Reconstructing visual stimuli from EEG signals is a crucial step in realizing brain-computer interfaces. In this paper, we propose a transformer-based EEG signal encoder integrating the Discrete Wavelet Transform (DWT) and the gating mechanism. Guided by the feature alignment and category-aware fusion losses, this encoder is used to extract features related to visual stimuli from EEG signals. Subsequently, with the aid of a pre-trained diffusion model, these features are reconstructed into visual stimuli. To verify the effectiveness of the model, we conducted EEG-to-image generation and classification tasks using the THINGS-EEG dataset. To address the limitations of quantitative analysis at the semantic level, we combined WordNet-based classification and semantic similarity metrics to propose a novel semantic-based score, emphasizing the ability of our model to transfer neural activities into visual representations. Experimental results show that our model significantly improves semantic alignment and classification accuracy, which achieves a maximum single-subject accuracy of 43%, outperforming other state-of-the-art methods. The source code is available at https://github.com/zes0v0inn/DWT_EEG_Reconstruction/.

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.

Computed Tomography (CT) is an advanced imaging technology. To obtain high-resolution (HR) CT images from low-resolution (LR) sinograms, we present a deep-learning (DL) based CT super-resolution (SR) method.The proposed method combines a SR model in the sinogram domain and the iterative framework into a CT SR algorithm. We unrolled the proposed method into a DL network (SRECT-Net) for adaptive estimation of inherent blurring effects causing by the insufficient sampling of LR X-Ray detector. For CT systems, if the scanning protocol is fixed, the system blur effect will remain relatively stable. Inspired by this fact, the proposed methods can be pre-trained with amounts of simulated datasets, effectively fine-tuned with just a single sample, and then obtain a machine-specific SR model. The proposed SRECT was evaluated via SR CT imaging of a Catphan700phantom and a ham, whose performance was compared to the other DL-based CT SR methods. The results show that the proposed SRECT can provide a CT SR reconstruction performance superior to the other state-of-the-art CT SR methods, demonstrating the potential use in improving CT resolution beyond its hardware limit, lowering the requirement of CT hardware, or reducing X-Ray dose during CT imaging.

Breast ultrasound (BUS) is an effective imaging modality for breast cancer diagnosis. The structural characteristics of breast lesions play an important role in computer-aided diagnosis. In this paper, a novel structure-aware triplet path network (SATPN) was designed to integrate classification and image reconstruction tasks to achieve accurate diagnosis on BUS images. Specifically, we enhanced clinically-approved structure characteristics of breast lesion by converting original BUS images to BI-RADS-oriented feature maps (BFMs) with a distance-transformation coupled Gaussian filter. Then, the converted BFMs were used as the inputs of the SATPN, which performed a supervised lesion classification task and two separate unsupervised stacked convolutional auto-encoder tasks for benign and malignant image reconstruction. We trained the SATPN with an alternative learning strategy by balancing image reconstruction error and classification label prediction error. The lesion label was determined by weighted voting of reconstruction error and label prediction error. We compared the performance of the SATPN with five deep learning methods using the original images and BFMs as inputs. Experimental results on two BUS datasets showed that SATPN performed the best among the six networks, with classification accuracy around 96%. These findings indicate that SATPN is promising for effective ultrasound computer-aided diagnosis of breast lesions.

Growing interests in multispectral semantic segmentation (MSS) have been witnessed in recent years, thanks to the unique advantages of combining RGB and thermal infrared images to tackle challenging scenarios with adverse conditions. However, unlike traditional RGB-only semantic segmentation, the lack of a large-scale MSS dataset has become a hindrance to the progress of this field. To address this issue, we introduce a SemanticRT dataset - the largest MSS dataset to date, comprising 11,371 high-quality, pixel-level annotated RGB-thermal image pairs. It is 7 times larger than the existing MFNet dataset, and covers a wide variety of challenging scenarios in adverse lighting conditions such as low-light and pitch black. Further, a novel Explicit Complement Modeling (ECM) framework is developed to extract modality-specific information, which is propagated through a robust cross-modal feature encoding and fusion process. Extensive experiments demonstrate the advantages of our approach and dataset over the existing counterparts. Our new dataset may also facilitate further development and evaluation of existing and new MSS algorithms.

Adversarial-based adaptation has dominated the area of domain adaptive detection over the past few years. Despite their general efficacy for various tasks, the learned representations may not capture the intrinsic topological structures of the whole images and thus are vulnerable to distributional shifts especially in real-world applications, such as geometric distortions across imaging devices in medical images. In this case, forcefully matching data distributions across domains cannot ensure precise knowledge transfer and are prone to result in the negative transfer. In this paper, we explore the problem of domain adaptive lesion detection from the perspective of relational reasoning, and propose a Graph-Structured Knowledge Transfer (GraphSKT) framework to perform hierarchical reasoning by modeling both the intra- and inter-domain topological structures. To be specific, we utilize cross-domain correspondence to mine meaningful foreground regions for representing graph nodes and explicitly endow each node with contextual information. Then, the intra- and inter-domain graphs are built on the top of instance-level features to achieve a high-level understanding of the lesion and whole medical image, and transfer the structured knowledge from source to target domains. The contextual and semantic information is propagated through graph nodes methodically, enhancing the expressive power of learned features for the lesion detection tasks. Extensive experiments on two types of challenging datasets demonstrate that the proposed GraphSKT significantly outperforms the state-of-the-art approaches for detection of polyps in colonoscopy images and of mass in mammographic images.

The COVID-19 pandemic has highlighted the importance of diagnosis and monitoring as early and accurately as possible. However, the reverse-transcription polymerase chain reaction (RT-PCR) test results in two issues: (1) protracted turnaround time from sample collection to testing result and (2) compromised test accuracy, as low as 67%, due to when and how the samples are collected, packaged, and delivered to the lab to conduct the RT-PCR test. Thus, we present ComputeCOVID19+, our computed tomography-based framework to improve the testing speed and accuracy of COVID-19 (plus its variants) via a deep learning-based network for CT image enhancement called DDnet, short for DenseNet and Deconvolution network. To demonstrate its speed and accuracy, we evaluate ComputeCOVID19+ across several sources of computed tomography (CT) images and on many heterogeneous platforms, including multi-core CPU, many-core GPU, and even FPGA. Our results show that ComputeCOVID19+ can significantly shorten the turnaround time from days to minutes and improve the testing accuracy to 91%.

Computed tomography (CT) has been widely used for medical diagnosis, assessment, and therapy planning and guidance. In reality, CT images may be affected adversely in the presence of metallic objects, which could lead to severe metal artifacts and influence clinical diagnosis or dose calculation in radiation therapy. In this article, we propose a generalizable framework for metal artifact reduction (MAR) by simultaneously leveraging the advantages of image domain and sinogram domain-based MAR techniques. We formulate our framework as a sinogram completion problem and train a neural network (SinoNet) to restore the metal-affected projections. To improve the continuity of the completed projections at the boundary of metal trace and thus alleviate new artifacts in the reconstructed CT images, we train another neural network (PriorNet) to generate a good prior image to guide sinogram learning, and further design a novel residual sinogram learning strategy to effectively utilize the prior image information for better sinogram completion. The two networks are jointly trained in an end-to-end fashion with a differentiable forward projection (FP) operation so that the prior image generation and deep sinogram completion procedures can benefit from each other. Finally, the artifact-reduced CT images are reconstructed using the filtered backward projection (FBP) from the completed sinogram. Extensive experiments on simulated and real artifacts data demonstrate that our method produces superior artifact-reduced results while preserving the anatomical structures and outperforms other MAR methods.

SUMMARY: Nowadays, it is feasible to collect massive features for quantitative representation and precision medicine, and thus, automatic ranking to figure out the most informative and discriminative ones becomes increasingly important. To address this issue, 42 feature ranking (FR) methods are integrated to form a MATLAB toolbox (matFR). The methods apply mutual information, statistical analysis, structure clustering and other principles to estimate the relative importance of features in specific measure spaces. Specifically, these methods are summarized, and an example shows how to apply a FR method to sort mammographic breast lesion features. The toolbox is easy to use and flexible to integrate additional methods. Importantly, it provides a tool to compare, investigate and interpret the features selected for various applications. AVAILABILITY AND IMPLEMENTATION: The toolbox is freely available at http://github.com/NicoYuCN/matFR. A tutorial and an example with a dataset are provided.

Sparse-view computed tomography (CT) holds great promise for speeding up data acquisition and reducing radiation dose in CT scans. Recent advances in reconstruction algorithms for sparse-view CT, such as iterative reconstruction algorithms, obtained high-quality image while requiring advanced computing power. Lately, deep learning (DL) has been widely used in various applications and has obtained many remarkable outcomes. In this paper, we propose a new method for sparse-view CT reconstruction based on the DL approach. The method can be divided into two steps. First, filter backprojection (FBP) was used to reconstruct the CT image from sparsely sampled sinogram. Then, the FBP results were fed to a DL neural network, which is a DenseNet and deconvolution-based network (DD-Net). The DD-Net combines the advantages of DenseNet and deconvolution and applies shortcut connections to concatenate DenseNet and deconvolution to accelerate the training speed of the network; all of those operations can greatly increase the depth of network while enhancing the expression ability of the network. After the training, the proposed DD-Net achieved a competitive performance relative to the state-of-the-art methods in terms of streaking artifacts removal and structure preservation. Compared with the other state-of-the-art reconstruction methods, the DD-Net method can increase the structure similarity by up to 18% and reduce the root mean square error by up to 42%. These results indicate that DD-Net has great potential for sparse-view CT image reconstruction.