arXiv subjects

Lixia Wang

Publications and source records attributed to Lixia Wang.

7 recordsLinked to original sources

arXiv2026

AutoIQ: An Ensemble Framework for Automatic Assessment of Geometric Distortion in Prostate Diffusion-Weighted Imaging

Geometric distortion in prostate diffusion-weighted imaging (DWI) can impair lesion localization and reduce the reliability of MRI-based clinical assessment. We propose AutoIQ, an ensemble machine learning framework for automatic quantification and classification of DWI geometric distortion severity. A total of 140 retrospective prostate biparametric MRI examinations were analyzed, including 33 scans with severe distortion requiring repeat acquisition and 107 scans with acceptable distortion based on expert radiologist assessment. AutoIQ combines two complementary distortion quantification strategies: a segmentation-based method measuring prostate boundary mismatch between T2-weighted imaging (T2WI) and DWI, and a registration-based method estimating deformation magnitude after DWI-to-T2WI alignment. The resulting distortion scores were used to train individual classifiers and a logistic-regression ensemble model. Both computational methods significantly differentiated severe from acceptable distortion cases (p < 0.001). On an independent test set, the ensemble model achieved an accuracy of 0.95, F1-score of 0.93, and AUC of 0.98, outperforming individual models. These results suggest that AutoIQ can provide automated, quantitative quality assessment for prostate DWI and may help identify scans that require repeat acquisition.

eess.IV↗

arXiv2026

Let Distortion Guide Restoration (DGR): A physics-informed learning framework for Prostate Diffusion MRI

We present Distortion-Guided Restoration (DGR), a physics-informed hybrid CNN-diffusion framework for acquisition-free correction of severe susceptibility-induced distortions in prostate single-shot EPI diffusion-weighted imaging (DWI). DGR is trained to invert a realistic forward distortion model using large-scale paired distorted and undistorted data synthesized from distortion-free prostate DWI and co-registered T2-weighted images from 410 multi-institutional studies, together with 11 measured B0 field maps from metal-implant cases incorporated into a forward simulator to generate low-b DWI (b = 50 s per mm squared), high-b DWI (b = 1400 s per mm squared), and ADC distortions. The network couples a CNN-based geometric correction module with conditional diffusion refinement under T2-weighted anatomical guidance. On a held-out synthetic validation set (n = 34) using ground-truth simulated distortion fields, DGR achieved higher PSNR and lower NMSE than FSL TOPUP and FUGUE. In 34 real clinical studies with severe distortion, including hip prostheses and marked rectal distension, DGR improved geometric fidelity and increased radiologist-rated image quality and diagnostic confidence. Overall, learning the inverse of a physically simulated forward process provides a practical alternative to acquisition-dependent distortion-correction pipelines for prostate DWI.

eess.IV↗

arXiv2026

A unified high-resolution ODE framework for first-order methods

For a generic discrete-time algorithm (DTA): $z^+=g(z,s)$, where $s$ is the step size, Lu (Math. Program., 194(1):1061--1112, 2022) proposed an $O(s^r)$-resolution ordinary differential equation (ODE) framework based on the backward error analysis, which can be used to analyze many DTAs satisfying the fixed point assumption $g(z,0)=z$ such as gradient descent, extra gradient method and primal-dual hybrid gradient (PDHG). However, most first-order methods with momentum violate this critical assumption. To address this issue, in this work, we introduce a novel $O((\sqrt{s})^r)$-resolution ODE framework for accelerated first-order methods allowing momentum and variable parameters, such as Nesterov accelerated gradient (NAG), heavy-ball (HB) method and accelerated mirror gradient. The proposed high-resolution framework provides deeper insight into the convergence properties of DTAs. Especially, although the $O(1)$-resolution ODEs for HB and NAG are identical, their $O(\sqrt{s})$-resolution ODEs differ from the subtle existence of the Hessian-driven damping. Moreover, we propose a high-resolution correction approach and apply it to PDHG and HB for provably convergent modifications that achieve global optimal convergence rates. Numerical results are reported to confirm the theoretical predictions.

math.OC↗

arXiv2026

From Global Radiomics to Parametric Maps: A Unified Workflow Fusing Radiomics and Deep Learning for PDAC Detection

Radiomics and deep learning both offer powerful tools for quantitative medical imaging, but most existing fusion approaches only leverage global radiomic features and overlook the complementary value of spatially resolved radiomic parametric maps. We propose a unified framework that first selects discriminative radiomic features and then injects them into a radiomics-enhanced nnUNet at both the global and voxel levels for pancreatic ductal adenocarcinoma (PDAC) detection. On the PANORAMA dataset, our method achieved AUC = 0.96 and AP = 0.84 in cross-validation. On an external in-house cohort, it achieved AUC = 0.95 and AP = 0.78, outperforming the baseline nnUNet; it also ranked second in the PANORAMA Grand Challenge. This demonstrates that handcrafted radiomics, when injected at both global and voxel levels, provide complementary signals to deep learning models for PDAC detection. Our code can be found at https://github.com/briandzt/dl-pdac-radiomics-global-n-paramaps

eess.IV↗

arXiv2025

An estimate for positive definite functions on finite abelian groups and its applications

This paper concentrates on positive definite functions on finite abelian groups, which are central to harmonic analysis and related fields. By leveraging the group structure and employing Fourier analysis, we establish a lower bound for the second largest value of positive definite functions. For illustrative purposes, we present three applications of our lower bound: (a) We obtain both lower and upper bounds for arbitrary functions on finite abelian groups; (b) We derive lower bounds for the relaxation and mixing times of random walks on finite abelian groups. Notably, our bound for the relaxation time achieves a quadratic improvement over the previously known one; (c) We determine a new lower bound for the size of the sumset of two subsets of finite abelian groups.

math.FA↗

arXiv2024

Geometric and electronic properties of two kinds of CrO2 magnetic monolayers: D3d and D2h phases

Due to the high magnetic coupling strength between the Cr elements, the bulk phase CrO2 is one of several ferromagnetic oxides known to have the highest Curie temperature. When the dimensionality of the material is reduced from 3D to 2D, the 2D CrO2 system material is expected to maintain a high Curie temperature. In this work, we predict two new phases of CrO2 monolayer (D3d and D2h) by using first-principles calculations. We have found that the Curie temperature of 2D CrO2 is much lower than that of its bulk phase, but still remains as high as 191K, which is comparable to that of Fe2Cr2Ge6. In addition, 1L D3d-CrO2 is in the ferromagnetic state, while 1L D2h-CrO2 is in the antiferromagnetic state. Also, the different geometric structure affects its electrical properties: the 1L D3d-CrO2 is a half-metal while 1L D2h-CrO2 is a semiconductor. Our studies have shown that there is a wealth of electrical and magnetic properties in CrO2.

cond-mat.mtrl-sci↗

arXiv2019

Fully Automated Multi-Organ Segmentation in Abdominal Magnetic Resonance Imaging with Deep Neural Networks

Segmentation of multiple organs-at-risk (OARs) is essential for radiation therapy treatment planning and other clinical applications. We developed an Automated deep Learning-based Abdominal Multi-Organ segmentation (ALAMO) framework based on 2D U-net and a densely connected network structure with tailored design in data augmentation and training procedures such as deep connection, auxiliary supervision, and multi-view. The model takes in multi-slice MR images and generates the output of segmentation results. Three-Tesla T1 VIBE (Volumetric Interpolated Breath-hold Examination) images of 102 subjects were collected and used in our study. Ten OARs were studied, including the liver, spleen, pancreas, left/right kidneys, stomach, duodenum, small intestine, spinal cord, and vertebral bodies. Two radiologists manually labeled and obtained the consensus contours as the ground-truth. In the complete cohort of 102, 20 samples were held out for independent testing, and the rest were used for training and validation. The performance was measured using volume overlapping and surface distance. The ALAMO framework generated segmentation labels in good agreement with the manual results. Specifically, among the 10 OARs, 9 achieved high Dice Similarity Coefficients (DSCs) in the range of 0.87-0.96, except for the duodenum with a DSC of 0.80. The inference completes within one minute for a 3D volume of 320x288x180. Overall, the ALAMO model matches the state-of-the-art performance. The proposed ALAMO framework allows for fully automated abdominal MR segmentation with high accuracy and low memory and computation time demands.

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