Search Results for author:
Found 17 papers, 3 papers with code
Non-Cartesian Self-Supervised Physics-Driven Deep Learning Reconstruction for Highly-Accelerated Multi-Echo Spiral fMRI
Functional MRI (fMRI) is an important tool for non-invasive studies of brain function.
On the Robustness of deep learning-based MRI Reconstruction to image transformations
We find a new instability source of MRI image reconstruction, i. e., the lack of reconstruction robustness against spatial transformations of an input, e. g., rotation and cutout.
Accelerated MRI With Deep Linear Convolutional Transform Learning
Recent studies show that deep learning (DL) based MRI reconstruction outperforms conventional methods, such as parallel imaging and compressed sensing (CS), in multiple applications.
Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging
We consider inverse problems with both linear and non-linear forward models for computational MRI, and review the classical approaches for solving these.
Unsupervised Deep Learning Methods for Biological Image Reconstruction and Enhancement
Recently, deep learning approaches have become the main research frontier for biological image reconstruction and enhancement problems thanks to their high performance, along with their ultra-fast inference times.
20-fold Accelerated 7T fMRI Using Referenceless Self-Supervised Deep Learning Reconstruction
High spatial and temporal resolution across the whole brain is essential to accurately resolve neural activities in fMRI.
Improved Simultaneous Multi-Slice Functional MRI Using Self-supervised Deep Learning
Self-supervised learning that does not require fully-sampled data has recently been proposed and has shown similar performance to supervised learning.
On Instabilities of Conventional Multi-Coil MRI Reconstruction to Small Adverserial Perturbations
Although deep learning (DL) has received much attention in accelerated MRI, recent studies suggest small perturbations may lead to instabilities in DL-based reconstructions, leading to concern for their clinical application.
Zero-Shot Self-Supervised Learning for MRI Reconstruction
Moreover, recent studies show that database-trained models may not generalize well when the unseen measurements differ in terms of sampling pattern, acceleration rate, SNR, image contrast, and anatomy.
Self-Supervised Physics-Guided Deep Learning Reconstruction For High-Resolution 3D LGE CMR
Late gadolinium enhancement (LGE) cardiac MRI (CMR) is the clinical standard for diagnosis of myocardial scar.
Improved Supervised Training of Physics-Guided Deep Learning Image Reconstruction with Multi-Masking
Physics-guided deep learning (PG-DL) via algorithm unrolling has received significant interest for improved image reconstruction, including MRI applications.
Multi-Mask Self-Supervised Learning for Physics-Guided Neural Networks in Highly Accelerated MRI
In this study, we propose an improved self-supervised learning strategy that more efficiently uses the acquired data to train a physics-guided reconstruction network without a database of fully-sampled data.
Noise2Inpaint: Learning Referenceless Denoising by Inpainting Unrolling
These methods rely on a masking approach that divides the image pixels into two disjoint sets, where one is used as input to the network while the other is used to define the loss.
High-Fidelity Accelerated MRI Reconstruction by Scan-Specific Fine-Tuning of Physics-Based Neural Networks
In addition, the proposed approach has the potential to reduce the risks of generalization to rare pathological conditions, which may be unavailable in the training data.
Self-Supervised Learning of Physics-Guided Reconstruction Neural Networks without Fully-Sampled Reference Data
Results: Results on five different knee sequences at acceleration rate of 4 shows that proposed self-supervised approach performs closely with supervised learning, while significantly outperforming conventional compressed sensing and parallel imaging, as characterized by quantitative metrics and a clinical reader study.
Dense Recurrent Neural Networks for Accelerated MRI: History-Cognizant Unrolling of Optimization Algorithms
These methods unroll iterative optimization algorithms to solve the inverse problem objective function, by alternating between domain-specific data consistency and data-driven regularization via neural networks.
Self-Supervised Physics-Based Deep Learning MRI Reconstruction Without Fully-Sampled Data
In this work, we tackle this issue and propose a self-supervised learning strategy that enables physics-based DL reconstruction without fully-sampled data.
