Data-Driven Learning of MRI Sampling Pattern

Data-Driven Learning of MRI Sampling Pattern

Marcelo Wust Zibetti, PhD

This is an example of results obtained through bias-accelerated subset selection (BASS) for optimizing the sampling pattern (SP) in accelerated MRI. In (A) we observe the improvement of normalized root mean squared error (NRMSE) across acceleration factors (AF) for compressed sensing reconstructions with low-rank regularization (CS-LR). (B) shows a Poisson disk SP with AF=24 and (E) shows sample images resulting from this non-optimized pattern. (C) shows an optimized SP, and (F) shows the resulting images. The images obtained with the optimized SP are of higher quality and more similar to the fully sampled images, shown in (D), than are the images obtained with the non-optimized SP.


We are making available a MATLAB implementation of a machine learning algorithm for rapidly learning the optimal sampling pattern for MRI.

In a forthcoming publication, “Fast Data-Driven Learning of MRI Sampling Pattern for Large Scale Problems,”, now available as a preprint in arXiv, we propose a new learning algorithm, called bias-accelerated subset selection (BASS), for learning an efficacious sampling pattern in accelerated parallel MRI. The approach is applicable when two conditions are met:

  1. A set of fully sampled Cartesian k-space data of specific anatomy is available for training;
  2. An image reconstruction for undersampled data or a direct k-space recovery method for parallel MRI that allows a free choice of the sampling pattern is used.

The learned sampling pattern allows fast data collection by capturing the key learned data that result in minor deterioration of reconstruction quality. Our proposed approach has a low computational cost and fast convergence, enabling the use of large datasets to optimize large sampling patterns—important features in high-resolution 3D-MRI, quantitative MRI, and dynamic MRI applications. In the related publication, we describe four parallel MRI reconstruction methods based on low-rankness and sparsity, each used with two different datasets. Our findings indicate that the sampling pattern learned by our method results in scan time nearly twice as fast as that obtained with variable density and Poisson disk sampling pattern for the same level of error.

Related Preprint

M. V. W. Zibetti, G. T. Herman, and R. R. Regatte, “Fast Data-Driven Learning of MRI Sampling Pattern for Large Scale Problems.” arXiv, 2020.


M. V. W. Zibetti, E. S. Helou, R. R. Regatte, and G. T. Herman, “Monotone FISTA with variable acceleration for compressed sensing magnetic resonance imaging.” IEEE Trans. Comput. Imaging. vol. 5, no. 1, pp. 109–119, Mar. 2019.

J. Zbontar, F. Knoll, A. Sriram, T. Murrell, Z. Huang, M. J. Muckley, A. Defazio, R. Stern, P. Johnson, M. Bruno, M. Parente, K. J. Geras, J. Katsnelson, H. Chandarana, Z. Zhang, M. Drozdzal, A. Romero, M. Rabbat, P. Vincent, N. Yakubova, J. Pinkerton, D. Wang, E. Owens, C. L. Zitnick, M. P. Recht, D. K. Sodickson, and Y. W. Lui, “fastMRI: An open dataset and benchmarks for accelerated MRI.” arXiv, 2019.

M. V. W. Zibetti, G. T. Herman, and R. R. Regatte, “Data-driven design of the sampling pattern for compressed sensing and low rank reconstructions on parallel MRI of human knee joint.” ISMRM Workshop on Data Sampling and Image Reconstruction, 2020.

This figure illustrates improvement in a sampling pattern (SP) achieved through bias-accelerated subset selection (BASS). The left panels show the averaged error map in k-space, with brighter areas indicating larger errors. The error map guides the machine learning algorithm to iteratively optimize the distribution of k-space points, resulting in a lower-error SP shown in the right panels. For example, staring with the Poisson disc, shown in top left, BASS learns the optimized SP shown in bottom right. The square area in the center is the auto-calibration area and the cloud of white dots represents the distribution of sampling k-space points determined by the algorithm.

PLEASE NOTE: The software available on this page is provided free of charge and comes without any warranty. CAI²R and the NYU School of Medicine do not take any liability for problems or damage of any kind resulting from the use of the files provided. Operation of the software is solely at the user's own risk. The software developments provided are not medical products and must not be used for making diagnostic decisions.

The software is provided for non-commercial, academic use only. Usage or distribution of the software for commercial purpose is prohibited. All rights belong to the author (Marcelo Wust Zibetti, PhD) and the NYU School of Medicine. If you use the software for academic work, please give credit to the author in publications and cite the related publications.

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01/22/2021 - 09:16
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Philanthropic Support

We gratefully acknowledge generous support for radiology research at NYU Langone Health from:
• The Big George Foundation
• Bernard and Irene Schwartz

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