Toward evaluation of multiresolution cortical thickness estimation with FreeSurfer, MaCRUISE, and BrainSuite

Author:

Nian Rui123ORCID,Gao Mingshan4,Zhang Shichang5,Yu Junjie1,Gholipour Ali23,Kong Shuang1,Wang Ruirui1,Sui Yao23ORCID,Velasco-Annis Clemente23,Tomas-Fernandez Xavier23,Li Qiuying1,Lv Hangyu1,Qian Yuqi1,Warfield Simon K23

Affiliation:

1. School of Electronic Engineering, Ocean University of China , 238 Songling Road, Qingdao, China

2. Harvard Medical School , 25 Shattuck Street, Boston, MA, United States

3. Boston Children’s Hospital , 300 Longwood Avenue, Boston, MA, United States

4. Citigroup Services and Technology Limited , 1000 Chenhi Road, Shanghai, China

5. Baidu, Inc. , Haidian District, Beijing, China

Abstract

Abstract Advances in Magnetic Resonance Imaging hardware and methodologies allow for promoting the cortical morphometry with submillimeter spatial resolution. In this paper, we generated 3D self-enhanced high-resolution (HR) MRI imaging, by adapting 1 deep learning architecture, and 3 standard pipelines, FreeSurfer, MaCRUISE, and BrainSuite, have been collectively employed to evaluate the cortical thickness. We systematically investigated the differences in cortical thickness estimation for MRI sequences at multiresolution homologously originated from the native image. It has been revealed that there systematically exhibited the preferences in determining both inner and outer cortical surfaces at higher resolution, yielding most deeper cortical surface placements toward GM/WM or GM/CSF boundaries, which directs a consistent reduction tendency of mean cortical thickness estimation; on the contrary, the lower resolution data will most probably provide a more coarse and rough evaluation in cortical surface reconstruction, resulting in a relatively thicker estimation. Although the differences of cortical thickness estimation at the diverse spatial resolution varied with one another, almost all led to roughly one-sixth to one-fifth significant reduction across the entire brain at the HR, independent to the pipelines we applied, which emphasizes on generally coherent improved accuracy in a data-independent manner and endeavors to cost-efficiency with quantitative opportunities.

Funder

National Science & Technology Pillar Program

Natural Science Foundation of P. R. China

National High-Tech R&D 863 Program

National Key R&D Program

National Program of International S&T Cooperation

Publisher

Oxford University Press (OUP)

Subject

Cellular and Molecular Neuroscience,Cognitive Neuroscience

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