Nuclear Medicine

Detection of transient neurotransmitter response using personalized neural networks.

Measurement of stimulus-induced dopamine release and other types of transient neurotransmitter response (TNR) from dynamic positron emission tomography (PET) images typically suffers from limited detection sensitivity and high false positive (FP) rates. Measurement of TNR of a voxel-level can be particularly problematic due to high image noise. In this work, we perform voxel-level TNR detection us...

Reducing scan time of paediatric Tc-DMSA SPECT via deep learning.

AIM: To investigate the feasibility of reducing the scan time of paediatric technetium 99m (Tc) dimercaptosuccinic acid (DMSA) single-photon-emission computed tomographic (SPECT) using a deep learning (DL) method.

Early prediction of neoadjuvant chemotherapy response for advanced breast cancer using PET/MRI image deep learning.

This study aimed to investigate the predictive efficacy of positron emission tomography/computed tomography (PET/CT) and magnetic resonance imaging (M...

Artificial intelligence applications for oncological positron emission tomography imaging.

Positron emission tomography (PET), a functional and dynamic molecular imaging technique, is generally used to reveal tumors' biological behavior. Rad...

Conditional Generative Adversarial Networks Aided Motion Correction of Dynamic F-FDG PET Brain Studies.

This work set out to develop a motion-correction approach aided by conditional generative adversarial network (cGAN) methodology that allows reliable,...

Comparison of 11 automated PET segmentation methods in lymphoma.

Segmentation of lymphoma lesions in FDG PET/CT images is critical in both assessing individual lesions and quantifying patient disease burden. Simple ...

An easy-to-use deep-learning model for highly accurate diagnosis of Parkinson's disease using SPECT images.

Accurate diagnosis of Parkinson's Disease (PD) at its early stages remains a challenge for modern clinicians. In this study, we utilize a convolutiona...

Deep learning with noise-to-noise training for denoising in SPECT myocardial perfusion imaging.

PURPOSE: Post-reconstruction filtering is often applied for noise suppression due to limited data counts in myocardial perfusion imaging (MPI) with si...

FBP-Net for direct reconstruction of dynamic PET images.

Dynamic positron emission tomography (PET) imaging can provide information about metabolic changes over time, used for kinetic analysis and auxiliary ...

Predicting lymph node metastasis in patients with oropharyngeal cancer by using a convolutional neural network with associated epistemic and aleatoric uncertainty.

There can be significant uncertainty when identifying cervical lymph node (LN) metastases in patients with oropharyngeal squamous cell carcinoma (OPSC...

Artificial Intelligence for Optimization and Interpretation of PET/CT and PET/MR Images.

Artificial intelligence (AI) has recently attracted much attention for its potential use in healthcare applications. The use of AI to improve and extr...

Artificial Intelligence for Response Evaluation With PET/CT.

Positron emission tomography (PET)/computed tomography (CT) are nuclear diagnostic imaging modalities that are routinely deployed for cancer staging a...

Obtaining PET/CT images from non-attenuation corrected PET images in a single PET system using Wasserstein generative adversarial networks.

Positron emission tomography (PET) imaging plays an indispensable role in early disease detection and postoperative patient staging diagnosis. However...

Prediction of amyloid β PET positivity using machine learning in patients with suspected cerebral amyloid angiopathy markers.

Amyloid-β(Aβ) PET positivity in patients with suspected cerebral amyloid angiopathy (CAA) MRI markers is predictive of a worse cognitive trajectory, a...

Radiomics in PET/CT: Current Status and Future AI-Based Evolutions.

This short review aims at providing the readers with an update on the current status, as well as future perspectives in the quickly evolving field of ...

Intraprostatic Tumor Segmentation on PSMA PET Images in Patients with Primary Prostate Cancer with a Convolutional Neural Network.

Accurate delineation of the intraprostatic gross tumor volume (GTV) is a prerequisite for treatment approaches in patients with primary prostate cance...

Attenuation correction using deep Learning and integrated UTE/multi-echo Dixon sequence: evaluation in amyloid and tau PET imaging.

PURPOSE: PET measures of amyloid and tau pathologies are powerful biomarkers for the diagnosis and monitoring of Alzheimer's disease (AD). Because cor...

A large-scale optical microscopy image dataset of potato tuber for deep learning based plant cell assessment.

We present a new large-scale three-fold annotated microscopy image dataset, aiming to advance the plant cell biology research by exploring different c...

Fully 3D Active Surface with Machine Learning for PET Image Segmentation.

In order to tackle three-dimensional tumor volume reconstruction from Positron Emission Tomography (PET) images, most of the existing algorithms rely ...

The prognostic role of end-of-treatment FDG-PET/CT in diffuse large B cell lymphoma: a pilot study application of neural networks to predict time-to-event.

PURPOSE: To evaluate the prognostic role of end-of-treatment (EoT) FDG-PET/CT parameters in diffuse large B cell lymphoma (DLBCL), and then to explore...