Nuclear Medicine

Incorporating label uncertainty during the training of convolutional neural networks improves performance for the discrimination between certain and inconclusive cases in dopamine transporter SPECT.

PURPOSE: Deep convolutional neural networks (CNN) hold promise for assisting the interpretation of dopamine transporter (DAT)-SPECT. For improved communication of uncertainty to the user it is crucial to reliably discriminate certain from inconclusive cases that might be misclassified by strict application of a predefined decision threshold on the CNN output. This study tested two methods to incor...

F-FDG PET/CT-based habitat radiomics combining stacking ensemble learning for predicting prognosis in hepatocellular carcinoma: a multi-center study.

BACKGROUND: This study aims to develop habitat radiomic models to predict overall survival (OS) for hepatocellular carcinoma (HCC), based on the characterization of the intratumoral heterogeneity reflected in F-FDG PET/CT images.

A deep learning method for the recovery of standard-dose imaging quality from ultra-low-dose PET on wavelet domain.

PURPOSE: Recent development in positron emission tomography (PET) dramatically increased the effective sensitivity by increasing the geometric coverag...

Multi-scale multimodal deep learning framework for Alzheimer's disease diagnosis.

Multimodal neuroimaging data, including magnetic resonance imaging (MRI) and positron emission tomography (PET), provides complementary information ab...

Automated Pipeline for Robust Cat Activity Detection Based on Deep Learning and Wearable Sensor Data.

The health, safety, and well-being of household pets such as cats has become a challenging task in previous years. To estimate a cat's behavior, objec...

Accuracy of deep learning-based attenuation correction in Tc-GSA SPECT/CT hepatic imaging.

INTRODUCTION: Attenuation correction (AC) is necessary for accurate assessment of radioactive distribution in single photon emission computed tomograp...

A F-FDG PET/CT-based deep learning-radiomics-clinical model for prediction of cervical lymph node metastasis in esophageal squamous cell carcinoma.

BACKGROUND: To develop an artificial intelligence (AI)-based model using Radiomics, deep learning (DL) features extracted from F-fluorodeoxyglucose (F...

Brain imaging and machine learning reveal uncoupled functional network for contextual threat memory in long sepsis.

Positron emission tomography (PET) utilizes radiotracers like [F]fluorodeoxyglucose (FDG) to measure brain activity in health and disease. Performing ...

Investigation of scatter energy window width and count levels for deep learning-based attenuation map estimation in cardiac SPECT/CT imaging.

Deep learning (DL) is becoming increasingly important in generating attenuation maps for accurate attenuation correction (AC) in cardiac perfusion SPE...

Edge Computing for AI-Based Brain MRI Applications: A Critical Evaluation of Real-Time Classification and Segmentation.

Medical imaging plays a pivotal role in diagnostic medicine with technologies like Magnetic Resonance Imagining (MRI), Computed Tomography (CT), Posit...

Cardiac motion correction with a deep learning network for perfusion defect assessment in single-photon emission computed tomography myocardial perfusion imaging.

BACKGROUND: In myocardial perfusion imaging (MPI) with single-photon emission computed tomography (SPECT), ungated studies are used for evaluation of ...

Presurgery and postsurgery: advancements in artificial intelligence and machine learning models for enhancing patient management in infective endocarditis.

Infective endocarditis (IE) is a severe infection of the inner lining of the heart, known as the endocardium. It is characterized by a range of sympto...

A novel meta learning based stacked approach for diagnosis of thyroid syndrome.

Thyroid syndrome, a complex endocrine disorder, involves the dysregulation of the thyroid gland, impacting vital physiological functions. Common cause...

Using interpretable deep learning radiomics model to diagnose and predict progression of early AD disease spectrum: a preliminary [F]FDG PET study.

OBJECTIVES: In this study, we propose an interpretable deep learning radiomics (IDLR) model based on [F]FDG PET images to diagnose the clinical spectr...

Tracer-Separator: A Deep Learning Model for Brain PET Dual-Tracer ( 18 F-FDG and Amyloid) Separation.

INTRODUCTION: Multiplexed PET imaging revolutionized clinical decision-making by simultaneously capturing various radiotracer data in a single scan, e...

GeSeNet: A General Semantic-Guided Network With Couple Mask Ensemble for Medical Image Fusion.

At present, multimodal medical image fusion technology has become an essential means for researchers and doctors to predict diseases and study patholo...

Clinical Pilot of a Deep Learning Elastic Registration Algorithm to Improve Misregistration Artifact and Image Quality on Routine Oncologic PET/CT.

RATIONALE AND OBJECTIVES: Misregistration artifacts between the PET and attenuation correction CT (CTAC) exams can degrade image quality and cause dia...

Image quality of virtual monochromatic and material density iodine images for evaluation of head and neck neoplasms using deep learning-based CT image reconstruction - A retrospective observational study.

PURPOSE: To compare the quality of deep learning image reconstructed (DLIR) virtual monochromatic images (VMI) and material density (MD) iodine images...

From microplastics to pixels: testing the robustness of two machine learning approaches for automated, Nile red-based marine microplastic identification.

Despite the urgent need for accurate and robust observations of microplastics in the marine environment to assess current and future environmental ris...

Deep learning-based approach for acquisition time reduction in ventilation SPECT in patients after lung transplantation.

We aimed to evaluate the image quality and diagnostic performance of chronic lung allograft dysfunction (CLAD) with lung ventilation single-photon emi...