132 citations · 705 across the 31 of their papers we have counts for
19 papers · 1 filter
AutoSamp: Autoencoding k-space Sampling via Variational Information Maximization for 3D MRI
Cagan Alkan, Morteza Mardani, Congyu Liao +3
Accelerated MRI protocols routinely involve a predefined sampling pattern that undersamples the k-space. Finding an optimal pattern can enhance the reconstruction quality, however…
Automated MRI Field of View Prescription from Region of Interest Prediction by Intra-stack Attention Neural Network
Ke Lei, Ali B. Syed, Xucheng Zhu +2
Manual prescription of the field of view (FOV) by MRI technologists is variable and prolongs the scanning process. Often, the FOV is too large or crops critical anatomy. We propose…
GLEAM: Greedy Learning for Large-Scale Accelerated MRI Reconstruction
Batu Ozturkler, Arda Sahiner, Tolga Ergen +6
Unrolled neural networks have recently achieved state-of-the-art accelerated MRI reconstruction. These networks unroll iterative optimization algorithms by alternating between phys…
Artifact- and content-specific quality assessment for MRI with image rulers
Ke Lei, John M. Pauly, Shreyas S. Vasanawala
In clinical practice MR images are often first seen by radiologists long after the scan. If image quality is inadequate either patients have to return for an additional scan, or a…
VORTEX: Physics-Driven Data Augmentations Using Consistency Training for Robust Accelerated MRI Reconstruction
Arjun D Desai, Beliz Gunel, Batu M Ozturkler +6
Deep neural networks have enabled improved image quality and fast inference times for various inverse problems, including accelerated magnetic resonance imaging (MRI) reconstructio…
Noise2Recon: Enabling Joint MRI Reconstruction and Denoising with Semi-Supervised and Self-Supervised Learning
Arjun D Desai, Batu M Ozturkler, Christopher M Sandino +7
Deep learning (DL) has shown promise for faster, high quality accelerated MRI reconstruction. However, supervised DL methods depend on extensive amounts of fully-sampled (labeled)…
NeRP: Implicit Neural Representation Learning with Prior Embedding for Sparsely Sampled Image Reconstruction
Liyue Shen, John Pauly, Lei Xing
Image reconstruction is an inverse problem that solves for a computational image based on sampled sensor measurement. Sparsely sampled image reconstruction poses addition challenge…
Least Squares Optimal Density Compensation for the Gridding Non-uniform Discrete Fourier Transform
Nicholas Dwork, Daniel O'Connor, Ethan M. I. Johnson +4
The Gridding algorithm has shown great utility for reconstructing images from non-uniformly spaced samples in the Fourier domain in several imaging modalities. Due to the non-unifo…
OUTCOMES: Rapid Under-sampling Optimization achieves up to 50% improvements in reconstruction accuracy for multi-contrast MRI sequences
Ke Wang, Enhao Gong, Yuxin Zhang +3
Multi-contrast Magnetic Resonance Imaging (MRI) acquisitions from a single scan have tremendous potential to streamline exams and reduce imaging time. However, maintaining clinical…
Unsupervised MRI Reconstruction with Generative Adversarial Networks
Elizabeth K. Cole, John M. Pauly, Shreyas S. Vasanawala +1
Deep learning-based image reconstruction methods have achieved promising results across multiple MRI applications. However, most approaches require large-scale fully-sampled ground…
multiMap: A Gradient Spoiled Sequence for Simultaneously Measuring B1+, B0, T1/M0, T2, T2*, and Fat Fraction of a Slice
Nicholas Dwork, Adam B. Kerr, Ethan M. I. Johnson +5
We propose multiMap, a single scan that can generate several quantitative maps simultaneously. The sequence acquires multiple images in a time-efficient manner, which can be modele…
Multi-coil Magnetic Resonance Imaging with Compressed Sensing Using Physically Motivated Regularization
Nicholas Dwork, Ethan M. I. Johnson, Daniel O'Connor +5
With the advent of multi-coil imaging and compressed sensing, a number of model based reconstruction algorithms have been created. They incorporate a multitude of different regular…
Analysis of Deep Complex-Valued Convolutional Neural Networks for MRI Reconstruction
Elizabeth K. Cole, Joseph Y. Cheng, John M. Pauly +1
Many real-world signal sources are complex-valued, having real and imaginary components. However, the vast majority of existing deep learning platforms and network architectures do…
Fast Variable Density Poisson-Disc Sample Generation with Directional Variation
Nicholas Dwork, Corey A. Baron, Ethan M. I. Johnson +3
We present a fast method for generating random samples according to a variable density Poisson-disc distribution. A minimum threshold distance is used to create a background grid a…
Utilizing the Wavelet Transform's Structure in Compressed Sensing
Nicholas Dwork, Daniel O'Connor, Corey A. Baron +4
Compressed sensing has empowered quality image reconstruction with fewer data samples than previously though possible. These techniques rely on a sparsifying linear transformation.…
Diagnostic Image Quality Assessment and Classification in Medical Imaging: Opportunities and Challenges
Jeffrey Ma, Ukash Nakarmi, Cedric Yue Sik Kin +6
Magnetic Resonance Imaging (MRI) suffers from several artifacts, the most common of which are motion artifacts. These artifacts often yield images that are of non-diagnostic qualit…
Wasserstein GANs for MR Imaging: from Paired to Unpaired Training
Ke Lei, Morteza Mardani, John M. Pauly +1
Lack of ground-truth MR images impedes the common supervised training of neural networks for image reconstruction. To cope with this challenge, this paper leverages unpaired advers…
Compressed Sensing: From Research to Clinical Practice with Data-Driven Learning
Joseph Y. Cheng, Feiyu Chen, Christopher Sandino +3
Compressed sensing in MRI enables high subsampling factors while maintaining diagnostic image quality. This technique enables shortened scan durations and/or improved image resolut…
Quantitative Susceptibility Mapping using Deep Neural Network: QSMnet
Jaeyeon Yoon, Enhao Gong, Itthi Chatnuntawech +10
Deep neural networks have demonstrated promising potential for the field of medical image reconstruction. In this work, an MRI reconstruction algorithm, which is referred to as qua…