Non-iterative image reconstruction from sparse magnetic resonance imaging radial data without priors

Purpose. To develop generic optimization strategies for image reconstruction using graphical processing units (GPUs) in magnetic resonance imaging (MRI) and to exemplarily report on our experience with a highly accelerated implementation of the nonlinear inversion (NLINV) algorithm for dynamic MRI with high frame rates. Methods. The NLINV algorithm is optimized and ported to run on a multi-GPU single-node server. The algorithm is mapped to multiple GPUs by decomposing the data domain along the channel dimension. Furthermore, the algorithm is decomposed along the temporal domain by relaxing a temporal regularization constraint, allowing the algorithm to work on multiple frames in parallel. Finally, an autotuning method is presented that is capable of combining different decomposition variants to achieve optimal algorithm performance in different imaging scenarios. Results. The algorithm is successfully ported to a multi-GPU system and allows online image reconstruction with high frame rates. Real-time reconstruction with low latency and frame rates up to 30 frames per second is demonstrated. Conclusion. Novel parallel decomposition methods are presented which are applicable to many iterative algorithms for dynamic MRI. Using these methods to parallelize the NLINV algorithm on multiple GPUs, it is possible to achieve online image reconstruction with high frame rates.

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Minimize the mean square error by data segregation approach for back-propagation artificial neural network with adaptive learning based image reconstruction in electron magnetic resonance imaging tomography

2015 Online International Conference on Green Engineering and Technologies (IC-GET) ◽

10.1109/get.2015.7453864 ◽

2015 ◽

Cited By ~ 1

Author(s):

Subramanian Kartheeswaran ◽

Daniel Dharmaraj Christopher Durairaj

Keyword(s):

Neural Network ◽

Magnetic Resonance Imaging ◽

Artificial Neural Network ◽

Magnetic Resonance ◽

Image Reconstruction ◽

Adaptive Learning ◽

Back Propagation ◽

Mean Square ◽

Resonance Imaging ◽

The Mean

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Deep Learning Applications in Magnetic Resonance Imaging: Has the Future Become Present?

Diagnostics ◽

10.3390/diagnostics11122181 ◽

2021 ◽

Vol 11 (12) ◽

pp. 2181

Author(s):

Sebastian Gassenmaier ◽

Thomas Küstner ◽

Dominik Nickel ◽

Judith Herrmann ◽

Rüdiger Hoffmann ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Deep Learning ◽

Clinical Practice ◽

Magnetic Resonance ◽

Image Reconstruction ◽

Learning Technologies ◽

Acquisition Time ◽

Resonance Imaging ◽

Major Player ◽

The Impact

Deep learning technologies and applications demonstrate one of the most important upcoming developments in radiology. The impact and influence of these technologies on image acquisition and reporting might change daily clinical practice. The aim of this review was to present current deep learning technologies, with a focus on magnetic resonance image reconstruction. The first part of this manuscript concentrates on the basic technical principles that are necessary for deep learning image reconstruction. The second part highlights the translation of these techniques into clinical practice. The third part outlines the different aspects of image reconstruction techniques, and presents a review of the current literature regarding image reconstruction and image post-processing in MRI. The promising results of the most recent studies indicate that deep learning will be a major player in radiology in the upcoming years. Apart from decision and diagnosis support, the major advantages of deep learning magnetic resonance imaging reconstruction techniques are related to acquisition time reduction and the improvement of image quality. The implementation of these techniques may be the solution for the alleviation of limited scanner availability via workflow acceleration. It can be assumed that this disruptive technology will change daily routines and workflows permanently.

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