Effects of image reconstruction on fiber orientation mapping from multichannel diffusion MRI: Reducing the noise floor using SENSE
Purpose To examine the effects of the reconstruction algorithm of magnitude images from multichannel diffusion MRI on fiber orientation estimation. Theory and Methods It is well established that the method used to combine signals from different coil elements in multichannel MRI can have an impact on...
| Main Authors: | , , , , , , , , , , , , |
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| 格式: | Journal article |
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2013
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| _version_ | 1826301741875855360 |
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| author | Sotiropoulos, S Moeller, S Moeller, S Jbabdi, S Xu, J Andersson, J Auerbach, E Auerbach, E Yacoub, E Feinberg, D Setsompop, K Setsompop, K Wald, L Behrens, T Behrens, T Ugurbil, K Lenglet, C |
| author_facet | Sotiropoulos, S Moeller, S Moeller, S Jbabdi, S Xu, J Andersson, J Auerbach, E Auerbach, E Yacoub, E Feinberg, D Setsompop, K Setsompop, K Wald, L Behrens, T Behrens, T Ugurbil, K Lenglet, C |
| author_sort | Sotiropoulos, S |
| collection | OXFORD |
| description | Purpose To examine the effects of the reconstruction algorithm of magnitude images from multichannel diffusion MRI on fiber orientation estimation. Theory and Methods It is well established that the method used to combine signals from different coil elements in multichannel MRI can have an impact on the properties of the reconstructed magnitude image. Using a root-sum-of-squares approach results in a magnitude signal that follows an effective noncentral-χ distribution. As a result, the noise floor, the minimum measurable in the absence of any true signal, is elevated. This is particularly relevant for diffusion-weighted MRI, where the signal attenuation is of interest. Results In this study, we illustrate problems that such image reconstruction characteristics may cause in the estimation of fiber orientations, both for model-based and model-free approaches, when modern 32-channel coils are used. We further propose an alternative image reconstruction method that is based on sensitivity encoding (SENSE) and preserves the Rician nature of the single-channel, magnitude MR signal. We show that for the same k-space data, root-sum-of-squares can cause excessive overfitting and reduced precision in orientation estimation compared with the SENSE-based approach. Conclusion These results highlight the importance of choosing the appropriate image reconstruction method for tractography studies that use multichannel receiver coils for diffusion MRI acquisition. Magn Reson Med 70:1682-1689, 2013. © 2013 Wiley Periodicals, Inc. Copyright © 2013 Wiley Periodicals, Inc. |
| first_indexed | 2024-03-07T05:36:57Z |
| format | Journal article |
| id | oxford-uuid:e43955b7-c869-4322-bc6c-fb62e96c9488 |
| institution | University of Oxford |
| last_indexed | 2024-03-07T05:36:57Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | oxford-uuid:e43955b7-c869-4322-bc6c-fb62e96c94882022-03-27T10:15:02ZEffects of image reconstruction on fiber orientation mapping from multichannel diffusion MRI: Reducing the noise floor using SENSEJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:e43955b7-c869-4322-bc6c-fb62e96c9488Symplectic Elements at Oxford2013Sotiropoulos, SMoeller, SMoeller, SJbabdi, SXu, JAndersson, JAuerbach, EAuerbach, EYacoub, EFeinberg, DSetsompop, KSetsompop, KWald, LBehrens, TBehrens, TUgurbil, KLenglet, CPurpose To examine the effects of the reconstruction algorithm of magnitude images from multichannel diffusion MRI on fiber orientation estimation. Theory and Methods It is well established that the method used to combine signals from different coil elements in multichannel MRI can have an impact on the properties of the reconstructed magnitude image. Using a root-sum-of-squares approach results in a magnitude signal that follows an effective noncentral-χ distribution. As a result, the noise floor, the minimum measurable in the absence of any true signal, is elevated. This is particularly relevant for diffusion-weighted MRI, where the signal attenuation is of interest. Results In this study, we illustrate problems that such image reconstruction characteristics may cause in the estimation of fiber orientations, both for model-based and model-free approaches, when modern 32-channel coils are used. We further propose an alternative image reconstruction method that is based on sensitivity encoding (SENSE) and preserves the Rician nature of the single-channel, magnitude MR signal. We show that for the same k-space data, root-sum-of-squares can cause excessive overfitting and reduced precision in orientation estimation compared with the SENSE-based approach. Conclusion These results highlight the importance of choosing the appropriate image reconstruction method for tractography studies that use multichannel receiver coils for diffusion MRI acquisition. Magn Reson Med 70:1682-1689, 2013. © 2013 Wiley Periodicals, Inc. Copyright © 2013 Wiley Periodicals, Inc. |
| spellingShingle | Sotiropoulos, S Moeller, S Moeller, S Jbabdi, S Xu, J Andersson, J Auerbach, E Auerbach, E Yacoub, E Feinberg, D Setsompop, K Setsompop, K Wald, L Behrens, T Behrens, T Ugurbil, K Lenglet, C Effects of image reconstruction on fiber orientation mapping from multichannel diffusion MRI: Reducing the noise floor using SENSE |
| title | Effects of image reconstruction on fiber orientation mapping from multichannel diffusion MRI: Reducing the noise floor using SENSE |
| title_full | Effects of image reconstruction on fiber orientation mapping from multichannel diffusion MRI: Reducing the noise floor using SENSE |
| title_fullStr | Effects of image reconstruction on fiber orientation mapping from multichannel diffusion MRI: Reducing the noise floor using SENSE |
| title_full_unstemmed | Effects of image reconstruction on fiber orientation mapping from multichannel diffusion MRI: Reducing the noise floor using SENSE |
| title_short | Effects of image reconstruction on fiber orientation mapping from multichannel diffusion MRI: Reducing the noise floor using SENSE |
| title_sort | effects of image reconstruction on fiber orientation mapping from multichannel diffusion mri reducing the noise floor using sense |
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