Improving in vivo human cerebral cortical surface reconstruction using data-driven super-resolution

Abstract

Accurate and automated reconstruction of the in vivo human cerebral cortical surface from anatomical magnetic resonance (MR) images facilitates the quantitative analysis of cortical structure. Anatomical MR images with sub-millimeter isotropic spatial resolution improve the accuracy of cortical surface and thickness estimation compared to the standard 1-millimeter isotropic resolution. Nonetheless, sub-millimeter resolution acquisitions require averaging multiple repetitions to achieve sufficient signal-to-noise ratio and are therefore long and potentially vulnerable to subject motion. We address this challenge by synthesizing sub-millimeter resolution images from standard 1-millimeter isotropic resolution images using a data-driven supervised machine learning-based super-resolution approach achieved via a deep convolutional neural network. We systematically characterize our approach using a large-scale simulated dataset and demonstrate its efficacy in empirical data. The super-resolution data provide improved cortical surfaces similar to those obtained from native sub-millimeter resolution data. The whole-brain mean absolute discrepancy in cortical surface positioning and thickness estimation is below 100 μm at the single-subject level and below 50 μm at the group level for the simulated data, and below 200 μm at the single-subject level and below 100 μm at the group level for the empirical data, making the accuracy of cortical surfaces derived from super-resolution sufficient for most applications.

Prospective Deployment of Deep Learning in MRI: A Framework for Important Considerations, Challenges, and Recommendations for Best Practices

“…36 In such scenarios, it becomes important not only to perform image quality optimization, but also to simultaneously evaluate the downstream utility and biases emanating from the reconstructed images. For example, it was shown that cardiac MRI super-resolution can be used to improve segmentation outcomes, 37 knee MRI super-resolution can improve segmentation of cartilage as well as detection of small osteophytes (Fig. 3), and brain MRI super-resolution can improve depiction of cortical parcellationpatterns (Fig.…”

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

“…T 1 -weighted MR images acquired with 1-mm isotropic spatial resolution have been long used for standard cortical surface reconstruction because the 1-mm voxels adequately sample the folded cortical ribbon in adult humans and because this acquisition provides an adequate trade-off between acquisition time, spatial resolution and signal-to-noise ratio (SNR). More recently, the Human Connectome Project (HCP) WU-Minn-Ox Consortium and several studies have shown the advantages of sub-millimeter isotropic spatial resolution for further improving the accuracy of the reconstructed cortical surfaces ( Bazin et al, 2014 , Glasser et al, 2013 , Lüsebrink et al, 2013 , MF Glasser et al, 2016, Q Tian et al, 2021 , Zaretskaya et al, 2018 ). In these studies using sub-millimeter cortical surface reconstruction, the cortical surface positioning and thickness estimation in cortical regions with diminished gray–white contrast on T 1 -weighted MR images exhibit improved accuracy due to reduced partial volume effects on the sub-millimeter resolution images.…”

“…In these studies using sub-millimeter cortical surface reconstruction, the cortical surface positioning and thickness estimation in cortical regions with diminished gray–white contrast on T 1 -weighted MR images exhibit improved accuracy due to reduced partial volume effects on the sub-millimeter resolution images. This effect is especially noticeable in heavily myelinated cortical areas (e.g., primary motor, somatosensory, visual and auditory cortex), where the gray matter signal appears more similar in signal intensity to the white matter, as well as in insular cortex where the superficial white matter in the extreme capsule immediately beneath the insular cortex appears darker due to partial volume effects with the claustrum ( Zaretskaya et al, 2018 , Q Tian et al, 2021 ). In general, T 1 -weighted MR images with 0.8-mm isotropic resolution (half of the minimum thickness of the cortex) or higher resolution are recommended by the HCP WU-Minn-Ox Consortium (MF Glasser et al, 2016).…”

High-fidelity fast volumetric brain MRI using synergistic wave-controlled aliasing in parallel imaging and a hybrid denoising generative adversarial network

“…The advantage of 2D GANs is that their parameters can be optimized on only a few subjects, because each image volume from a subject provides millions of voxels as training samples for calculating the voxel-wise loss for the generator and hundreds of image slices as training samples for calculating the image-wise loss for the discriminator. However, the image synthesis performance of 2D generators is often limited compared to 3D generators, which can incorporate complementary information from an additional spatial dimension 22,38,39 (Supplementary Information Figure 1). Moreover, there may be boundary artifacts across synthesized 2D image slices along the cross-sectional direction.…”

“…For volumetric imaging, 3D convolution in the generator network is advantageous for increasing the data redundancy from an additional spatial dimension for improved image synthesis performance and smooth transition between 2D image slices along all directions [22][23][24] , but 3D discriminators in GANs require a large number of training data, which are challenging to acquire in practice. Chen et al utilized data from 1113 subjects to train a 3D GAN for brain MRI super-resolution 19 .…”