Dimitrios G. Gkotsoulias^{1}, Roland Müller ^{1}, Torsten Schlumm^{1}, Niklas Alsleben^{1}, Carsten Jäger^{1}, Jennifer Jaffe^{2,3}, André Pampel^{1}, Catherine Crockford^{2,3}, Roman Wittig^{2,3}, and Harald Möller^{1}

^{1}Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, ^{2}Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany, ^{3}Tai Chimpanzee Project, Centre Suisse de Recherches Scientifiques en Cote d'Ivoire, Abidjan, Cote D'ivoire

Calculation Of Susceptibility through Multiple Orientation Sampling (COSMOS) is assessed comparing the optimal, a clinically feasible and multiple-orientation schemes. The optimal COSMOS estimation is used as a gold standard and is compared to the other schemes using the similarity index (SSIM), mean absolute error (MAE) and Pearson’s coefficient (PC). Further comparisons include Thresholded K-space Division (TKD) quantitative susceptibility mapping. For selected white-matter regions, linear regression is used to assess the similarities between the different estimations.

Most QSM applications employ post-acquisition solutions to the first problem, with Thresholded K-space Division (TKD)

Multi-orientation phase volumes were registered to a reference employing transformations that were derived by registering the corresponding magnitude volumes using FSL

Laplacian phase unwrapping and background-phase removal using V-SHARP

Visual inspection of the susceptibility maps obtained with optimal COSMOS, in vivo-feasible COSMOS and TKD-QSM (Figure 3A) indicate differences in WM and GM regions. The high-angular COSMOS implementations (4 to 10 orientations; Figure 3B) exhibit higher resemblance to the optimal-scheme results when limiting the number of orientations (4 and 5).

The visual observations are corroborated by the different comparison metrics PC, SSIM and MAE. Using the optimal 3-orientation COSMOS scheme as reference, the 4-orientation COSMOS estimates indicate the smallest error in addition to higher PC and SSIM values, while higher angular-resolution schemes yielded slightly increased error and decreased PC/SSIM until a plateau was reached around 8 orientations. TKD-QSM yielded the highest errors and lowest PC and SSIM. However, its accuracy was still is comparable to the in vivo-feasible COSMOS scheme (Figure 4A).

In exemplarily selected WM ROIs, linear regression of the optimal COSMOS susceptibility estimates with TKD-QSM and the in vivo-feasible COSMOS results indicate similar dispersion (Figure 5), validating the other statistical comparisons. Higher angular-resolution COSMOS schemes in the same WM ROIs also followed the trend observed in the general statistical comparisons, with 4- and 5-orientation schemes indicating the least dispersion. Again, with more than 5 orientations, the dispersion increased without significant differences in the estimations beyond 7 orientations.

This work was funded by the EU through the ITN “INSPiRE-MED” (H2020-MSCA-ITN-2018, #813120).

We are grateful to the Evolution of Brain Connectivity (EBC) project, the Ministère de l’Enseignement Supérieur et de la Recherche Scientifique, the Ministère de Eaux et Fôrests in Côte d’Ivoire, and the Office Ivoirien des Parcs et Réserves for permitting the study, and to the staff of the Taï Chimpanzee Project.

1. Möller HE, Bossoni L, Connor JR, et al. Iron, myelin, and the brain: Neuroimaging meets neurobiology. Trends Neurosci. 2019; 42: 384-401.

2. Deistung A, Schweser F, Reichenbach JR. Overview of quantitative susceptibility mapping. NMR Biomed. 2017; 30: e3569.

3. Liu T, Spincemaille P, de Rochefort L, et al. Calculation of susceptibility through multiple orientation sampling (COSMOS): A method for conditioning the inverse problem from measured magnetic field map to susceptibility source image in MRI. Magn Reson Med. 2009; 61: 196-204.

4. Wharton S, Schäfer A, Bowtell R. Susceptibility mapping in the human brain using threshold-based k-space division. Magn Res Med. 2010; 63: 1292-1304.

5. Uecker M, Lustig M. Estimating absolute-phase maps using ESPIRiT and virtual conjugate coils. Magn Reson Med. 2017; 77: 1201-1207.

6. Bilgic B, Polimeni JR, Wald LL, et al. Automated tissue phase and QSM estimation from multichannel data. Proceedings of the 24th Annual Meeting of ISMRM. Singapore 2016; 2849.

7. Jenkinson M, Beckmann CF, Behrens TE, et al. FSL. NeuroImage. 2012; 62: 782-790.

8. Özbay PS, Deistung A, Feng X, et al. A comprehensive numerical analysis of background phase correction with V-SHARP. NMR Biomed. 2017; 30: e3550.

9. Langkammer C, Schweser F, Shmueli K, et al. Quantitative susceptibility mapping: Report from the 2016 reconstruction challenge. Magn Reson Med. 2018; 79: 1661-1673.