Deep‐TOF‐PET: Deep learning‐guided generation of time‐of‐flight from non‐TOF brain PET images in the image and projection domains
Sign Up to like & getrecommendations! Published in 2022 at "Human Brain Mapping"
DOI: 10.1002/hbm.26068
Abstract: We aim to synthesize brain time‐of‐flight (TOF) PET images/sinograms from their corresponding non‐TOF information in the image space (IS) and sinogram space (SS) to increase the signal‐to‐noise ratio (SNR) and contrast of abnormalities, and decrease… read more here.
Keywords: tof; non tof; brain; tof pet ... See more keywords
Virtual high-count PET image generation using a deep-learning method.
Sign Up to like & getrecommendations! Published in 2022 at "Medical physics"
DOI: 10.1002/mp.15867
Abstract: PURPOSE Recently, deep learning-based methods have been established to denoise the low-count PET images and predict their standard-count image counterparts, which could achieve reduction of injected dosage and scan time, and improve image quality for… read more here.
Keywords: standard count; count; count pet; image ... See more keywords
Whole-body tumor segmentation from PET/CT images using a two-stage cascaded neural network with camouflaged object detection mechanisms.
Sign Up to like & getrecommendations! Published in 2023 at "Medical physics"
DOI: 10.1002/mp.16438
Abstract: BACKGROUND Whole-body Metabolic Tumor Volume (MTVwb) is an independent prognostic factor for overall survival in lung cancer patients. Automatic segmentation methods have been proposed for MTV calculation. Nevertheless, most of existing methods for patients with… read more here.
Keywords: whole body; code net; body; segmentation ... See more keywords
Histologic subtype classification of non-small cell lung cancer using PET/CT images
Sign Up to like & getrecommendations! Published in 2020 at "European Journal of Nuclear Medicine and Molecular Imaging"
DOI: 10.1007/s00259-020-04771-5
Abstract: Purposes To evaluate the capability of PET/CT images for differentiating the histologic subtypes of non-small cell lung cancer (NSCLC) and to identify the optimal model from radiomics-based machine learning/deep learning algorithms. Methods In this study,… read more here.
Keywords: machine; feature selection; pet images; accuracy ... See more keywords
Direct attenuation correction of brain PET images using only emission data via a deep convolutional encoder-decoder (Deep-DAC)
Sign Up to like & getrecommendations! Published in 2019 at "European Radiology"
DOI: 10.1007/s00330-019-06229-1
Abstract: To obtain attenuation-corrected PET images directly from non-attenuation-corrected images using a convolutional encoder-decoder network. Brain PET images from 129 patients were evaluated. The network was designed to map non-attenuation-corrected (NAC) images to pixel-wise continuously valued… read more here.
Keywords: pet; pet images; encoder decoder; brain pet ... See more keywords
Dual-modality multi-atlas segmentation of torso organs from [18F]FDG-PET/CT images
Sign Up to like & getrecommendations! Published in 2018 at "International Journal of Computer Assisted Radiology and Surgery"
DOI: 10.1007/s11548-018-1879-3
Abstract: AbstractPurposeAutomated segmentation of torso organs from positron emission tomography/computed tomography (PET/CT) images is a prerequisite step for nuclear medicine image analysis. However, accurate organ segmentation from clinical PET/CT is challenging due to the poor soft… read more here.
Keywords: image; pet; pet images; segmentation torso ... See more keywords
Leveraging RSF and PET images for prognosis of multiple myeloma at diagnosis
Sign Up to like & getrecommendations! Published in 2019 at "International Journal of Computer Assisted Radiology and Surgery"
DOI: 10.1007/s11548-019-02015-y
Abstract: Purpose Multiple myeloma (MM) is a bone marrow cancer that accounts for 10% of all hematological malignancies. It has been reported that FDG PET imaging provides prognostic information for both baseline and therapeutic follow-up of… read more here.
Keywords: pet images; selection; multiple myeloma; rsf pet ... See more keywords
Regional 18F-fluoromisonidazole PET images generated from multiple advanced MR images using neural networks in glioblastoma
Sign Up to like & getrecommendations! Published in 2022 at "Medicine"
DOI: 10.1097/md.0000000000029572
Abstract: Generated 18F-fluoromisonidazole (18F-FMISO) positron emission tomography (PET) images for glioblastoma are highly sought after because 18F-FMISO can be radioactive, and the imaging procedure is not easy. This study aimed to explore the feasibility of using… read more here.
Keywords: 18f fmiso; regional 18f; advanced images; fmiso images ... See more keywords
Comparison of 18F-FDOPA and 18F-MFBG PET/CT Images of Metastatic Pheochromocytoma.
Sign Up to like & getrecommendations! Published in 2023 at "Clinical nuclear medicine"
DOI: 10.1097/rlu.0000000000004664
Abstract: ABSTRACT A 30-year-old man with pheochromocytoma was hospitalized for hemoptysis without inducement. CT revealed a mass in the left lung, and biopsy pathology under the bronchoscope suggested that it was a pheochromocytoma metastasis. To further… read more here.
Keywords: 18f mfbg; fdopa 18f; 18f fdopa; mfbg pet ... See more keywords
Learning to Denoise Gated Cardiac PET Images Using Convolutional Neural Networks
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Access"
DOI: 10.1109/access.2021.3122194
Abstract: Noise and motion artifacts in Positron emission tomography (PET) scans can interfere in diagnosis and result in inaccurate interpretations. PET gating techniques effectively reduce motion blurring, but at the cost of increasing noise, as only… read more here.
Keywords: gated pet; pet; pet images; neural networks ... See more keywords
A Novel Lung Nodule Accurate Segmentation of PET-CT Images Based on Convolutional Neural Network and Graph Model
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Access"
DOI: 10.1109/access.2023.3262729
Abstract: Positron Emission Tomography and Computed Tomography(PET/CT) imaging could obtain functional metabolic feature information and anatomical localization information of the patient body. However, tumor segmentation in PET/CT images is significantly challenging for fusing of dual-modality characteristic… read more here.
Keywords: information; pet; segmentation pet; segmentation ... See more keywords