[HTML][HTML] What's new and what's next in diffusion MRI preprocessing
Diffusion MRI (dMRI) provides invaluable information for the study of tissue microstructure
and brain connectivity, but suffers from a range of imaging artifacts that greatly challenge the …
and brain connectivity, but suffers from a range of imaging artifacts that greatly challenge the …
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 …
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 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 …
Deep residual learning for accelerated MRI using magnitude and phase networks
Objective: Accelerated magnetic resonance (MR) image acquisition with compressed
sensing (CS) and parallel imaging is a powerful method to reduce MR imaging scan time …
sensing (CS) and parallel imaging is a powerful method to reduce MR imaging scan time …
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 …
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 …
A general framework for compressed sensing and parallel MRI using annihilating filter based low-rank Hankel matrix
Parallel MRI (pMRI) and compressed sensing MRI (CS-MRI) have been considered as two
distinct reconstruction problems. Inspired by recent k-space interpolation methods, an …
distinct reconstruction problems. Inspired by recent k-space interpolation methods, an …
Multi‐shot sensitivity‐encoded diffusion data recovery using structured low‐rank matrix completion (MUSSELS)
Purpose To introduce a novel method for the recovery of multi‐shot diffusion weighted (MS‐
DW) images from echo‐planar imaging (EPI) acquisitions. Methods Current EPI‐based MS …
DW) images from echo‐planar imaging (EPI) acquisitions. Methods Current EPI‐based MS …
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) …