AIMC Topic: Positron-Emission Tomography

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Multicenter validation of [F]-FDG PET and support-vector machine discriminant analysis in automatically classifying patients with amyotrophic lateral sclerosis versus controls.

Amyotrophic lateral sclerosis & frontotemporal degeneration
OBJECTIVE: F-Fluorodeoxyglucose (F-FDG) positron emission tomography (PET) single-center studies using support vector machine (SVM) approach to differentiate amyotrophic lateral sclerosis (ALS) from controls have shown high overall accuracy on an ind...

Machine learning in the integration of simple variables for identifying patients with myocardial ischemia.

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
BACKGROUND: A significant number of variables are obtained when characterizing patients suspected with myocardial ischemia or at risk of MACE. Guidelines typically use a handful of them to support further workup or therapeutic decisions. However, it ...

Adaptive template generation for amyloid PET using a deep learning approach.

Human brain mapping
Accurate spatial normalization (SN) of amyloid positron emission tomography (PET) images for Alzheimer's disease assessment without coregistered anatomical magnetic resonance imaging (MRI) of the same individual is technically challenging. In this st...

Identification of Alzheimer's disease and mild cognitive impairment using multimodal sparse hierarchical extreme learning machine.

Human brain mapping
Different modalities such as structural MRI, FDG-PET, and CSF have complementary information, which is likely to be very useful for diagnosis of AD and MCI. Therefore, it is possible to develop a more effective and accurate AD/MCI automatic diagnosis...

Enhancing the Image Quality via Transferred Deep Residual Learning of Coarse PET Sinograms.

IEEE transactions on medical imaging
Increasing the image quality of positron emission tomography (PET) is an essential topic in the PET community. For instance, thin-pixelated crystals have been used to provide high spatial resolution images but at the cost of sensitivity and manufactu...

Multiscale deep neural network based analysis of FDG-PET images for the early diagnosis of Alzheimer's disease.

Medical image analysis
Alzheimer's disease (AD) is one of the most common neurodegenerative diseases with a commonly seen prodromal mild cognitive impairment (MCI) phase where memory loss is the main complaint progressively worsening with behavior issues and poor self-care...

Using convolutional neural networks to estimate time-of-flight from PET detector waveforms.

Physics in medicine and biology
Although there have been impressive strides in detector development for time-of-flight positron emission tomography, most detectors still make use of simple signal processing methods to extract the time-of-flight information from the detector signals...

Use of multimodality imaging and artificial intelligence for diagnosis and prognosis of early stages of Alzheimer's disease.

Translational research : the journal of laboratory and clinical medicine
Alzheimer's disease (AD) is a major neurodegenerative disease and the most common cause of dementia. Currently, no treatment exists to slow down or stop the progression of AD. There is converging belief that disease-modifying treatments should focus ...

Interpretation criteria for FDG PET/CT in multiple myeloma (IMPeTUs): final results. IMPeTUs (Italian myeloma criteria for PET USe).

European journal of nuclear medicine and molecular imaging
UNLABELLED: ᅟ: FDG PET/CT (F-fluoro-deoxy-glucose positron emission tomography/computed tomography) is a useful tool to image multiple myeloma (MM). However, simple and reproducible reporting criteria are still lacking and there is the need for harmo...

Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: A systematic review.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The high incidence of breast cancer in women has increased significantly in the recent years. Physician experience of diagnosing and detecting breast cancer can be assisted by using some computerized features extraction and ...