69 citations · 232 across the 21 of their papers we have counts for
22 papers
On the Foundation Model for Cardiac MRI Reconstruction
Chi Zhang, Michael Loecher, Cagan Alkan +3
In recent years, machine learning (ML) based reconstruction has been widely investigated and employed in cardiac magnetic resonance (CMR) imaging. ML-based reconstructions can deli…
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…
Coil Sketching for computationally-efficient MR iterative reconstruction
Julio A. Oscanoa, Frank Ong, Siddharth S. Iyer +6
Purpose: Parallel imaging and compressed sensing reconstructions of large MRI datasets often have a prohibitive computational cost that bottlenecks clinical deployment, especially…
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…
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…
Memory-efficient Learning for High-Dimensional MRI Reconstruction
Ke Wang, Michael Kellman, Christopher M. Sandino +5
Deep learning (DL) based unrolled reconstructions have shown state-of-the-art performance for under-sampled magnetic resonance imaging (MRI). Similar to compressed sensing, DL can…
SLfRank: Shinnar-Le-Roux Pulse Design with Reduced Energy and Accurate Phase Profiles using Rank Factorization
Frank Ong, Zheng Zhong, Congyu Liao +3
The Shinnar-Le-Roux (SLR) algorithm is widely used to design frequency selective pulses with large flip angles. We improve its design process to generate pulses with lower energy (…
Spectral Decomposition in Deep Networks for Segmentation of Dynamic Medical Images
Edgar A. Rios Piedra, Morteza Mardani, Frank Ong +3
Dynamic contrast-enhanced magnetic resonance imaging (DCE- MRI) is a widely used multi-phase technique routinely used in clinical practice. DCE and similar datasets of dynamic medi…
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…
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…
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…
Accelerating cardiac cine MRI using a deep learning-based ESPIRiT reconstruction
Christopher M. Sandino, Peng Lai, Shreyas S. Vasanawala +1
A novel neural network architecture, known as DL-ESPIRiT, is proposed to reconstruct rapidly acquired cardiac MRI data without field-of-view limitations which are present in previo…
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…
Extreme MRI: Large-Scale Volumetric Dynamic Imaging from Continuous Non-Gated Acquisitions
Frank Ong, Xucheng Zhu, Joseph Y. Cheng +4
Purpose: To develop a framework to reconstruct large-scale volumetric dynamic MRI from rapid continuous and non-gated acquisitions, with applications to pulmonary and dynamic contr…
Degrees of Freedom Analysis of Unrolled Neural Networks
Morteza Mardani, Qingyun Sun, Vardan Papyan +3
Unrolled neural networks emerged recently as an effective model for learning inverse maps appearing in image restoration tasks. However, their generalization risk (i.e., test mean-…
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…
Uncertainty Quantification in Deep MRI Reconstruction
Vineet Edupuganti, Morteza Mardani, Shreyas Vasanawala +1
Reliable MRI is crucial for accurate interpretation in therapeutic and diagnostic tasks. However, undersampling during MRI acquisition as well as the overparameterized and non-tran…
Deep Residual Network for Off-Resonance Artifact Correction with Application to Pediatric Body Magnetic Resonance Angiography with 3D Cones
David Y Zeng, Jamil Shaikh, Dwight G Nishimura +2
Purpose: Off-resonance artifact correction by deep-learning, to facilitate rapid pediatric body imaging with a scan time efficient 3D cones trajectory. Methods: A residual convolut…
Clinically Deployed Distributed Magnetic Resonance Imaging Reconstruction: Application to Pediatric Knee Imaging
Michael J. Anderson, Jonathan I. Tamir, Javier S. Turek +4
Magnetic resonance imaging is capable of producing volumetric images without ionizing radiation. Nonetheless, long acquisitions lead to prohibitively long exams. Compressed sensing…
Highly Scalable Image Reconstruction using Deep Neural Networks with Bandpass Filtering
Joseph Y. Cheng, Feiyu Chen, Marcus T. Alley +2
To increase the flexibility and scalability of deep neural networks for image reconstruction, a framework is proposed based on bandpass filtering. For many applications, sensing me…
Development and Clinical Implementation of Next Generation Very Light Weight and Extremely Flexible Receiver Arrays for Pediatric MRI
Shreyas S. Vasanawala, Robert Stormont, Scott Lindsay +8
We develop a novel next generation light-weight highly flexible pediatric coil array, combine it with a high-density pediatric posterior array or conventional posterior phased arra…