Latest AI and machine learning research in nuclear medicine for healthcare professionals.
PURPOSE: To provide a holistic and complete comparison of the five most advanced AI models in the augmentation of low-dose F-FDG PET data over the entire dose reduction spectrum.
Treatment of hepatocellular carcinoma (HCC) with Y90 radioembolization segmentectomy (Y90-RE) demonstrates a tumor dose-response threshold, where dose estimates are highly dependent on accurate SPECT/CT acquisition, registration, and reconstruction. Any error can result in distorted absorbed dose distributions and inaccurate estimates of treatment success. This study improves upon the voxel-based ...
BACKGROUND: Intravenous contrast agents are routinely used in CT imaging to enable the visualization of intravascular pathology, such as with abdomina...
OBJECTIVE: Patients receiving radionuclide 131I treatment expose radiation to others, and there was no clinical trial to verify the effectiveness and ...
PURPOSE: We use neural networks to evaluate and compare the spatial resolution of two different simulated monolithic PET detector elements. The effect...
BACKGROUND: Beta amyloid in the brain, which was originally confirmed by post-mortem examinations, can now be confirmed in living patients using amylo...
BACKGROUND: In the setting of primary hyperparathyroidism (PHPT), [F]fluorocholine PET/CT (FCH-PET) has excellent diagnostic performance, with experie...
PURPOSE: Attenuation correction and scatter compensation (AC/SC) are two main steps toward quantitative PET imaging, which remain challenging in PET-o...
. The goal of this work is to experimentally compare the 3D spatial and energy resolution of a semi-monolithic detector suitable for total-body positr...
PURPOSE: To explore a multidomain fusion model of radiomics and deep learning features based on F-fluorodeoxyglucose positron emission tomography/comp...
PURPOSE: Although SPECT myocardial perfusion imaging (MPI) is susceptible to artifacts from soft tissue attenuation, most scans are performed without ...
OBJECTIVES: PET/CT is a first-line tool for the diagnosis of lung cancer. The accuracy of quantification may suffer from various factors throughout th...
In the last few years, deep learning has made a breakthrough and established its position in machine learning classification problems in medical image...
The study aims to implement a convolutional neural network framework that uses the 18F-FDG PET modality of brain imaging to detect multiple stages of ...
Our aim was to identify and quantify high coronary artery calcium (CAC) with deep learning (DL)-powered CAC scoring (CACS) in oncological patients wit...
An objective biomarker to predict the outcome of isolated rapid eye movement sleep behavior disorder (iRBD) is crucial for the management. This study ...
We evaluated and described the impact of prostatic indocyanine green (ICG) injection on extended pelvic lymph node (LN) dissection (ePLND) in robotic-...
OBJECTIVES: Pet robots are technology-based substitutes for live animals that have demonstrated psychosocial benefits for people living with dementia ...
BACKGROUND: The World Health Organization reported that cardiovascular disease is the most common cause of death worldwide. On average, one person die...
PURPOSE: We want to develop a model for predicting lymph node status based on positron emission computed tomography (PET) images of untreated ovarian ...