmentation-, atlas-, and deep learning-based algorithms.
Deep learning-based image reconstruction for TOF PET with DIRECT data partitioning format.
Physics in medicine and biology
Aug 9, 2021
Conventional positron emission tomography (PET) image reconstruction is achieved by the statistical iterative method. Deep learning provides another opportunity for speeding up the image reconstruction process. However, conventional deep learning-bas...
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