Nuclear Medicine

Predicting O-Water PET cerebral blood flow maps from multi-contrast MRI using a deep convolutional neural network with evaluation of training cohort bias.

To improve the quality of MRI-based cerebral blood flow (CBF) measurements, a deep convolutional neural network (dCNN) was trained to combine single- and multi-delay arterial spin labeling (ASL) and structural images to predict gold-standard O-water PET CBF images obtained on a simultaneous PET/MRI scanner. The dCNN was trained and tested on 64 scans in 16 healthy controls (HC) and 16 cerebrovascu...

De novo Gastrinoma: A Case Report.

Gastrinomas are neuroendocrine tumors characterized by gastrin overexpression - 80% are sporadic and 20% are associated with multiple endocrine neoplasia type 1. A 75-year-old male patient, surgically treated at the age of 50 years for gastrinoma, followed on an outpatient basis because of chronic non-bloody diarrhea, was admitted to our hospital because of abdominal pain, watery diarrhea, and non...

Independent brain F-FDG PET attenuation correction using a deep learning approach with Generative Adversarial Networks.

OBJECTIVE: Attenuation correction (AC) of positron emission tomography (PET) data poses a challenge when no transmission data or computed tomography (...

Attenuation correction using 3D deep convolutional neural network for brain 18F-FDG PET/MR: Comparison with Atlas, ZTE and CT based attenuation correction.

One of the main technical challenges of PET/MRI is to achieve an accurate PET attenuation correction (AC) estimation. In current systems, AC is accomp...

Effect of storage on the quality of processed palm oil collected from local milling points within Ile-Ife, Osun State, Nigeria.

The influence of storage practices on physicochemical and microbial changes in crude palm oil (CPO) from milling points in Ile-Ife, Nigeria were inves...

Intelligent Imaging: Radiomics and Artificial Neural Networks in Heart Failure.

BACKGROUND: Our previous work with iodine meta-iodobenzylguanidine (I-mIBG) radionuclide imaging among patients with cardiomyopathy reported limitatio...

Image reconstruction for positron emission tomography based on patch-based regularization and dictionary learning.

PURPOSE: Positron emission tomography (PET) is an important tool for nuclear medical imaging. It has been widely used in clinical diagnosis, scientifi...

Three-dimensional convolutional neural networks for simultaneous dual-tracer PET imaging.

Dual-tracer positron emission tomography (PET) is a promising technique to measure the distribution of two tracers in the body by a single scan, which...

Machine learning for radiomics-based multimodality and multiparametric modeling.

Due to the recent developments of both hardware and software technologies, multimodality medical imaging techniques have been increasingly applied in ...

Quantifying brain metabolism from FDG-PET images into a probability of Alzheimer's dementia score.

F-fluorodeoxyglucose positron emission tomography (FDG-PET) enables in-vivo capture of the topographic metabolism patterns in the brain. These images...

Automatic classification of dopamine transporter SPECT: deep convolutional neural networks can be trained to be robust with respect to variable image characteristics.

PURPOSE: This study investigated the potential of deep convolutional neural networks (CNN) for automatic classification of FP-CIT SPECT in multi-site ...

PET image denoising using unsupervised deep learning.

PURPOSE: Image quality of positron emission tomography (PET) is limited by various physical degradation factors. Our study aims to perform PET image d...

Predicting PET-derived demyelination from multimodal MRI using sketcher-refiner adversarial training for multiple sclerosis.

Multiple sclerosis (MS) is the most common demyelinating disease. In MS, demyelination occurs in the white matter of the brain and in the spinal cord....

An investigation of quantitative accuracy for deep learning based denoising in oncological PET.

Reducing radiation dose is important for PET imaging. However, reducing injection doses causes increased image noise and low signal-to-noise ratio (SN...

mDixon-Based Synthetic CT Generation for PET Attenuation Correction on Abdomen and Pelvis Jointly Using Transfer Fuzzy Clustering and Active Learning-Based Classification.

We propose a new method for generating synthetic CT images from modified Dixon (mDixon) MR data. The synthetic CT is used for attenuation correction (...

Fully automated analysis for bone scintigraphy with artificial neural network: usefulness of bone scan index (BSI) in breast cancer.

OBJECTIVE: Artificial neural network (ANN) technology has been developed for clinical use to analyze bone scintigraphy with metastatic bone tumors. It...

High-Resolution SPECT Imaging of Stimuli-Responsive Soft Microrobots.

Untethered small-scale robots have great potential for biomedical applications. However, critical barriers to effective translation of these miniaturi...

Machine learning for differentiating metastatic and completely responded sclerotic bone lesion in prostate cancer: a retrospective radiomics study.

OBJECTIVE: Using CT texture analysis and machine learning methods, this study aims to distinguish the lesions imaged via 68Ga-prostate-specific membra...

Flexible Prediction of CT Images From MRI Data Through Improved Neighborhood Anchored Regression for PET Attenuation Correction.

Given the complicated relationship between the magnetic resonance imaging (MRI) signals and the attenuation values, the attenuation correction in hybr...

Use of a Tracer-Specific Deep Artificial Neural Net to Denoise Dynamic PET Images.

Application of kinetic modeling (KM) on a voxel level in dynamic PET images frequently suffers from high levels of noise, drastically reducing the pre...