AIMC Topic: Positron-Emission Tomography
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Utilizing deep learning techniques to improve image quality and noise reduction in preclinical low-dose PET images in the sinogram domain.
Medical physics
Nov 15, 2023
BACKGROUND: Low-dose positron emission tomography (LD-PET) imaging is commonly employed in preclinical research to minimize radiation exposure to animal subjects. However, LD-PET images often exhibit poor quality and high noise levels due to the low ...
A narrative review of radiomics and deep learning advances in neuroblastoma: updates and challenges.
Pediatric radiology
Nov 10, 2023
Neuroblastoma is an extremely heterogeneous tumor that commonly occurs in children. The diagnosis and treatment of this tumor pose considerable challenges due to its varied clinical presentations and intricate genetic aberrations. Presently, various ...
Applications of machine learning and deep learning in SPECT and PET imaging: General overview, challenges and future prospects.
Pharmacological research
Nov 7, 2023
The integration of positron emission tomography (PET) and single-photon emission computed tomography (SPECT) imaging techniques with machine learning (ML) algorithms, including deep learning (DL) models, is a promising approach. This integration enha...
Image Denoising of Low-Dose PET Mouse Scans with Deep Learning: Validation Study for Preclinical Imaging Applicability.
Molecular imaging and biology
Oct 24, 2023
PURPOSE: Positron emission tomography (PET) image quality can be improved by higher injected activity and/or longer acquisition time, but both may often not be practical in preclinical imaging. Common preclinical radioactive doses (10 MBq) have been ...
Deep learning techniques in PET/CT imaging: A comprehensive review from sinogram to image space.
Computer methods and programs in biomedicine
Oct 21, 2023
Positron emission tomography/computed tomography (PET/CT) is increasingly used in oncology, neurology, cardiology, and emerging medical fields. The success stems from the cohesive information that hybrid PET/CT imaging offers, surpassing the capabili...
Deep learning for diagnosis of head and neck cancers through radiographic data: a systematic review and meta-analysis.
Oral radiology
Oct 19, 2023
PURPOSE: This study aims to review deep learning applications for detecting head and neck cancer (HNC) using magnetic resonance imaging (MRI) and radiographic data.
Deep learning based diagnosis of Alzheimer's disease using FDG-PET images.
Neuroscience letters
Oct 17, 2023
PURPOSE: The aim of this study is to develop a deep neural network to diagnosis Alzheimer's disease and categorize the stages of the disease using FDG-PET scans. Fluorodeoxyglucose positron emission tomography (FDG-PET) is a highly effective diagnost...
Advancements in Positron Emission Tomography Detectors: From Silicon Photomultiplier Technology to Artificial Intelligence Applications.
PET clinics
Oct 4, 2023
This review article focuses on PET detector technology, which is the most crucial factor in determining PET image quality. The article highlights the desired properties of PET detectors, including high detection efficiency, spatial resolution, energy...
A Shortened Model for Logan Reference Plot Implemented via the Self-Supervised Neural Network for Parametric PET Imaging.
IEEE transactions on medical imaging
Oct 2, 2023
Dynamic PET imaging provides superior physiological information than conventional static PET imaging. However, the dynamic information is gained at the cost of a long scanning protocol; this limits the clinical application of dynamic PET imaging. We ...
Distinct subtypes of spatial brain metabolism patterns in Alzheimer's disease identified by deep learning-based FDG PET clusters.
European journal of nuclear medicine and molecular imaging
Sep 22, 2023
PURPOSE: Alzheimer's disease (AD) is a heterogeneous disease that presents a broad spectrum of clinicopathologic profiles. To date, objective subtyping of AD independent of disease progression using brain imaging has been required. Our study aimed to...
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