Search Results for author: Peder E. Z. Larson

Found 13 papers, 5 papers with code

Physics-driven Deep Learning for PET/MRI

no code implementations11 Jun 2022 Abhejit Rajagopal, Andrew P. Leynes, Nicholas Dwork, Jessica E. Scholey, Thomas A. Hope, Peder E. Z. Larson

In this paper, we review physics- and data-driven reconstruction techniques for simultaneous positron emission tomography (PET) / magnetic resonance imaging (MRI) systems, which have significant advantages for clinical imaging of cancer, neurological disorders, and heart disease.

MRI Reconstruction

Federated Learning with Research Prototypes for Multi-Center MRI-based Detection of Prostate Cancer with Diverse Histopathology

1 code implementation11 Jun 2022 Abhejit Rajagopal, Ekaterina Redekop, Anil Kemisetti, Rushi Kulkarni, Steven Raman, Kirti Magudia, Corey W. Arnold, Peder E. Z. Larson

Early prostate cancer detection and staging from MRI are extremely challenging tasks for both radiologists and deep learning algorithms, but the potential to learn from large and diverse datasets remains a promising avenue to increase their generalization capability both within- and across clinics.

Federated Learning

Scan-specific Self-supervised Bayesian Deep Non-linear Inversion for Undersampled MRI Reconstruction

1 code implementation1 Mar 2022 Andrew P. Leynes, Srikantan S. Nagarajan, Peder E. Z. Larson

Although unsupervised or self-supervised deep learning methods have emerged to address the limitations of supervised deep learning approaches, they still require a database of images.

Bayesian Inference MRI Reconstruction

Utilizing the Structure of the Curvelet Transform with Compressed Sensing

no code implementations24 Jul 2021 Nciholas Dwork, Peder E. Z. Larson

The discrete curvelet transform decomposes an image into a set of fundamental components that are distinguished by direction and size as well as a low-frequency representation.


Least Squares Optimal Density Compensation for the Gridding Non-uniform Discrete Fourier Transform

no code implementations12 Jun 2021 Nicholas Dwork, Daniel O'Connor, Ethan M. I. Johnson, Corey A. Baron, Jeremy W. Gordon, John M. Pauly, Peder E. Z. Larson

The Gridding algorithm has shown great utility for reconstructing images from non-uniformly spaced samples in the Fourier domain in several imaging modalities.

Anisotropic field-of-views in radial imaging

1 code implementation12 Jan 2021 Peder E. Z. Larson, Paul T. Gurney, Dwight G. Nishimura

One drawback is that standard implementations do not support anisotropic field-of-view (FOV) shapes, which are used to match the imaging parameters to the object or region-of-interest.

Predicting Generalization in Deep Learning via Local Measures of Distortion

no code implementations13 Dec 2020 Abhejit Rajagopal, Vamshi C. Madala, Shivkumar Chandrasekaran, Peder E. Z. Larson

We study generalization in deep learning by appealing to complexity measures originally developed in approximation and information theory.


Fast Variable Density Poisson-Disc Sample Generation with Directional Variation

no code implementations14 Apr 2020 Nicholas Dwork, Corey A. Baron, Ethan M. I. Johnson, Daniel O'Connor, John M. Pauly, Peder E. Z. Larson

We present a fast method for generating random samples according to a variable density Poisson-disc distribution.

Di-chromatic Interpolation of Magnetic Resonance Metabolic Imagery

no code implementations11 Mar 2020 Nicholas Dwork, Jeremy W. Gordon, Shuyu Tang, Daniel O'Connor, Esben Sovso Szocska Hansen, Christoffer Laustsen, Peder E. Z. Larson

Magnetic resonance imaging with hyperpolarized contrast agents can provide unprecedented \textit{in-vivo} measurements of metabolism, but yields images that are lower resolution than that achieved with proton anatomical imaging.

Utilizing the Wavelet Transform's Structure in Compressed Sensing

no code implementations11 Feb 2020 Nicholas Dwork, Daniel O'Connor, Corey A. Baron, Ethan M. I. Johnson, Adam B. Kerr, John M. Pauly, Peder E. Z. Larson

In this work, we take advantage of the structure of this wavelet transform and identify an affine transformation that increases the sparsity of the result.

Denoising Image Reconstruction

Extreme MRI: Large-Scale Volumetric Dynamic Imaging from Continuous Non-Gated Acquisitions

1 code implementation30 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

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