A python-based open-source package, “MRIPY” combines the existing MRI reconstruction methods, i.e. compressed sensing and parallel imaging, with deep neural networks that are implemented in the Tensorflow software.
Purpose
The purpose of this research project is to develop and validate an open-source toolbox for next-generation accelerated MRI reconstruction. This abstract presents a python-based open-source package as the output of this project, developed to combine the existing MRI reconstruction methods, i.e. compressed sensing and parallel imaging, with deep neural networks that can be integrated with software such as Tensorflow and PyTorch.Methods
Fig. 1 shows a few key functions of this python package (https://github.com/LarsonLab/mripy/):
Results
Figures 2-5 show sample outputs generated from MRIPY, including a range of MRI reconstruction methods as well as integration with deep neural networks:
Fig. 2 shows an example of parallel imaging and compressed sensing reconstruction with alternating direction method of multipliers (ADMM) and wavelet L1 or total variation minimization regularization. Raw MRI data was from website (http://people.eecs.berkeley.edu/~mlustig/CS.html).
Fig. 3 shows NUFFT based reconstruction for UTE images (after MIP) acquired using a 3D UTE Cones sequence.
Fig. 4 shows whole brain segmentation/relaxometry as the direct output of a deep neural network trained with the synthetic MRI data. The in vivo brain MRI data was from Dr. Jing Liu.
Fig. 5 shows another more complex task that is the detection/segmentation of myocardium from MRI image. A fully convolutional network performed the segmentation as an example provide in MRIPY. The MRI data was from Dr. Yan Wang.
Conclusion
The new reconstruction and neural network segmentation methods developed will provide improved precise measurements in MRI.1. Lustig, M. & Pauly, J. M. SPIRiT: Iterative self-consistent parallel imaging reconstruction from arbitrary k-space. Magn. Reson. Med. 64, 457–471 (2010).
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