2 code implementations • 5 Jun 2023 • Cagan Alkan, Morteza Mardani, Shreyas S. Vasanawala, John M. Pauly
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.
no code implementations • 10 May 2023 • Julio A. Oscanoa, Frank Ong, Siddharth S. Iyer, Zhitao Li, Christopher M. Sandino, Batu Ozturkler, Daniel B. Ennis, Mert Pilanci, Shreyas S. Vasanawala
Results: First, we performed ablation experiments to validate the sketching matrix design on both Cartesian and non-Cartesian datasets.
no code implementations • 9 Nov 2022 • Ke Lei, Ali B. Syed, Xucheng Zhu, John M. Pauly, Shreyas S. Vasanawala
We propose a deep-learning framework, trained by radiologists' supervision, for automating FOV prescription.
no code implementations • 6 Nov 2021 • Ke Lei, John M. Pauly, Shreyas S. Vasanawala
We propose a framework with multi-task CNN model trained with calibrated labels and inferenced with image rulers.
no code implementations • 6 Mar 2021 • Ke Wang, Michael Kellman, Christopher M. Sandino, Kevin Zhang, Shreyas S. Vasanawala, Jonathan I. Tamir, Stella X. Yu, Michael Lustig
Deep learning (DL) based unrolled reconstructions have shown state-of-the-art performance for under-sampled magnetic resonance imaging (MRI).
1 code implementation • 29 Aug 2020 • Elizabeth K. Cole, John M. Pauly, Shreyas S. Vasanawala, Frank Ong
Deep learning-based image reconstruction methods have achieved promising results across multiple MRI applications.
1 code implementation • 3 Apr 2020 • Elizabeth K. Cole, Joseph Y. Cheng, John M. Pauly, Shreyas S. Vasanawala
Many real-world signal sources are complex-valued, having real and imaginary components.
no code implementations • 13 Nov 2019 • Christopher M. Sandino, Peng Lai, Shreyas S. Vasanawala, Joseph Y. Cheng
Feasibility of this approach is demonstrated in reconstructions of prospectively undersampled data which were acquired in a single heartbeat per slice.
no code implementations • 15 Oct 2019 • Ke Lei, Morteza Mardani, John M. Pauly, Shreyas S. Vasanawala
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.
1 code implementation • 30 Sep 2019 • Frank Ong, Xucheng Zhu, Joseph Y. Cheng, Kevin M. Johnson, Peder E. Z. Larson, Shreyas S. Vasanawala, Michael Lustig
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
1 code implementation • 19 Mar 2019 • Joseph Y. Cheng, Feiyu Chen, Christopher Sandino, Morteza Mardani, John M. Pauly, Shreyas S. Vasanawala
Data-driven learning provides a solution to address these challenges.
no code implementations • 28 Sep 2018 • David Y Zeng, Jamil Shaikh, Dwight G. Nishimura, Shreyas S. Vasanawala, Joseph Y. Cheng
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).
no code implementations • 11 Sep 2018 • Michael J. Anderson, Jonathan I. Tamir, Javier S. Turek, Marcus T. Alley, Theodore L. Willke, Shreyas S. Vasanawala, Michael Lustig
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.
1 code implementation • 8 May 2018 • Joseph Y. Cheng, Feiyu Chen, Marcus T. Alley, John M. Pauly, Shreyas S. Vasanawala
To increase the flexibility and scalability of deep neural networks for image reconstruction, a framework is proposed based on bandpass filtering.