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Positron-Emission Tomography

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Multimodal deep learning model on interim [F]FDG PET/CT for predicting primary treatment failure in diffuse large B-cell lymphoma.

European radiology
OBJECTIVES: The prediction of primary treatment failure (PTF) is necessary for patients with diffuse large B-cell lymphoma (DLBCL) since it serves as a prominent means for improving front-line outcomes. Using interim F-fluoro-2-deoxyglucose ([F]FDG) ...

Virtual high-count PET image generation using a deep learning method.

Medical physics
PURPOSE: Recently, deep learning-based methods have been established to denoise the low-count positron emission tomography (PET) images and predict their standard-count image counterparts, which could achieve reduction of injected dosage and scan tim...

Automated Lung Cancer Segmentation Using a PET and CT Dual-Modality Deep Learning Neural Network.

International journal of radiation oncology, biology, physics
PURPOSE: To develop an automated lung tumor segmentation method for radiation therapy planning based on deep learning and dual-modality positron emission tomography (PET) and computed tomography (CT) images.

Deep-learning prediction of amyloid deposition from early-phase amyloid positron emission tomography imaging.

Annals of nuclear medicine
OBJECTIVE: While the use of biomarkers for the detection of early and preclinical Alzheimer's Disease has become essential, the need to wait for over an hour after injection to obtain sufficient image quality can be challenging for patients with susp...

3D Segmentation Guided Style-Based Generative Adversarial Networks for PET Synthesis.

IEEE transactions on medical imaging
Potential radioactive hazards in full-dose positron emission tomography (PET) imaging remain a concern, whereas the quality of low-dose images is never desirable for clinical use. So it is of great interest to translate low-dose PET images into full-...

Quantitative evaluation of a deep learning-based framework to generate whole-body attenuation maps using LSO background radiation in long axial FOV PET scanners.

European journal of nuclear medicine and molecular imaging
PURPOSE: Attenuation correction is a critically important step in data correction in positron emission tomography (PET) image formation. The current standard method involves conversion of Hounsfield units from a computed tomography (CT) image to cons...

A personalized deep learning denoising strategy for low-count PET images.

Physics in medicine and biology
. 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
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
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
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...