Nuclear Medicine

Residual Conditional Variational Autoencoder for Multi-Center PET/CT Radiomic Feature Harmonization with Integrated Modeling of Batch Effects and Clinical Covariates.

This study proposes a Residual Conditional Variational Autoencoder model (ResCVAE-Harmonizer) that integrates batch information and clinical covariates for multi-center feature harmonization and systematically and comprehensively evaluates its harmonization performance. This study collected 806 cases from 9 different centers. After preprocessing, three types of features were extracted from PET and...

Improving transthyretin cardiac amyloidosis detection from electrocardiograms through the Willem artificial intelligence platform.

BACKGROUND: Transthyretin amyloid cardiomyopathy (ATTR-CM) is a frequently underdiagnosed disease in which delay in diagnosis limits the efficacy of treatments. Artificial intelligence (AI) applied to standard 12-lead electrocardiograms (ECGs) is promising and may help improve ATTR-CM early detection. OBJECTIVES: The goals of this study were to extend the Willem AI cloud platform for ATTR-CM detec...

UniPET: A universal network for high-quality PET image denoising across varied dose reduction factors.

Most existing deep learning-based PET image denoising methods assume a fixed and known dose reduction factor (DRF) for low-dose PET images. However, t...

From Quantum Mechanics to Coarse-Grained Models: Bridging the Gap toward Polymer Rational Design.

In the rational design of novel polymers, the role of simulation methods based on classical physics is often hindered by the limited accuracy and tran...

Predicting ventilation from single breathing phase non-contrast CT using Swin Transformers.

BACKGROUND: Pulmonary ventilation imaging enables functional avoidance radiotherapy treatment plans by quantifying regional lung function. However, cu...

Interpretable LightGBM model with SHAP analysis predicts non-excellent response to initial radioiodine therapy in differentiated thyroid carcinoma.

PURPOSE: To identify independent determinants influencing therapeutic outcomes of initial radioactive iodine (1 3 1I) therapy in differentiated thyroi...

Testosterone-Sparing Treatment Strategies for Biochemically Recurrent Prostate Cancer After Radical Prostatectomy.

PURPOSE OF REVIEW: Biochemical recurrence (BCR) after radical prostatectomy occurs in up to one-third of patients and increases the risk of metastasis...

Imaging Modalities in Tuberculosis.

Tuberculosis (TB) remains a major global health challenge, with increasing prevalence of multidrug-resistant and extrapulmonary forms complicating dia...

A unified deep learning framework for cross-platform harmonization of multi-tracer PET quantification in neurodegenerative disease.

Quantitative PET underpins diagnosis and treatment monitoring in neurodegenerative disease, yet systematic biases between PET-MRI and PET-CT preclude ...

4f-5d orbital tag-team catalysis empowers high-loading zinc-iodine batteries.

Conversion-type positive electrodes offer high theoretical energy density for next-generation energy storage, but their practical application is limit...

Evaluating Deep Learning Attenuation Correction for SPECT MPI: Lessons From Clinical Validation.

Personalized mapping of body homeostasis using whole-body PET connectomics and routine FDG PET imaging.

BACKGROUND: Immuno-inflammation and systemic alterations are key features of chronic diseases. While PET molecular imaging is widely used in precision...

FDG-PET Medullary Total Tumor Volume Highlights High-Risk Newly Diagnosed Multiple Myeloma Patients in CASSIOPEIA Trial.

This study aimed to assess the prognostic value of medullary total metabolic tumor volume (mTMTV) derived from fluorodeoxyglucose-positron emission to...

Automated acquisition of explainable knowledge from unannotated colposcopic images for predicting the natural course of CIN2.

OBJECTIVE: Cervical intraepithelial neoplasia grade 2 (CIN2) represents a critical turning point in cervical cancer progression. While its natural reg...

SPECT and PET imaging of Alzheimer's disease revisited: from biomarkers to artificial intelligence-based prediction.

Machine-learning approach on echocardiography to improve the detection of transthyretin amyloid cardiomyopathy: GRAAL algorithm.

AIMS: Transthyretin amyloid cardiomyopathy (ATTR-CM) is an increasingly recognized cause of heart failure, yet detection remains challenging due to it...

Bone Metastasis Detection at CT with Deep Learning Models Trained Using Multicenter, Multimodal Reference Standards: Development and Evaluation.

Purpose To develop and validate deep learning models for detecting bone metastases on abdominal and thoracic CT scans, considering lesion visibility, ...

U-Net-based transfer learning for automated tumour segmentation enabling fully automated [18F]F-DOPA PET analysis in paediatric gliomas.

BACKGROUND: PET imaging with [18F]F-DOPA shows great promise for assessing paediatric gliomas. Manual tumour delineation and parameter extraction are ...

A convolutional neural network for fully automated total metabolic tumor volume delineation in patients with aggressive Non-Hodgkin lymphoma.

PURPOSE: The [18F]FDG-PET-derived total metabolic tumor volume (TMTV) has a high prognostic value in patients with Hodgkin and Non-Hodgkin lymphoma. H...

A pipeline for meso- and microplastic identification in aquatic systems using machine learning and hyperspectral imaging.

Microplastic pollution presents major environmental and health challenges, requiring accurate identification and quantification to assess its distribu...