Michael Lustig — accepted AI-conference papers

2023

ResoNet: Noise-Trained Physics-Informed MRI Off-Resonance Correction

Alfredo De Goyeneche, Shreya Ramachandran, Ke Wang, Ekin Karasan, Joseph Yitan Cheng, Stella X. Yu +1

NeurIPS 2023poster

Magnetic Resonance Imaging (MRI) is a powerful medical imaging modality that offers diagnostic information without harmful ionizing radiation. Unlike optical imaging, MRI sequentially samples the spatial Fourier domain (k-space) of the image. Measurements are collected in multiple shots, or readout…

Cited by 4 Source ↗PDF ↗code ↗Scholar ↗

2017

Barker-Coded node-pore resistive pulse sensing with built-in coincidence correction

Michael R. Kellman, François Rivest, Alina Pechacek, Lydia Sohn, Michael Lustig

ICASSP 2017accepted

A resistive pulse sensing device is able to extract quantities such as concentration and size distribution of particles, e.g. cells or microspheres, as they flow through the device's sensor region, i.e. channel, in an electrolyte solution. The dynamic range of detectable particle sizes is limited by…

Cited by 11 Source ↗Scholar ↗

2016

Beyond low rank + sparse: Multi-scale low rank matrix decomposition

Frank Ong, Michael Lustig

ICASSP 2016accepted

The recent low rank + sparse matrix decomposition [1,2] enables us to decompose a matrix into sparse and globally low rank components. In this paper, we present a natural generalization and consider the decomposition of matrices into low rank components of multiple scales. The proposed multi-scale l…

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