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• Conference paper
  Dayarathna S, Islam KT, Zhuang B, Yang G, Cai J, Law M, Chen Zet al., 2025,

  McCaD: Multi-Contrast MRI Conditioned, Adaptive Adversarial Diffusion Model for High-Fidelity MRI Synthesis

  , Pages: 670-679

  Magnetic Resonance Imaging (MRI) is instrumental in clinical diagnosis, offering diverse contrasts that provide comprehensive diagnostic information. However, acquiring multiple MRI contrasts is often constrained by high costs, long scanning durations, and patient discomfort. Current synthesis methods, typically focused on single-image contrasts, fall short in capturing the collective nuances across various contrasts. Moreover, existing methods for multi-contrast MRI synthesis often fail to accurately map feature-level information across multiple imaging contrasts. We introduce McCaD (Multi-Contrast MRI Conditioned Adaptive Adversarial Diffusion), a novel framework leveraging an adversarial diffusion model conditioned on multiple contrasts for high-fidelity MRI synthesis. McCaD significantly enhances synthesis accuracy by employing a multi-scale, feature-guided mechanism, incorporating denoising and semantic encoders. An adaptive feature maximization strategy and a spatial feature-attentive loss have been introduced to capture more intrinsic features across multiple contrasts. This facilitates a precise and comprehensive feature-guided denoising process. Extensive experiments on tumor and healthy multi-contrast MRI datasets demonstrated that the McCaD outperforms state-of-the-art baselines quantitively and qualitatively. The code is available at https://github.com/sanuwanihewa/M.cCaD.

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  • Citations: 2
• Conference paper
  Wang F, Wen K, Luo Y, Wu Y, Huang J, Pennell DJ, Ferreira PF, Scott AD, Nielles-Vallespin S, Yang Get al., 2025,

  Is Fitting Error a Reliable Metric for Assessing Deformable Motion Correction in Quantitative MRI?

  , ISSN: 1945-7928

  Quantitative MR (qMR) can provide numerical values representing the physical and chemical properties of the tissues. To collect a series of frames under varying settings, retrospective motion correction is essential to align the corresponding anatomical points or features. Under the assumption that the misalignment makes the discrepancy between the corresponding features larger, fitting error is a commonly used evaluation metric for motion correction in qMR. This study evaluates the reliability of the fitting error metric in cardiac diffusion tensor imaging (cDTI) after deformable registration. We found that while fitting error correlates with the negative eigenvalues, the negative Jacobian Determinant increases with broken cardiomyocytes, indicated by helix angle gradient line profiles. Since fitting error measures the distance between moved points and their re-rendered counterparts, the fitting parameter itself may be adjusted due to poor registration. Therefore, fitting error in deformable registration itself is a necessary but not sufficient metric and should be combined with other metrics.

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• Conference paper
  Wu Y, Huang J, Wang F, Gao M, Liao C, Yang G, Setsompop Ket al., 2025,

  Enhancing Diffusion-Weighted Images (DWI) for Diffusion MRI: Is it Enough without Non-Diffusion-Weighted B=0 Reference?

  , ISSN: 1945-7928

  Diffusion MRI (dMRI) is essential for studying brain microstructure, but high-resolution imaging remains challenging due to the inherent trade-offs between acquisition time and signal-to-noise ratio (SNR). Conventional methods often optimize only the diffusion-weighted images (DWIs) without considering their relationship with the non-diffusion-weighted (b=0) reference images. However, calculating diffusion metrics, such as the apparent diffusion coefficient (ADC) and diffusion tensor with its derived metrics like fractional anisotropy (FA) and mean diffusivity (MD), relies on the ratio between each DWI and the b=0 image, which is crucial for clinical observation and diagnostics. In this study, we demonstrate that solely enhancing DWIs using a conventional pixel-wise mean squared error (MSE) loss is insufficient, as the error in ratio between generated DWIs and b=0 diverges. We propose a novel ratio loss, defined as the MSE loss between the predicted and ground-truth log of DWI/b=0 ratios. Our results show that incorporating the ratio loss significantly improves the convergence of this ratio error, achieving lower ratio MSE and slightly enhancing the peak signal-to-noise ratio (PSNR) of generated DWIs. This leads to improved dMRI super-resolution and better preservation of b=0 ratio-based features for the derivation of diffusion metrics.

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• Conference paper
  Boyer-Chammard J, Wu Y, Zhang C, Jewell S, Strong A, Yang G, Boutelle Met al., 2025,

  Spreading Depolarization Detection in Electrocorticogram Spectrogram Imaging by Deep Learning: Is it Just About Delta Band?

  , ISSN: 1945-7928

  Prevention of secondary brain injury is a core aim of neurocritical care, with Spreading Depolarizations (SDs) recognized as a significant independent cause. SDs are typically monitored through invasive, high-frequency electrocorticography (ECoG); however, detection remains challenging due to signal artifacts that obscure critical SD-related electrophysiological changes, such as power attenuation and DC drifting. Recent studies suggest spectrogram analysis could improve SD detection; however, brain injury patients often show power reduction across all bands except delta, causing class imbalance. Previous methods focusing solely on delta mitigates imbalance but overlooks features in other frequencies, limiting detection performance. This study explores using multi-frequency spectrogram analysis, revealing that essential SD-related features span multiple frequency bands beyond the most active delta band. This study demonstrated that further integration of both alpha and delta bands could result in enhanced SD detection accuracy by a deep learning model.

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• Conference paper
  Yue Z, Fang Y, Yang L, Baid N, Walsh S, Yang Get al., 2025,

  Enhancing Weakly Supervised Semantic Segmentation for Fibrosis via Controllable Image Generation

  , ISSN: 1945-7928

  Fibrotic Lung Disease (FLD) is a severe condition marked by lung stiffening and scarring, leading to respiratory decline. High-resolution computed tomography (HRCT) is critical for diagnosing and monitoring FLD; however, fibrosis appears as irregular, diffuse patterns with unclear boundaries, leading to high inter-observer variability and time-intensive manual annotation. To tackle this challenge, we propose DiffSeg, a novel weakly supervised semantic segmentation (WSSS) method that uses image-level annotations to generate pixel-level fibrosis segmentation, reducing the need for fine-grained manual labeling. Additionally, our DiffSeg incorporates a diffusion-based generative model to synthesize HRCT images with different levels of fibrosis from healthy slices, enabling the generation of the fibrosis-injected slices and their paired fibrosis location. Experiments indicate that our method significantly improves the accuracy of pseudo masks generated by existing WSSS methods, greatly reducing the complexity of manual labeling and enhancing the consistency of the generated masks.

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• Conference paper
  Ning J, Xing X, Zhang S, Ma X, Yang Get al., 2025,

  Unveiling the Capabilities of Latent Diffusion Models for Classification of Lung Diseases in Chest X-Rays

  , ISSN: 1945-7928

  Diffusion models have demonstrated remarkable ability in synthesizing chest X-ray (CXR) images, particularly by generating high-quality samples to address the scarcity and imbalance of annotated CXR datasets. While these models excel in generating realistic samples-suggesting that they contain rich discriminative information effectively harnessing this capability for disease classification and decomposition remains a challenge. This study investigates an approach that leverages latent conditional diffusion models, which are conditioned on corresponding radiology reports, for lung disease classification in CXRs. Specifically, we employ a pre-trained latent conditional diffusion model for CXRs to predict noise estimates for a noisy input lung CXR under various disease conditions. By comparing the noise estimation errors associated with different class prompts, we determine the most probable disease classification based on the minimal error. Through the experiments, we demonstrate that the CXR diffusion-based classifier not only achieves zero-shot classification performance comparable to existing models but also reveals lesion regions aligning with ground truth lesion areas in CXRs.

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  • Citations: 1
• Journal article
  Wang H, Chen Y, Chen W, Xu H, Zhao H, Sheng B, Fu H, Yang G, Zhu Let al., 2025,

  Serp-Mamba: Advancing High-Resolution Retinal Vessel Segmentation with Selective State-Space Model

  , IEEE Transactions on Medical Imaging, ISSN: 0278-0062

  Ultra-Wide-Field Scanning Laser Ophthalmoscopy (UWF-SLO) images capture high-resolution views of the retina with typically spanning 200 degrees. Accurate segmentation of vessels in UWF-SLO images is essential for detecting and diagnosing fundus disease. Recent studies highlight that Mamba’s selective State Space Model (SSM) excels in modeling long-range dependencies with linear computational complexity, making it highly suitable for preserving the continuity of elongated vessel structures, especially for high-resolution UWF images. Inspired by this, we propose the Serpentine Mamba (Serp-Mamba) network to address this challenging task. Specifically, we recognize the intricate, varied, and delicate nature of the tubular structure of vessels. Furthermore, the high-resolution of UWF-SLO images exacerbates the imbalance between the vessel and background categories. Based on the above observations, we first devise a Serpentine Interwoven Adaptive (SIA) scan mechanism, which scans UWF-SLO images along curved vessel structures in a snake-like crawling manner. This approach, consistent with vascular texture transformations, ensures the effective and continuous capture of curved vascular structure features. Second, we propose an Ambiguity-Driven Dual Recalibration (ADDR) module to address the category imbalance problem intensified by high-resolution images. Our ADDR module delineates pixels by two learnable thresholds and refines ambiguous pixels through a dual-driven strategy, thereby accurately distinguishing vessels and background regions. Experiment results on three datasets demonstrate the superior performance of our Serp-Mamba on high-resolution vessel segmentation. We also conduct a series of ablation studies to verify the impact of our designs.

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  • Citations: 1
• Journal article
  Wang N, Jin W, Jing S, Bi H, Yang Get al., 2025,

  Learning with noisy labels via Mamba and entropy KNN framework

  , APPLIED SOFT COMPUTING, Vol: 169, ISSN: 1568-4946
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  • Citations: 2
• Journal article
  Huang J, Yang L, Wang F, Wu Y, Nan Y, Wu W, Wang C, Shi K, Aviles-Rivero AI, Schoenlieb C-B, Zhang D, Yang Get al., 2025,

  Enhancing global sensitivity and uncertainty quantification in medical image reconstruction with Monte Carlo arbitrary-masked mamba

  , MEDICAL IMAGE ANALYSIS, Vol: 99, ISSN: 1361-8415
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• Journal article
  Huang J, Wu Y, Wang F, Fang Y, Nan Y, Alkan C, Abraham D, Liao C, Xu L, Gao Z, Wu W, Zhu L, Chen Z, Lally P, Bangerter N, Setsompop K, Guo Y, Rueckert D, Wang G, Yang Get al., 2025,

  Data- and Physics-Driven Deep Learning Based Reconstruction for Fast MRI: Fundamentals and Methodologies

  , IEEE REVIEWS IN BIOMEDICAL ENGINEERING, Vol: 18, Pages: 152-171, ISSN: 1937-3333
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  • Citations: 5

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