AIMC Topic: Positron-Emission Tomography
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PET scatter estimation using deep learning U-Net architecture.
Physics in medicine and biology
Mar 10, 2023
Positron emission tomography (PET) image reconstruction needs to be corrected for scatter in order to produce quantitatively accurate images. Scatter correction is traditionally achieved by incorporating an estimated scatter sinogram into the forward...
Deep learning for improving PET/CT attenuation correction by elastic registration of anatomical data.
European journal of nuclear medicine and molecular imaging
Mar 8, 2023
BACKGROUND: For PET/CT, the CT transmission data are used to correct the PET emission data for attenuation. However, subject motion between the consecutive scans can cause problems for the PET reconstruction. A method to match the CT to the PET would...
Deep learning model integrating positron emission tomography and clinical data for prognosis prediction in non-small cell lung cancer patients.
BMC bioinformatics
Feb 6, 2023
BACKGROUND: Lung cancer is the leading cause of cancer-related deaths worldwide. The majority of lung cancers are non-small cell lung cancer (NSCLC), accounting for approximately 85% of all lung cancer types. The Cox proportional hazards model (CPH),...
Deep learning-based attenuation map generation with simultaneously reconstructed PET activity and attenuation and low-dose application.
Physics in medicine and biology
Jan 24, 2023
. In PET/CT imaging, CT is used for positron emission tomography (PET) attenuation correction (AC). CT artifacts or misalignment between PET and CT can cause AC artifacts and quantification errors in PET. Simultaneous reconstruction (MLAA) of PET act...
Machine learning-based approach reveals essential features for simplified TSPO PET quantification in ischemic stroke patients.
Zeitschrift fur medizinische Physik
Jan 20, 2023
INTRODUCTION: Neuroinflammation evaluation after acute ischemic stroke is a promising option for selecting an appropriate post-stroke treatment strategy. To assess neuroinflammation in vivo, translocator protein PET (TSPO PET) can be used. However, t...
Low-count whole-body PET/MRI restoration: an evaluation of dose reduction spectrum and five state-of-the-art artificial intelligence models.
European journal of nuclear medicine and molecular imaging
Jan 12, 2023
PURPOSE: To provide a holistic and complete comparison of the five most advanced AI models in the augmentation of low-dose F-FDG PET data over the entire dose reduction spectrum.
Technological advancements in cancer diagnostics: Improvements and limitations.
Cancer reports (Hoboken, N.J.)
Jan 6, 2023
BACKGROUND: Cancer is characterized by the rampant proliferation, growth, and infiltration of malignantly transformed cancer cells past their normal boundaries into adjacent tissues. It is the leading cause of death worldwide, responsible for approxi...
Resolution estimation in different monolithic PET detectors using neural networks.
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Jan 5, 2023
PURPOSE: We use neural networks to evaluate and compare the spatial resolution of two different simulated monolithic PET detector elements. The effects of mixing events with single photoeffect interactions and multiple Compton scatterings are also st...
Automatic lesion detection and segmentation in F-flutemetamol positron emission tomography images using deep learning.
Biomedical engineering online
Dec 20, 2022
BACKGROUND: Beta amyloid in the brain, which was originally confirmed by post-mortem examinations, can now be confirmed in living patients using amyloid positron emission tomography (PET) tracers, and the accuracy of diagnosis can be improved by beta...
Decentralized collaborative multi-institutional PET attenuation and scatter correction using federated deep learning.
European journal of nuclear medicine and molecular imaging
Dec 12, 2022
PURPOSE: Attenuation correction and scatter compensation (AC/SC) are two main steps toward quantitative PET imaging, which remain challenging in PET-only and PET/MRI systems. These can be effectively tackled via deep learning (DL) methods. However, t...
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