AIMC Topic: Scattering, Radiation
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Unsupervised Adaptive Deep Learning Framework for Video Denoising in Light Scattering Imaging.
Analytical chemistry
Jun 3, 2025
Light scattering is a powerful tool that has been widely applied in various scenarios, such as nanoparticle analysis, single-cell measurement, and blood flow monitoring. However, noise is always a concerning and challenging issue in light scattering ...
A CT-free deep-learning-based attenuation and scatter correction for copper-64 PET in different time-point scans.
Radiological physics and technology
Jun 1, 2025
This study aimed to develop and evaluate a deep-learning model for attenuation and scatter correction in whole-body 64Cu-based PET imaging. A swinUNETR model was implemented using the MONAI framework. Whole-body PET-nonAC and PET-CTAC image pairs wer...
Scatter and beam hardening effect corrections in pelvic region cone beam CT images using a convolutional neural network.
Radiological physics and technology
Jun 1, 2025
The aim of this study is to remove scattered photons and beam hardening effect in cone beam CT (CBCT) images and make an image available for treatment planning. To remove scattered photons and beam hardening effect, a convolutional neural network (CN...
Breaking through scattering: The H-Net CNN model for image retrieval.
Computer methods and programs in biomedicine
Jun 1, 2025
BACKGROUND: In scattering media, traditional optical imaging techniques often find it significantly challenging to accurately reconstruct images owing to rapid light scattering. Thus, to address this problem, we propose a convolutional neural network...
Feasibility of Deep Learning-Guided Attenuation and Scatter Correction of Whole-Body 68Ga-PSMA PET Studies in the Image Domain.
Clinical nuclear medicine
Aug 1, 2021
OBJECTIVE: This study evaluates the feasibility of direct scatter and attenuation correction of whole-body 68Ga-PSMA PET images in the image domain using deep learning.
Use of Artificial Intelligence to Reduce Radiation Exposure at Fluoroscopy-Guided Endoscopic Procedures.
The American journal of gastroenterology
Apr 1, 2020
OBJECTIVES: Exposure to ionizing radiation remains a hazard for patients and healthcare providers. We evaluated the utility of an artificial intelligence (AI)-enabled fluoroscopy system to minimize radiation exposure during image-guided endoscopic pr...
Convolutional neural network-based approach to estimate bulk optical properties in diffuse optical tomography.
Applied optics
Feb 10, 2020
Deep learning has been actively investigated for various applications such as image classification, computer vision, and regression tasks, and it has shown state-of-the-art performance. In diffuse optical tomography (DOT), the accurate estimation of ...
Fast honey classification using infrared spectrum and machine learning.
Mathematical biosciences and engineering : MBE
Jul 29, 2019
Honey has been one previous natural food in human history. However, as the supply cannot satisfy the market demand, many incidents of adulterated and fraudulent honey have been reported. In Taiwan, some common adulterated honey and fraudulent honey i...
STATISTICAL APPROACH FOR HUMAN ELECTROMAGNETIC EXPOSURE ASSESSMENT IN FUTURE WIRELESS ATTO-CELL NETWORKS.
Radiation protection dosimetry
May 1, 2019
In this article, we study human electromagnetic exposure to the radiation of an ultra dense network of nodes integrated in a floor denoted as ATTO-cell floor, or ATTO-floor. ATTO-cells are a prospective 5 G wireless networking technology, in which hu...
Efficient estimation of subdiffusive optical parameters in real time from spatially resolved reflectance by artificial neural networks.
Optics letters
Jun 15, 2018
Subdiffusive reflectance captured at short source-detector separations provides increased sensitivity to the scattering phase function and hence allows superficial probing of the tissue ultrastructure. Consequently, estimation of subdiffusive optical...
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