Nuclear Medicine

Deep learning-based denoising of low-dose SPECT myocardial perfusion images: quantitative assessment and clinical performance.

PURPOSE: This work was set out to investigate the feasibility of dose reduction in SPECT myocardial perfusion imaging (MPI) without sacrificing diagnostic accuracy. A deep learning approach was proposed to synthesize full-dose images from the corresponding low-dose images at different dose reduction levels in the projection space.

Deep-learning image-reconstruction algorithm for dual-energy CT angiography with reduced iodine dose: preliminary results.

AIM: To evaluate the computed tomography (CT) attenuation values, background noise, arterial depiction, and image quality in whole-body dual-energy CT angiography (DECTA) at 40 keV with a reduced iodine dose using deep-learning image reconstruction (DLIR) and compare them with hybrid iterative reconstruction (IR).

Leveraging deep neural networks to improve numerical and perceptual image quality in low-dose preclinical PET imaging.

The amount of radiotracer injected into laboratory animals is still the most daunting challenge facing translational PET studies. Since low-dose imagi...

Application of Deep Learning Models for Automated Identification of Parkinson's Disease: A Review (2011-2021).

Parkinson's disease (PD) is the second most common neurodegenerative disorder affecting over 6 million people globally. Although there are symptomatic...

Post-reconstruction attenuation correction for SPECT myocardium perfusion imaging facilitated by deep learning-based attenuation map generation.

BACKGROUND: Attenuation correction can improve the quantitative accuracy of single-photon emission computed tomography (SPECT) images. Existing SPECT-...

Performance evaluation in [18F]Florbetaben brain PET images classification using 3D Convolutional Neural Network.

High accuracy has been reported in deep learning classification for amyloid brain scans, an important factor in Alzheimer's disease diagnosis. However...

Comparing the Prognostic Value of Stress Myocardial Perfusion Imaging by Conventional and Cadmium-Zinc Telluride Single-Photon Emission Computed Tomography through a Machine Learning Approach.

We compared the prognostic value of myocardial perfusion imaging (MPI) by conventional- (C-) single-photon emission computed tomography (SPECT) and ca...

Feasibility evaluation of PET scan-time reduction for diagnosing amyloid-β levels in Alzheimer's disease patients using a deep-learning-based denoising algorithm.

PURPOSE: To shorten positron emission tomography (PET) scanning time in diagnosing amyloid-β levels thus increasing the workflow in centers involving ...

Prediction of post-stroke cognitive impairment using brain FDG PET: deep learning-based approach.

PURPOSE: Post-stroke cognitive impairment can affect up to one third of stroke survivors. Since cognitive function greatly contributes to patients' qu...

A Large-Scale Fully Annotated Low-Cost Microscopy Image Dataset for Deep Learning Framework.

This work presents a large-scale three-fold annotated, low-cost microscopy image dataset of potato tubers for plant cell analysis in deep learning (DL...

Multiclass classification of whole-body scintigraphic images using a self-defined convolutional neural network with attention modules.

PURPOSE: A self-defined convolutional neural network is developed to automatically classify whole-body scintigraphic images of concern (i.e., the norm...

MRI-guided attenuation correction in torso PET/MRI: Assessment of segmentation-, atlas-, and deep learning-based approaches in the presence of outliers.

PURPOSE: We compare the performance of three commonly used MRI-guided attenuation correction approaches in torso PET/MRI, namely segmentation-, atlas-...

Automatic identification of suspicious bone metastatic lesions in bone scintigraphy using convolutional neural network.

BACKGROUND: We aimed to construct an artificial intelligence (AI) guided identification of suspicious bone metastatic lesions from the whole-body bone...

Synthetic pulmonary perfusion images from 4DCT for functional avoidance using deep learning.

To develop and evaluate the performance of a deep learning model to generate synthetic pulmonary perfusion images from clinical 4DCT images for patien...

dSPIC: a deep SPECT image classification network for automated multi-disease, multi-lesion diagnosis.

BACKGROUND: Functional imaging especially the SPECT bone scintigraphy has been accepted as the effective clinical tool for diagnosis, treatment, evalu...

Deep learning-based image reconstruction for TOF PET with DIRECT data partitioning format.

Conventional positron emission tomography (PET) image reconstruction is achieved by the statistical iterative method. Deep learning provides another o...

Prediction of the local treatment outcome in patients with oropharyngeal squamous cell carcinoma using deep learning analysis of pretreatment FDG-PET images.

BACKGROUND: This study aimed to assess the utility of deep learning analysis using pretreatment FDG-PET images to predict local treatment outcome in o...

Artificial Intelligence-Based Data Corrections for Attenuation and Scatter in Position Emission Tomography and Single-Photon Emission Computed Tomography.

Recent developments in artificial intelligence (AI) technology have enabled new developments that can improve attenuation and scatter correction in PE...

Anatomy and Physiology of Artificial Intelligence in PET Imaging.

Artificial intelligence (AI) has seen an explosion in interest within nuclear medicine. This interest is driven by the rapid progress and eye-catching...

Application of Pet-CT Fusion Deep Learning Imaging in Precise Radiotherapy of Thyroid Cancer.

This article explores the value of wall F-FDG PET/Cr imaging in the diagnosis of thyroid cancer, studies its ability to distinguish benign and maligna...