Evaluation of multi-channel phase reconstruction methods for quantitative susceptibility mapping on postmortem human brain

• Published in: Journal of Magnetic Resonance Open Vol. 14-15; p. 100097
• Main Authors: Otsuka, Fábio Seiji, Otaduy, Maria Concepcion Garcia, Azevedo, José Henrique Monteiro, Chaim, Khallil Taverna, Salmon, Carlos Ernesto Garrido
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.06.2023; Elsevier
• Subjects: Magnetic resonance imaging; MRI phase reconstruction; Postmortem; Quantitative susceptibility mapping; Quantitative susceptibility mapping; MRI phase reconstruction; Magnetic resonance imaging; Postmortem
• ISSN: 2666-4410; 2666-4410
• DOI: 10.1016/j.jmro.2023.100097

•Different phase combination methods based on magnitude-weighted phase sum at different powers for a proper phase reconstruction.•Results from simulated dataset indicates different behaviors of Root-Mean Squared Error among the methods depending on the region of interest.•Both MCPC3D-S and VRC deviates from the ground truth QSM, which is most likely due to the other processing steps of the QSM.•Evaluations of postmortem datasets indicates that MCPC3D-S and VRC are highly correlated, with little differences between them.•Evaluations of postmortem datasets indicates that the weighting factor (k = 1 or k = 2) have little influence on the QSM maps, however overall k = 1 showed better results on simulated data. Quantitative Susceptibility Mapping (QSM) is an established Magnetic Resonance Imaging (MRI) technique with high potential in brain iron studies associated to several neurodegenerative diseases. Unlike other MRI techniques, QSM relies on phase images to estimate tissue's relative susceptibility, therefore requiring a reliable phase data. Phase images from a multi-channel acquisition should be reconstructed in a proper way. On this work it was compared the performance of combination of phase matching algorithms (MCPC3D-S and VRC) and phase combination methods based on a complex weighted sum of phases, considering the magnitude at different powers (k = 0 to 4) as the weighting factor. These reconstruction methods were applied in two datasets: a simulated brain dataset for a 4-coil array and data of 22 postmortem subjects acquired at a 7T scanner using a 32 channels coil. For the simulated dataset, differences between the ground truth and the Root Mean Squared Error (RMSE) were evaluated. For both simulated and postmortem data, the mean (MS) and standard deviation (SD) of susceptibility values of five deep gray matter regions were calculated. For the postmortem subjects, MS and SD were statistically compared across all subjects. A qualitative analysis indicated no differences between methods, except for the Adaptive approach on postmortem data, which showed intense artifacts. In the 20% noise level case, the simulated data showed increased noise in central regions. Quantitative analysis showed that both MS and SD were not statistically different when comparing k=1 and k=2 on postmortem brain images, however visual inspection showed some boundaries artifacts on k=2. Furthermore, the RMSE decreased (on regions near the coils) and increased (on central regions and on overall QSM) with increasing k. In conclusion, for reconstruction of phase images from multiple coils with no reference available, alternative methods are needed. In this study it was found that overall, the phase combination with k=1 is preferred over other powers of k. [Display omitted]