arXiv subjects

Deqiang Qiu

Publications and source records attributed to Deqiang Qiu.

At least 19 recordsLinked to original sources

arXiv2026

XAI-Refine: An Automated Explanation-Knowledge Loop for Brain-Age Prediction

Brain-age prediction models are commonly evaluated by predictive accuracy, yet accurate predictions alone do not establish that a model relies on reproducible or neurobiologically supported mechanisms. Post-hoc explanation methods can expose these mechanisms, but existing workflows typically stop at diagnosis or require correction targets to be specified before model analysis. We propose XAI-Refine, an automated explanation-knowledge loop for brain-age prediction from resting-state functional connectivity. At each iteration, XAI-Refine consolidates complementary post-hoc analyses across repeated training runs into reliable, structured model explanations. It converts each reliable explanation into a neutral neurobiological question, retrieves and verifies relevant literature, and compiles the verified evidence into an admissible set in the same typed explanation space. The target for refinement is defined as the minimal projection of the current model explanation onto the admissible set induced by applicable verified knowledge. This revised explanation is then translated into a differentiable constraint while preserving the originating model variable, measurement operator, and applicable scope. Candidate updates are promoted only when multi-seed validation confirms target-directed explanatory movement, predictive performance remains within a prespecified guardrail, and non-target explanatory drift remains bounded. Experiments on functional-connectivity-based brain-age prediction evaluate predictive performance, explanation reliability, literature alignment, and target-specific model revision, illustrating a structured route from post-hoc analysis to evidence-guided model refinement.

cs.LG↗

arXiv2026

Laplace-Mixture Dipole Inversion for Quantitative Susceptibility Mapping

Purpose: To develop an automatic dipole inversion method for quantitative susceptibility mapping (QSM) that preserves fine anatomical structures without the need for manual regularization-parameter tuning. Theory: The original approximate message passing with parameter estimation (AMP-PE) framework models image gradients with a single Laplace prior, which does not fully capture the heavy-tailed gradient distribution of brain susceptibility maps. This prior mismatch can lead to over-regularization and blocky reconstructions. We address this limitation by modeling the gradients with a two-component Laplace mixture prior. Methods: We propose a Laplace-Mixture Dipole Inversion (LAMDI) method by incorporating a two-component Laplace mixture prior into the AMP-PE framework with automatic parameter estimation. LAMDI was evaluated on a public in vivo dataset. Its performance was compared with FANSI, MEDI, and AMP-PE with a single-Laplace prior (AMP-PE-L1) under both standard default and reference-tuned settings. Results: On a public multi-orientation QSM dataset, LAMDI achieved NRMSE and SSIM comparable to AMP-PE-L1 while substantially reducing HFEN, suggesting improved preservation of high-frequency anatomical detail. Under reference-based tuning, FANSI and MEDI achieved the best performance for some metrics, but LAMDI remained competitive without requiring reference maps or manual regularization tuning. Conclusion: LAMDI provides an effective and automatic parameter-estimation alternative for QSM dipole inversion by combining competitive reconstruction accuracy with improved preservation of fine anatomical detail.

eess.IV↗

arXiv2026

Probabilistic Joint and Individual Variation Explained (ProJIVE) for Data Integration

Collecting multiple types of data on the same set of subjects is common in modern scientific applications including, genomics, metabolomics, and neuroimaging. Joint and Individual Variance Explained (JIVE) seeks a low-rank approximation of the joint variation between two or more sets of features captured on common subjects and isolates this variation from that unique to eachset of features. We develop an expectation-maximization (EM) algorithm to estimate a probabilistic model for the JIVE framework. The model extends probabilistic principal components analysis to multiple data sets. Our maximum likelihood approach simultaneously estimates joint and individual components, which can lead to greater accuracy compared to other methods. We apply ProJIVE to measures of brain morphometry and cognition in Alzheimer's disease. ProJIVE learns biologically meaningful courses of variation, and the joint morphometry and cognition subject scores are strongly related to more expensive existing biomarkers. Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Code to reproduce the analysis is available on our GitHub page.

stat.ML↗

arXiv2026

MIRAGE: Knowledge Graph-Guided Cross-Cohort MRI Synthesis for Alzheimer's Disease Prediction

Reliable Alzheimer's disease (AD) diagnosis increasingly relies on multimodal assessments combining structural Magnetic Resonance Imaging (MRI) and Electronic Health Records (EHR). However, deploying these models is bottlenecked by modality missingness, as MRI scans are expensive and frequently unavailable in many patient cohorts. Furthermore, synthesizing de novo 3D anatomical scans from sparse, high-dimensional tabular records is technically challenging and poses severe clinical risks. To address this, we introduce MIRAGE, a novel framework that reframes the missing-MRI problem as an anatomy-guided cross-modal latent distillation task. First, MIRAGE leverages a Biomedical Knowledge Graph (KG) and Graph Attention Networks to map heterogeneous EHR variables into a unified embedding space that can be propagated from cohorts with real MRIs to cohorts without them. To bridge the semantic gap and enforce physical spatial awareness, we employ a frozen pre-trained 3D U-Net decoder strictly as an auxiliary regularization engine. Supported by a novel cohort-aggregated skip feature compensation strategy, this decoder acts as a rigorous structural penalty, forcing 1D latent representations to encode biologically plausible, macro-level pathological semantics. By exclusively utilizing this distilled "diagnostic-surrogate" representation during inference, MIRAGE completely bypasses computationally expensive 3D voxel reconstruction. Experiments demonstrate that our framework successfully bridges the missing-modality gap, improving the AD classification rate by 13% compared to unimodal baselines in cohorts without real MRIs.

cs.CV↗

arXiv2025

Diffusion-Based Synthesis of 3D T1w MPRAGE Images from Multi-Echo GRE with Multi-Parametric MRI Integration

Multi-echo Gradient Echo (mGRE) sequences provide valuable quantitative parametric maps, such as Quantitative Susceptibility Mapping (QSM) and transverse relaxation rate (R2*), sensitive to tissue iron and myelin. However, structural morphometry typically relies on separate T1-weighted MPRAGE acquisitions, prolonging scan times. We propose a deep learning framework to synthesize high-contrast 3D T1w MPRAGE images directly from mGRE data, streamlining neuroimaging protocols. We developed a novel multi-parametric conditional diffusion model based on the Fast-DDPM architecture. Unlike conventional intensity-based synthesis, our approach integrates iron-sensitive QSM and R2* maps as physical priors to address contrast ambiguity in iron-rich deep gray matter. We trained and validated the model on 175 healthy subjects. Performance was evaluated against established U-Net and GAN-based baselines using perceptual metrics and downstream segmentation accuracy. Uniquely, we assessed the biological plausibility of synthesized images by replicating population-level statistical associations with age and sex. The proposed framework significantly outperformed baselines, achieving superior perceptual quality and segmentation accuracy, particularly in subcortical regions like the thalamus and pallidum. Crucially, synthesized images preserved essential biological dependencies: regression analyses showed high concordance in age-related atrophy rates, aging effect sizes, and sexual dimorphism patterns compared to ground truth. By effectively leveraging quantitative MRI priors, our diffusion-based method generates strictly biologically plausible T1w images suitable for reliable clinical morphometric analysis. This approach offers a promising pathway to reduce acquisition time by deriving structural contrasts retrospectively from quantitative mGRE sequences.

eess.IV↗

arXiv2025

Non-linear associations of amyloid-$\beta$ with resting-state functional networks and their cognitive relevance in a large community-based cohort of cognitively normal older adults

Background: Non-linear alterations in brain network connectivity may represent early neural signatures of Alzheimer's disease (AD) pathology in cognitively normal older adults. Understanding these changes and their cognitive relevance may help clarify early network vulnerability associated with AD pathology. Most prior studies recruited participants from memory clinics, often with subjective memory concerns, limiting generalizability. Methods: We examined 14 large-scale functional brain networks in 968 cognitively normal older adults recruited from the community using resting-state functional MRI, cerebrospinal fluid (CSF) biomarkers (amyloid-$\beta$ 1-42 [A$\beta$], total tau, phosphorylated tau 181), and neuropsychological assessments. Functional networks were identified using group independent component analysis. Results: Inverted U-shaped associations between CSF A$\beta$ and functional connectivity were observed in the precuneus network and ventral default mode network (DMN), but not in the dorsal DMN, indicating network-specific vulnerability to early amyloid pathology. Higher connectivity in A$\beta$-related networks, including dorsal and ventral DMN, precuneus, and posterior salience networks, was associated with better visual memory, visuospatial, and executive performance. No significant relationships were observed between CSF tau and functional connectivity. Conclusions: Using a large, community-based cohort, we demonstrate that non-linear alterations in functional connectivity occur in specific networks even during the asymptomatic phase of AD. Moreover, A$\beta$-related network connectivity is cognitively relevant, highlighting early network vulnerability and its functional consequences in amyloid pathology.

q-bio.NC↗

arXiv2025

Atlas-free Brain Network Transformer

Current atlas-based approaches to brain network analysis rely heavily on standardized anatomical or connectivity-driven brain atlases. However, these fixed atlases often introduce significant limitations, such as spatial misalignment across individuals, functional heterogeneity within predefined regions, and atlas-selection biases, collectively undermining the reliability and interpretability of the derived brain networks. To address these challenges, we propose a novel atlas-free brain network transformer (atlas-free BNT) that leverages individualized brain parcellations derived directly from subject-specific resting-state fMRI data. Our approach computes ROI-to-voxel connectivity features in a standardized voxel-based feature space, which are subsequently processed using the BNT architecture to produce comparable subject-level embeddings. Experimental evaluations on sex classification and brain-connectome age prediction tasks demonstrate that our atlas-free BNT consistently outperforms state-of-the-art atlas-based methods, including elastic net, BrainGNN, Graphormer and the original BNT. Our atlas-free approach significantly improves the precision, robustness, and generalizability of brain network analyses. This advancement holds great potential to enhance neuroimaging biomarkers and clinical diagnostic tools for personalized precision medicine. Reproducible code is available at https://github.com/shuai-huang/atlas_free_bnt

q-bio.NC↗

arXiv2025

DeepJIVE: Learning Joint and Individual Variation Explained from Multimodal Data Using Deep Learning

Conventional multimodal data integration methods provide a comprehensive assessment of the shared or unique structure within each individual data type but suffer from several limitations such as the inability to handle high-dimensional data and identify nonlinear structures. In this paper, we introduce DeepJIVE, a deep-learning approach to performing Joint and Individual Variance Explained (JIVE). We perform mathematical derivation and experimental validations using both synthetic and real-world 1D, 2D, and 3D datasets. Different strategies of achieving the identity and orthogonality constraints for DeepJIVE were explored, resulting in three viable loss functions. We found that DeepJIVE can successfully uncover joint and individual variations of multimodal datasets. Our application of DeepJIVE to the Alzheimer's Disease Neuroimaging Initiative (ADNI) also identified biologically plausible covariation patterns between the amyloid positron emission tomography (PET) and magnetic resonance (MR) images. In conclusion, the proposed DeepJIVE can be a useful tool for multimodal data analysis.

cs.CV↗

arXiv2024

Improving the Test-retest Reliability of Quantitative Susceptibility Mapping

Motivation - The test-retest reliability of quantitative susceptibility mapping (QSM) is affected by parameters of the acquisition protocol such as the angulation of acquisition plane with respect to the B0 field direction and spatial resolution. Goal - We aim to reduce the protocol-dependent biases/errors that might overshadow subtle changes of the susceptibility values due to pathology in the brain. Approach - The magnitude and phase images used for QSM are registered according to a standard reference protocol in the k-space through nonuniform fast Fourier transform (NUFFT). Results - With the help of our proposed approach, the test-retest reliability of scans acquired from different protocols is notably improved. Impact - The improved test-retest reliability of QSM makes it easier to detect subtle susceptibility changes in the early stages of neurodegeneration such as Alzheimer's disease and Parkinson's disease.

eess.IV↗

arXiv2024

A deep spatio-temporal attention model of dynamic functional network connectivity shows sensitivity to Alzheimer's in asymptomatic individuals

Alzheimer's disease (AD) progresses from asymptomatic changes to clinical symptoms, emphasizing the importance of early detection for proper treatment. Functional magnetic resonance imaging (fMRI), particularly dynamic functional network connectivity (dFNC), has emerged as an important biomarker for AD. Nevertheless, studies probing at-risk subjects in the pre-symptomatic stage using dFNC are limited. To identify at-risk subjects and understand alterations of dFNC in different stages, we leverage deep learning advancements and introduce a transformer-convolution framework for predicting at-risk subjects based on dFNC, incorporating spatial-temporal self-attention to capture brain network dependencies and temporal dynamics. Our model significantly outperforms other popular machine learning methods. By analyzing individuals with diagnosed AD and mild cognitive impairment (MCI), we studied the AD progression and observed a higher similarity between MCI and asymptomatic AD. The interpretable analysis highlights the cognitive-control network's diagnostic importance, with the model focusing on intra-visual domain dFNC when predicting asymptomatic AD subjects.

cs.CE↗

arXiv2024

Motion and temporal B0 shift corrections for quantitative susceptibility mapping (QSM) and R2* mapping using dual-echo spiral navigators and conjugate-phase reconstruction

Purpose: To develop an efficient navigator-based motion and temporal B0 shift correction technique for 3D multi-echo gradient-echo (ME-GRE) MRI for quantitative susceptibility mapping (QSM) and R2* mapping. Theory and Methods: A dual-echo 3D spiral navigator was designed to interleave with the Cartesian ME-GRE acquisitions, allowing the acquisition of both low- and high-echo time signals. We additionally designed a novel conjugate-phase based reconstruction method for the joint correction of motion and temporal B0 shifts. We performed both numerical simulation and in vivo human scans to assess the performance of the methods. Results: Numerical simulation and human brain scans demonstrated that the proposed technique successfully corrected artifacts induced by both head motions and temporal B0 changes. Efficient B0-change correction with conjugate-phase reconstruction can be performed on less than 10 clustered k-space segments. In vivo scans showed that combining temporal B0 correction with motion correction further reduced artifacts and improved image quality in both R2* and QSM images. Conclusion: Our proposed approach of using 3D spiral navigators and a novel conjugate-phase reconstruction method can improve susceptibility-related measurements using MR.

physics.med-ph↗

arXiv2023

Model-based T1, T2* and Proton Density Mapping Using a Bayesian Approach with Parameter Estimation and Complementary Undersampling Patterns

Purpose: To achieve automatic hyperparameter estimation for the joint recovery of quantitative MR images, we propose a Bayesian formulation of the reconstruction problem that incorporates the signal model. Additionally, we investigate the use of complementary undersampling patterns to determine optimal undersampling schemes for quantitative MRI. Theory: We introduce a novel nonlinear approximate message passing framework, referred to as ``AMP-PE'', that enables the simultaneous recovery of distribution parameters and quantitative maps. Methods: We employed the variable flip angle multi-echo (VFA-ME) method to acquire measurements. Both retrospective and prospective undersampling approaches were utilized to obtain Fourier measurements using variable-density and Poisson-disk patterns. Furthermore, we extensively explored various undersampling schemes, incorporating complementary patterns across different flip angles and/or echo times. Results: AMP-PE adopts a model-based joint recovery strategy, it outperforms the $l_1$-norm minimization approach that follows a decoupled recovery strategy. A comparison with an existing joint-recovery approach further demonstrates the advantageous outcomes of AMP-PE. For quantitative $T_1$ mapping using VFA-ME, employing identical k-space sampling patterns across different echo times produced the best performance. Whereas for $T_2^*$ and proton density mappings, using complementary sampling patterns across different flip angles yielded the best performance. Conclusion: AMP-PE is equipped with built-in parameter estimation, and works naturally in clinical settings with varying acquisition protocols and scanners. It also achieves improved performance by combining information from the MR signal model and the sparse prior on images.

eess.IV↗

arXiv2023

Concentric Tube Robot Redundancy Resolution via Velocity/Compliance Manipulability Optimization

Concentric Tube Robots (CTR) have the potential to enable effective minimally invasive surgeries. While extensive modeling and control schemes have been proposed in the past decade, limited efforts have been made to improve the trajectory tracking performance from the perspective of manipulability , which can be critical to generate safe motion and feasible actuator commands. In this paper, we propose a gradient-based redundancy resolution framework that optimizes velocity/compliance manipulability-based performance indices during trajectory tracking for a kinematically redundant CTR. We efficiently calculate the gradients of manipulabilities by propagating the first- and second-order derivatives of state variables of the Cosserat rod model along the CTR arc length, reducing the gradient computation time by 68\% compared to finite difference method. Task-specific performance indices are optimized by projecting the gradient into the null-space of trajectory tracking. The proposed method is validated in three exemplary scenarios that involve trajectory tracking, obstacle avoidance, and external load compensation, respectively. Simulation results show that the proposed method is able to accomplish the required tasks while commonly used redundancy resolution approaches underperform or even fail.

cs.RO↗

arXiv2022

Robust Quantitative Susceptibility Mapping via Approximate Message Passing with Parameter Estimation

Purpose: For quantitative susceptibility mapping (QSM), the lack of ground-truth in clinical settings makes it challenging to determine suitable parameters for the dipole inversion. We propose a probabilistic Bayesian approach for QSM with built-in parameter estimation, and incorporate the nonlinear formulation of the dipole inversion to achieve a robust recovery of the susceptibility maps. Theory: From a Bayesian perspective, the image wavelet coefficients are approximately sparse and modelled by the Laplace distribution. The measurement noise is modelled by a Gaussian-mixture distribution with two components, where the second component is used to model the noise outliers. Through probabilistic inference, the susceptibility map and distribution parameters can be jointly recovered using approximate message passing (AMP). Methods: We compare our proposed AMP with built-in parameter estimation (AMP-PE) to the state-of-the-art L1-QSM, FANSI and MEDI approaches on the simulated and in vivo datasets, and perform experiments to explore the optimal settings of AMP-PE. Reproducible code is available at https://github.com/EmoryCN2L/QSM_AMP_PE Results: On the simulated Sim2Snr1 dataset, AMP-PE achieved the lowest NRMSE, DFCM and the highest SSIM, while MEDI achieved the lowest HFEN. On the in vivo datasets, AMP-PE is robust and successfully recovers the susceptibility maps using the estimated parameters, whereas L1-QSM, FANSI and MEDI typically require additional visual fine-tuning to select or double-check working parameters. Conclusion: AMP-PE provides automatic and adaptive parameter estimation for QSM and avoids the subjectivity from the visual fine-tuning step, making it an excellent choice for the clinical setting.

eess.IV↗

arXiv2022

Approximate Message Passing with Parameter Estimation for Heavily Quantized Measurements

Designing efficient sparse recovery algorithms that could handle noisy quantized measurements is important in a variety of applications -- from radar to source localization, spectrum sensing and wireless networking. We take advantage of the approximate message passing (AMP) framework to achieve this goal given its high computational efficiency and state-of-the-art performance. In AMP, the signal of interest is assumed to follow certain prior distribution with unknown parameters. Previous works focused on finding the parameters that maximize the measurement likelihood via expectation maximization -- an increasingly difficult problem to solve in cases involving complicated probability models. In this paper, we treat the parameters as unknown variables and compute their posteriors via AMP. The parameters and signal of interest can then be jointly recovered. Compared to previous methods, the proposed approach leads to a simple and elegant parameter estimation scheme, allowing us to directly work with 1-bit quantization noise model. We then further extend our approach to general multi-bit quantization noise model. Experimental results show that the proposed framework provides significant improvement over state-of-the-art methods across a wide range of sparsity and noise levels.

eess.SP↗

arXiv2021

A Probabilistic Bayesian Approach to Recover $R_2^*$ map and Phase Images for Quantitative Susceptibility Mapping

Purpose: Undersampling is used to reduce the scan time for high-resolution 3D magnetic resonance imaging. In order to achieve better image quality and avoid manual parameter tuning, we propose a probabilistic Bayesian approach to recover $R_2^*$ map and phase images for quantitative susceptibility mapping (QSM), while allowing automatic parameter estimation from undersampled data. Theory: Sparse prior on the wavelet coefficients of images is interpreted from a Bayesian perspective as sparsity-promoting distribution. A novel nonlinear approximate message passing (AMP) framework that incorporates a mono-exponential decay model is proposed. The parameters are treated as unknown variables and jointly estimated with image wavelet coefficients. Results: The proposed AMP with parameter estimation (AMP-PE) approach successfully recovers $R_2^*$ maps and phase images for QSM across various undersampling rates. It is more computationally efficient, and performs better than the state-of-the-art $l_1$-norm regularization (L1) approach in general, except a few cases where the L1 approach performs as well as AMP-PE. Conclusion: AMP-PE achieves better performance by drawing information from both the sparse prior and the mono-exponential decay model. It does not require parameter tuning, and works with a clinical, prospective undersampling scheme where parameter tuning is often impossible or difficult due to the lack of ground-truth image.

eess.IV↗

arXiv2020

Fast Nonconvex $T_2^*$ Mapping Using ADMM

Magnetic resonance (MR)-$T_2^*$ mapping is widely used to study hemorrhage, calcification and iron deposition in various clinical applications, it provides a direct and precise mapping of desired contrast in the tissue. However, the long acquisition time required by conventional 3D high-resolution $T_2^*$ mapping method causes discomfort to patients and introduces motion artifacts to reconstructed images, which limits its wider applicability. In this paper we address this issue by performing $T_2^*$ mapping from undersampled data using compressive sensing (CS). We formulate the reconstruction as a nonconvex problem that can be decomposed into two subproblems. They can be solved either separately via the standard approach or jointly via the alternating direction method of multipliers (ADMM). Compared to previous CS-based approaches that only apply sparse regularization on the spin density $\boldsymbol X_0$ and the relaxation rate $\boldsymbol R_2^*$, our formulation enforces additional sparse priors on the $T_2^*$-weighted images at multiple echoes to improve the reconstruction performance. We performed convergence analysis of the proposed algorithm, evaluated its performance on in vivo data, and studied the effects of different sampling schemes. Experimental results showed that the proposed joint-recovery approach generally outperforms the state-of-the-art method, especially in the low-sampling rate regime, making it a preferred choice to perform fast 3D $T_2^*$ mapping in practice. The framework adopted in this work can be easily extended to other problems arising from MR or other imaging modalities with non-linearly coupled variables.

eess.IV↗

arXiv2020

Bayesian Massive MIMO Channel Estimation with Parameter Estimation Using Low-Resolution ADCs

In order to reduce hardware complexity and power consumption, massive multiple-input multiple-output (MIMO) systems employ low-resolution analog-to-digital converters (ADCs) to acquire quantized measurements $\boldsymbol y$. This poses new challenges to the channel estimation problem, and the sparse prior on the channel coefficient vector $\boldsymbol x$ in the angle domain is often used to compensate for the information lost during quantization. By interpreting the sparse prior from a probabilistic perspective, we can assume $\boldsymbol x$ follows certain sparse prior distribution and recover it using approximate message passing (AMP). However, the distribution parameters are unknown in practice and need to be estimated. Due to the increased computational complexity in the quantization noise model, previous works either use an approximated noise model or manually tune the noise distribution parameters. In this paper, we treat both signals and parameters as random variables and recover them jointly within the AMP framework. The proposed approach leads to a much simpler parameter estimation method, allowing us to work with the quantization noise model directly. Experimental results show that the proposed approach achieves state-of-the-art performance under various noise levels and does not require parameter tuning, making it a practical and maintenance-free approach for channel estimation.

cs.IT↗