AIMC Topic: Positron-Emission Tomography
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The Use of Random Forests to Identify Brain Regions on Amyloid and FDG PET Associated With MoCA Score.
Clinical nuclear medicine
Jun 1, 2020
PURPOSE: The aim of this study was to evaluate random forests (RFs) to identify ROIs on F-florbetapir and F-FDG PET associated with Montreal Cognitive Assessment (MoCA) score.
[Generation of the Pseudo CT Image Based on the Deep Learning Technique Aimed for the Attenuation Correction of the PET Image].
Nihon Hoshasen Gijutsu Gakkai zasshi
Jan 1, 2020
Computed tomography (CT) is used for the attenuation correction (AC) of [F-18] fluoro-deoxy-glucose positron emission tomography (PET) image. However, acquisition of a CT image for this purpose requires increasing the radiation dose of the patient. T...
Radiomics and artificial Intelligence for PET imaging analysis.
Nuclear medicine review. Central & Eastern Europe
Jan 1, 2020
In recent years, processing of the imaging signal derived from CT, MR or positron emission has proven to be able to predict outcome parameters in cancer patients. The processing techniques of the signal constitute the discipline of radiomics. The qua...
Diagnostic Performance of the Support Vector Machine Model for Breast Cancer on Ring-Shaped Dedicated Breast Positron Emission Tomography Images.
Journal of computer assisted tomography
Jan 1, 2020
OBJECTIVE: The aim of this study was to evaluate the diagnostic ability of support vector machine (SVM) for early breast cancer (BC) using dedicated breast positron emission tomography (dbPET).
Predicting Amyloid-β Levels in Amnestic Mild Cognitive Impairment Using Machine Learning Techniques.
Journal of Alzheimer's disease : JAD
Jan 1, 2020
BACKGROUND: Amyloid-β positivity (Aβ+) based on PET imaging is part of the enrollment criteria for many of the clinical trials of Alzheimer's disease (AD), particularly in trials for amyloid-targeted therapy. Predicting Aβ positivity prior to PET ima...
The Use of Random Forests to Classify Amyloid Brain PET.
Clinical nuclear medicine
Oct 1, 2019
PURPOSE: To evaluate random forests (RFs) as a supervised machine learning algorithm to classify amyloid brain PET as positive or negative for amyloid deposition and identify key regions of interest for stratification.
Full-Dose PET Image Estimation from Low-Dose PET Image Using Deep Learning: a Pilot Study.
Journal of digital imaging
Oct 1, 2019
Positron emission tomography (PET) imaging is an effective tool used in determining disease stage and lesion malignancy; however, radiation exposure to patients and technicians during PET scans continues to draw concern. One way to minimize radiation...
The Role of Generative Adversarial Networks in Radiation Reduction and Artifact Correction in Medical Imaging.
Journal of the American College of Radiology : JACR
Sep 1, 2019
Adversarial networks were developed to complete powerful image-processing tasks on the basis of example images provided to train the networks. These networks are relatively new in the field of deep learning and have proved to have unique strengths th...
Radiomics: Data Are Also Images.
Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Sep 1, 2019
The aim of this review is to provide readers with an update on the state of the art, pitfalls, solutions for those pitfalls, future perspectives, and challenges in the quickly evolving field of radiomics in nuclear medicine imaging and associated onc...
A 3D Deep Residual Convolutional Neural Network for Differential Diagnosis of Parkinsonian Syndromes on F-FDG PET Images.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2019
Idiopathic Parkinsons disease and atypical parkinsonian syndromes have similar symptoms at early disease stages, which makes the early differential diagnosis difficult. Positron emission tomography with F-FDG shows the ability to assess early neurona...
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