AIMC Topic: Positron-Emission Tomography

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Residual Neural Networks for the Prediction of the Regularization Parameters in PET Reconstruction.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Positron Emission Tomography (PET) is a medical imaging modality relying on numerical methods that integrate the statistical properties of the measurements and prior assumptions about the images. In order to maximize the computed image quality, PET r...

PET Myocardial Flow Reserve Estimation from 4D-Coronary-CT using Deep Neural Network.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The myocardial flow reserve (MFR) index proves to be a highly effective means of assessing the severity of myocardial ischemic disease. An MFR value below two commonly indicates impaired coronary artery perfusion function. Nevertheless, the measureme...

AmyloidPETNet: Classification of Amyloid Positivity in Brain PET Imaging Using End-to-End Deep Learning.

Radiology
Background Visual assessment of amyloid PET scans relies on the availability of radiologist expertise, whereas quantification of amyloid burden typically involves MRI for processing and analysis, which can be computationally expensive. Purpose To dev...

In vivo neuropil density from anatomical MRI and machine learning.

Cerebral cortex (New York, N.Y. : 1991)
Brain energy budgets specify metabolic costs emerging from underlying mechanisms of cellular and synaptic activities. While current bottom-up energy budgets use prototypical values of cellular density and synaptic density, predicting metabolism from ...

[Changes in FDG-PET Images of Small Lung and Liver Masses Caused by the Deep Learning-based Time-of-flight Processing].

Nihon Hoshasen Gijutsu Gakkai zasshi
PURPOSE: The deep learning time-of-flight (DL-ToF) aims to replicate the ToF effects through post-processing, applying deep learning-based enhancement to PET images. This study evaluates the effectiveness of DL-ToF using a chest-abdomen phantom that ...

Unveiling New Strategies Facilitating the Implementation of Artificial Intelligence in Neuroimaging for the Early Detection of Alzheimer's Disease.

Journal of Alzheimer's disease : JAD
Alzheimer's disease (AD) is a chronic neurodegenerative disorder with a global impact. The past few decades have witnessed significant strides in comprehending the underlying pathophysiological mechanisms and developing diagnostic methodologies for A...

MRI-based Deep Learning Assessment of Amyloid, Tau, and Neurodegeneration Biomarker Status across the Alzheimer Disease Spectrum.

Radiology
Background PET can be used for amyloid-tau-neurodegeneration (ATN) classification in Alzheimer disease, but incurs considerable cost and exposure to ionizing radiation. MRI currently has limited use in characterizing ATN status. Deep learning techniq...

[To promote the clinical application of PET/MRI in oncology].

Zhonghua yi xue za zhi
PET/MRI integrates anatomical, functional and metabolic information, and is increasingly used in the field of clinical oncology, including early diagnosis of disease, local staging, detection of systemic metastases, evaluation of treatment efficacy a...

Fast and low-dose medical imaging generation empowered by hybrid deep-learning and iterative reconstruction.

Cell reports. Medicine
Fast and low-dose reconstructions of medical images are highly desired in clinical routines. We propose a hybrid deep-learning and iterative reconstruction (hybrid DL-IR) framework and apply it for fast magnetic resonance imaging (MRI), fast positron...