Fenqiang Zhao

• Zhejiang University

Fetal Magnetic Resonance Imaging (MRI) is challenged by fetal movements and maternal breathing. Although fast MRI sequences allow artifact free acquisition of individual 2D slices, motion frequently occurs in the acquisition of spatially adjacent slices. Motion correction for each slice is thus critical for the reconstruction of 3D fetal brain MRI....

Multi-modal neuroimaging data, e.g., magnetic resonance imaging (MRI) and positron emission tomography (PET), has greatly advanced computer-aided diagnosis of Alzheimer’s disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). However, incomplete multi-modality data often limits the diagnostic performance of deep learning-based...

Modern multi-site neuroimaging studies are known to be biased by significant site effects observed in imaging data and their derived structural and functional features. Although many statistical models and deep learning methods have been proposed to eliminate the site effects while maintaining biological characteristics, they have two major drawbac...

The cerebellum (i.e., little brain) plays an important role in motion and balances control abilities, despite its much smaller size and deeper sulci compared to the cerebrum. Previous cerebellum studies mainly relied on and focused on conventional volumetric analysis, which ignores the extremely deep and highly convoluted nature of the cerebellar c...

Exploring the relationship between the cognitive ability and infant cortical structural and functional development is critically important to advance our understanding of early brain development, which, however, is very challenging due to the complex and dynamic brain development in early postnatal stages. Conventional approaches typically use eith...

During the early postnatal period, the human brain undergoes rapid and dynamic development. Over the past decades, there has been increased attention in studying the cognitive and cortical development of infants. However, accurate prediction of the infant cognitive and cortical development at an individual-level is a significant challenge, due to t...

Precise segmentation of subcortical structures from infant brain magnetic resonance (MR) images plays an essential role in studying early subcortical structural and functional developmental patterns and diagnosis of related brain disorders. However, due to the dynamic appearance changes, low tissue contrast, and tiny subcortical size in infant brai...

Motivated by the recent great success of attention modeling in computer vision, it is highly desired to extend the Transformer architecture from the conventional Euclidean space to non-Euclidean spaces. Given the intrinsic spherical topology of brain cortical surfaces in neuroimaging, in this study, we propose a novel Spherical Transformer, an effe...

Spherical mapping of cortical surface meshes provides a more convenient and accurate space for cortical surface registration and analysis and thus has been widely adopted in neuroimaging field. Conventional approaches typically first inflate and project the original cortical surface mesh onto a sphere to generate an initial spherical mesh which con...

Deep learning approaches, especially convolutional neural networks, have become the method of choice in the field of medical image analysis over the last few years. This prevalence is attributed to their excellent abilities of learning features in a more effective and efficient manner, not only for 2D/3D images in the Euclidean space, but also for...

Spatiotemporal (four-dimensional) infant-dedicated brain atlases are essential for neuroimaging analysis of early dynamic brain development. However, due to the substantial technical challenges in the acquisition and processing of infant brain MR images, 4D atlases densely covering the dynamic brain development during infancy are still scarce. Few...

Brain cortical surfaces, which have an intrinsic spherical topology, are typically represented by triangular meshes and mapped onto a spherical manifold in neuroimaging analysis. Inspired by the strong capability of feature learning in Convolutional Neural Networks (CNNs), spherical CNNs have been developed accordingly and achieved many successes i...

Longitudinal brain imaging atlases with densely sampled time-points and ancillary anatomical information are of fundamental importance in studying early developmental characteristics of human and non-human primate brains during infancy, which feature extremely dynamic imaging appearance, brain shape and size. However, for non-human primates, which...

Longitudinal infant dedicated cerebellum atlases play a fundamental role in characterizing and understanding the dynamic cerebellum development during infancy. However, due to the limited spatial resolution, low tissue contrast, tiny folding structures, and rapid growth of the cerebellum during this stage, it is challenging to build such atlases wh...

Cortical surface registration and parcellation are two essential steps in neuroimaging analysis. Conventionally, they are performed independently as two tasks, ignoring the inherent connections of these two closely-related tasks. Essentially, both tasks rely on meaningful cortical feature representations, so they can be jointly optimized by learnin...

Spatiotemporal (4D) cortical surface atlas during infancy plays an important role for surface-based visualization, normalization and analysis of the dynamic early brain development. Conventional atlas construction methods typically rely on classical group-wise registration on sub-populations and ignore longitudinal constraints, thus having three ma...

Brain atlases are of fundamental importance for analyzing the dynamic neurodevelopment in fetal brain studies. Since the brain size, shape, and anatomical structures change rapidly during the prenatal period, it is essential to construct a spatiotemporal (4D) atlas equipped with tissue probability maps, which can preserve sharper early brain foldin...

Cortical surface registration is an essential step and prerequisite for surface-based neuroimaging analysis. It aligns cortical surfaces across individuals and time points to establish cross-sectional and longitudinal cortical correspondences to facilitate neuroimaging studies. Though achieving good performance, available methods are either time co...

Convolutional Neural Networks (CNNs) have achieved overwhelming success in learning-related problems for 2D/3D images in the Euclidean space. However, unlike in the Euclidean space, the shapes of many structures in medical imaging have an inherent spherical topology in a manifold space, e.g., the convoluted brain cortical surfaces represented by tr...

The human cerebral cortex undergoes dynamic and regionally heterogeneous development during infancy. Cortical surface-based analysis, which explicitly reconstructs topologically-correct and geometrically-accurate surface representations of the highly-folded, thin cerebral cortex, is the key to precisely measure, integrate, and map brain structural,...

As non-human primates, macaques have a close phylogenetic relationship to human beings and have been proven to be a valuable and widely used animal model in human neuroscience research. Accurate skull stripping (aka. brain extraction) of brain magnetic resonance imaging (MRI) is a crucial prerequisite in neuroimaging analysis of macaques. Most of t...

Non-human primates, especially macaque monkeys, with close phylogenetic relationship to humans, are highly valuable and widely used animal models for human neuroscience studies. In neuroimaging analysis of macaques, brain extraction or skull stripping of magnetic resonance imaging (MRI) is a crucial step for following processing. However, the curre...

Fetal Magnetic Resonance Imaging (MRI) is challenged by the fetal movements and maternal breathing. Although fast MRI sequences allow artifact free acquisition of individual 2D slices, motion commonly occurs in between slices acquisitions. Motion correction for each slice is thus very important for reconstruction of 3D fetal brain MRI, but is highl...

Quality assessment (QA) and brain extraction (BE) are two fundamental steps in 3D fetal brain MRI reconstruction and quantification. Conventionally, QA and BE are performed independently, ignoring the inherent relation of the two closely-related tasks. However, both of them focus on the brain region representation, so they can be jointly optimized...

Current spherical surface registration methods achieve good performance on alignment and spatial normalization of cortical surfaces across individuals in neuroimaging analysis. However, they are computationally intensive, since they have to optimize an objective function independently for each pair of surfaces. In this paper, we present a fast lear...

Structured illumination microscopy (SIM) surpasses the optical diffraction limit and offers a two-fold enhancement in resolution over diffraction limited microscopy. However, it requires both intense illumination and multiple acquisitions to produce a single high-resolution image. Using deep learning to augment SIM, we obtain a five-fold reduction...

Using deep learning to augment structured illumination microscopy (SIM), we obtained a fivefold reduction in the number of raw images required for super-resolution SIM, and generated images under extreme low light conditions (100X fewer photons). We validated the performance of deep neural networks on different cellular structures and achieved mult...

Automatic parcellation of cortical surfaces into anatomically meaningful regions of interest (ROIs) is of great importance in brain analysis. Due to the complex shape of the convoluted cerebral cortex, conventional methods generally require three steps to obtain the parcellations. First, the original cortical surface is iteratively inflated and map...

Increasing multi-site infant neuroimaging datasets are facilitating the research on understanding early brain development with larger sample size and bigger statistical power. However, a joint analysis of cortical properties (e.g., cortical thickness) is unavoidably facing the problem of non-biological variance introduced by differences in MRI scan...

Fetal brain extraction is one of the most essential steps for prenatal brain MRI reconstruction and analysis. However, due to the fetal movement within the womb, it is a challenging task to extract fetal brains from sparsely-acquired imaging stacks typically with motion artifacts. To address this problem, we propose an automatic brain extraction me...

Convolutional Neural Networks (CNNs) have been providing the state-of-the-art performance for learning-related problems involving 2D/3D images in Euclidean space. However, unlike in the Euclidean space, the shapes of many structures in medical imaging have a spherical topology in a manifold space, e.g., brain cortical or subcortical surfaces repres...

Convolutional Neural Networks (CNNs) have been providing the state-of-the-art performance for learning-related problems involving 2D/3D images in Euclidean space. However, unlike in the Euclidean space, the shapes of many structures in medical imaging have a spherical topology in a manifold space, e.g., brain cortical or subcortical surfaces repres...

In human brain MRI studies, it is of great importance to accurately parcellate cortical surfaces into anatomically and functionally meaningful regions. In this paper, we propose a novel end-to-end deep learning method by formulating surface parcellation as a semantic segmentation task on the sphere. To extend the convolutional neural networks (CNNs...

Spatiotemporal (4D) neonatal cortical surface atlases with densely sampled ages are important tools for understanding the dynamic early brain development. Conventionally, after non-linear co-registration, surface atlases were constructed by simple Euclidean average of cortical attributes across different subjects, which leads to blurred folding pat...

Surgical tool detection is attracting increasing attention from the medical image analysis community. The goal generally is not to precisely locate tools in images, but rather to indicate which tools are being used by the surgeon at each instant. The main motivation for annotating tool usage is to design efficient solutions for surgical workflow an...