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Ye Wu

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18 papers

2 author rows

YNIMG Journal 2026 Journal Article

Detailed connectomic cluster resource for white matter mapping from ultra-high-field diffusion MRI

Hiuying Yip
Yifei He
Yu Xie
Fan Zhang
Ye Wu

Large-scale brain mapping initiatives have underscored the necessity for white matter atlases that extend beyond the currently identified pathways, particularly in underexplored regions such as the superficial and cerebellar white matter. To address this gap, we develop a data-driven fiber-cluster atlas using ultra-high-field 7T structural and diffusion MRI data from 171 participants in the Human Connectome Project (HCP). Following preprocessing, we construct the whole-brain tractogram, comprising probabilistic and deterministic tractography from multi-tissue fiber orientation dispersion functions to mitigate streamline-tracking bias. Data from multiple algorithms are registered to the MNI space and subsequently aggregated. We cluster streamlines connecting seven cortical networks and nine subcortical regions using cosine k-means clustering along with two-level consensus filtering. The resulting atlas comprises 33,256 clusters for a seven-network scheme and 65,184 clusters for a seventeen-network scheme, encompassing both deep and superficial white matter. Across participants, the overlap between individual and population clusters exceeds 97%, and the median Davies-Bouldin scores are below 0.35, indicating high reproducibility and anatomical compactness. Importantly, classical tracts such as the arcuate fasciculus and corticospinal tract are subdivided into anatomically coherent subclusters, and numerous previously uncharacterized U-fibers are also identified. This open-access 7T resource aims to facilitate research on structure-function relationships, algorithm benchmarking, and precision connectomics.

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JBHI Journal 2026 Journal Article

Dual-Branch Deep Unfolding Network for Compressed Sensing MRI Reconstruction

Yujie Liu
Yu Luo
Jie Ling
Lieqing Lin
Ye Wu
Shun Yao

In the field of compressed sensing magnetic resonance imaging (CS-MRI), deep unfolding networks (DUNs) achieve high interpretability and superior performance. However, existing DUN-based methods often treat different components of the MR image uniformly without considering their respective unique characteristics, leading to insufficient detail capture and suboptimal performance. To address this issue, we propose a Dual-BrancH Deep Unfolding Network (DBH-Net), which employs parallel under-complete (UC) and over-complete (OC) branches to alternately reconstruct different components from the under-sampled MR image. The UC branch focuses on extracting low-frequency features by expanding the receptive field, while the OC branch emphasizes high-frequency features by restricting the receptive field. Besides the independent descriptive abilities of dual-branch, the unique characteristics of DUN facilitate a tighter integration between the two branches. Additionally, we introduce an Auxiliary Information Fusion Block (AIFB) to transfer multi-channel auxiliary information between stages, effectively reducing information loss. Extensive experiments on three datasets demonstrate that our proposed DBH-Net outperforms existing state-of-the-art methods.

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NeurIPS Conference 2025 Conference Paper

CryptoMoE: Privacy-Preserving and Scalable Mixture of Experts Inference via Balanced Expert Routing

Yifan Zhou
Tianshi Xu
Jue Hong
Ye Wu
Meng Li

Private large language model (LLM) inference based on cryptographic primitives offers a promising path towards privacy-preserving deep learning. However, existing frameworks only support dense LLMs like LLaMA-1 and struggle to scale to mixture-of-experts (MoE) architectures. The key challenge comes from securely evaluating the dynamic routing mechanism in MoE layers, which may reveal sensitive input information if not fully protected. In this paper, we propose CryptoMoE, the first framework that enables private, efficient, and accurate inference for MoE-based models. CryptoMoE balances expert loads to protect expert routing information and proposes novel protocols for secure expert dispatch and combine. CryptoMoE also develops a confidence-aware token selection strategy and a batch matrix multiplication protocol to improve accuracy and efficiency further. Extensive experiments on DeepSeekMoE-16. 4B, OLMoE-6. 9B, and QWenMoE-14. 3B show that CryptoMoE achieves $2. 8\sim3. 5\times$ end-to-end latency reduction and $3\sim6\times$ communication reduction over a dense baseline with minimum accuracy loss. We also adapt CipherPrune (ICLR'25) for MoE inference and demonstrate CryptoMoE can reduce the communication by up to $4. 3 \times$.

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YNIMG Journal 2025 Journal Article

Deep learning-based diffusion MRI tractography: Integrating spatial and anatomical information

Yiqiong Yang
Yitian Yuan
Baoxing Ren
Ye Wu
Yanqiu Feng
Xinyuan Zhang

Diffusion MRI tractography technique enables non-invasive visualization of the white matter pathways in the brain. It plays a crucial role in neuroscience and clinical fields by facilitating the study of brain connectivity and neurological disorders. However, the accuracy of reconstructed tractograms has been a longstanding challenge. Recently, deep learning methods have been applied to improve tractograms for better white matter coverage, but often comes at the expense of generating excessive false-positive connections. This is largely due to their reliance on local information to predict long-range streamlines. To improve the accuracy of streamline propagation predictions, we introduce a novel deep learning framework that integrates image-domain spatial information and anatomical information along tracts, with the former extracted through convolutional layers and the latter modeled via a Transformer-decoder. Additionally, we employ a weighted loss function to address fiber class imbalance encountered during training. We evaluate the proposed method on the simulated ISMRM 2015 Tractography Challenge dataset, achieving a valid streamline rate of 66.2 %, white matter coverage of 63.8 %, and successfully reconstructing 24 out of 25 bundles. Furthermore, on the multi-site Tractoinferno dataset, the proposed method demonstrates its ability to handle various diffusion MRI acquisition schemes, achieving a 5.7 % increase in white matter coverage and a 4.1 % decrease in overreach compared to RNN-based methods.

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YNICL Journal 2025 Journal Article

Differential patterns of axonal loss associated with threat-related adversity in atypical depression and non-atypical depression

Huifeng Zhang
Lei Ding
Lanxiang He
Rubai Zhou
Wenxian Lu
Tenghuan Xu
Ye Wu
Daihui Peng

BACKGROUND: Major depressive disorder (MDD) encompasses a broad spectrum of heterogeneous symptoms arising from distinct etiological mechanisms. Phenotypic markers of psychopathology are most likely influenced by exposure to childhood maltreatment, yielding distinct subtypes within conventional diagnostic boundaries. However, the biological interactions between MDD subtypes and types of childhood trauma remain unclear. METHODS: 50 atypical depression (AD) patients, 97 non-AD patients and 50 healthy controls were included to complete multi-shell diffusion MRI scans and clinical assessments. Differential tractography was performed to clarify the axonal injury between the AD and non-AD groups. Moreover, correlational tractography was employed to individually assess the relationship between quantitative anisotropy (QA) and all types of childhood trauma in each depressed subgroup. RESULTS: Our study found that AD and non-AD patients had differential axonal loss primarily involving the bilateral superior longitudinal fasciculus, arcuate fasciculus, inferior longitudinal fasciculus, parietal aslant tract, and corpus callosum. Furthermore, AD patients showed significantly negative associations between QA values, childhood trauma total scores, and threat-related adversity, while significantly positive associations were observed in non-AD patients. However, similar phenomena were not observed for deprivation-related adversities. DISCUSSION: Our findings indicate differential spatial patterns of axonal alterations associated with threat-related adversity in atypical depression and non-atypical depression. Efforts to attenuate the consequences of childhood maltreatment for MDD should consider the associations between specific patterns of adversity and specific clinical manifestations.

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AAAI Conference 2025 Conference Paper

Portcullis: A Scalable and Verifiable Privacy Gateway for Third-Party LLM Inference

Jiangou Zhan
Wenhui Zhang
Zheng Zhang
Huanran Xue
Yao Zhang
Ye Wu

Businesses using third-party LLMs face privacy risks from exposed prompts. This paper presents Portcullis, a privacy-preserving gateway that safeguards sensitive data while supporting efficient and accurate LLM responses. Portcullis functions as a mediator, anonymizing sensitive data in prompts through parallel substitution, securely interacting with LLMs, and accurately reconstructing responses. It ensures all data processing occurs within secure encrypted memory. The gateway is attested to ensure trustworthiness and protect user privacy. Portcullis is the first of its kind, offering a verifiable and scalable privacy gateway for third-party LLM inferences. We assess Portcullis's efficiency as a confidential container platform, demonstrating that its startup time scales linearly, ensuring scalability. Additionally, we evaluate its runtime performance using the PII and Enron Email Dataset. For masking and unmasking workloads, Portcullis outperforms Hide-and-Seek by 96x speed up, while maintaining equal or better false positive and false negative rates compared to existing solutions. On the Enron dataset, Portcullis achieves notably higher accuracy, surpassing Hide-and-Seek by over 0.1 for GPT-4o mini.

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NeurIPS Conference 2025 Conference Paper

PubSub-VFL: Towards Efficient Two-Party Split Learning in Heterogeneous Environments via Publisher/Subscriber Architecture

Yi Liu
Yang Liu
Leqian Zheng
Jue Hong
Junjie Shi
Qingyou Yang
Ye Wu
Cong Wang

With the rapid advancement of the digital economy, data collaboration between organizations has become a well-established business model, driving the growth of various industries. However, privacy concerns make direct data sharing impractical. To address this, Two-Party Split Learning (a. k. a. Vertical Federated Learning (VFL)) has emerged as a promising solution for secure collaborative learning. Despite its advantages, this architecture still suffers from low computational resource utilization and training efficiency. Specifically, its synchronous dependency design increases training latency, while resource and data heterogeneity among participants further hinder efficient computation. To overcome these challenges, we propose \texttt{PubSub-VFL}, a novel VFL paradigm with a Publisher/Subscriber architecture optimized for two-party collaborative learning with high computational efficiency. \texttt{PubSub-VFL} leverages the decoupling capabilities of the Pub/Sub architecture and the data parallelism of the parameter server architecture to design a hierarchical asynchronous mechanism, reducing training latency and improving system efficiency. Additionally, to mitigate the training imbalance caused by resource and data heterogeneity, we formalize an optimization problem based on participants’ system profiles, enabling the selection of optimal hyperparameters while preserving privacy. We conduct a theoretical analysis to demonstrate that \texttt{PubSub-VFL} achieves stable convergence and is compatible with security protocols such as differential privacy. Extensive case studies on five benchmark datasets further validate its effectiveness, showing that \texttt{PubSub-VFL} compared to state-of-the-art baselines not only accelerates training by $2 \sim 7\times$ without compromising accuracy but also achieves computational resource utilization by up to 91. 07\%.

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IJCAI Conference 2025 Conference Paper

SAP: Privacy-Preserving Fine-Tuning on Language Models with Split-and-Privatize Framework

Xicong Shen
Yang Liu
Yi Liu
Peiran Wang
Huiqi Liu
Jue Hong
Bing Duan
Zirui Huang

Pre-trained Language Models (PLM) have enabled a cost-effective approach to handling various downstream applications via Parameter-Efficient-Fine-Tuning (PEFT) techniques. In this context, service providers have introduced a popular fine-tuning-based product service known as Model-as-a-Service (MaaS). This service offers users access to extensive PLMs and training resources. With MaaS, users can fine-tune, deploy, and utilize their customized models seamlessly, leveraging a one-stop platform that allows them to work with their private datasets efficiently. However, this service paradigm has recently been exposed to the possibility of leaking user private data. To this end, we identify the data privacy leakage risks in MaaS-based PEFT and propose a Split-and-Privatize (SAP) framework, mitigating the privacy leakage by integrating split learning and differential privacy into MaaS PEFT. Furthermore, we propose Contributing-Token-Identification (CTI), a novel method to balance model utility degradation and privacy leakage. As a result, the proposed framework is comprehensively evaluated, demonstrating a 65% improvement in empirical privacy with only a 1% degradation in model performance on the Stanford Sentiment Treebank dataset, outperforming existing state-of-the-art baselines.

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IROS Conference 2025 Conference Paper

SimWorld: A Unified Benchmark for Simulator-Conditioned Scene Generation via World Model

Xinqing Li
Ruiqi Song
Qingyu Xie
Ye Wu
Nanxin Zeng
Yunfeng Ai

With the rapid advancement of autonomous driving technology, a lack of data has become a major obstacle to enhancing perception model accuracy. Researchers are now exploring controllable data generation using world models to diversify datasets. However, previous work has been limited to studying image generation quality on specific public datasets. There is still relatively little research on how to build data generation engines for real-world application scenes to achieve large-scale data generation for challenging scenes. In this paper, a simulator-conditioned scene generation engine based on world model is proposed. By constructing a simulation system consistent with real-world scenes, simulation data and labels, which serve as the conditions for data generation in the world model, for any scenes can be collected. It is a novel data generation pipeline by combining the powerful scene simulation capabilities of the simulation engine with the robust data generation capabilities of the world model. In addition, a benchmark with proportionally constructed virtual and real data, is provided for exploring the capabilities of world models in real-world scenes. Quantitative results show that these generated images significantly improve downstream perception models performance. Finally, we explored the generative performance of the world model in urban autonomous driving scenarios. All the data and code will be available at https://github.com/Li-Zn-H/SimWorld.

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JBHI Journal 2025 Journal Article

Spherical Harmonics-Based Deep Learning Achieves Generalized and Accurate Diffusion Tensor Imaging

Yunwei Chen
Jialong Li
Qiqi Lu
Ye Wu
Xiaoming Liu
Yuanyuan Gao
Yanqiu Feng
Zhicheng Zhang

Diffusion tensor imaging (DTI) is a prevalent magnetic resonance imaging (MRI) technique, widely used in clinical and neuroscience research. However, the reliability of DTI is affected by the low signal-to-noise ratio inherent in diffusion-weighted (DW) images. Deep learning (DL) has shown promise in improving the quality of DTI, but its limited generalization to variable acquisition schemes hinders practical applications. This study aims to develop a generalized, accurate, and efficient DL-based DTI method. By leveraging the representation of voxel-wise diffusion MRI (dMRI) signals on the sphere using spherical harmonics (SH), we propose a novel approach that utilizes SH coefficient maps as input to a network for predicting the diffusion tensor (DT) field, enabling improved generalization. Extensive experiments were conducted on simulated and in-vivo datasets, covering various DTI application scenarios. The results demonstrate that the proposed SH-DTI method achieves advanced performance in both quantitative and qualitative analyses of DTI. Moreover, it exhibits remarkable generalization capabilities across different acquisition schemes, centers, and scanners, ensuring its broad applicability in diverse settings.

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ICML Conference 2024 Conference Paper

GroupCover: A Secure, Efficient and Scalable Inference Framework for On-device Model Protection based on TEEs

Zheng Zhang
Na Wang 0003
Ziqi Zhang
Yao Zhang
Tianyi Zhang
Jianwei Liu 0001
Ye Wu

Due to the high cost of training DNN models, how to protect the intellectual property of DNN models, especially when the models are deployed to users’ devices, is becoming an important topic. One practical solution is to use Trusted Execution Environments (TEEs) and researchers have proposed various model obfuscation solutions to make full use of the high-security guarantee of TEEs and the high performance of collocated GPUs. In this paper, we first identify a common vulnerability, namely the fragility of randomness, that is shared by existing TEE-based model obfuscation solutions. This vulnerability benefits model-stealing attacks and allows the adversary to recover about 97% of the secret model. To improve the security of TEE-shielded DNN models, we further propose a new model obfuscation approach GroupCover, which uses sufficient randomization and mutual covering obfuscation to protect model weights. Experimental results demonstrate that GroupCover can achieve a comparable security level as the upper-bound (black-box protection), which is remarkably over 3x compared with existing solutions. Besides, GroupCover introduces 19% overhead and negligible accuracy loss compared to model unprotected scheme.

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YNIMG Journal 2024 Journal Article

Spherical-deconvolution informed filtering of tractograms changes laterality of structural connectome

Yifei He
Yoonmi Hong
Ye Wu

Diffusion MRI-driven tractography, a non-invasive technique that reveals how the brain is connected, is widely used in brain lateralization studies. To improve the accuracy of tractography in showing the underlying anatomy of the brain, various tractography filtering methods were applied to reduce false positives. Based on different algorithms, tractography filtering methods are able to identify the fibers most consistent with the original diffusion data while removing fibers that do not align with the original signals, ensuring the tractograms are as biologically accurate as possible. However, the impact of tractography filtering on the lateralization of the brain connectome remains unclear. This study aims to investigate the relationship between fiber filtering and laterality changes in brain structural connectivity. Three typical tracking algorithms were used to construct the raw tractography, and two popular fiber filtering methods(SIFT and SIFT2) were employed to filter the tractography across a range of parameters. Laterality indices were computed for six popular biological features, including four microstructural measures (AD, FA, RD, and T1/T2 ratio) and two structural features (fiber length and connectivity) for each brain region. The results revealed that tractography filtering may cause significant laterality changes in more than 10% of connections, up to 25% for probabilistic tracking, and deterministic tracking exhibited minimal laterality changes compared to probabilistic tracking, experiencing only about 6%. Except for tracking algorithms, different fiber filtering methods, along with the various biological features themselves, displayed more variable patterns of laterality change. In conclusion, this study provides valuable insights into the intricate relationship between fiber filtering and laterality changes in brain structural connectivity. These findings can be used to develop improved tractography filtering methods, ultimately leading to more robust and reliable measurements of brain asymmetry in lateralization studies.

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YNIMG Journal 2023 Journal Article

Tractography passes the test: Results from the diffusion-simulated connectivity (disco) challenge

Gabriel Girard
Jonathan Rafael-Patiño
Raphaël Truffet
Dogu Baran Aydogan
Nagesh Adluru
Veena A. Nair
Vivek Prabhakaran
Barbara B. Bendlin

Estimating structural connectivity from diffusion-weighted magnetic resonance imaging is a challenging task, partly due to the presence of false-positive connections and the misestimation of connection weights. Building on previous efforts, the MICCAI-CDMRI Diffusion-Simulated Connectivity (DiSCo) challenge was carried out to evaluate state-of-the-art connectivity methods using novel large-scale numerical phantoms. The diffusion signal for the phantoms was obtained from Monte Carlo simulations. The results of the challenge suggest that methods selected by the 14 teams participating in the challenge can provide high correlations between estimated and ground-truth connectivity weights, in complex numerical environments. Additionally, the methods used by the participating teams were able to accurately identify the binary connectivity of the numerical dataset. However, specific false positive and false negative connections were consistently estimated across all methods. Although the challenge dataset doesn't capture the complexity of a real brain, it provided unique data with known macrostructure and microstructure ground-truth properties to facilitate the development of connectivity estimation methods.

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YNIMG Journal 2022 Journal Article

Insights from the IronTract challenge: Optimal methods for mapping brain pathways from multi-shell diffusion MRI

Chiara Maffei
Gabriel Girard
Kurt G. Schilling
Dogu Baran Aydogan
Nagesh Adluru
Andrey Zhylka
Ye Wu
Matteo Mancini

Limitations in the accuracy of brain pathways reconstructed by diffusion MRI (dMRI) tractography have received considerable attention. While the technical advances spearheaded by the Human Connectome Project (HCP) led to significant improvements in dMRI data quality, it remains unclear how these data should be analyzed to maximize tractography accuracy. Over a period of two years, we have engaged the dMRI community in the IronTract Challenge, which aims to answer this question by leveraging a unique dataset. Macaque brains that have received both tracer injections and ex vivo dMRI at high spatial and angular resolution allow a comprehensive, quantitative assessment of tractography accuracy on state-of-the-art dMRI acquisition schemes. We find that, when analysis methods are carefully optimized, the HCP scheme can achieve similar accuracy as a more time-consuming, Cartesian-grid scheme. Importantly, we show that simple pre- and post-processing strategies can improve the accuracy and robustness of many tractography methods. Finally, we find that fiber configurations that go beyond crossing (e.g., fanning, branching) are the most challenging for tractography. The IronTract Challenge remains open and we hope that it can serve as a valuable validation tool for both users and developers of dMRI analysis methods.

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YNICL Journal 2021 Journal Article

Multiscale neural modeling of resting-state fMRI reveals executive-limbic malfunction as a core mechanism in major depressive disorder

Guoshi Li
Yujie Liu
Yanting Zheng
Ye Wu
Danian Li
Xinyu Liang
Yaoping Chen
Ying Cui

Major depressive disorder (MDD) represents a grand challenge to human health and society, but the underlying pathophysiological mechanisms remain elusive. Previous neuroimaging studies have suggested that MDD is associated with abnormal interactions and dynamics in two major neural systems including the default mode - salience (DMN-SAL) network and the executive - limbic (EXE-LIM) network, but it is not clear which network plays a central role and which network plays a subordinate role in MDD pathophysiology. To address this question, we refined a newly developed Multiscale Neural Model Inversion (MNMI) framework and applied it to test whether MDD is more affected by impaired circuit interactions in the DMN-SAL network or the EXE-LIM network. The model estimates the directed connection strengths between different neural populations both within and between brain regions based on resting-state fMRI data collected from normal healthy subjects and patients with MDD. Results show that MDD is primarily characterized by abnormal circuit interactions in the EXE-LIM network rather than the DMN-SAL network. Specifically, we observe reduced frontoparietal effective connectivity that potentially contributes to hypoactivity in the dorsolateral prefrontal cortex (dlPFC), and decreased intrinsic inhibition combined with increased excitation from the superior parietal cortex (SPC) that potentially lead to amygdala hyperactivity, together resulting in activation imbalance in the PFC-amygdala circuit that pervades in MDD. Moreover, the model reveals reduced PFC-to-hippocampus excitation but decreased SPC-to-thalamus inhibition in MDD population that potentially lead to hypoactivity in the hippocampus and hyperactivity in the thalamus, consistent with previous experimental data. Overall, our findings provide strong support for the long-standing limbic-cortical dysregulation model in major depression but also offer novel insights into the multiscale pathophysiology of this debilitating disease.

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YNIMG Journal 2018 Journal Article

An anatomically curated fiber clustering white matter atlas for consistent white matter tract parcellation across the lifespan

Fan Zhang
Ye Wu
Isaiah Norton
Laura Rigolo
Yogesh Rathi
Nikos Makris
Lauren J. O'Donnell

This work presents an anatomically curated white matter atlas to enable consistent white matter tract parcellation across different populations. Leveraging a well-established computational pipeline for fiber clustering, we create a tract-based white matter atlas including information from 100 subjects. A novel anatomical annotation method is proposed that leverages population-based brain anatomical information and expert neuroanatomical knowledge to annotate and categorize the fiber clusters. A total of 256 white matter structures are annotated in the proposed atlas, which provides one of the most comprehensive tract-based white matter atlases covering the entire brain to date. These structures are composed of 58 deep white matter tracts including major long range association and projection tracts, commissural tracts, and tracts related to the brainstem and cerebellar connections, plus 198 short and medium range superficial fiber clusters organized into 16 categories according to the brain lobes they connect. Potential false positive connections are annotated in the atlas to enable their exclusion from analysis or visualization. In addition, the proposed atlas allows for a whole brain white matter parcellation into 800 fiber clusters to enable whole brain connectivity analyses. The atlas and related computational tools are open-source and publicly available. We evaluate the proposed atlas using a testing dataset of 584 diffusion MRI scans from multiple independently acquired populations, across genders, the lifespan (1 day–82 years), and different health conditions (healthy control, neuropsychiatric disorders, and brain tumor patients). Experimental results show successful white matter parcellation across subjects from different populations acquired on multiple scanners, irrespective of age, gender or disease indications. Over 99% of the fiber tracts annotated in the atlas were detected in all subjects on average. One advantage in terms of robustness is that the tract-based pipeline does not require any cortical or subcortical segmentations, which can have limited success in young children and patients with brain tumors or other structural lesions. We believe this is the first demonstration of consistent automated white matter tract parcellation across the full lifespan from birth to advanced age.

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YNIMG Journal 2018 Journal Article

Investigation into local white matter abnormality in emotional processing and sensorimotor areas using an automatically annotated fiber clustering in major depressive disorder

Ye Wu
Fan Zhang
Nikos Makris
Yuping Ning
Isaiah Norton
Shenglin She
Hongjun Peng
Yogesh Rathi

This work presents an automatically annotated fiber cluster (AAFC) method to enable identification of anatomically meaningful white matter structures from the whole brain tractography. The proposed method consists of 1) a study-specific whole brain white matter parcellation using a well-established data-driven groupwise fiber clustering pipeline to segment tractography into multiple fiber clusters, and 2) a novel cluster annotation method to automatically assign an anatomical tract annotation to each fiber cluster by employing cortical parcellation information across multiple subjects. The novelty of the AAFC method is that it leverages group-wise information about the fiber clusters, including their fiber geometry and cortical terminations, to compute a tract anatomical label for each cluster in an automated fashion. We demonstrate the proposed AAFC method in an application of investigating white matter abnormality in emotional processing and sensorimotor areas in major depressive disorder (MDD). Seven tracts of interest related to emotional processing and sensorimotor functions are automatically identified using the proposed AAFC method as well as a comparable method that uses a cortical parcellation alone. Experimental results indicate that our proposed method is more consistent in identifying the tracts across subjects and across hemispheres in terms of the number of fibers. In addition, we perform a between-group statistical analysis in 31 MDD patients and 62 healthy subjects on the identified tracts using our AAFC method. We find statistical differences in diffusion measures in local regions within a fiber tract (e. g. 4 fiber clusters within the identified left hemisphere cingulum bundle (consisting of 14 clusters) are significantly different between the two groups), suggesting the ability of our method in identifying potential abnormality specific to subdivisions of a white matter structure.

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AIIM Journal 2015 Journal Article

Sparse deconvolution of higher order tensor for fiber orientation distribution estimation

Yuanjing Feng
Ye Wu
Yogesh Rathi
Carl-Fredrik Westin

Purpose Higher order tensor (HOT) imaging approaches based on the spherical deconvolution framework have attracted much interest for their effectiveness in estimating fiber orientation distribution (FOD). However, sparse regularization techniques are still needed to obtain stable FOD in solving the deconvolution problem, particularly in very high orders. Our goal is to adequately characterize the actual sparsity lying in the FOD domain to develop accurate estimation approach for fiber orientation in HOT framework. Materials and methods We propose a sparse HOT regularization model by enforcing the sparse constraint directly on the representation of FOD instead of imposing it on coefficients of basis function. Then, we incorporate both the stabilizing effect of the l 2 penalty and the sparsity encouraging effect of the l 1 penalty in the sparse model to adequately characterize the actual sparsity lying in the FOD domain. Furthermore, a weighted regularization scheme is developed to iteratively solve the deconvolution problem. The deconvolution technique is compared against existing methods using l 2 or l 1 regularizer and tested on synthetic data and real human brain. Results Experiments were conducted on synthetic data and real human brain data. The synthetic experimental results indicate that crossing fibers are more easily detected and the angular resolution limit is improved by our method by approximately 20°–30° compared to existing HOT method. The detection accuracy is considerably improved compared with that of spherical deconvolution approaches using the l 2 regularizer and the reweighted l 1 scheme. Conclusions Results of testing the deconvolution technique demonstrate that it allows HOTs to obtain increasingly clean and sharp FOD, which in turn significantly increases the angular resolution of current HOT methods. With sparsity on FOD domain, this method efficiently improves the ability of HOT in resolving crossing fibers.

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