Cerebrovascular diseases (CVDs) such as aneurysms, arteriovenous malformations, stenosis, and Moyamoya disease are major public health concerns. Accurate classification of these conditions is essential for timely intervention, yet current computer-aided methods often exhibit limited representational capacity, feature redundancy, and insufficient interpretability, restricting clinical applicability. We propose PASAformer, a Swin-Transformer-based framework for cerebrovascular disease classification on DSA. PASAformer incorporates a Pathology-Aware Sparse Attention (PASA) module that emphasizes lesion-related regions while suppressing background redundancy. Inserted into the Swin backbone, PASA replaces dense window self-attention, improving computational efficiency while preserving the hierarchical architecture. We further employ the MiAMix data augmenter to increase sample diversity, and incorporate a CombinedAdapter encoder that injects anatomical priors from the frozen Medical Segment Anything Model (MED-SAM) into early-stage representations, strengthening discriminative power under limited supervision. To support research in this underexplored area, we curate CDSA-NEO, a proprietary DSA dataset comprising more than 1,700 static images across four major cerebrovascular disease categories, constituting the first large-scale benchmark of its kind. Furthermore, an external cohort of angiographic runs with sequential, unselected frames is used to assess robustness in realistic temporal workflows. Extensive experiments on CDSA-NEO and public vascular datasets demonstrate that PASAformer achieves competitive precision and balanced accuracy compared to representative state-of-the-art models, while providing more focused visual explanations. These results suggest that PASAformer can support automated cerebrovascular disease classification on angiography, and that CDSA-NEO provides a benchmark for future method development and evaluation.

Deep learning-based methods have shown promising results in multi-label chest X-ray (CXR) image classification. However, most existing methods rely on large-scale fully-annotated datasets, which are costly and laborious to obtain. Therefore, training a high-performance model with limited annotation remains a significant challenge in practice. To address this issue, we propose a Generalized Nesterov-Boosted Adversarial Data Augmentation (GN-ADA) framework for multi-label CXR image classification. First, we generate pseudo labels based on the model predictions on weakly-augmented images. Next, we propose a Generalized Nesterov Iterative Fast Gradient Sign Method (GNI-FGSM) to generate effective adversarial examples as strongly-augmented data. Then, we introduce an adversarial-augmentation-based consistency regularization to perform supervision of model predictions on the above adversarial examples. We note that the proposed GNI-FGSM is a higher-order FGSM variant, which is capable of generating more effective adversarial examples for model training within a constrained time frame, thereby improving the overall classification performance. Extensive experiments on two large CXR datasets (CheXpert and MIMIC-CXR) demonstrate the effectiveness of the proposed GN-ADA framework for multi-label CXR image classification under limited-annotation scenario.

Despite the effectiveness of single-photon emission computed tomography (SPECT) imaging in clinics, the ionizing radiation induced by its radiotracer poses a potential hazard to human health. Clinically, a lower radiation dose can be achieved by reducing the activity of administered radiotracer, which inevitably leads to increased Poisson noise, severe artifacts and degraded spatial resolution in the sinogram domain. Although existing sinogram restoration methods for the low-dose scenario have made significant progress in noise suppression, they still fail to effectively recover the detailed sinusoidal features and intrinsic contrast within the sinogram. In addition, existing methods seldom explore the sinogram innerstructure, which may hinder further improvement on reconstructed image quality. To address these issues, we propose a residual framework based on the diffusion model that leverages the frequency characteristics of sinograms. Indeed, the proposed framework consists of two stages. The first stage employs the Residual Denoising Diffusion Model (RDDM) to denoise the low-dose sinogram, thereby producing a noise-suppressed coarse output. In the second stage, we develop a novel Wavelet-based Sinogram Structure Interaction network (WSSI-net) to explicitly and selectively process high- and low-frequency features of the coarse output. In particular, we propose a High-Frequency Restoration Module (HFRM) to further enhance high-frequency features, as well as a low-frequency Graph Convolution block (GC block) to effectively exploit the inherent inner-structure within low-frequency components. Moreover, we further introduce a Cross-Frequency Interaction Module (CFIM) to achieve correlation learning between high- and low-frequency features. Extensive experiments demonstrate that the proposed framework achieves superior reconstruction performance compared to the state-of-the-art methods.

Sparse-view tomographic reconstruction is a pivotal direction for reducing radiation dose and augmenting clinical applicability. While many research works have proposed the reconstruction of tomographic images from sparse 2D projections, existing models tend to excessively focus on high-frequency information while overlooking low-frequency components within the sparse input images. This bias towards high-frequency information often leads to overfitting, particularly intense at edges and boundaries in the reconstructed slices. In this paper, we introduce the Frequency Regularized Neural Attenuation/Activity Field (Freq-NAF) for self-supervised sparse-view tomographic reconstruction. Freq-NAF mitigates overfitting by incorporating frequency regularization, directly controlling the visible frequency bands in the neural network input. This approach effectively balances high-frequency and low-frequency information. We conducted numerical experiments on CBCT and SPECT datasets, and our method demonstrates state-of-the-art accuracy.

Deep learning approaches for multi-label Chest X-ray (CXR) images classification usually require large-scale datasets. However, acquiring such datasets with full annotations is costly, time-consuming, and prone to noisy labels. Therefore, we introduce a weakly supervised learning problem called Single Positive Multi-label Learning (SPML) into CXR images classification (abbreviated as SPML-CXR), in which only one positive label is annotated per image. A simple solution to SPML-CXR problem is to assume that all the unannotated pathological labels are negative, however, it might introduce false negative labels and decrease the model performance. To this end, we present a Multi-level Pseudo-label Consistency (MPC) framework for SPML-CXR. First, inspired by the pseudo-labeling and consistency regularization in semi-supervised learning, we construct a weak-to-strong consistency framework, where the model prediction on weakly-augmented image is treated as the pseudo label for supervising the model prediction on a strongly-augmented version of the same image, and define an Image-level Perturbation-based Consistency (IPC) regularization to recover the potential mislabeled positive labels. Besides, we incorporate Random Elastic Deformation (RED) as an additional strong augmentation to enhance the perturbation. Second, aiming to expand the perturbation space, we design a perturbation stream to the consistency framework at the feature-level and introduce a Feature-level Perturbation-based Consistency (FPC) regularization as a supplement. Third, we design a Transformer-based encoder module to explore the sample relationship within each mini-batch by a Batch-level Transformer-based Correlation (BTC) regularization. Extensive experiments on the CheXpert and MIMIC-CXR datasets have shown the effectiveness of our MPC framework for solving the SPML-CXR problem.

Multi-contrast magnetic resonance imaging (MRI) is widely used in clinical diagnosis. However, it is time-consuming to obtain MR data of multi-contrasts and the long scanning time may bring unexpected physiological motion artifacts. To obtain MR images of higher quality within limited acquisition time, we propose an effective model to reconstruct images from under-sampled k-space data of one contrast by utilizing another fully-sampled contrast of the same anatomy. Specifically, multiple contrasts from the same anatomical section exhibit similar structures. Enlightened by the fact that co-support of an image provides an appropriate characterization of morphological structures, we develop a similarity regularization of the co-supports across multi-contrasts. In this case, the guided MRI reconstruction problem is naturally formulated as a mixed integer optimization model consisting of three terms, the data fidelity of k-space, smoothness-enforcing regularization, and co-support regularization. An effective algorithm is developed to solve this minimization model alternatively. In the numerical experiments, T2-weighted images are used as the guidance to reconstruct T1-weighted/T2-weighted-Fluid-Attenuated Inversion Recovery (T2-FLAIR) images and PD-weighted images are used as the guidance to reconstruct PDFS-weighted images, respectively, from their under-sampled k-space data. The experimental results demonstrate that the proposed model outperforms other state-of-the-art multi-contrast MRI reconstruction methods in terms of both quantitative metrics and visual performance at various sampling ratios.

We investigated the imaging performance of a fast convergent ordered-subsets algorithm with subiteration-dependent preconditioners (SDPs) for positron emission tomography (PET) image reconstruction. In particular, we considered the use of SDP with the block sequential regularized expectation maximization (BSREM) approach with the relative difference prior (RDP) regularizer due to its prior clinical adaptation by vendors. Because the RDP regularization promotes smoothness in the reconstructed image, the directions of the gradients in smooth areas more accurately point toward the objective function's minimizer than those in variable areas. Motivated by this observation, two SDPs have been designed to increase iteration step-sizes in the smooth areas and reduce iteration step-sizes in the variable areas relative to a conventional expectation maximization preconditioner. The momentum technique used for convergence acceleration can be viewed as a special case of SDP. We have proved the global convergence of SDP-BSREM algorithms by assuming certain characteristics of the preconditioner. By means of numerical experiments using both simulated and clinical PET data, we have shown that the SDP-BSREM algorithms substantially improve the convergence rate, as compared to conventional BSREM and a vendor's implementation as Q.Clear. Specifically, SDP-BSREM algorithms converge 35%-50% faster in reaching the same objective function value than conventional BSREM and commercial Q.Clear algorithms. Moreover, we showed in phantoms with hot, cold and background regions that the SDP-BSREM algorithms approached the values of a highly converged reference image faster than conventional BSREM and commercial Q.Clear algorithms.

Existing single-photon emission computed tomography (SPECT) reconstruction methods are mostly based on discrete models that may be viewed as piecewise constant approximations of a continuous data acquisition process. Due to low accuracy order of piecewise constant approximations, a traditional discrete model introduces irreducible model errors which are a bottleneck of the quality improvement of reconstructed images in clinical applications. To overcome this drawback, we develop a higher-order polynomial method for SPECT reconstruction. Specifically, we represent the data acquisition of SPECT imaging by using an integral equation model, approximate the solution of the underlying integral equation by higher-order piecewise polynomials leading to a new discrete system and introduce two novel regularizers for the system, by exploring the a priori knowledge of the radiotracer distribution, suitable for the approximation. The proposed higher-order polynomial method outperforms significantly the cutting edge reconstruction method based on a traditional discrete model in terms of model error reduction, noise suppression, and artifact reduction. In particular, the coefficient of variation of images reconstructed by the piecewise linear polynomial method is reduced by a factor of 10 in comparison to that of a traditional discrete model-based method.

This paper presents a preconditioned Krasnoselskii-Mann (KM) algorithm with an improved EM preconditioner (IEM-PKMA) for higher-order total variation (HOTV) regularized positron emission tomography (PET) image reconstruction. The PET reconstruction problem can be formulated as a three-term convex optimization model consisting of the Kullback-Leibler (KL) fidelity term, a nonsmooth penalty term, and a nonnegative constraint term which is also nonsmooth. We develop an efficient KM algorithm for solving this optimization problem based on a fixed-point characterization of its solution, with a preconditioner and a momentum technique for accelerating convergence. By combining the EM precondtioner, a thresholding, and a good inexpensive estimate of the solution, we propose an improved EM preconditioner that can not only accelerate convergence but also avoid the reconstructed image being "stuck at zero." Numerical results in this paper show that the proposed IEM-PKMA outperforms existing state-of-the-art algorithms including, the optimization transfer descent algorithm and the preconditioned L-BFGS-B algorithm for the differentiable smoothed anisotropic total variation regularized model, the preconditioned alternating projection algorithm, and the alternating direction method of multipliers for the nondifferentiable HOTV regularized model. Encouraging initial experiments using clinical data are presented.

In this paper, we propose an efficient adaptive total variation regularization scheme for ROF image denoising problem. By smoothing the non-differentiable convex function appearing in the traditional total variation by its Moreau envelope and selecting the smoothing factor to be inversely proportional to the likelihood of the presence of an edge at discrete image location, the proposed adaptive total variation can remove the stair casing effects caused by total variation as well as preserve sharp edges well in the restored image. Moreover, the proposed adaptive total variation facilitates us to employ some accelerated techniques to solve the generated ROF model. Our numerical experiments demonstrate the efficiency of the proposed method.