Review and prospect: artificial intelligence in advanced medical imaging
Artificial intelligence (AI) as an emerging technology is gaining momentum in medical
imaging. Recently, deep learning-based AI techniques have been actively investigated in …
imaging. Recently, deep learning-based AI techniques have been actively investigated in …
Deep learning for fast MR imaging: A review for learning reconstruction from incomplete k-space data
Magnetic resonance imaging is a powerful imaging modality that can provide versatile
information. However, it has a fundamental challenge that is time consuming to acquire …
information. However, it has a fundamental challenge that is time consuming to acquire …
Deep learning for compressive sensing: a ubiquitous systems perspective
AL Machidon, V Pejović - Artificial Intelligence Review, 2023 - Springer
Compressive sensing (CS) is a mathematically elegant tool for reducing the sensor
sampling rate, potentially bringing context-awareness to a wider range of devices …
sampling rate, potentially bringing context-awareness to a wider range of devices …
Deep variational autoencoder for mapping functional brain networks
In the neuroimaging and brain mapping communities, researchers have proposed a variety
of computational methods to map functional brain networks (FBNs). Recently, it has been …
of computational methods to map functional brain networks (FBNs). Recently, it has been …
Dual-domain faster Fourier convolution based network for MR image reconstruction
Deep learning methods for fast MRI have shown promise in reconstructing high-quality
images from undersampled multi-coil k-space data, leading to reduced scan duration …
images from undersampled multi-coil k-space data, leading to reduced scan duration …
Image Reconstruction for Accelerated MR Scan with Faster Fourier Convolutional Neural Networks
High quality image reconstruction from undersampled-space data is key to accelerating MR
scanning. Current deep learning methods are limited by the small receptive fields in …
scanning. Current deep learning methods are limited by the small receptive fields in …
Deep Learning Techniques for Compressive Sensing-Based Reconstruction and Inference--A Ubiquitous Systems Perspective
AL Machidon, V Pejovic - arXiv preprint arXiv:2105.13191, 2021 - arxiv.org
Compressive sensing (CS) is a mathematically elegant tool for reducing the sampling rate,
potentially bringing context-awareness to a wider range of devices. Nevertheless, practical …
potentially bringing context-awareness to a wider range of devices. Nevertheless, practical …
Cascade Multiscale Swin-Conv Network for Fast MRI Reconstruction
S Ye, X Xie, D Xiong, L Ouyang, X Zhang - Chinese Conference on …, 2022 - Springer
Compressed sensing magnetic resonance imaging (CS-MRI) is an important and effective
tool for the fast MR imaging, which enables superior performance in restoring the anatomy of …
tool for the fast MR imaging, which enables superior performance in restoring the anatomy of …
[PDF][PDF] Exploring compressed sensing fMRI time series
Z Stoebner - zstoebs.github.io
Compressed sensing reconstructs signals by solving underdetermined linear systems under
the conditions that the measurements are sparse in the domain and incoherent [1]. In …
the conditions that the measurements are sparse in the domain and incoherent [1]. In …
Creating parallel-transmission-style MRI with deep learning (deepPTx): a feasibility study using high-resolution whole-brain diffusion at 7T
Parallel transmission (pTx) has proven capable of addressing two RF-related challenges at
ultrahigh fields (≥ 7 Tesla): RF non-uniformity and power deposition in tissues. However …
ultrahigh fields (≥ 7 Tesla): RF non-uniformity and power deposition in tissues. However …