Compressed sensing MRI: a review from signal processing perspective
JC Ye - BMC Biomedical Engineering, 2019 - Springer
Magnetic resonance imaging (MRI) is an inherently slow imaging modality, since it acquires
multi-dimensional k-space data through 1-D free induction decay or echo signals. This often …
multi-dimensional k-space data through 1-D free induction decay or echo signals. This often …
A robust volumetric transformer for accurate 3D tumor segmentation
We propose a Transformer architecture for volumetric segmentation, a challenging task that
requires keeping a complex balance in encoding local and global spatial cues, and …
requires keeping a complex balance in encoding local and global spatial cues, and …
Image reconstruction is a new frontier of machine learning
Over past several years, machine learning, or more generally artificial intelligence, has
generated overwhelming research interest and attracted unprecedented public attention. As …
generated overwhelming research interest and attracted unprecedented public attention. As …
Framing U-Net via deep convolutional framelets: Application to sparse-view CT
X-ray computed tomography (CT) using sparse projection views is a recent approach to
reduce the radiation dose. However, due to the insufficient projection views, an analytic …
reduce the radiation dose. However, due to the insufficient projection views, an analytic …
Image reconstruction: From sparsity to data-adaptive methods and machine learning
The field of medical image reconstruction has seen roughly four types of methods. The first
type tended to be analytical methods, such as filtered backprojection (FBP) for X-ray …
type tended to be analytical methods, such as filtered backprojection (FBP) for X-ray …
Deep convolutional framelets: A general deep learning framework for inverse problems
Recently, deep learning approaches with various network architectures have achieved
significant performance improvement over existing iterative reconstruction methods in …
significant performance improvement over existing iterative reconstruction methods in …
Deep convolutional framelet denosing for low-dose CT via wavelet residual network
Model-based iterative reconstruction algorithms for low-dose X-ray computed tomography
(CT) are computationally expensive. To address this problem, we recently proposed a deep …
(CT) are computationally expensive. To address this problem, we recently proposed a deep …
Boosting the signal-to-noise of low-field MRI with deep learning image reconstruction
Recent years have seen a resurgence of interest in inexpensive low magnetic field (< 0.3 T)
MRI systems mainly due to advances in magnet, coil and gradient set designs. Most of these …
MRI systems mainly due to advances in magnet, coil and gradient set designs. Most of these …
Efficient B-mode ultrasound image reconstruction from sub-sampled RF data using deep learning
In portable, 3-D, and ultra-fast ultrasound imaging systems, there is an increasing demand
for the reconstruction of high-quality images from a limited number of radio-frequency (RF) …
for the reconstruction of high-quality images from a limited number of radio-frequency (RF) …
Unpaired MR motion artifact deep learning using outlier-rejecting bootstrap aggregation
Recently, deep learning approaches for MR motion artifact correction have been extensively
studied. Although these approaches have shown high performance and lower …
studied. Although these approaches have shown high performance and lower …