Nuclear Medicine

A system-level DOI discrimination method based on SSDA for a brain-dedicated DOI-PET scanner.

OBJECTIVE: Depth-of-interaction (DOI) information is essential for improving the spatial resolution in positron emission tomography (PET) imaging. Existing deep learning-based DOI discrimination methods rely on side-irradiation measurements for each detector to obtain labeled data, making them impractical for system-level calibration. APPROACH: This study proposes a system-level DOI discrimination...

Overview of State-of-the-Art Learning-Based Classification Methods in Medical Imaging.

Learning-based image classification has become central to modern medical imaging, but the field is changing rapidly: foundation models, vision-language models (VLMs), and label-efficient pretraining are reshaping which methods are clinically useful. This review focuses on the state of the art rather than re-explaining well-established models. We summarize learning paradigms, contrast classical mac...

Investigating AD and non-AD [18F]flortaucipir distribution patterns in a memory clinic cohort.

PURPOSE: Amyloid (A) deposition represents a specific pathological hallmark of Alzheimer's disease (AD). Clinical diagnostic protocols frequently rely...

A deep learning framework for lesion-level treatment response prediction in hodgkin lymphoma using PET/CT tensor radiomics.

BACKGROUND: Accurate prediction of treatment response in Hodgkin lymphoma (HL) is crucial for personalized therapy. The Tensor Radiomics (TR) paradigm...

Multimodal multitask deep learning for grading management system in non-small cell lung cancer.

Accurate histologic subtyping, tumor node metastasis classification (TNM) staging and prognostic assessment are central to clinical management of non-...

Investigation of the molecular network underlying PET-MPs-induced inflammatory bowel disease via integrated machine learning and molecular docking approaches.

Polyethylene terephthalate microplastics (PET-MPs), as environmental contaminants, have raised significant concerns due to their potential toxicity, l...

Comprehensive framework for evaluation of deep neural networks in detection and quantification of lymphoma from PET/CT images: Clinical insights, pitfalls, and observer agreement analyses.

PURPOSE: This study addresses critical gaps in automated lymphoma segmentation from PET/CT imaging, often overlooked in prior work. While deep learnin...

Comparison of ChatGPT and dietitians in formulating diet plans and recommendations for patients with cardiometabolic diseases.

BACKGROUND: Artificial Intelligence (AI) is transforming personalized medicine, yet its efficacy constitutes a dynamic factor in the field of health a...

A methodological framework for integrating generalisable deep learning and radiomics fusion model for early lung cancer detection across multi-centre imaging datasets.

Early detection of lung cancer remains one of the most effective strategies for improving survival; however, diagnostic performance is limited by vari...

Functional Imaging in Hereditary Endocrine Neoplasms: Evolving Modalities and Clinical Implications.

Hereditary endocrine neoplastic syndromes require structured, lifelong surveillance owing to their multisystem involvement, variable penetrance, and h...

Spinal cord FDG PET uptake in healthy adults: an AI-driven segmentation study.

PURPOSE: 18F-fluorodeoxyglucose (FDG) Positron Emission Tomography (PET)/Computerized Tomography (CT) is an important imaging modality in oncology, bu...

Swin UNETR-based prediction of [68Ga]Ga-FAPI-46 PET/CT dose rate maps in cancer patients: quantitative comparison with [18F]FDG PET/CT.

BACKGROUND: Fibroblast activation protein (FAP) is a promising theranostic target due to its high stroma expression in numerous malignancies. This stu...

QSM vs dopamine transporter PET: χ+ Mapping Shows Stronger Longitudinal Associations with Nigral Degeneration than Conventional QSM in Parkinson's Disease.

BACKGROUND: Iron accumulation in the substantia nigra (SN) is a hallmark of Parkinson's disease (PD). Quantitative susceptibility mapping (QSM) includ...

Hybrid-CMLP: Hybrid CNN-MLP Networks for Low-to-standard-dose PET Synthesis.

Positron Emission Tomography (PET) is a critical modality in medical imaging for detecting abnormalities and diagnosing diseases. However, the radiati...

Scan-wise generalized PET denoising with contrastive adversarial learning.

Objective.Deep learning has significantly advanced low-count positron emission tomography (PET) denoising. However, models trained on specific distrib...

Beyond 0.29±0.02 mm intrinsic spatial resolution based on monolithic crystals using convolutional neural network: a simulation study.

High spatial resolution is important for a Positron Emission Tomography (PET) system. Monolithic crystal based detectors are promising to deliver ...

The impact of a novel deep learning reconstruction algorithm on image quality in ultralow-dose CT: a quantitative phantom study.

OBJECTIVE: The aim of this study is to evaluate the performance of a novel deep learning image reconstruction (DLIR) algorithm in noise reduction, con...

A tissue-informed deep learning-based method for positron range correction in preclinical [Formula: see text]Ga PET imaging.

PURPOSE: Positron range (PR) limits spatial resolution and quantitative accuracy in PET imaging, particularly for high-energy positron-emitting radion...

Integrated CT-PET radiogenomics and graph-based multi-task learning for preoperative prediction of key glioma molecular markers.

BACKGROUND: The clinical management of glioma is increasingly dependent on the tumor's molecular profile, particularly the mutation status of Isocitra...

[18F] PSMA-3Q PET/CT radiomics at 45% SUVmax threshold: predicting post-surgical ISUP grade ≥ 4 and extraprostatic extension for noninvasive stratification in prostate cancer management.

OBJECTIVE: To systematically evaluate radiomic features extracted from [18F] PSMA-3Q PET/CT using 40%, 45%, and 50% SUVmax thresholds for their abilit...