MRI-guided robot intervention—current state-of-the-art and new challenges
Abstract Magnetic Resonance Imaging (MRI) is now a widely used modality for providing
multimodal, high-quality soft tissue contrast images with good spatiotemporal resolution but …
multimodal, high-quality soft tissue contrast images with good spatiotemporal resolution but …
Synthesizing MR image contrast enhancement using 3D high-resolution ConvNets
Objective: Gadolinium-based contrast agents (GBCAs) have been widely used to better
visualize disease in brain magnetic resonance imaging (MRI). However, gadolinium …
visualize disease in brain magnetic resonance imaging (MRI). However, gadolinium …
A deep unrolled neural network for real-time MRI-guided brain intervention
Accurate navigation and targeting are critical for neurological interventions including biopsy
and deep brain stimulation. Real-time image guidance further improves surgical planning …
and deep brain stimulation. Real-time image guidance further improves surgical planning …
Frequency Learning via Multi-Scale Fourier Transformer for MRI Reconstruction
Since Magnetic Resonance Imaging (MRI) requires a long acquisition time, various methods
were proposed to reduce the time, but they ignored the frequency information and non-local …
were proposed to reduce the time, but they ignored the frequency information and non-local …
Fast MRI reconstruction via edge attention
Fast and accurate MRI reconstruction is a key concern in modern clinical practice. Recently,
numerous Deep-Learning methods have been proposed for MRI reconstruction, however …
numerous Deep-Learning methods have been proposed for MRI reconstruction, however …
Accelerated dynamic MR imaging with joint balanced low‐rank tensor and sparsity constraints
J He, C Mi, X Liu, Y Zhao - Medical Physics, 2023 - Wiley Online Library
Background Dynamic magnetic resonance imaging (DMRI) is an essential medical imaging
technique, but the slow data acquisition process limits its further development. Purpose By …
technique, but the slow data acquisition process limits its further development. Purpose By …
MRI reconstruction via multi transforms learning and logarithm ratio for vectorized groups
J Cao, S Liu - Applied Soft Computing, 2023 - Elsevier
Compressed sensing (CS) has been demonstrated to substantially reduce the scan time of
magnetic resonance imaging (MRI) by undersampling k-space data. The desirable …
magnetic resonance imaging (MRI) by undersampling k-space data. The desirable …
Positive incentive CNN structure coupled nonconvex model for image super-resolution
J Liu, Y Shi, G Ni - Physica Scripta, 2024 - iopscience.iop.org
This paper studies super-resolution (SR) technique to reconstruct high-quality images for
deep image analysis. Currently, the convolutional neural networks (CNNs) are well …
deep image analysis. Currently, the convolutional neural networks (CNNs) are well …
Feasibility of Ambiguity Analysis for Binary Classification from Non-Stationary Signals
Non-stationary signal analysis for classification of signals is the recent area of research.
Various researchers have contributed significantly in this field. However the inherent …
Various researchers have contributed significantly in this field. However the inherent …
[PDF][PDF] Low-Rank Dynamic MRI Imaging Model Based on MC Penalty Function.
Z Luo, L Wang, Z Zhu, Y Li - IAENG International Journal of Applied …, 2024 - iaeng.org
In the research of dynamic MRI imaging, we have utilized the minimax-concave (MC)
function to replace the traditional nuclear norm, thereby significantly enhancing the model's …
function to replace the traditional nuclear norm, thereby significantly enhancing the model's …