Results 71 to 80 of about 32,314 (198)

DeepRelaxo: Fast Mono‐Exponential Magnitude Brain R2* Mapping With Reduced Echoes Using Self‐Supervised Deep Learning

Magnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose We introduce DeepRelaxo, a fast and generalizable deep learning method for estimating brain R2* maps from multi‐echo gradient echo (ME‐GRE) acquisitions with arbitrary echo configurations, including shortened echo trains for accelerated scans.
Samiha Prima, Zhuang Xiong, Alan H. Wilman, Hongfu Sun   +3 more
wiley   +1 more source

Time‐Conditioned Zero‐Shot Self‐Supervised Reconstruction for Accelerated 3D Ultra‐Low‐Field MRI

Magnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose Ultra‐low‐field (ULF) MRI provides a cost‐effective, portable imaging option but has relatively low SNR and long acquisition times compared to standard clinical scans. This study presents a time‐conditioned zero‐shot self‐supervised learning image reconstruction framework (ULF‐ZS‐SSL) to accelerate 3D‐acquired single‐coil ULF MRI ...
Mart W. J. van Straten, Beatrice Lena, Chloé Najac, Ruben van den Broek, Peter Börnert, Andrew Webb, Yiming Dong   +6 more
wiley   +1 more source

Noise Impact Analysis in Computer-Generated Holography Based on Dual Metrics Evaluation via Peak Signal-to-Noise Ratio and Structural Similarity Index Measure

Applied Sciences
This study investigates the noise impact on reconstructed images in computer-generated holography (CGH) through theoretical analysis and Matlab 2015b simulations.
Yucheng Li, Yang Zhang, Deyu Jia, Song Gao, Muqun Zhang   +4 more
doaj   +1 more source

Physics‐Guided Neural Network for Quantitative Parameter Mapping Using Balanced Steady State Free Precession MRI

Magnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose To propose a new method using a physics‐guided neural network for quantitative parameter mapping in balanced steady‐state free precession (bSSFP) imaging. Theory and Methods We trained physics‐guided neural networks with a multilayer perceptron using simulated bSSFP signals generated from tissue parameters (T1$$ {T}_1 $$, T2$$ {T}_2 $$,
Hye‐Ryeong Choi, Huan Minh Luu, Sung‐Hong Park   +2 more
wiley   +1 more source

SelExNet: A Self‐Supervised Physics‐Informed Framework for Multi‐Channel Joint RF and Gradient Waveform Optimization in 2D Spatially Selective Excitation

Magnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose To introduce SelExNet: a self‐supervised framework for two‐dimensional spatially selective excitation that jointly optimizes radiofrequency (RF) pulses and gradient waveforms, and extends to multi‐channel transmission MRI. Methods Building on prior RF‐only and joint RF‐gradient optimization approaches, SelExNet couples neural RF and ...
Yuliang Xiao, Jason Rock, Zhe Wu, Jamie Near, Mark Chiew, Simon J. Graham   +5 more
wiley   +1 more source

Deep learning‐based super‐resolution reconstruction and improved YOLOv9 for efficient benthos detection: a case study at Lake Hamana, Japan

Remote Sensing in Ecology and Conservation, EarlyView.
This study presents a UAV‐based framework that integrates deep learning‐based super‐resolution reconstruction and an enhanced YOLO detector to improve centimetre‐scale benthic organism monitoring. Using hermit crabs in Lake Hamana, a coastal lagoon in Japan, as a case study, the method substantially enhanced small‐object detection performance ...
Fan Zhao, Bangzhang Ma, Dianhan Xi, Jiaqi Wang, Yijia Chen, Yongying Liu, Xinlei Shao, Mowen Zhang, Guocheng Zhang, Jundong Chen, Katsunori Mizuno   +10 more
wiley   +1 more source

Computed tomography images de-noising using a novel two stage adaptive algorithm

Journal of Medical Signals and Sensors, 2015
In this paper, an optimal algorithm is presented for de-noising of medical images. The presented algorithm is based on improved version of local pixels grouping and principal component analysis.
Mojtaba Fadaee, Mousa Shamsi, Hamidreza Saberkari, Mohammad Hossein Sedaaghi   +3 more
doaj  

Robust steganographic framework for securing sensitive healthcare data of telemedicine using convolutional neural network

CAAI Transactions on Intelligence Technology, EarlyView.
Abstract Data is the key element that runs the modern society. Large amounts of data are being released day by day as a result of many activities. The digital data is transferred through the Internet which may be vulnerable to attacks while transmitting. Especially, the medical data is observed to be of at most importance.
Rupa Ch, Venkayya Yadlapalli, Suhana Sulthana Sk, G. Thippa Reddy, Sandeep Kautish   +4 more
wiley   +1 more source

Infrared–Visible Image Fusion via Cross-Modal Guided Dual-Branch Networks

Applied Sciences
In the field of low-altitude aerial drone data fusion, the fusion of infrared and visible light images remains challenging due to issues such as large modal differences, insufficient cross-modal alignment, and limited global context modeling. Traditional
Tingyu Zhu, Jinyong Chen, Gang Wang
doaj   +1 more source

DrLS: Distortion‐Resistant Lossless Steganography via Colour Depth Interpolation

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT The lossless data steganography is to hide a certain amount of information into a container image. Previous lossless steganography methods fail to strike a balance between capacity, imperceptibility, accuracy, and robustness, commonly vulnerable to distortion on container images.
Youmin Xu, Xuanyu Zhang, Xiandong Meng, Jian Zhang   +3 more
wiley   +1 more source