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Positron-Emission Tomography

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A Robust Deep Model for Improved Classification of AD/MCI Patients.

IEEE journal of biomedical and health informatics
Accurate classification of Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI), plays a critical role in possibly preventing progression of memory impairment and improving quality of life for AD patients. Among many rese...

Manifold regularized multitask feature learning for multimodality disease classification.

Human brain mapping
Multimodality based methods have shown great advantages in classification of Alzheimer's disease (AD) and its prodromal stage, that is, mild cognitive impairment (MCI). Recently, multitask feature selection methods are typically used for joint select...

Evaluating prostate cancer diagnostic methods: The role and relevance of digital rectal examination in modern era.

Investigative and clinical urology
This review examines diagnostic methods for prostate cancer, focusing on the role of digital rectal examination (DRE) alongside modern advancements like prostate-specific antigen (PSA) testing, Prostate Health Index (PHI), magnetic resonance imaging ...

Machine learning prediction of tau-PET in Alzheimer's disease using plasma, MRI, and clinical data.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: Tau positron emission tomography (PET) is a reliable neuroimaging technique for assessing regional load of tau pathology in the brain, but its routine clinical use is limited by cost and accessibility barriers.

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 ...