Nuclear Medicine

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

The systematic literature review was performed on the use of artificial intelligence (AI) algorithms in nonsmall cell lung cancer (NSCLC) prognostication. Studies were evaluated for the type of input data (histology and whether CT, PET, and MRI were used), cancer therapy intervention, prognosis performance, and comparisons to clinical prognosis systems such as TNM staging. Further comparisons were...

The role of neuro-imaging in multiple system atrophy.

Neuroimaging plays a crucial role in diagnosing multiple system atrophy and monitoring progressive neurodegeneration in this fatal disease. Advanced MRI techniques and post-processing methods have demonstrated significant volume loss and microstructural changes in brain regions well known to be affected by MSA pathology. These observations can be exploited to support the differential diagnosis of ...

Effect of data-driven motion correction for respiratory movement on lesion detectability in PET-CT: a phantom study.

PURPOSE: While data-driven motion correction (DDMC) techniques have proven to enhance the visibility of lesions affected by motion, their impact on ov...

Machine learning to identify suitable boundaries for band-pass spectral analysis of dynamic [ C]Ro15-4513 PET scan and voxel-wise parametric map generation.

BACKGROUND: Spectral analysis is a model-free PET quantification technique that treats the time-space signal as an impulse response to a bolus injecti...

Limited-angle SPECT image reconstruction using deep image prior.

. In single-photon emission computed tomography (SPECT) image reconstruction, limited-angle conditions lead to a loss of frequency components, which d...

Differentiated thyroid cancer and positron emission computed tomography: when, how and why?

INTRODUCTION: Fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) has become an indispensable tool in oncology, offering ...

Deep supervised transformer-based noise-aware network for low-dose PET denoising across varying count levels.

BACKGROUND: Reducing radiation dose from PET imaging is essential to minimize cancer risks; however, it often leads to increased noise and degraded im...

AI-enhanced patient-specific dosimetry in I-131 planar imaging with a single oblique view.

This study aims to enhance the dosimetry accuracy in I planar imaging by utilizing a single oblique view and Monte Carlo (MC) validated dose point ker...

Artificial intelligence in cardiac sarcoidosis: ECG, Echo, CPET and MRI.

PURPOSE OF REVIEW: Cardiac sarcoidosis is a form of inflammatory cardiomyopathy that varies in its clinical presentation. It is associated with signif...

Gut microbiota composition is related to anxiety and aggression scores in companion dogs.

There is mounting evidence for a link between behaviour and the gut microbiome in animal and human health. However, the role of the gut microbiome in ...

Prediction of Motor Symptom Progression of Parkinson's Disease Through Multimodal Imaging-Based Machine Learning.

The unrelenting progression of Parkinson's disease (PD) leads to severely impaired quality of life, with considerable variability in progression rates...

Harnessing protein language model for structure-based discovery of highly efficient and robust PET hydrolases.

Plastic waste, particularly polyethylene terephthalate (PET), presents significant environmental challenges, driving extensive research into enzymatic...

Unveiling degradation patterns in dye-sensitized solar cells: a machine learning perspective.

This study examines the time-dependent degradation of dye-sensitized solar cells (DSSCs) by systematically investigating several critical parameters, ...

A Novel Two-step Classification Approach for Differentiating Bone Metastases From Benign Bone Lesions in SPECT/CT Imaging.

RATIONALE AND OBJECTIVES: This study aims to develop and validate a novel two-step deep learning framework for the automated detection, segmentation, ...

[AI-based applications in medical image computing].

The processing of medical images plays a central role in modern diagnostics and therapy. Automated processing and analysis of medical images can effic...

Treatment Response Evaluation in Prostate Cancer Using PSMA PET/CT.

In recent years, there has been a headlong rush into the use of prostate-specific membrane antigen (PSMA)-targeted PET for the staging and restaging o...

GAN-based Denoising for Scan Time Reduction and Motion Correction of 18F FP-CIT PET/CT: A Multicenter External Validation Study.

PURPOSE: AI-driven scan time reduction is rapidly transforming medical imaging with benefits such as improved patient comfort and enhanced efficiency....

F-FDG dose reduction using deep learning-based PET reconstruction.

BACKGROUND: A deep learning-based image reconstruction (DLR) algorithm that can reduce the statistical noise has been developed for PET/CT imaging. It...

Machine learning model for predicting Amyloid-β positivity and cognitive status using early-phase F-Florbetaben PET and clinical features.

This study developed machine learning models to predict Aβ positivity in Alzheimer's disease by integrating early-phase F-Florbetaben PET and clinical...

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-PET/CT images according to the 9th Edition of the...