Visible-infrared vehicle re-identification has become crucial for stable 24-h surveillance of Visual Internet of Things (VIoT). It aims to leverage the shared information between different modalities to retrieve specific objects. Previous works primarily focus on projecting images from two modalities into a common embedding space to measure their similarity scores. However, the inherent distribution discrepancies between different modalities often lead models to rely on spurious features that are unrelated to vehicle identity, making effective feature fusion challenging. To address this unique problem, we propose the Progressive Optimization and Robust Spatial Convolution for Hybrid Enhancement (PORSCHE) model, which reduces the negative effects of spurious correlations and biases toward training pairs. Specifically, we introduce the Patch-wise Matching (PAM) module, which performs initial coarse-grained alignment between different modalities. Building upon this foundation, we develop the Point-wise Matching (POM) module to achieve fine-grained discriminative alignment through precise point-level feature matching, thereby enhancing identity-specific representation. To optimize these complementary PAM and POM components effectively, we implement a progressive training strategy that hierarchically refines feature representations from local patches to global structures, ensuring stable learning of modality-invariant characteristics. This coarse-to-fine architecture enables gradual fusion and alignment across modalities at both patch and point levels, effectively capturing the essential discriminative features required for robust cross-modality retrieval. Extensive experimental results on MSVR310, WMVeID863, and RGBN300 benchmarks demonstrate the effectiveness of our proposed method. The code will be available at https://github.com/HowardLiu28/PORSCHE.

CT report generation (CTRG) aims to automatically generate diagnostic reports for 3D volumes, relieving clinicians' workload and improving patient care. Despite clinical value, existing works fail to effectively incorporate diagnostic information from multiple anatomical views and lack related clinical expertise essential for accurate and reliable diagnosis. To resolve these limitations, we propose a novel Multi-view perception Knowledge-enhanced TansfoRmer (MvKeTR) to mimic the diagnostic workflow of clinicians. Just as radiologists first examine CT scans from multiple planes, a Multi-View Perception Aggregator (MVPA) with view-aware attention is proposed to synthesize diagnostic information from multiple anatomical views effectively. Then, inspired by how radiologists further refer to relevant clinical records to guide diagnostic decision-making, a Cross-Modal Knowledge Enhancer (CMKE) is devised to retrieve the most similar reports based on the query volume to incorporate domain knowledge into the diagnosis procedure. Furthermore, instead of traditional MLPs, we employ Kolmogorov-Arnold Networks (KANs) as the fundamental building blocks of both modules, which exhibit superior parameter efficiency and reduced spectral bias to better capture high-frequency components critical for CT interpretation while mitigating overfitting. Extensive experiments on the public CTRG-Chest-548 K dataset demonstrate that our method outpaces prior state-of-the-art (SOTA) models across almost all metrics.

The generation of synthetic data using physics-based modeling provides a solution to limited or lacking real-world training samples in deep learning methods for rapid quantitative magnetic resonance imaging (qMRI). However, synthetic data distribution differs from real-world data, especially under complex imaging conditions, resulting in gaps between domains and limited generalization performance in real scenarios. Recently, a single-shot qMRI method, multiple overlapping-echo detachment imaging (MOLED), was proposed, quantifying tissue transverse relaxation time ( $\text {T}_{{2}}$ ) in the order of milliseconds with the help of a trained network. Previous works leveraged a Bloch-based simulator to generate synthetic data for network training, which leaves the domain gap between synthetic and real-world scenarios and results in limited generalization. In this study, we proposed a $\text {T}_{{2}}$ mapping method via MOLED from the perspective of domain adaptation, which obtained accurate mapping performance without real-label training and reduced the cost of sequence research at the same time. Experiments demonstrate that our method outshined in the restoration of MR anatomical structures.

Use of synthetic data has provided a potential solution for addressing unavailable or insufficient training samples in deep learning-based magnetic resonance imaging (MRI). However, the challenge brought by domain gap between synthetic and real data is usually encountered, especially under complex experimental conditions. In this study, by combining Bloch simulation and general MRI models, we propose a framework for addressing the lack of training data in supervised learning scenarios, termed MOST-DL. A challenging application is demonstrated to verify the proposed framework and achieve motion-robust [Formula: see text] mapping using single-shot overlapping-echo acquisition. We decompose the process into two main steps: (1) calibrationless parallel reconstruction for ultra-fast pulse sequence and (2) intra-shot motion correction for [Formula: see text] mapping. To bridge the domain gap, realistic textures from a public database and various imperfection simulations were explored. The neural network was first trained with pure synthetic data and then evaluated with in vivo human brain. Both simulation and in vivo experiments show that the MOST-DL method significantly reduces ghosting and motion artifacts in [Formula: see text] maps in the presence of unpredictable subject movement and has the potential to be applied to motion-prone patients in the clinic. Our code is available at https://github.com/qinqinyang/MOST-DL.

The automatic pulmonary nodule detection in thoracic computed tomography (CT) scans plays a crucial role in the early diagnosis of lung cancer. In this paper, we propose a novel framework with a 3D convolutional network (ConvNet) for pulmonary nodule detection. To improve the efficiency and flexibility, we adopt one-stage process without the false positive reduction stage. Specially, the great challenge of the nodule detection is the recall rate of small nodules. We propose two methods to solve this issue. Firstly, we set the classification label by the intersection over union (IoU) self-normalization, which enables to eliminate the loss of regression information caused by misleading classification confidence. Secondly, pulmonary nodules differ in size, shape and density, leading to large intra-class variations. We introduce maxout unit to solve this problem. Overall, we achieve an average FROC score of 0.912 on LUNA16 dataset, outperforming all other one-stage models as far as we know.

Quantitative magnetic resonance imaging (MRI) is of great value to both clinical diagnosis and scientific research. However, most MRI experiments remain qualitative, especially dynamic MRI, because repeated sampling with variable weighting parameter makes quantitative imaging time-consuming and sensitive to motion artifacts. A single-shot quantitative T2mapping method based on multiple overlapping-echo acquisition (dubbed MOLED-4) was proposed to obtain reliable T2mapping in milliseconds. Different from traditional MRI acceleration methods, such as compressed sensing and parallel imaging, MOLED-4 accelerates quantitative T2mapping via synchronized multisampling and then deep learning to map the complex nonlinear relationship that is difficult to solve by traditional optimization-based methods. The results of simulation, phantom, and in vivo human brain experiments show the great performance of the proposed method. The principle of MOLED-4 may be extended to other ultrafast quantitative parameter mappings and potentially lead to new dynamic MRI with high efficiency to catch quantitative variation of tissue properties.

Compressed sensing theory facilitates the fast magnetic resonance imaging by reducing the required number of measurements for reconstruction. Conventional compressed sensing magnetic resonance imaging(CSMRI) method utilize the partial k-space measurements as a whole without considering their intrinsic property. Some recent researches have shown the advantage of dealing the high and low frequency image content separately. Based on this, we propose a novel CSMRI algorithm based on total variation regularization with k-space decomposition. First we decompose k-space into high frequency band and low frequency band, then we reconstruct the corresponding high and low MR images which will be used for integration later. All the steps can be unified into a objective function. We will show that the proposed objective function can be split into several subproblems to solve iteratively using ADMM technique. The experimental results show that the proposed method outperforms the conventional CSMRI method. Besides, the proposed method can be extended to other image processing applications as well.

The reconstruction of MR quantitative susceptibility mapping (QSM) from local phase measurements is an ill posed inverse problem and different regularization strategies incorporating a priori information extracted from magnitude and phase images have been proposed. However, the anatomy observed in magnitude and phase images does not always coincide spatially with that in susceptibility maps, which could give erroneous estimation in the reconstructed susceptibility map. In this paper, we develop a structural feature based collaborative reconstruction (SFCR) method for QSM including both magnitude and susceptibility based information. The SFCR algorithm is composed of two consecutive steps corresponding to complementary reconstruction models, each with a structural feature based l 1 norm constraint and a voxel fidelity based l 2 norm constraint, which allows both the structure edges and tiny features to be recovered, whereas the noise and artifacts could be reduced. In the M-step, the initial susceptibility map is reconstructed by employing a k -space based compressed sensing model incorporating magnitude prior. In the S-step, the susceptibility map is fitted in spatial domain using weighted constraints derived from the initial susceptibility map from the M-step. Simulations and in vivo human experiments at 7T MRI show that the SFCR method provides high quality susceptibility maps with improved RMSE and MSSIM. Finally, the susceptibility values of deep gray matter are analyzed in multiple head positions, with the supine position most approximate to the gold standard COSMOS result.

There is a growing interest in quantifying tissue susceptibility in MRI. However, the zeros in the dipole kernel makes the calculation of the magnetic susceptibility from the measured field to be an ill-posed problem. Recently, Bayesian regularization approaches have been utilized to enable accurate quantitative susceptibility mapping(QSM), such as L2 norm gradient minimization and TV. In this work, we propose an efficient QSM method by using a sparsity promoting regularization which called L0 norm of gradient to reconstruct susceptibility map. The use of L0 norm allows us to yield high quality image and prevent penalizing salient edges. Since the L0 minimization is an NP-hard problem, a special alternating optimization strategy by introducing an auxiliary variable is adopted to solve the problem and it only takes 1-2 mins to reconstruct the whole 3D susceptibility data. Both numerical phantom simulations and human brain tests are performed to demonstrate the superior performance of the proposed method compared with previous methods.