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Phantoms, Imaging

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Fast and accurate modeling of transient-state, gradient-spoiled sequences by recurrent neural networks.

NMR in biomedicine
Fast and accurate modeling of MR signal responses are typically required for various quantitative MRI applications, such as MR fingerprinting. This work uses a new extended phase graph (EPG)-Bloch model for accurate simulation of transient-state, gra...

A novel multi-DoF surgical robotic system for brachytherapy on liver tumor: Design and control.

International journal of computer assisted radiology and surgery
PURPOSE: Radioactive seed implantation is an effective invasive treatment method for malignant liver tumors in hepatocellular carcinomas. However, challenges of the manual procedure may degrade the efficacy of the technique, such as the high accuracy...

DeepRegularizer: Rapid Resolution Enhancement of Tomographic Imaging Using Deep Learning.

IEEE transactions on medical imaging
Optical diffraction tomography measures the three-dimensional refractive index map of a specimen and visualizes biochemical phenomena at the nanoscale in a non-destructive manner. One major drawback of optical diffraction tomography is poor axial res...

Metal artefact reduction in the oral cavity using deep learning reconstruction algorithm in ultra-high-resolution computed tomography: a phantom study.

Dento maxillo facial radiology
OBJECTIVES: This study aimed to improve the impact of the metal artefact reduction (MAR) algorithm for the oral cavity by assessing the effect of acquisition and reconstruction parameters on an ultra-high-resolution CT (UHRCT) scanner.

Deep learning-based denoising algorithm in comparison to iterative reconstruction and filtered back projection: a 12-reader phantom study.

European radiology
OBJECTIVES: (1) To compare low-contrast detectability of a deep learning-based denoising algorithm (DLA) with ADMIRE and FBP, and (2) to compare image quality parameters of DLA with those of reconstruction methods from two different CT vendors (ADMIR...

Uncertainty measurement of radiomics features against inherent quantum noise in computed tomography imaging.

European radiology
OBJECTIVES: Quantum noise is a random process in X-ray-based imaging systems. We addressed and measured the uncertainty of radiomics features against this quantum noise in computed tomography (CT) images.

Protocol Optimization Considerations for Implementing Deep Learning CT Reconstruction.

AJR. American journal of roentgenology
Previous advances over filtered back projection (FBP) have incorporated model-based iterative reconstruction. The purpose of this study was to characterize the latest advance in image reconstruction, that is, deep learning. The focus was on applying...

Objective evaluation of biomaterial effects after injection laryngoplasty - Introduction of artificial intelligence-based ultrasonic image analysis.

Clinical otolaryngology : official journal of ENT-UK ; official journal of Netherlands Society for Oto-Rhino-Laryngology & Cervico-Facial Surgery
OBJECTIVE: Hyaluronic acid (HA) can be degraded over time. However, persistence of the effects after injection laryngoplasty (IL) for unilateral vocal fold paralysis (UVFP), longer than expected from HA longevity, has been observed. The purpose of th...

Deep learning-enhanced T mapping with spatial-temporal and physical constraint.

Magnetic resonance in medicine
PURPOSE: To propose a reconstruction framework to generate accurate T maps for a fast MR T mapping sequence.

Reverberation Noise Suppression in Ultrasound Channel Signals Using a 3D Fully Convolutional Neural Network.

IEEE transactions on medical imaging
Diffuse reverberation is ultrasound image noise caused by multiple reflections of the transmitted pulse before returning to the transducer, which degrades image quality and impedes the estimation of displacement or flow in techniques such as elastogr...