AIMC Topic: Positron-Emission Tomography
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A personalized deep learning denoising strategy for low-count PET images.
Physics in medicine and biology
Jul 13, 2022
. Deep learning denoising networks are typically trained with images that are representative of the testing data. Due to the large variability of the noise levels in positron emission tomography (PET) images, it is challenging to develop a proper tra...
Comparison of the performances of machine learning and deep learning in improving the quality of low dose lung cancer PET images.
Japanese journal of radiology
Jul 9, 2022
PURPOSE: To compare the performances of machine learning (ML) and deep learning (DL) in improving the quality of low dose (LD) lung cancer PET images and the minimum counts required.
Improving Breast Tumor Segmentation in PET via Attentive Transformation Based Normalization.
IEEE journal of biomedical and health informatics
Jul 1, 2022
Positron Emission Tomography (PET) has become a preferred imaging modality for cancer diagnosis, radiotherapy planning, and treatment responses monitoring. Accurate and automatic tumor segmentation is the fundamental requirement for these clinical ap...
Deep learning based low-activity PET reconstruction of [C]PiB and [F]FE-PE2I in neurodegenerative disorders.
NeuroImage
Jun 24, 2022
PURPOSE: Positron Emission Tomography (PET) can support a diagnosis of neurodegenerative disorder by identifying disease-specific pathologies. Our aim was to investigate the feasibility of using activity reduction in clinical [F]FE-PE2I and [C]PiB PE...
Eliminating CT radiation for clinical PET examination using deep learning.
European journal of radiology
Jun 23, 2022
Clinical PET/CT examinations rely on CT modality for anatomical localization and attenuation correction of the PET data. However, the use of CT significantly increases the risk of ionizing radiation exposure for patients. We propose a deep learning f...
A neural network-based algorithm for simultaneous event positioning and timestamping in monolithic scintillators.
Physics in medicine and biology
Jun 21, 2022
. Monolithic scintillator crystals coupled to silicon photomultiplier (SiPM) arrays are promising detectors for PET applications, offering spatial resolution around 1 mm and depth-of-interaction information. However, their timing resolution has alway...
Simulation study on 3D convolutional neural networks for time-of-flight prediction in monolithic PET detectors using digitized waveforms.
Physics in medicine and biology
Jun 15, 2022
We investigate the use of 3D convolutional neural networks for gamma arrival time estimation in monolithic scintillation detectors.The required data is obtained by Monte Carlo simulation in GATE v8.2, based on a 50 × 50 × 16 mmmonolithic LYSO crystal...
Deep-learning-based methods of attenuation correction for SPECT and PET.
Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
Jun 9, 2022
Attenuation correction (AC) is essential for quantitative analysis and clinical diagnosis of single-photon emission computed tomography (SPECT) and positron emission tomography (PET). In clinical practice, computed tomography (CT) is utilized to gene...
Deep learning based time-to-event analysis with PET, CT and joint PET/CT for head and neck cancer prognosis.
Computer methods and programs in biomedicine
Jun 9, 2022
OBJECTIVES: Recent studies have shown that deep learning based on pre-treatment positron emission tomography (PET) or computed tomography (CT) is promising for distant metastasis (DM) and overall survival (OS) prognosis in head and neck cancer (HNC)....
Use of deep learning-based radiomics to differentiate Parkinson's disease patients from normal controls: a study based on [F]FDG PET imaging.
European radiology
Jun 8, 2022
OBJECTIVES: We proposed a novel deep learning-based radiomics (DLR) model to diagnose Parkinson's disease (PD) based on [F]fluorodeoxyglucose (FDG) PET images.
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