Nuclear Medicine

Radiomics in PET/CT: Current Status and Future AI-Based Evolutions.

This short review aims at providing the readers with an update on the current status, as well as future perspectives in the quickly evolving field of radiomics applied to the field of PET/CT imaging. Numerous pitfalls have been identified in study design, data acquisition, segmentation, features calculation and modeling by the radiomics community, and these are often the same issues across all ima...

Intraprostatic Tumor Segmentation on PSMA PET Images in Patients with Primary Prostate Cancer with a Convolutional Neural Network.

Accurate delineation of the intraprostatic gross tumor volume (GTV) is a prerequisite for treatment approaches in patients with primary prostate cancer (PCa). Prostate-specific membrane antigen PET (PSMA PET) may outperform MRI in GTV detection. However, visual GTV delineation underlies interobserver heterogeneity and is time consuming. The aim of this study was to develop a convolutional neural n...

Attenuation correction using deep Learning and integrated UTE/multi-echo Dixon sequence: evaluation in amyloid and tau PET imaging.

PURPOSE: PET measures of amyloid and tau pathologies are powerful biomarkers for the diagnosis and monitoring of Alzheimer's disease (AD). Because cor...

A large-scale optical microscopy image dataset of potato tuber for deep learning based plant cell assessment.

We present a new large-scale three-fold annotated microscopy image dataset, aiming to advance the plant cell biology research by exploring different c...

Fully 3D Active Surface with Machine Learning for PET Image Segmentation.

In order to tackle three-dimensional tumor volume reconstruction from Positron Emission Tomography (PET) images, most of the existing algorithms rely ...

The prognostic role of end-of-treatment FDG-PET/CT in diffuse large B cell lymphoma: a pilot study application of neural networks to predict time-to-event.

PURPOSE: To evaluate the prognostic role of end-of-treatment (EoT) FDG-PET/CT parameters in diffuse large B cell lymphoma (DLBCL), and then to explore...

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BACKGROUND: Currently there is an ever increasing interest in Lu-177 targeted radionuclide therapies, which target neuro-endocrine and prostate tumour...

Artificial intelligence-based detection of lymph node metastases by PET/CT predicts prostate cancer-specific survival.

INTRODUCTION: Lymph node metastases are a key prognostic factor in prostate cancer (PCa), but detecting lymph node lesions from PET/CT images is a sub...

Non-invasive decision support for NSCLC treatment using PET/CT radiomics.

Two major treatment strategies employed in non-small cell lung cancer, NSCLC, are tyrosine kinase inhibitors, TKIs, and immune checkpoint inhibitors, ...

Potentials and caveats of AI in hybrid imaging.

State-of-the-art patient management frequently mandates the investigation of both anatomy and physiology of the patients. Hybrid imaging modalities su...

Prognostic value of FDG-PET radiomics with machine learning in pancreatic cancer.

Patients with pancreatic cancer have a poor prognosis, therefore identifying particular tumor characteristics associated with prognosis is important. ...

Deep neural network based artificial intelligence assisted diagnosis of bone scintigraphy for cancer bone metastasis.

Bone scintigraphy (BS) is one of the most frequently utilized diagnostic techniques in detecting cancer bone metastasis, and it occupies an enormous w...

Noise reduction approach in pediatric abdominal CT combining deep learning and dual-energy technique.

OBJECTIVES: To evaluate the image quality of low iodine concentration, dual-energy CT (DECT) combined with a deep learning-based noise reduction techn...

Machine learning-based FDG PET-CT radiomics for outcome prediction in larynx and hypopharynx squamous cell carcinoma.

AIM: To determine whether machine learning-based radiomic feature analysis of baseline integrated 2-[F]-fluoro-2-deoxy-d-glucose (FDG) positron-emissi...

Formal caregivers' perceptions and experiences of using pet robots for persons living with dementia in long-term care: A meta-ethnography.

AIM: To explore the formal caregivers' perceptions and experiences of using pet robots for persons living with dementia residing in long-term care set...

Visual interpretation of [F]Florbetaben PET supported by deep learning-based estimation of amyloid burden.

PURPOSE: Amyloid PET which has been widely used for noninvasive assessment of cortical amyloid burden is visually interpreted in the clinical setting....

Machine Learning in Nuclear Medicine: Part 2-Neural Networks and Clinical Aspects.

This article is the second part in our machine learning series. Part 1 provided a general overview of machine learning in nuclear medicine. Part 2 foc...

Truncation compensation and metallic dental implant artefact reduction in PET/MRI attenuation correction using deep learning-based object completion.

The susceptibility of MRI to metallic objects leads to void MR signal and missing information around metallic implants. In addition, body truncation o...

Applications of artificial intelligence and deep learning in molecular imaging and radiotherapy.

This brief review summarizes the major applications of artificial intelligence (AI), in particular deep learning approaches, in molecular imaging and ...

Approximating anatomically-guided PET reconstruction in image space using a convolutional neural network.

In the last two decades, it has been shown that anatomically-guided PET reconstruction can lead to improved bias-noise characteristics in brain PET im...