MRI‐based artificial intelligence in rectal cancer
C Wong, Y Fu, M Li, S Mu, X Chu, J Fu… - Journal of Magnetic …, 2023 - Wiley Online Library
Rectal cancer (RC) accounts for approximately one‐third of colorectal cancer (CRC), with
death rates increasing in patients younger than 50 years old. Magnetic resonance imaging …
death rates increasing in patients younger than 50 years old. Magnetic resonance imaging …
TranSMS: Transformers for super-resolution calibration in magnetic particle imaging
Magnetic particle imaging (MPI) offers exceptional contrast for magnetic nanoparticles
(MNP) at high spatio-temporal resolution. A common procedure in MPI starts with a …
(MNP) at high spatio-temporal resolution. A common procedure in MPI starts with a …
[HTML][HTML] Artificial intelligence and radiomics in magnetic resonance imaging of rectal cancer: A review
G Di Costanzo, R Ascione, A Ponsiglione… - … of Targeted Anti …, 2023 - ncbi.nlm.nih.gov
Rectal cancer (RC) is one of the most common tumours worldwide in both males and
females, with significant morbidity and mortality rates, and it accounts for approximately one …
females, with significant morbidity and mortality rates, and it accounts for approximately one …
DEQ-MPI: A deep equilibrium reconstruction with learned consistency for magnetic particle imaging
Magnetic particle imaging (MPI) offers unparalleled contrast and resolution for tracing
magnetic nanoparticles. A common imaging procedure calibrates a system matrix (SM) that …
magnetic nanoparticles. A common imaging procedure calibrates a system matrix (SM) that …
One-dimensional deep low-rank and sparse network for accelerated MRI
Deep learning has shown astonishing performance in accelerated magnetic resonance
imaging (MRI). Most state-of-the-art deep learning reconstructions adopt the powerful …
imaging (MRI). Most state-of-the-art deep learning reconstructions adopt the powerful …
Equilibrated zeroth-order unrolled deep network for parallel MR imaging
In recent times, model-driven deep learning has evolved an iterative algorithm into a
cascade network by replacing the regularizer's first-order information, such as the (sub) …
cascade network by replacing the regularizer's first-order information, such as the (sub) …
Self-score: Self-supervised learning on score-based models for mri reconstruction
Recently, score-based diffusion models have shown satisfactory performance in MRI
reconstruction. Most of these methods require a large amount of fully sampled MRI data as a …
reconstruction. Most of these methods require a large amount of fully sampled MRI data as a …
Swin deformable attention u-net transformer (sdaut) for explainable fast mri
Fast MRI aims to reconstruct a high fidelity image from partially observed measurements.
Exuberant development in fast MRI using deep learning has been witnessed recently …
Exuberant development in fast MRI using deep learning has been witnessed recently …
K-UNN: k-space interpolation with untrained neural network
Recently, untrained neural networks (UNNs) have shown satisfactory performances for MR
image reconstruction on random sampling trajectories without using additional full-sampled …
image reconstruction on random sampling trajectories without using additional full-sampled …
A simultaneous multi‐slice T2 mapping framework based on overlapping‐echo detachment planar imaging and deep learning reconstruction
Purpose Quantitative MRI (qMRI) is of great importance to clinical medicine and scientific
research. However, most qMRI techniques are time‐consuming and sensitive to motion …
research. However, most qMRI techniques are time‐consuming and sensitive to motion …