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J. Imaging 2017, 3(4), 67; doi:10.3390/jimaging3040067

Restoration of Bi-Contrast MRI Data for Intensity Uniformity with Bayesian Coring of Co-Occurrence Statistics

1
Department of Electrical Engineering and Informatics, Cyprus University of Technology, Pavlou Mela, Limassol 3036, Cyprus
2
Department of Diagnostic and Interventional Neuroradiology, University Medical Center Goettingen, University of Goettingen, Robert Koch Straße 40, 37075 Goettingen, Germany
3
Department of Neurology, University Medical Center Goettingen, University of Goettingen, Robert Koch Straße 40, 37075 Goettingen, Germany
4
Department of Neurosurgery, University Medical Center Goettingen, University of Goettingen, Robert Koch Straße 40, 37075 Goettingen, Germany
5
Institute for Diagnostic and Interventional Radiology, University of Jena, Am Klinikum 1, 07747 Jena, Germany
6
Institute of Radiology, Suedharz Hospital Nordhausen, Dr.-Robert-Koch Straße 39, 99734 Nordhausen, Germany
*
Author to whom correspondence should be addressed.
Received: 30 August 2017 / Revised: 7 December 2017 / Accepted: 12 December 2017 / Published: 15 December 2017
(This article belongs to the Special Issue Selected Papers from “MIUA 2017”)
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Abstract

The reconstruction of MRI data assumes a uniform radio-frequency field. However, in practice, the radio-frequency field is inhomogeneous and leads to anatomically inconsequential intensity non-uniformities across an image. An anatomic region can be imaged with multiple contrasts reconstructed independently and be suffering from different non-uniformities. These artifacts can complicate the further automated analysis of the images. A method is presented for the joint intensity uniformity restoration of two such images. The effect of the intensity distortion on the auto-co-occurrence statistics of each image as well as on the joint-co-occurrence statistics of the two images is modeled and used for their non-stationary restoration followed by their back-projection to the images. Several constraints that ensure a stable restoration are also imposed. Moreover, the method considers the inevitable differences between the signal regions of the two images. The method has been evaluated extensively with BrainWeb phantom brain data as well as with brain anatomic data from the Human Connectome Project (HCP) and with data of Parkinson’s disease patients. The performance of the proposed method has been compared with that of the N4ITK tool. The proposed method increases tissues contrast at least 4 . 62 times more than the N4ITK tool for the BrainWeb images. The dynamic range with the N4ITK method for the same images is increased by up to +29.77%, whereas, for the proposed method, it has a corresponding limited decrease of - 1 . 15 % , as expected. The validation has demonstrated the accuracy and stability of the proposed method and hence its ability to reduce the requirements for additional calibration scans. View Full-Text
Keywords: bi-contrast MRI intensity restoration; MRI bias field correction; joint co-occurrence statistics; non-stationary restoration; Bayesian coring; Van Cittert deconvolution bi-contrast MRI intensity restoration; MRI bias field correction; joint co-occurrence statistics; non-stationary restoration; Bayesian coring; Van Cittert deconvolution
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Hadjidemetriou, S.; Psychogios, M.N.; Lingor, P.; von Eckardstein, K.; Papageorgiou, I. Restoration of Bi-Contrast MRI Data for Intensity Uniformity with Bayesian Coring of Co-Occurrence Statistics. J. Imaging 2017, 3, 67.

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