Search Results for author:
Found 18 papers, 11 papers with code
K-band: Self-supervised MRI Reconstruction via Stochastic Gradient Descent over K-space Subsets
In each training iteration, rather than using the fully sampled k-space for computing gradients, we use only a small k-space portion.
High-fidelity Direct Contrast Synthesis from Magnetic Resonance Fingerprinting
Here we propose a supervised learning-based method that directly synthesizes contrast-weighted images from the MRF data without going through the quantitative mapping and spin-dynamics simulation.
Generative Adversarial Network
Magnetic Resonance Fingerprinting
+1
Subtle Data Crimes: Naively training machine learning algorithms could lead to overly-optimistic results
We demonstrate this phenomenon for inverse problem solvers and show how their biased performance stems from hidden data preprocessing pipelines.
High Fidelity Deep Learning-based MRI Reconstruction with Instance-wise Discriminative Feature Matching Loss
By adding an additional loss function on the low-dimensional feature space during training, the reconstruction frameworks from under-sampled or corrupted data can reproduce more realistic images that are closer to the original with finer textures, sharper edges, and improved overall image quality.
Memory-efficient Learning for High-Dimensional MRI Reconstruction
Deep learning (DL) based unrolled reconstructions have shown state-of-the-art performance for under-sampled magnetic resonance imaging (MRI).
How to do Physics-based Learning
The goal of this tutorial is to explain step-by-step how to implement physics-based learning for the rapid prototyping of a computational imaging system.
Memory-efficient Learning for Large-scale Computational Imaging
Critical aspects of computational imaging systems, such as experimental design and image priors, can be optimized through deep networks formed by the unrolled iterations of classical model-based reconstructions (termed physics-based networks).
Memory-efficient Learning for Large-scale Computational Imaging -- NeurIPS deep inverse workshop
Computational imaging systems jointly design computation and hardware to retrieve information which is not traditionally accessible with standard imaging systems.
Extreme MRI: Large-Scale Volumetric Dynamic Imaging from Continuous Non-Gated Acquisitions
We demonstrate the feasibility of the proposed method on DCE imaging acquired with a golden-angle ordered 3D cones trajectory and pulmonary imaging acquired with a bit-reversed ordered 3D radial trajectory.
Medical Physics Image and Video Processing
Accelerating Non-Cartesian MRI Reconstruction Convergence using k-space Preconditioning
We propose a k-space preconditioning formulation for accelerating the convergence of iterative Magnetic Resonance Imaging (MRI) reconstructions from non-uniformly sampled k-space data.
Medical Physics
SURE-based Automatic Parameter Selection For ESPIRiT Calibration
The purpose of this work is to automatically select parameters in ESPIRiT for more robust and consistent performance across a variety of exams.
Medical Physics
Clinically Deployed Distributed Magnetic Resonance Imaging Reconstruction: Application to Pediatric Knee Imaging
Our improvements to the pipeline on a single machine provide a 3x overall reconstruction speedup, which allowed us to add algorithmic changes improving image quality.
General Phase Regularized Reconstruction using Phase Cycling
Purpose: To develop a general phase regularized image reconstruction method, with applications to partial Fourier imaging, water-fat imaging and flow imaging.
Better than Real: Complex-valued Neural Nets for MRI Fingerprinting
The task of MRI fingerprinting is to identify tissue parameters from complex-valued MRI signals.
ENLIVE: An Efficient Nonlinear Method for Calibrationless and Robust Parallel Imaging
Robustness against data inconsistencies, imaging artifacts and acquisition speed are crucial factors limiting the possible range of applications for magnetic resonance imaging (MRI).
Medical Physics
On the Empirical Effect of Gaussian Noise in Under-sampled MRI Reconstruction
The effects of lower SNR and the underdetermined system are coupled during reconstruction, making it difficult to isolate the impact of lower SNR on image quality.
Beyond Low Rank + Sparse: Multi-scale Low Rank Matrix Decomposition
We present a natural generalization of the recent low rank + sparse matrix decomposition and consider the decomposition of matrices into components of multiple scales.
Systems and Control Information Theory Numerical Analysis Information Theory Optimization and Control
Estimating Absolute-Phase Maps Using ESPIRiT and Virtual Conjugate Coils
Based on this method, a new post-processing step is proposed for the explicit computation of coil sensitivities that include the absolute phase of the image.
