Nuclear Medicine

Deep learning with multiresolution handcrafted features for brain MRI segmentation.

The segmentation of magnetic resonance (MR) images is a crucial task for creating pseudo computed tomography (CT) images which are used to achieve positron emission tomography (PET) attenuation correction. One of the main challenges of creating pseudo CT images is the difficulty to obtain an accurate segmentation of the bone tissue in brain MR images. Deep convolutional neural networks (CNNs) have...

A personalized deep learning denoising strategy for low-count PET images.

. Deep learning denoising networks are typically trained with images that are representative of the testing data. Due to the large variability of the noise levels in positron emission tomography (PET) images, it is challenging to develop a proper training set for general clinical use. Our work aims to develop a personalized denoising strategy for the low-count PET images at various noise levels.We...

Feasibility study of three-material decomposition in dual-energy cone-beam CT imaging with deep learning.

In this work, a dedicated end-to-end deep convolutional neural network, named as Triple-CBCT, is proposed to demonstrate the feasibility of reconstruc...

Comparison of the performances of machine learning and deep learning in improving the quality of low dose lung cancer PET images.

PURPOSE: To compare the performances of machine learning (ML) and deep learning (DL) in improving the quality of low dose (LD) lung cancer PET images ...

Improving Breast Tumor Segmentation in PET via Attentive Transformation Based Normalization.

Positron Emission Tomography (PET) has become a preferred imaging modality for cancer diagnosis, radiotherapy planning, and treatment responses monito...

Deep learning exploration for SPECT MPI polar map images classification in coronary artery disease.

OBJECTIVE: The exploration and the implementation of a deep learning method using a state-of-the-art convolutional neural network for the classificati...

Deep learning based low-activity PET reconstruction of [C]PiB and [F]FE-PE2I in neurodegenerative disorders.

PURPOSE: Positron Emission Tomography (PET) can support a diagnosis of neurodegenerative disorder by identifying disease-specific pathologies. Our aim...

Eliminating CT radiation for clinical PET examination using deep learning.

Clinical PET/CT examinations rely on CT modality for anatomical localization and attenuation correction of the PET data. However, the use of CT signif...

Colposcopic multimodal fusion for the classification of cervical lesions.

: Cervical cancer is one of the two biggest killers of women and early detection of cervical precancerous lesions can effectively improve the survival...

A neural network-based algorithm for simultaneous event positioning and timestamping in monolithic scintillators.

. Monolithic scintillator crystals coupled to silicon photomultiplier (SiPM) arrays are promising detectors for PET applications, offering spatial res...

Adapting a low-count acquisition of the bone scintigraphy using deep denoising super-resolution convolutional neural network.

PURPOSE: Deep-layer learning processing may improve contrast imaging with greater precision in low-count acquisition. However, no data on noise reduct...

Deep-learning-based methods of attenuation correction for SPECT and PET.

Attenuation correction (AC) is essential for quantitative analysis and clinical diagnosis of single-photon emission computed tomography (SPECT) and po...

Deep learning based time-to-event analysis with PET, CT and joint PET/CT for head and neck cancer prognosis.

OBJECTIVES: Recent studies have shown that deep learning based on pre-treatment positron emission tomography (PET) or computed tomography (CT) is prom...

Use of deep learning-based radiomics to differentiate Parkinson's disease patients from normal controls: a study based on [F]FDG PET imaging.

OBJECTIVES: We proposed a novel deep learning-based radiomics (DLR) model to diagnose Parkinson's disease (PD) based on [F]fluorodeoxyglucose (FDG) PE...

Determinants of implementing of pet robots in nursing homes for dementia care.

BACKGROUND: Pet robots have been employed as viable substitutes to pet therapy in nursing homes. Despite their potential to enhance the psychosocial h...

Deep learning-based automated segmentation of eight brain anatomical regions using head CT images in PET/CT.

OBJECTIVE: We aim to propose a deep learning-based method of automated segmentation of eight brain anatomical regions in head computed tomography (CT)...

Computer-aided detection and segmentation of malignant melanoma lesions on whole-body F-FDG PET/CT using an interpretable deep learning approach.

BACKGROUND AND OBJECTIVE: In oncology, 18-fluorodeoxyglucose (F-FDG) positron emission tomography (PET) / computed tomography (CT) is widely used to i...

Deep learning-based multimodal image analysis for cervical cancer detection.

Cervical cancer is the fourth most common cancer in women, and its precise detection plays a critical role in disease treatment and prognosis predicti...

A deep learning method for translating 3DCT to SPECT ventilation imaging: First comparison with Kr-gas SPECT ventilation imaging.

PURPOSE: This study aimed to evaluate the accuracy of deep learning (DL)-based computed tomography (CT) ventilation imaging (CTVI).

Increasing angular sampling through deep learning for stationary cardiac SPECT image reconstruction.

BACKGROUND: The GE Discovery NM (DNM) 530c/570c are dedicated cardiac SPECT scanners with 19 detector modules designed for stationary imaging. This st...