Nuclear Medicine

The utility of a deep learning-based algorithm for bone scintigraphy in patient with prostate cancer.

OBJECTIVE: Bone scintigraphy has often been used to evaluate bone metastases. Its functionality is evident in detecting bone metastasis in patients with malignant tumor including prostate cancer, as appropriate treatment and prognosis are dependent on the presence and degree of bone metastasis. The development of a deep learning-based algorithm in the field of information processing has been remar...

A machine learning framework with anatomical prior for online dose verification using positron emitters and PET in proton therapy.

We developed a machine learning framework in order to establish the correlation between dose and activity distributions in proton therapy. A recurrent neural network was used to predict dose distribution in three dimensions based on the information of proton-induced positron emitters. Hounsfield Unit (HU) information from CT images and analytically derived stopping power (SP) information were inco...

Noise reduction with cross-tracer and cross-protocol deep transfer learning for low-dose PET.

Previous studies have demonstrated the feasibility of reducing noise with deep learning-based methods for low-dose fluorodeoxyglucose (FDG) positron e...

Artificial Intelligence and Machine Learning in Nuclear Medicine: Future Perspectives.

Artificial intelligence and machine learning based approaches are increasingly finding their way into various areas of nuclear medicine imaging. With ...

Natural language processing for automated quantification of bone metastases reported in free-text bone scintigraphy reports.

BACKGROUND: The widespread use of electronic patient-generated health data has led to unprecedented opportunities for automated extraction of clinical...

Intelligent Imaging in Nuclear Medicine: the Principles of Artificial Intelligence, Machine Learning and Deep Learning.

The emergence of artificial intelligence (AI) in nuclear medicine has occurred over the last 50 years but more recent developments in machine learning...

Adjunctive dental therapies in caries-active children: Shifting the cariogenic salivary microbiome from dysbiosis towards non-cariogenic health.

BACKGROUND: The oral microbiome is a complex assembly of microbial species, whose constituents can tilt the balance towards progression of oral diseas...

Deep-Learning Generation of Synthetic Intermediate Projections Improves Lu SPECT Images Reconstructed with Sparsely Acquired Projections.

The aims of this study were to decrease the Lu-SPECT acquisition time by reducing the number of projections and to circumvent image degradation by add...

Human thermal sensation over a mountainous area, revealed by the application of ANNs: the case of Ainos Mt., Kefalonia Island, Greece.

Mt. Ainos in Kefalonia Island, Greece, hosts a large variety of plant species, some of them endemic to the region. Because of its rich biodiversity, a...

Improving depth-of-interaction resolution in pixellated PET detectors using neural networks.

Parallax error is a common issue in high-resolution preclinical positron emission tomography (PET) scanners as well as in clinical scanners that have ...

Development of Convolutional Neural Networks to identify bone metastasis for prostate cancer patients in bone scintigraphy.

OBJECTIVE: The main aim of this work is to build a robust Convolutional Neural Network (CNN) algorithm that efficiently and quickly classifies bone sc...

Relationship between Coronary Iodine Concentration Determined Using Spectral CT and the Outcome of Percutaneous Coronary Intervention in Patients with Chronic Total Occlusion.

PURPOSE: To evaluate the feasibility of coronary iodine concentration (CIC) by using spectral CT in the assessment of the outcome of percutaneous coro...

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model.

Machine learning (ML) algorithms permit the integration of different features into a model to perform classification or regression tasks with an accur...

Bone metastasis classification using whole body images from prostate cancer patients based on convolutional neural networks application.

Bone metastasis is one of the most frequent diseases in prostate cancer; scintigraphy imaging is particularly important for the clinical diagnosis of ...

Morphological Characterization of Functional Brain Imaging by Isosurface Analysis in Parkinson's Disease.

Finding new biomarkers to model Parkinson's Disease (PD) is a challenge not only to help discerning between Healthy Control (HC) subjects and patients...

Performance of Ga-68 PSMA PET/CT for diagnosis and grading of local prostate cancer.

BACKGROUND: We aimed to evaluate the utility of prostate-specific membrane antigen (PSMA) PET/CT for the detection of local disease within the prostat...

AI-driven attenuation correction for brain PET/MRI: Clinical evaluation of a dementia cohort and importance of the training group size.

INTRODUCTION: Robust and reliable attenuation correction (AC) is a prerequisite for accurate quantification of activity concentration. In combined PET...

Motion correction of respiratory-gated PET images using deep learning based image registration framework.

Artifacts caused by patient breathing and movement during PET data acquisition affect image quality. Respiratory gating is commonly used to gate the l...

Improved myocardial perfusion PET imaging using artificial neural networks.

Myocardial perfusion (MP) PET imaging plays a key role in risk assessment and stratification of patients with coronary artery disease. In this work, w...

Reinventing radiation therapy with machine learning and imaging bio-markers (radiomics): State-of-the-art, challenges and perspectives.

Radiation therapy is a pivotal cancer treatment that has significantly progressed over the last decade due to numerous technological breakthroughs. Im...