Search Results for author:
Found 14 papers, 6 papers with code
AutoSamp: Autoencoding MRI Sampling via Variational Information Maximization
Experiments on public MRI datasets show improved reconstruction quality of the proposed AutoSamp method over the prevailing variable density and variable density Poisson disc sampling.
Coil Sketching for computationally-efficient MR iterative reconstruction
Results: First, we performed ablation experiments to validate the sketching matrix design on both Cartesian and non-Cartesian datasets.
Automated MRI Field of View Prescription from Region of Interest Prediction by Intra-stack Attention Neural Network
We propose a deep-learning framework, trained by radiologists' supervision, for automating FOV prescription.
Artifact- and content-specific quality assessment for MRI with image rulers
We propose a framework with multi-task CNN model trained with calibrated labels and inferenced with image rulers.
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).
Unsupervised MRI Reconstruction with Generative Adversarial Networks
Deep learning-based image reconstruction methods have achieved promising results across multiple MRI applications.
Analysis of Deep Complex-Valued Convolutional Neural Networks for MRI Reconstruction
Many real-world signal sources are complex-valued, having real and imaginary components.
Accelerating cardiac cine MRI using a deep learning-based ESPIRiT reconstruction
Feasibility of this approach is demonstrated in reconstructions of prospectively undersampled data which were acquired in a single heartbeat per slice.
Wasserstein GANs for MR Imaging: from Paired to Unpaired Training
The reconstruction networks consist of a generator which suppresses the input image artifacts, and a discriminator using a pool of (unpaired) labels to adjust the reconstruction quality.
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
Compressed Sensing: From Research to Clinical Practice with Data-Driven Learning
Data-driven learning provides a solution to address these challenges.
Deep Residual Network for Off-Resonance Artifact Correction with Application to Pediatric Body Magnetic Resonance Angiography with 3D Cones
Long-readout scans, with greater off-resonance artifacts but shorter scan time, were corrected by autofocus and Off-ResNet and compared to short-readout scans by normalized root-mean-square error (NRMSE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR).
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.
Highly Scalable Image Reconstruction using Deep Neural Networks with Bandpass Filtering
To increase the flexibility and scalability of deep neural networks for image reconstruction, a framework is proposed based on bandpass filtering.
