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TED toolbox

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TED: Temporal Differences (TED) Compressed Sensing

TED is a Compressed Sensing method for temperature reconstrcution from sub-sampled dynamic MRI data.

Paper: https://onlinelibrary.wiley.com/doi/abs/10.1002/nbm.4352

Summary slide:

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What this code does:

This Matlab toolbox contains an implementation of the TED algorithm [1] and demos with two datasets:

1. Gel phantom data (GE scanner).
2. Agar phantom data (Philips scanner).

In both cases, fully sampled data was acquired in-vitro and then retrospectively subsampled offline. A reference (gold standard) temperature map was computed from the fully-sampled dataset, and the TED reconstruction was computed from sub-sampled data.

TED is compared with two well-established methods: l1-SPIRiT [2] and the K-space Hybrid Method [3].

Generalizability

While we proposed TED for temperature reconstruction, TED is in fact a general dynamic MRI method. It can hence be implemented to other dynamic MRI applications, such as cardiac MRI. If you find a cool implementation - let us know!

Prerequisites

Matlab is required. The code was tested with Matlab2017R.

Getting Started

Clone or download the code.

Running the examples

Open the demo_start.m function in Matlab, choose one example from the following list, set the desired reduction factor (R), and run the code.

Demo 1 - gel phantom - expected output:

Demo 2 - agar phantom - expected output:

Acknowledgments

The TED toolbox was built upon the l1-SPIRiT toolbox that was created by Prof. Michael (Miki) Lustig and is available at his website: http://people.eecs.berkeley.edu/~mlustig/Software.html

The agar phantom data and the code for the K-space Hybrid Method are courtesy of Prof. William Grissom, Vanderbilt University, TA, USA.

The gel phantom data is courtesy of INSIGHTEC Ltd.

Contact

If you have any questions or if you found a cool app of TED and want to let us know, please contact me: Efrat Shimron,

You can also see my other projects here: https://sites.google.com/view/efratshimron/home

References

[1] Shimron E., Grissom W., Azhari H. (2020) "Temporal Differences (TED) Compressed Sensing: A Method for Fast MRgHIFU Temperature Imaging". NMR in Biomedicine, in press.

[2] Murphy M, et al. (2012) "Fast l₁-SPIRiT Compressed Sensing Parallel Imaging MRI: Scalable Parallel Implementation and Clinically Feasible Runtime". IEEE TMI.

[3] Gaur P, Grissom WA. (2015) Accelerated MRI thermometry by Direct Estimation of Temperature from Undersampled K-space Data. MRM.