Partial volume model for brain MRI scan using MP2RAGE

Duché, Quentin; Saint‐Jalmes, Hervé; Acosta, Oscar; Raniga, Parnesh; Bourgeat, Pierrick; Doré, Vincent; Egan, Gary F.; Salvado, Olivier

doi:10.1002/hbm.23719

Cited by 9 publications

(8 citation statements)

References 40 publications

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“…Alternative approaches have been to normalize an MPRAGE scan by an additionally acquired gradient echo scan (Van de Moortele et al, 2009; Lüsebrink et al, 2013), or to using the MP2RAGE method, which similarly can remove this bias (Marques et al, 2010). However, these alternate acquisition schemes can influence the positioning of the cortical surface reconstruction (Fujimoto et al, 2014), and, due to nonlinear combination of the two inversion images used in MP2RAGE, assumptions of partial volume estimation algorithms used in morphometry studies may be violated (Duché et al, 2017). Nevertheless, these bias correction methods can greatly improve the robustness of the surface reconstruction.…”

Section: Discussionmentioning

confidence: 99%

Advantages of cortical surface reconstruction using submillimeter 7 T MEMPRAGE

et al. 2018

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Recent advances in MR technology have enabled increased spatial resolution for routine functional and anatomical imaging, which has created demand for software tools that are able to process these data. The availability of high-resolution data also raises the question of whether higher resolution leads to substantial gains in accuracy of quantitative morphometric neuroimaging procedures, in particular the cortical surface reconstruction and cortical thickness estimation. In this study we adapted the FreeSurfer cortical surface reconstruction pipeline to process structural data at native submillimeter resolution. We then quantified the differences in surface placement between meshes generated from (0.75 mm) isotropic resolution data acquired in 39 volunteers and the same data downsampled to the conventional 1 mm voxel size. We find that when processed at native resolution, cortex is estimated to be thinner in most areas, but thicker around the Cingulate and the Calcarine sulci as well as in the posterior bank of the Central sulcus. Thickness differences are driven by two kinds of effects. First, the gray-white surface is found closer to the white matter, especially in cortical areas with high myelin content, and thus low contrast, such as the Calcarine and the Central sulci, causing local increases in thickness estimates. Second, the gray-CSF surface is placed more interiorly, especially in the deep sulci, contributing to local decreases in thickness estimates. We suggest that both effects are due to reduced partial volume effects at higher spatial resolution. Submillimeter voxel sizes can therefore provide improved accuracy for measuring cortical thickness.

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Section: Discussionmentioning

confidence: 99%

Advantages of cortical surface reconstruction using submillimeter 7 T MEMPRAGE

et al. 2018

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“…Indeed, a recent study by Madan () has shown that head motion can cause a significant decrease of numerical FD estimates. Moreover, just as reference values for blood tests may differ depending on the laboratory where they are measured, fractal analysis may be influenced by the type of scanning equipment, sequences, preprocessing software or estimation method, as has been shown for other morphometric analyses (e.g., Wonderlick et al, ; Madan & Kensinger, 2017; Duché et al, ). In this context, it is noteworthy that the MASSIVE and the MSC data were acquired on scanning systems from two different manufacturers.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of the 3D fractal dimension as a marker of structural brain complexity in multiple‐acquisition MRI

Krohn

Froeling

Leemans

et al. 2019

Human Brain Mapping

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Fractal analysis represents a promising new approach to structural neuroimaging data, yet systematic evaluation of the fractal dimension (FD) as a marker of structural brain complexity is scarce. Here we present in-depth methodological assessment of FD estimation in structural brain MRI. On the computational side, we show that spatial scale optimization can significantly improve FD estimation accuracy, as suggested by simulation studies with known FD values. For empirical evaluation, we analyzed two recent open-access neuroimaging data sets (MASSIVE and Midnight Scan Club), stratified by fundamental image characteristics including registration, sequence weighting, spatial resolution, segmentation procedures, tissue type, and image complexity. Deviation analyses showed high repeated-acquisition stability of the FD estimates across both data sets, with differential deviation susceptibility according to image characteristics. While less frequently studied in the literature, FD estimation in T2-weighted images yielded robust outcomes. Importantly, we observed a significant impact of image registration on absolute FD estimates. Applying different registration schemes, we found that unbalanced registration induced (a) repeated-measurement deviation clusters around the registration target, (b) strong bidirectional correlations among image analysis groups, and (c) spurious associations between the FD and an index of structural similarity, and these effects were strongly attenuated by reregistration in both data sets. Indeed, differences in FD between scans did not simply track differences in structure per se, suggesting that structural complexity and structural similarity represent distinct aspects of structural brain MRI. In conclusion, scale optimization can improve FD estimation accuracy, and empirical FD estimates are reliable yet sensitive to image characteristics. K E Y W O R D S fractal analysis, MRI biomarker, structural brain complexity, structural similarity, imaging validation Carsten Finke and Francisco J. Esteban contributed equally to the manuscript.

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“…2009; Madan and Kensinger 2017;Duché et al 2017. In this context, however, it is noteworthy that the MASSIVE and the MSC data were acquired on scanning systems from two dierent manufacturers.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of the 3D fractal dimension as a marker of structural brain complexity in multiple-acquisition MRI

Krohn

Froeling

Leemans

et al. 2017

Preprint

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Keywords: fractal analysis, structural brain complexity, MRI biomarker, computational image analysis 1 All rights reserved. No reuse allowed without permission.(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/124206 doi: bioRxiv preprint first posted online Apr. 5, 2017; AbstractFractal analysis, i.e. the estimation of an object's fractal dimension (FD) as a marker of its morphometric complexity, has attracted increasing interest as a versatile tool for the analysis of structural neuroimaging data in both health and disease. However, a number of important methodological questions regarding fractal analysis in magnetic resonance images have so far remained unaddressed. This includes the stability of the FD over repeated within-subject measurements, i.e. the susceptibility of fractal analysis to noise, a formal assessment of its sampling distribution, and the impact of image acquisition and processing parameters. Importantly, fractal analysis has not yet been explored in detail in T2 contrast images. To address these issues, we analyzed structural images from the recently published MASSIVE data set (Multiple Acquisitions for Standardization of Structural Imaging Validation and Evaluation). We conduct a fine-grained stratification of image parameters, leading to 32 distinct analysis groups as a combination of image contrast, spatial resolution, segmentation procedures, tissue type, and image complexity. We estimate 3D tissue models based on the thus obtained input volumes and compute the FDs as the box-counting regression on these models. Furthermore, we present a detailed deviation analysis including resampling methods, composite normality assessment, outlier detection, and multivariate comparisons to establish the susceptibility of the FD to noise. We find that in both T1 and T2 contrasts, the FD of gray matter (GM) segmentations was generally higher than in white matter volumes (WM). FDs in both image contrasts were sampled in comparable range and showed similar responses to processing parameters, e.g. as regards the effects of binary vs. partial volume segmentation and a decrease in FD by image skeletization. Lower spatial resolution invariably resulted in decreased FDs in unskeletized images, while the response depended on the segmentation procedure in image skeletons. Furthermore, in multiple measurements, the FD can be assumed to be sampled from an underlying normal distribution. We tested different options for a sensible within-group deviation criterion and found that outlier detection by Grubbs testing and a 2 standard-deviation interval around the sample mean performed very well in this regard. Even with the more conservative threshold, the overall robustness of the FD to noise was well above 90 %. Most deviations were found in T1-weighted images, and binarized image skeletons were most susceptible to deviations. Importantly, our analysis was able to detect sample-w...

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Partial volume model for brain MRI scan using MP2RAGE

Cited by 9 publications

References 40 publications

Advantages of cortical surface reconstruction using submillimeter 7 T MEMPRAGE

Advantages of cortical surface reconstruction using submillimeter 7 T MEMPRAGE

Evaluation of the 3D fractal dimension as a marker of structural brain complexity in multiple‐acquisition MRI

Evaluation of the 3D fractal dimension as a marker of structural brain complexity in multiple-acquisition MRI

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