International journal of radiation oncology, biology, physics
Aug 4, 2022
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.
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...
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-...
European journal of nuclear medicine and molecular imaging
Jul 19, 2022
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...
. 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...
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.
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...
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...
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...
. 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...
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