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Phantoms, Imaging

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Impact of deep learning reconstructions on image quality and liver lesion detectability in dual-energy CT: An anthropomorphic phantom study.

Medical physics
BACKGROUND: Deep learning image reconstruction (DLIR) algorithms allow strong noise reduction while preserving noise texture, which may potentially improve hypervascular focal liver lesions.

Enhancement of structural and functional photoacoustic imaging based on a reference-inputted convolutional neural network.

Optics express
Photoacoustic microscopy has demonstrated outstanding performance in high-resolution functional imaging. However, in the process of photoacoustic imaging, the photoacoustic signals will be polluted by inevitable background noise. Besides, the image q...

Design of a Cost-Effective Ultrasound Force Sensor and Force Control System for Robotic Extra-Body Ultrasound Imaging.

Sensors (Basel, Switzerland)
Ultrasound imaging is widely valued for its safety, non-invasiveness, and real-time capabilities but is often limited by operator variability, affecting image quality and reproducibility. Robot-assisted ultrasound may provide a solution by delivering...

Fluorescence Lifetime Endoscopy with a Nanosecond Time-Gated CAPS Camera with IRF-Free Deep Learning Method.

Sensors (Basel, Switzerland)
Fluorescence imaging has been widely used in fields like (pre)clinical imaging and other domains. With advancements in imaging technology and new fluorescent labels, fluorescence lifetime imaging is gradually gaining recognition. Our research departm...

Robot-Assisted CT-Guided Biopsy with an Artificial Intelligence-Based Needle-Path Generator: An Experimental Evaluation Using a Phantom Model.

Journal of vascular and interventional radiology : JVIR
PURPOSE: To investigate the feasibility of a robotic system with artificial intelligence-based lesion detection and path planning for computed tomography (CT)-guided biopsy compared with the conventional freehand technique.

Quantification of tissue stiffness with magnetic resonance elastography and finite difference time domain (FDTD) simulation-based spatiotemporal neural network.

Magnetic resonance imaging
Quantification of tissue stiffness with magnetic resonance elastography (MRE) is an inverse problem that is sensitive to noise. Conventional methods for the purpose include direct inversion (DI) and local frequency estimation (LFE). In this study, we...

Deep-ER: Deep Learning ECCENTRIC Reconstruction for fast high-resolution neurometabolic imaging.

NeuroImage
INTRODUCTION: Altered neurometabolism is an important pathological mechanism in many neurological diseases and brain cancer, which can be mapped non-invasively by Magnetic Resonance Spectroscopic Imaging (MRSI). Advanced MRSI using non-cartesian comp...

Deep learning initialized compressed sensing (Deli-CS) in volumetric spatio-temporal subspace reconstruction.

Magma (New York, N.Y.)
OBJECT: Spatio-temporal MRI methods offer rapid whole-brain multi-parametric mapping, yet they are often hindered by prolonged reconstruction times or prohibitively burdensome hardware requirements. The aim of this project is to reduce reconstruction...

Deep learning aided determination of the optimal number of detectors for photoacoustic tomography.

Biomedical physics & engineering express
Photoacoustic tomography (PAT) is a non-destructive, non-ionizing, and rapidly expanding hybrid biomedical imaging technique, yet it faces challenges in obtaining clear images due to limited data from detectors or angles. As a result, the methodology...