Nuclear Medicine

Supporting intraoperative margin assessment using deep learning for automatic tumour segmentation in breast lumpectomy micro-PET-CT.

Complete tumour removal is vital in curative breast cancer (BCa) surgery to prevent recurrence. Rece...

Deep unrolled primal dual network for TOF-PET list-mode image reconstruction.

OBJECTIVE: Time-of-flight (TOF) information provides more accurate location data for annihilation ph...

Development and validation of a transformer-based deep learning model for predicting distant metastasis in non-small cell lung cancer using FDG PET/CT images.

BACKGROUND: This study aimed to develop and validate a hybrid deep learning (DL) model that integrat...

Memory-enhanced and multi-domain learning-based deep unrolling network for medical image reconstruction.

Reconstructing high-quality images from corrupted measurements remains a fundamental challenge in me...

Development of a deep learning based approach for multi-material decomposition in spectral CT: a proof of principle in silico study.

Conventional approaches to material decomposition in spectral CT face challenges related to precise ...

Enhanced detection of ovarian cancer using AI-optimized 3D CNNs for PET/CT scan analysis.

This study investigates how deep learning (DL) can enhance ovarian cancer diagnosis and staging usin...

Impact of artificial intelligence assistance on bone scintigraphy diagnosis.

Bone scintigraphy is an important tool for detecting bone lesions. This study aimed to improve and e...

Predicting disinfection by-products (DBPs) in supply water within a real water distribution network using an artificial neural network.

This study develops an artificial neural network (ANN) model using advanced learning algorithm techn...

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

Advancements in lung cancer: molecular insights, innovative therapies, and future prospects.

Still among the most common and deadly cancers worldwide, lung cancer causes major morbidity and dea...

Fully automated 3D multi-modal deep learning model for preoperative T-stage prediction of colorectal cancer using F-FDG PET/CT.

PURPOSE: This study aimed to develop a fully automated 3D multi-modal deep learning model using preo...

MSA-Net: a multi-scale and adversarial learning network for segmenting bone metastases in low-resolution SPECT imaging.

BACKGROUND: Single-photon emission computed tomography (SPECT) plays a crucial role in detecting bon...

Malignancy classification of thyroid incidentalomas using 18F-fluorodeoxy-d-glucose PET/computed tomography-derived radiomics.

BACKGROUND: Thyroid incidentalomas (TIs) are incidental thyroid lesions detected on fluorodeoxy-d-gl...

Biological tumor volume predicts survival in recurrent High-Grade glioma: A multiparametric [F]FET PET/MRI study.

BACKGROUND AND PURPOSE: Single-session, multiparametric [¹⁸F]FET PET/MRI is used to detect tumor rec...

Ultra-low dose imaging in a standard axial field-of-view PET.

Though ultra-low dose (ULD) imaging offers notable benefits, its widespread clinical adoption faces ...

Artificial Intelligence for Tumor [F]FDG PET Imaging: Advancements and Future Trends - Part II.

The integration of artificial intelligence (AI) into [F]FDG PET/CT imaging continues to expand, offe...

Innovations in clinical PET image reconstruction: advances in Bayesian penalized likelihood algorithm and deep learning.

Recent advances in PET image reconstruction have focused on achieving high image quality and quantit...

AI Prognostication in Nonsmall Cell Lung Cancer: A Systematic Review.

The systematic literature review was performed on the use of artificial intelligence (AI) algorithms...

Clinical Translation of Integrated PET-MRI for Neurodegenerative Disease.

The prevalence of Alzheimer's disease and other dementias is increasing as populations live longer l...

An interpretable machine learning model for predicting bone marrow invasion in patients with lymphoma via F-FDG PET/CT: a multicenter study.

PURPOSE: Accurate identification of bone marrow invasion (BMI) is critical for determining the progn...

F-FDG PET-based liver segmentation using deep-learning.

Organ segmentation using F-FDG PET images alone has not been extensively explored. Segmentation base...