Improved cortical surface reconstruction using sub-millimeter resolution MPRAGE by image denoising

Year: 2021

Type: article

Abstract: Automatic cerebral cortical surface reconstruction is a useful tool for cortical anatomy quantification, analysis and visualization. Recently, the Human Connectome Project and several studies have shown the advantages of using T 1 -weighted magnetic resonance (MR) images with sub-millimeter isotropic spatial resolution instead of the standard 1-millimeter isotropic resolution for improved accuracy of cortical surface positioning and thickness estimation. Nonetheless, sub-millimeter resolution images are noisy by nature and require averaging multiple repetitions to increase the signal-to-noise ratio for precisely delineating the cortical boundary. The prolonged acquisition time and potential motion artifacts pose significant barriers to the wide adoption of cortical surface reconstruction at sub-millimeter resolution for a broad range of neuroscientific and clinical applications. We address this challenge by evaluating the cortical surface reconstruction resulting from denoised single-repetition sub-millimeter T 1 -weighted images. We systematically characterized the effects of image denoising on empirical data acquired at 0.6 mm isotropic resolution using three classical denoising methods, including denoising convolutional neural network (DnCNN), block-matching and 4-dimensional filtering (BM4D) and adaptive optimized non-local means (AONLM). The denoised single-repetition images were found to be highly similar to 6-repetition averaged images, with a low whole-brain averaged mean absolute difference of ∼0.016, high whole-brain averaged peak signal-to-noise ratio of ∼33.5 dB and structural similarity index of 0.92, and minimal gray matter–white matter contrast loss (2% to 9%). The whole-brain mean absolute discrepancies in gray–white surface placement, gray–CSF surface placement and cortical thickness estimation were lower than 165 μm, 155 μm and 145 μm—sufficiently accurate for most applications. The denoising performance is equivalent to averaging ∼2.5 repetitions of the data in terms of image similarity, and 1.6–2.2 repetitions in terms of the cortical surface placement accuracy. The scan-rescan precision of the cortical surface positioning and thickness estimation was lower than 170 μm. Our unique dataset and systematic characterization support the use of denoising methods for improved cortical surface reconstruction sub-millimeter resolution.

Source: NeuroImage

DOI: 10.1016/j.neuroimage.2021.117946

Authors: Qiyuan Tian, Natalia Zaretskaya, Qiuyun Fan, Chanon Ngamsombat, Berkin Bilgic, Jonathan R. Polimeni, Susie Y. Huang

Institutions: Harvard University, Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, University of Graz, BioTechMed-Graz, Siriraj Hospital, Mahidol University, Harvard–MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology

Language: English

Topic: Image and Signal Denoising Methods

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,300

Funders: Massachusetts General Hospital, Centre d'Imagerie BioMédicale, National Institutes of Health, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, National Institute of Biomedical Imaging and Bioengineering, National Center for Research Resources

OpenAlex ID: W3133626900