Nuclear Medicine

Assessing Image Quality and Diagnostic Performance of Quadruple-Low Coronary CT Angiography with Deep Learning in High-BMI Patients.

RATIONALE AND OBJECTIVES: To examine the feasibility of a quadruple-low protocol in coronary computed tomography angiography (CCTA) assisted by the deep learning image reconstruction (DLIR) for participants with high body mass index (BMI). MATERIALS AND METHODS: This prospective study involved 180 participants (BMI of ≥ 25 kg/m2). Participants were randomly assigned to three groups: the standard-d...

MSML-DenseXmer: harnessing vision transformers through integration with novel dense networks for medical image fusion.

The integration of multiple modalities in medical imaging allows a thorough representation of structural and functional details, resulting in improved diagnosis and treatment. Deep learning methods outperform conventional methods by automating the extraction of pertinent features and fusing them while preserving both structural and textural integrity. Existing methods lack the ability to capture c...

Fully automatic left ventricle segmentation in [Formula: see text]Rb PET/CT Using a semi-supervised nnU-net.

BACKGROUND: Quantification of myocardial blood flow (MBF) with [Formula: see text]Rb PET/CT requires accurate delineation of the left ventricle (LV). ...

MMTC-Net: Multimodal Temporal Cervical Network for HSIL+ Recognition in Precancer Screening.

Cervical precancer screening is essential for reducing disease-related mortality. In colposcopic practice, clinicians jointly assess dynamic acetic-ac...

Beyond target lesions: Prognostic value of longitudinal AI-derived whole-body [1⁸F]FDG PET/CT metrics in metastatic melanoma.

PURPOSE: To investigate the prognostic value of an artificial intelligence (AI)-based semi-automated tool for longitudinal whole-body quantification o...

Leveraging the non-contrast CT component of PET/CT: an AI-driven delta-radiomics approach to monitor treatment response in metastatic breast cancer.

PURPOSE: 18 F-FDG PET/CT is the standard modality for monitoring treatment response in metastatic breast cancer. This study aims to evaluate the predi...

Unsupervised anomaly detection for longitudinal comparison in whole-body PET/CT images.

PURPOSE: This study investigates the utility of unsupervised anomaly detection for longitudinal comparison of whole-body 18F-fluorodeoxyglucose (FDG)-...

An interpretable 1⁸F-PSMA PET/CT-MRI model for optimizing prostate biopsy decisions.

PURPOSE: Distinguishing indolent from clinically significant prostate cancer (csPCa) in biopsy-naïve men remains a diagnostic challenge, often leading...

Clinically reliable and stable automated segmentation of DLBCL lesions on PET/CT using self-configuring nnU-Net for robust TMTV quantification.

PURPOSE: Accurate lesion segmentation on 18F-FDG PET/CT is essential for the effective management of Diffuse Large B-cell Lymphoma (DLBCL). While deep...

A rapid total-body PET imaging approach for pediatric patients using non-attenuation-corrected PET scans.

BACKGROUND: Pediatric lymphoma patients undergo multiple 18F-FDG PET/CT examinations for staging and response assessment, raising concerns about cumul...

Simultaneous partial volume correction and denoising of brain PET images, using transformers and transfer learning.

BACKGROUND: Positron emission tomography (PET) is a key tool for quantitative brain imaging, but its image quality and quantitative reliability are st...

Can LLMs Turn French PET/CT Narrative Reports into Structured Knowledge?

This study evaluates large language models (LLMs) for information extraction from French PET/CT reports related to cognitive impairment, focusing on d...

Acquisition time/dose reduction in pediatric PET imaging using patch-based deep learning.

BACKGROUND: Deep learning (DL)-based denoising methods have shown promise for reducing radiation dose and/or acquisition time in pediatric PET imaging...

FDG PET for cardiac sarcoidosis: Protocol optimization, quantification, pitfalls, and multimodality imaging integration.

Cardiac sarcoidosis (CS) is a clinically heterogeneous disorder associated with significant morbidity and mortality, including heart failure, conducti...

The Brain Imaging and Neurophysiology Dataset of large-scale multimodal neural data.

The Brain Imaging and Neurophysiology Dataset (BIND) represents one of the largest multi-institutional, multimodal, clinical neuroimaging repositories...

Innovations in pediatric imaging: a scoping review of the past decade with case illustrations.

BACKGROUND: Imaging plays a fundamental and increasing role in the diagnostic work-up of pediatric patients. Non-invasive imaging methods include ultr...

Half-dose contrast media protocol using 70 kVp abdominal dynamic CT with super-resolution deep learning reconstruction: Evaluation of image quality and contrast performance.

OBJECTIVE: To evaluate contrast enhancement and image quality in 70 kVp abdominal dynamic CT using super-resolution deep learning reconstruction (SR-D...

Prognostic Significance of Baseline 18F-FDG PET/CT Parameters in Combination with an Artificial Intelligence-Based Pleural Effusion Segmentation Model for Malignant Pleural Effusion.

OBJECTIVES: We aimed to use an artificial intelligence (AI)-based pleural effusion segmentation model on baseline 18F-FDG positron emission tomography...

Evaluation of 2D and 3D nnU-Net models with two-label and three-label strategies for automatic segmentation and total metabolic tumor volume estimation of metastatic differentiated thyroid carcinoma on FDG-PET/CT.

PURPOSE: To evaluate the segmentation performance and total metabolic tumor volume (TMTV) prediction accuracy of 2D and 3D nnU-Net models under two-la...

An interpretable machine learning model integrating [18F]FDG PET/CT radiomics and clinical features for predicting perforation following chemotherapy in gastrointestinal lymphoma: a multicenter study.

BACKGROUND: Perforation following chemotherapy in gastrointestinal lymphoma (PFCGL) is a rare but severe and life-threatening complication. Early pre-...