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.

This work develops a novel framework that learns a microstructural codebook to enable accurate, rapid, and multi-parameter microstructure imaging on diffusion MRI, thereby resolving the limited generalization across protocols and inextensibility to new microstructural indices. By integrating the spherical mean technique with a hybrid Mamba-CNN network and learnable tissue-compartment kernels, our approach effectively captures multi-scale spatial dependencies and links spherical mean signals to biophysical microstructure models, enhancing both interpretability and adaptability. The model supports robust estimation of 24 microstructural metrics from 8 biophysical diffusion models, even under undersampled acquisition settings. Furthermore, it generalizes across varying acquisition protocols and enables seamless adaptation to new microstructural indices with minimal fine-tuning. Extensive experiments on multiple datasets validate the effectiveness of our method, demonstrating its excellence in accuracy, generalization, and transferability on microstructure estimation. This work contributes to the development of a foundation model for microstructure imaging, offering a unified framework that bridges biophysical modeling and deep learning for more interpretable and adaptable dMRI analysis. Code is available at https://github.com/1nlandempire/dMRI_codebook_imaging.

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.

Accurate polyp segmentation plays a critical role in the diagnosis and treatment of colorectal cancer from colonoscopy images. While deep learning-based polyp segmentation models have made significant progress, they often suffer from performance degradation when applied to unseen target domain datasets collected from different imaging devices. To address this challenge, unsupervised domain adaptation (UDA) methods have gained attention by leveraging labeled source data and unlabeled target data to reduce the domain gap. However, existing UDA methods primarily focus on capturing class-wise representations, neglecting domain-wise representations. Additionally, uncertainty in pseudo-labels could hinder the segmentation performance. To tackle these issues, we propose a novel Domain-interactive Contrastive Learning and Prototype-guided Self-training (DCL-PS) framework for cross-domain polyp segmentation. Specifically, domain-interactive contrastive learning (DCL) with a domain-mixed prototype updating strategy is proposed to discriminate class-wise feature representations across domains. Then, to enhance the feature extraction ability of the encoder, we present a contrastive learning-based cross-consistency training (CL-CCT) strategy, which is imposed on both the prototypes obtained by the outputs of the main decoder and perturbed auxiliary outputs. Furthermore, we propose a prototype-guided self-training (PS) strategy, which dynamically assigns a weight for each pixel during self-training, filtering out unreliable pixels and improving the quality of pseudo-labels. Experimental results demonstrate the superiority of DCL-PS in improving polyp segmentation performance in the target domain. The code is released at https://github.com/taozh2017/DCLPS.

Diffusion MRI (dMRI) allows for examining microarchitecture profiles and tissue changes using specific microstructure modeling but depends on advanced acquisition protocol and well-established biological model assumptions. It is also limited in clinical applications due to lengthy acquisition time and dense q-space sampling requirements. In this study, we present a method that aims to estimate multiple functional microstructural parameters robustly, thereby enhancing clinical applications with different acquisition protocols. Instead of learning the mapping between dMRI and microstructure maps directly, our approach learns a set of explainable over-completed tissue-compartment kernels between parameterized spherical mean signal and widely-used microstructure maps and apply into the joint characterization of multiple biological models. We validate the effectiveness of our method using an in vivo dataset across different health conditions collected by a clinical scanner. This work sets the foundation for future techniques that integrate clinical data acquisition protocols and enhance sensitivity to microstructure measures. The code is provided at https://github.com/1nlandempire/JEMAP.

Fine-Grained Image Recognition (FGIR) is a fundamental and challenging task in computer vision and multimedia that plays a crucial role in Intellectual Economy and Industrial Internet applications. However, the absence of a unified open-source software library covering various paradigms in FGIR poses a significant challenge for researchers and practitioners in the field. To address this gap, we present Hawkeye, a PyTorch-based library for FGIR with deep learning. Hawkeye is designed with a modular architecture, emphasizing high-quality code and human-readable configuration, providing a comprehensive solution for FGIR tasks. In Hawkeye, we have implemented 16 state-of-the-art fine-grained methods, covering 6 different paradigms, enabling users to explore various approaches for FGIR. To the best of our knowledge, Hawkeye represents the first open-source PyTorch-based library dedicated to FGIR. It is publicly available at https://github.com/Hawkeye-FineGrained/Hawkeye/, providing researchers and practitioners with a powerful tool to advance their research and development in the field of FGIR.

In this paper, we present a robust reconstruction scheme for diffusion MRI (dMRI) data acquired using slice-interleaved diffusion encoding (SIDE). When combined with SIDE undersampling and simultaneous multi-slice (SMS) imaging, our reconstruction strategy is capable of significantly reducing the amount of data that needs to be acquired, enabling high-speed diffusion imaging for pediatric, elderly, and claustrophobic individuals. In contrast to the conventional approach of acquiring a full diffusion-weighted (DW) volume per diffusion wavevector, SIDE acquires in each repetition time (TR) a volume that consists of interleaved slice groups, each group corresponding to a different diffusion wavevector. This strategy allows SIDE to rapidly acquire data covering a large number of wavevectors within a short period of time. The proposed reconstruction method uses a diffusion spectrum model and multi-dimensional total variation to recover full DW images from DW volumes that are slice-undersampled due to unacquired SIDE volumes. We formulate an inverse problem that can be solved efficiently using the alternating direction method of multipliers (ADMM). Experiment results demonstrate that DW images can be reconstructed with high fidelity even when the acquisition is accelerated by 25 folds.

During the first years of life, the human brain undergoes dynamic spatially-heterogeneous changes, invo- lving differentiation of neuronal types, dendritic arbori- zation, axonal ingrowth, outgrowth and retraction, synaptogenesis, and myelination. To better quantify these changes, this article presents a method for probing tissue microarchitecture by characterizing water diffusion in a spectrum of length scales, factoring out the effects of intra-voxel orientation heterogeneity. Our method is based on the spherical means of the diffusion signal, computed over gradient directions for a set of diffusion weightings (i.e., b -values). We decompose the spherical mean profile at each voxel into a spherical mean spectrum (SMS), which essentially encodes the fractions of spin packets undergoing fine- to coarse-scale diffusion proce- sses, characterizing restricted and hindered diffusion stemming respectively from intra- and extra-cellular water compartments. From the SMS, multiple orientation distribution invariant indices can be computed, allowing for example the quantification of neurite density, microscopic fractional anisotropy ( μ FA), per-axon axial/radial diffusivity, and free/restricted isotropic diffusivity. We show that these indices can be computed for the developing brain for greater sensitivity and specificity to development related changes in tissue microstructure. Also, we demonstrate that our method, called spherical mean spectrum imaging (SMSI), is fast, accurate, and can overcome the biases associated with other state-of-the-art microstructure models.

Diffusion MRI is a powerful tool for non-invasive probing of brain tissue microstructure. Recent multi-center efforts in the acquisition and analysis of diffusion MRI data significantly increase sample sizes and hence improve sensitivity and reliability in detecting subtle changes associated with development, aging, and diseases. However, discrepancies resulting from different scanner vendors, acquisition protocols, and image reconstruction algorithms can cause data incompatibility across imaging centers. In this paper, we introduce a model-free method that is based on the method of moments for the direct harmonization of diffusion MRI data to reduce site-specific variations. Our method directly harmonizes diffusion-attenuated signal without the need to fit any diffusion model. Moreover, our method allows the explicit definition of well-behaved mapping functions with properties such as invertibility, smoothness, and injectivity. We show that our method is effective in lowering the variations of diffusion scalars of traveling human phantoms scanned at different sites from 1%-3% to less than 0.9% for fractional anisotropy (FA) and mean diffusivity and from 1%-2.5% to 0.3%-1.2% for generalized FA. We also demonstrate its ability in preserving individual differences and in increasing across-site consistency in tractography and white matter connectivity.