AIMC Topic: Positron-Emission Tomography
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Deep learning detection of prostate cancer recurrence with F-FACBC (fluciclovine, Axumin®) positron emission tomography.
European journal of nuclear medicine and molecular imaging
Jun 17, 2020
PURPOSE: To evaluate the performance of deep learning (DL) classifiers in discriminating normal and abnormal F-FACBC (fluciclovine, Axumin®) PET scans based on the presence of tumor recurrence and/or metastases in patients with prostate cancer (PC) a...
Generalization of deep learning models for ultra-low-count amyloid PET/MRI using transfer learning.
European journal of nuclear medicine and molecular imaging
Jun 13, 2020
PURPOSE: We aimed to evaluate the performance of deep learning-based generalization of ultra-low-count amyloid PET/MRI enhancement when applied to studies acquired with different scanning hardware and protocols.
Deep-Learning F-FDG Uptake Classification Enables Total Metabolic Tumor Volume Estimation in Diffuse Large B-Cell Lymphoma.
Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Jun 12, 2020
Total metabolic tumor volume (TMTV), calculated from F-FDG PET/CT baseline studies, is a prognostic factor in diffuse large B-cell lymphoma (DLBCL) whose measurement requires the segmentation of all malignant foci throughout the body. No consensus cu...
Application of Deep Learning to Predict Standardized Uptake Value Ratio and Amyloid Status on F-Florbetapir PET Using ADNI Data.
AJNR. American journal of neuroradiology
Jun 4, 2020
BACKGROUND AND PURPOSE: Cortical amyloid quantification on PET by using the standardized uptake value ratio is valuable for research studies and clinical trials in Alzheimer disease. However, it is resource intensive, requiring co-registered MR imagi...
Deep learning-guided joint attenuation and scatter correction in multitracer neuroimaging studies.
Human brain mapping
May 21, 2020
PET attenuation correction (AC) on systems lacking CT/transmission scanning, such as dedicated brain PET scanners and hybrid PET/MRI, is challenging. Direct AC in image-space, wherein PET images corrected for attenuation and scatter are synthesized f...
Deep learning-guided estimation of attenuation correction factors from time-of-flight PET emission data.
Medical image analysis
May 19, 2020
PURPOSE: Attenuation correction (AC) is essential for quantitative PET imaging. In the absence of concurrent CT scanning, for instance on hybrid PET/MRI systems or dedicated brain PET scanners, an accurate approach for synthetic CT generation is high...
Augmented deep learning model for improved quantitative accuracy of MR-based PET attenuation correction in PSMA PET-MRI prostate imaging.
European journal of nuclear medicine and molecular imaging
May 11, 2020
PURPOSE: Estimation of accurate attenuation maps for whole-body positron emission tomography (PET) imaging in simultaneous PET-MRI systems is a challenging problem as it affects the quantitative nature of the modality. In this study, we aimed to impr...
Applications of radiomics and machine learning for radiotherapy of malignant brain tumors.
Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]
May 11, 2020
BACKGROUND: Magnetic resonance imaging (MRI) and amino acid positron-emission tomography (PET) of the brain contain a vast amount of structural and functional information that can be analyzed by machine learning algorithms and radiomics for the use o...
Radiotherapy Planning, Computer-Assisted
DNA Modification Methylases
DNA Repair Enzymes
Tumor Suppressor Proteins
Neuroimaging
Radiosurgery
Promoter Regions, Genetic
Isocitrate Dehydrogenase
Neoplasm Grading
Neoplasm Recurrence, Local
Progression-Free Survival
Positron-Emission Tomography
Humans
Neoplasm Proteins
Sensitivity and Specificity
Glioblastoma
Imaging Genomics
Glioma
Brain Neoplasms
Radiation Oncology
Magnetic Resonance Imaging
Deep Learning
DNA Methylation
Image Processing, Computer-Assisted
Diagnosis, Differential
Computational Biology
AI approach of cycle-consistent generative adversarial networks to synthesize PET images to train computer-aided diagnosis algorithm for dementia.
Annals of nuclear medicine
Apr 20, 2020
OBJECTIVE: An artificial intelligence (AI)-based algorithm typically requires a considerable amount of training data; however, few training images are available for dementia with Lewy bodies and frontotemporal lobar degeneration. Therefore, this stud...
Multi-Modality Medical Image Fusion Using Convolutional Neural Network and Contrast Pyramid.
Sensors (Basel, Switzerland)
Apr 11, 2020
Medical image fusion techniques can fuse medical images from different morphologies to make the medical diagnosis more reliable and accurate, which play an increasingly important role in many clinical applications. To obtain a fused image with high v...
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