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 in magnetic resonance image reconstruction
Magnetic resonance (MR) imaging visualises soft tissue contrast in exquisite detail without
harmful ionising radiation. In this work, we provide a state‐of‐the‐art review on the use of …
harmful ionising radiation. In this work, we provide a state‐of‐the‐art review on the use of …
DuDoRNet: learning a dual-domain recurrent network for fast MRI reconstruction with deep T1 prior
MRI with multiple protocols is commonly used for diagnosis, but it suffers from a long
acquisition time, which yields the image quality vulnerable to say motion artifacts. To …
acquisition time, which yields the image quality vulnerable to say motion artifacts. To …
Deep-learning-based multi-modal fusion for fast MR reconstruction
T1-weighted image (T1WI) and T2-weighted image (T2WI) are the two routinely acquired
magnetic resonance (MR) modalities that can provide complementary information for clinical …
magnetic resonance (MR) modalities that can provide complementary information for clinical …
DSFormer: A dual-domain self-supervised transformer for accelerated multi-contrast MRI reconstruction
Abstract Multi-contrast MRI (MC-MRI) captures multiple complementary imaging modalities
to aid in radiological decision-making. Given the need for lowering the time cost of multiple …
to aid in radiological decision-making. Given the need for lowering the time cost of multiple …
Spherical deformable u-net: Application to cortical surface parcellation and development prediction
Convolutional Neural Networks (CNNs) have achieved overwhelming success in learning-
related problems for 2D/3D images in the Euclidean space. However, unlike in the …
related problems for 2D/3D images in the Euclidean space. However, unlike in the …
Single-breath-hold T2WI liver MRI with deep learning-based reconstruction: a clinical feasibility study in comparison to conventional multi-breath-hold T2WI liver MRI
Objective To investigate the clinical feasibility of single-breath-hold (SBH) T2-weighted
(T2WI) liver MRI with deep learning-based reconstruction in the evaluation of image quality …
(T2WI) liver MRI with deep learning-based reconstruction in the evaluation of image quality …
Reconstruction of multicontrast MR images through deep learning
Purpose Magnetic resonance (MR) imaging with a long scan time can lead to degraded
images due to patient motion, patient discomfort, and increased costs. For these reasons …
images due to patient motion, patient discomfort, and increased costs. For these reasons …
Multimodal MRI reconstruction assisted with spatial alignment network
In clinical practice, multi-modal magnetic resonance imaging (MRI) with different contrasts is
usually acquired in a single study to assess different properties of the same region of interest …
usually acquired in a single study to assess different properties of the same region of interest …
Joint cross-attention network with deep modality prior for fast MRI reconstruction
Current deep learning-based reconstruction models for accelerated multi-coil magnetic
resonance imaging (MRI) mainly focus on subsampled k-space data of single modality using …
resonance imaging (MRI) mainly focus on subsampled k-space data of single modality using …