Search Results for author:
Found 24 papers, 13 papers with code
χ-sepnet: Deep neural network for magnetic susceptibility source separation
Magnetic susceptibility source separation ($\chi$-separation), an advanced quantitative susceptibility mapping (QSM) method, enables the separate estimation of para- and diamagnetic susceptibility source distributions in the brain.
Fast Whole-Brain MR Multi-Parametric Mapping with Scan-Specific Self-Supervised Networks
However, Joint MAPLE suffers from prohibitively long reconstruction time to estimate the maps from a multi-echo, multi-flip angle (MEMFA) dataset at high resolution in a scan-specific manner.
NLCG-Net: A Model-Based Zero-Shot Learning Framework for Undersampled Quantitative MRI Reconstruction
Typical quantitative MRI (qMRI) methods estimate parameter maps after image reconstructing, which is prone to biases and error propagation.
SubZero: Subspace Zero-Shot MRI Reconstruction
Recently introduced zero-shot self-supervised learning (ZS-SSL) has shown potential in accelerated MRI in a scan-specific scenario, which enabled high-quality reconstructions without access to a large training dataset.
Improved Multi-Shot Diffusion-Weighted MRI with Zero-Shot Self-Supervised Learning Reconstruction
In this study, we introduce a novel msEPI reconstruction approach called zero-MIRID (zero-shot self-supervised learning of Multi-shot Image Reconstruction for Improved Diffusion MRI).
Zero-DeepSub: Zero-Shot Deep Subspace Reconstruction for Rapid Multiparametric Quantitative MRI Using 3D-QALAS
Using an ISMRM/NIST system phantom, the accuracy and reproducibility of the T1 and T2 maps estimated using the proposed methods were evaluated by comparing them with reference techniques.
SSL-QALAS: Self-Supervised Learning for Rapid Multiparameter Estimation in Quantitative MRI Using 3D-QALAS
Conclusion: The proposed SSL-QALAS method enabled rapid reconstruction of multiparametric maps from 3D-QALAS measurements without an external dictionary or labeled ground-truth training data.
3D-EPI Blip-Up/Down Acquisition (BUDA) with CAIPI and Joint Hankel Structured Low-Rank Reconstruction for Rapid Distortion-Free High-Resolution T2* Mapping
Purpose: This work aims to develop a novel distortion-free 3D-EPI acquisition and image reconstruction technique for fast and robust, high-resolution, whole-brain imaging as well as quantitative T2* mapping.
SRNR: Training neural networks for Super-Resolution MRI using Noisy high-resolution Reference data
Neural network (NN) based approaches for super-resolution MRI typically require high-SNR high-resolution reference data acquired in many subjects, which is time consuming and a barrier to feasible and accessible implementation.
Latent Signal Models: Learning Compact Representations of Signal Evolution for Improved Time-Resolved, Multi-contrast MRI
Purpose: Training auto-encoders on simulated signal evolution and inserting the decoder into the forward model improves reconstructions through more compact, Bloch-equation-based representations of signal in comparison to linear subspaces.
High-Dimensional Sparse Bayesian Learning without Covariance Matrices
Sparse Bayesian learning (SBL) is a powerful framework for tackling the sparse coding problem.
Wave-Encoded Model-based Deep Learning for Highly Accelerated Imaging with Joint Reconstruction
Purpose: To propose a wave-encoded model-based deep learning (wave-MoDL) strategy for highly accelerated 3D imaging and joint multi-contrast image reconstruction, and further extend this to enable rapid quantitative imaging using an interleaved look-locker acquisition sequence with T2 preparation pulse (3D-QALAS).
Accurate parameter estimation using scan-specific unsupervised deep learning for relaxometry and MR fingerprinting
We propose an unsupervised convolutional neural network (CNN) for relaxation parameter estimation.
Improving accuracy and uncertainty quantification of deep learning based quantitative MRI using Monte Carlo dropout
Dropout is conventionally used during the training phase as regularization method and for quantifying uncertainty in deep learning.
SDnDTI: Self-supervised deep learning-based denoising for diffusion tensor MRI
The noise in diffusion-weighted images (DWIs) decreases the accuracy and precision of diffusion tensor magnetic resonance imaging (DTI) derived microstructural parameters and leads to prolonged acquisition time for achieving improved signal-to-noise ratio (SNR).
eRAKI: Fast Robust Artificial neural networks for K-space Interpolation (RAKI) with Coil Combination and Joint Reconstruction
RAKI can perform database-free MRI reconstruction by training models using only auto-calibration signal (ACS) from each specific scan.
Highly Accelerated EPI with Wave Encoding and Multi-shot Simultaneous Multi-Slice Imaging
We introduce wave encoded acquisition and reconstruction techniques for highly accelerated echo planar imaging (EPI) with reduced g-factor penalty and image artifacts.
Covariance-Free Sparse Bayesian Learning
The most popular inference algorithms for SBL exhibit prohibitively large computational costs for high-dimensional problems due to the need to maintain a large covariance matrix.
Scan Specific Artifact Reduction in K-space (SPARK) Neural Networks Synergize with Physics-based Reconstruction to Accelerate MRI
Conclusion: SPARK synergizes with physics-based acquisition and reconstruction techniques to improve accelerated MRI by training scan-specific models to estimate and correct reconstruction errors in k-space.
SRDTI: Deep learning-based super-resolution for diffusion tensor MRI
High-resolution diffusion tensor imaging (DTI) is beneficial for probing tissue microstructure in fine neuroanatomical structures, but long scan times and limited signal-to-noise ratio pose significant barriers to acquiring DTI at sub-millimeter resolution.
Nonlinear Dipole Inversion (NDI) enables Quantitative Susceptibility Mapping (QSM) without parameter tuning
We propose Nonlinear Dipole Inversion (NDI) for high-quality Quantitative Susceptibility Mapping (QSM) without regularization tuning, while matching the image quality of state-of-the-art reconstruction techniques.
Highly Accelerated Multishot EPI through Synergistic Machine Learning and Joint Reconstruction
While msEPI can mitigate these artifacts, high-quality msEPI has been elusive because of phase mismatch arising from shot-to-shot variations which preclude the combination of the multiple-shot data into a single image.
Rice Classification Using Spatio-Spectral Deep Convolutional Neural Network
In this work, we develop a non-destructive rice variety classification system that benefits from the synergy between hyperspectral imaging and deep convolutional neural network (CNN).
Quantitative Susceptibility Mapping using Deep Neural Network: QSMnet
The QSMnet maps of the test dataset were compared with those from TKD and MEDI for image quality and consistency in multiple head orientations.
Image and Video Processing
