Nullspace-Based Sensitivity Map Estimation with PISCO

Update: May 30, 2023. The software available from this page has been updated from Version 1.0 to Version 1.1. This version provides new features that will help automatically identify the number of iterations needed for power iteration to converge. The code will now notify users (with warnings and error messages) if convergence is unlikely to have been reached, and there are new settings that enable the code to automatically select the number of iterations. The original Version 1.0 is still available on request, although we encourage the use of Version 1.1.

This page provides a MATLAB software implementation for the fast subspace-based sensitivity map estimation methods described in:

R. A. Lobos, C.-C. Chan, J. P. Haldar.
New Theory and Faster Computations for Subspace-Based Sensitivity Map Estimation in Multichannel MRI.
Submitted.
[link]

R. A. Lobos, C.-C. Chan, J. P. Haldar.
Extended Version of ‘New Theory and Faster Computations for Subspace-Based Sensitivity Map Estimation in Multichannel MRI’.
arXiv:2302.13431.
[link]

A detailed description of this software is provided in the technical report:

R. A. Lobos, C.-C. Chan, J. P. Haldar.
PISCO Software Version 1.0.
University of Southern California, Los Angeles, CA.
Technical Report USC-SIPI-458, 2023.
[link]

The estimation of sensitivity maps from k-space calibration data is a common task in many multichannel MRI applications. Subspace-based estimation methods have gained popularity within the MRI community due to their accuracy and robustness. However, these methods can be computationally demanding, and their underlying theoretical principles can be nontrivial to understand. In the references listed above, we have recently proposed a novel theoretical framework for subspace-based sensitivity map estimation. This new framework relies on theoretical concepts from the literature on linear predictability and structured low-rank matrix modeling, and we expect it may be more intuitive and easier to understand for some readers. In addition, we have also introduced a set of computational techniques (that we collectively call PISCO) for accelerated subspace-based sensitivity map estimation. The PISCO techniques enable substantial improvements in computation time (up to a 100-fold improvement in the cases we have tried) relative to conventional subspace-based sensitivity map estimation implementations.

The following MPRAGE example shows a comparison of sensitivity map estimation using the proposed nullspace-based algorithm with (Nullspace + PISCO) and without (Nullspace – PISCO) the use of PISCO techniques. As can be seen, there is no noticeable difference between the two cases. On one of our computers, Nullspace + PISCO allowed sensitivity map estimation in ~2.3 seconds, which is considerably faster than the ~34.8 seconds used by Nullspace – PISCO. Our software download includes the code and data needed to reproduce this example.

The software and data can be downloaded using the form below

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    Permission to use, copy, modify, and distribute this software and its documentation for educational, research and non-profit purposes, without fee, and without a written agreement is hereby granted, provided that the above copyright notice, this paragraph and the following three paragraphs appear in all copies. If you use this code or its derivatives in your own work, you are required to cite at least the PISCO V1.0 technical report [1] and either the journal article (once it is published) [2] or the extended preprint [3].

    [1] R. A. Lobos, C.-C. Chan, J. P. Haldar. PISCO Software Version 1.0. University of Southern California, Los Angeles, CA, Technical Report USC-SIPI-458, 2023.

    [2] R. A. Lobos, C.-C. Chan, J. P. Haldar. New Theory and Faster Computations for Subspace-Based Sensitivity Map Estimation in Multichannel MRI. Submitted.

    [3] R. A. Lobos, C.-C. Chan, J. P. Haldar. Extended Version of “New Theory and Faster Computations for Subspace-Based Sensitivity Map Estimation in Multichannel MRI”. arXiv:2302.13431, 2023.

    Permission to make commercial use of this software may be obtained by contacting:
    USC Stevens Center for Innovation
    University of Southern California
    1150 S. Olive Street, Suite 2300
    Los Angeles, CA 90115, USA

    This software program and documentation are copyrighted by The University of Southern California. The software program and documentation are supplied "as is", without any accompanying services from USC. USC does not warrant that the operation of the program will be uninterrupted or error-free. The end-user understands that the program was developed for research purposes and is advised not to rely exclusively on the program for any reason.

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