Nuclear Medicine

Thoracic staging of lung cancers by FDG-PET/CT: impact of artificial intelligence on the detection of associated pulmonary nodules.

This study focuses on automating the classification of certain thoracic lung cancer stages in 3D FDG...

Assessment of quantitative staging PET/computed tomography parameters using machine learning for early detection of progression in diffuse large B-cell lymphoma.

OBJECTIVE: This study aimed to investigate the role of volumetric and dissemination parameters obtai...

Deep Learning-Based Prediction of PET Amyloid Status Using MRI.

BACKGROUND AND PURPOSE: Identifying amyloid-beta (Aβ)-positive patients is essential for Alzheimer's...

Artificial intelligence in coronary CT angiography: transforming the diagnosis and risk stratification of atherosclerosis.

Coronary CT Angiography (CCTA) is essential for assessing atherosclerosis and coronary artery diseas...

Development of an anomaly detection system for Gibbs artifact identification in amyloid PET imaging.

The PET Imaging Site Qualification Program for amyloid positron emission tomography (PET) in Japan i...

Letter by Sacoransky Regarding Article, "Deep Learning-Derived Cardiac Chamber Volumes and Mass From PET/CT Attenuation Scans: Associations With Myocardial Flow Reserve and Heart Failure".

Machine learning-based construction and validation of an radiomics model for predicting ISUP grading in prostate cancer: a multicenter radiomics study based on [68Ga]Ga-PSMA PET/CT.

PURPOSE: The International Society of Urological Pathology (ISUP) grading of prostate cancer (PCa) i...

Machine learning-based prediction of amyloid positivity using early-phase F-18 flutemetamol PET.

BackgroundPrevious studies have suggested that early-phase imaging of amyloid positron emission tomo...

Impact of Field-of-view Zooming and Segmentation Batches on Radiomics Features Reproducibility and Machine Learning Performance in Thyroid Scintigraphy.

BACKGROUND: Thyroid diseases are the second most common hormonal disorders, necessitating accurate d...

A Robust Residual Three-dimensional Convolutional Neural Networks Model for Prediction of Amyloid-β Positivity by Using FDG-PET.

BACKGROUND: Widely used in oncology PET, 2-deoxy-2- 18 F-FDG PET is more accessible and affordable t...

AI based automatic measurement of split renal function in [F]PSMA-1007 PET/CT.

BACKGROUND: Prostate-specific membrane antigen (PSMA) is an important target for positron emission t...

Deep learning estimations of the production cross sections of Br medical radionuclide.

Bromine-77 has a half-life of 56 h and decays nearly exclusively (99.3 %) by electron capture, with ...

A strategy for the automatic diagnostic pipeline towards feature-based models: a primer with pleural invasion prediction from preoperative PET/CT images.

BACKGROUND: This study aims to explore the feasibility to automate the application process of nomogr...

Summary Report of the SNMMI AI Task Force Radiomics Challenge 2024.

In medical imaging, challenges are competitions that aim to provide a fair comparison of different m...

Fusion of FDG and FMZ PET Reduces False-Positives in Predicting Epileptogenic Zone.

BACKGROUND AND PURPOSE: Epilepsy, a globally prevalent neurologic disorder, necessitates precise ide...

Deep-learning-based Partial Volume Correction in 99mTc-TRODAT-1 SPECT for Parkinson's Disease: A Preliminary Study on Clinical Translation.

Tc-TRODAT-1 SPECT is effective for the early detection of Parkinson's disease (PD). However, SPECT i...

A radiogenomics study on F-FDG PET/CT in endometrial cancer by a novel deep learning segmentation algorithm.

OBJECTIVE: To create an automated PET/CT segmentation method and radiomics model to forecast Mismatc...

Ensemble of weak spectral total-variation learners: a PET-CT case study.

Solving computer vision problems through machine learning, one often encounters lack of sufficient t...

From Model Development to Mitigation: Machine Learning for Predicting and Minimizing Iodinated Trihalomethanes in Water Treatment.

Disinfection processes in water treatment produce disinfection byproducts (DBPs), such as iodinated ...

Direct parametric reconstruction in dynamic PET using deep image prior and a novel parameter magnification strategy.

BACKGROUND/PURPOSE: Multiple parametric imaging in positron emission tomography (PET) is challenging...

Performance of AI methods in PET-based imaging for outcome prediction in lymphoma: A systematic review and meta-analysis.

OBJECTIVES: To evaluate the predictive performance of artificial intelligence (AI) methods using pre...