Physics-driven synthetic data learning for biomedical magnetic resonance: The imaging physics-based data synthesis paradigm for artificial intelligence
Deep learning (DL) has driven innovation in the field of computational imaging. One of its
bottlenecks is unavailable or insufficient training data. This article reviews an emerging …
bottlenecks is unavailable or insufficient training data. This article reviews an emerging …
Deep learning for accelerated and robust MRI reconstruction
Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic
resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides …
resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides …
Revealing hidden patterns in deep neural network feature space continuum via manifold learning
Deep neural networks (DNNs) extract thousands to millions of task-specific features during
model training for inference and decision-making. While visualizing these features is critical …
model training for inference and decision-making. While visualizing these features is critical …
An end‐to‐end AI‐based framework for automated discovery of rapid CEST/MT MRI acquisition protocols and molecular parameter quantification (AutoCEST)
Purpose To develop an automated machine‐learning‐based method for the discovery of
rapid and quantitative chemical exchange saturation transfer (CEST) MR fingerprinting …
rapid and quantitative chemical exchange saturation transfer (CEST) MR fingerprinting …
Accelerating CEST imaging using a model‐based deep neural network with synthetic training data
Purpose To develop a model‐based deep neural network for high‐quality image
reconstruction of undersampled multi‐coil CEST data. Theory and Methods Inspired by the …
reconstruction of undersampled multi‐coil CEST data. Theory and Methods Inspired by the …
Accelerated and quantitative three‐dimensional molecular MRI using a generative adversarial network
J Weigand‐Whittier, M Sedykh, K Herz… - Magnetic resonance …, 2023 - Wiley Online Library
Purpose To substantially shorten the acquisition time required for quantitative three‐
dimensional (3D) chemical exchange saturation transfer (CEST) and semisolid …
dimensional (3D) chemical exchange saturation transfer (CEST) and semisolid …
Bloch simulator–driven deep recurrent neural network for magnetization transfer contrast MR fingerprinting and CEST imaging
Purpose To develop a unified deep‐learning framework by combining an ultrafast Bloch
simulator and a semisolid macromolecular magnetization transfer contrast (MTC) MR …
simulator and a semisolid macromolecular magnetization transfer contrast (MTC) MR …
CEST MR fingerprinting (CEST‐MRF) for brain tumor quantification using EPI readout and deep learning reconstruction
Purpose To develop a clinical CEST MR fingerprinting (CEST‐MRF) method for brain tumor
quantification using EPI acquisition and deep learning reconstruction. Methods A CEST …
quantification using EPI acquisition and deep learning reconstruction. Methods A CEST …
MOdel-Based SyntheTic Data-Driven Learning (MOST-DL): Application in Single-Shot T2 Mapping With Severe Head Motion Using Overlapping-Echo Acquisition
Q Yang, Y Lin, J Wang, J Bao, X Wang… - … on Medical Imaging, 2022 - ieeexplore.ieee.org
Use of synthetic data has provided a potential solution for addressing unavailable or
insufficient training samples in deep learning-based magnetic resonance imaging (MRI) …
insufficient training samples in deep learning-based magnetic resonance imaging (MRI) …
MR fingerprinting for semisolid magnetization transfer and chemical exchange saturation transfer quantification
Chemical exchange saturation transfer (CEST) MRI has positioned itself as a promising
contrast mechanism, capable of providing molecular information at sufficient resolution and …
contrast mechanism, capable of providing molecular information at sufficient resolution and …