scholarly journals Image distortion correction for MRI in low field permanent magnet systems with strong B0 inhomogeneity and gradient field nonlinearities

Author(s):  
Kirsten Koolstra ◽  
Thomas O’Reilly ◽  
Peter Börnert ◽  
Andrew Webb

Abstract Objective To correct for image distortions produced by standard Fourier reconstruction techniques on low field permanent magnet MRI systems with strong $${B}_{0}$$ B 0 inhomogeneity and gradient field nonlinearities. Materials and methods Conventional image distortion correction algorithms require accurate $${\Delta B}_{0}$$ Δ B 0 maps which are not possible to acquire directly when the $${B}_{0}$$ B 0 inhomogeneities also produce significant image distortions. Here we use a readout gradient time-shift in a TSE sequence to encode the $${B}_{0}$$ B 0 field inhomogeneities in the k-space signals. Using a non-shifted and a shifted acquisition as input, $$\Delta {B}_{0}$$ Δ B 0 maps and images were reconstructed in an iterative manner. In each iteration, $$\Delta {B}_{0}$$ Δ B 0 maps were reconstructed from the phase difference using Tikhonov regularization, while images were reconstructed using either conjugate phase reconstruction (CPR) or model-based (MB) image reconstruction, taking the reconstructed field map into account. MB reconstructions were, furthermore, combined with compressed sensing (CS) to show the flexibility of this approach towards undersampling. These methods were compared to the standard fast Fourier transform (FFT) image reconstruction approach in simulations and measurements. Distortions due to gradient nonlinearities were corrected in CPR and MB using simulated gradient maps. Results Simulation results show that for moderate field inhomogeneities and gradient nonlinearities, $$\Delta {B}_{0}$$ Δ B 0 maps and images reconstructed using iterative CPR result in comparable quality to that for iterative MB reconstructions. However, for stronger inhomogeneities, iterative MB reconstruction outperforms iterative CPR in terms of signal intensity correction. Combining MB with CS, similar image and $$\Delta {B}_{0}$$ Δ B 0 map quality can be obtained without a scan time penalty. These findings were confirmed by experimental results. Discussion In case of $${B}_{0}$$ B 0 inhomogeneities in the order of kHz, iterative MB reconstructions can help to improve both image quality and $$\Delta {B}_{0}$$ Δ B 0 map estimation.

2012 ◽  
Vol 25 (7) ◽  
pp. 075013 ◽  
Author(s):  
Chao Liu ◽  
Yi Zhang ◽  
Longqing Qiu ◽  
Hui Dong ◽  
Hans-Joachim Krause ◽  
...  

Author(s):  
Jia-Lin Tang ◽  
Hao-Nan Huang ◽  
Long-Chao Shi ◽  
Ze-Bin Chen ◽  
Yi-Ying Lu ◽  
...  

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 80310-80327 ◽  
Author(s):  
Jia Gong ◽  
Shao Ying Huang ◽  
Zhi Hua Ren ◽  
Wenwei Yu

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Gilad Liberman ◽  
Benedikt A. Poser

AbstractModern sequences for Magnetic Resonance Imaging (MRI) trade off scan time with computational challenges, resulting in ill-posed inverse problems and the requirement to account for more elaborated signal models. Various deep learning techniques have shown potential for image reconstruction from reduced data, outperforming compressed sensing, dictionary learning and other advanced techniques based on regularization, by characterization of the image manifold. In this work we suggest a framework for reducing a “neural” network to the bare minimum required by the MR physics, reducing the network depth and removing all non-linearities. The networks performed well both on benchmark simulated data and on arterial spin labeling perfusion imaging, showing clear images while preserving sensitivity to the minute signal changes. The results indicate that the deep learning framework plays a major role in MR image reconstruction, and suggest a concrete approach for probing into the contribution of additional elements.


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