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Spatiotemporally constrained 3D reconstruction from biplanar digital subtraction angiography

INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY 2025年 1-13页

作者： Frisken, Sarah Gopalakrishnan, Vivek Chlorogiannis, David Dimitris Haouchine, Nazim Cafaro, Alexandre Golby, Alexandra J. Wells iii, William M. Du, Rose Brigham & Womens Hosp Boston MA 02115 USA Harvard Med Sch Boston MA 02115 USA MIT Cambridge MA USA TheraPanacea Paris France

PurposeOur goal is to reconstruct 3D cerebral vessels from two 2D digital subtraction angiography (DSA) images acquired using a biplane scanner. This could provide intraoperative 3D imaging with 2-5 x spatial and 20 x temporal resolution of 3D magnetic resonance angiography, computed tomography angiography (CTA), or rotational DSA. Because many interventional radiology suites have biplane scanners, our method could be easily integrated into clinical *** present a constrained 3D reconstruction method that utilizes vessel centerlines, radii, and the flow of contrast agent through vessels from DSA. The reconstructed volume samples 'vesselness' at each voxel, i.e., its probability of containing a vessel. We present evaluation metrics which we used to optimize reconstruction parameters and evaluate our method on synthetic data. We provide preliminary results on clinical data. To handle clinical data, we developed a software tool for extracting vessel centerlines, radii, and contrast arrival times from clinical DSA. We provide an automated method for registering DSA to CTA which allows us to compare reconstructed vessels with vessels extracted from *** method reduced reconstruction artifacts in vesselness volumes for both synthetic and clinical data. In synthetic DSA, where 3D ground-truth vessel centerlines are available, our constrained reconstruction method improved accuracy, selectivity, and Dice scores with two views compared to existing sparse reconstruction methods with up to 16 *** additional constraints into 3D reconstruction can successfully reduce artifacts introduced when a complex 3D structure like the brain vasculature is reconstructed from a small number of 2D views.

关键词： 3D image reconstruction Neurovascular imaging Cerebrovascular disease Digital subtraction angiography

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A Spatial Transformation Based Next Frame Predictor

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IEEE ACCESS 2025年 13卷 5406-5423页

作者： Mokssit, Saad Licea, Daniel Bonilla Guermah, Bassma Ghogho, Mounir Int Univ Rabat Coll Engn & Architecture TICLab Rabat 11103 Morocco Mohammed VI Polytech Univ Coll Comp Ben Guerir 43150 Morocco Univ Leeds Fac Engn Leeds LS2 9JT England

In recent years, the automobile industry has achieved astonishing success in making autonomous cars safer, more affordable, and more reliable. However, current autonomous driving technology is mainly based on reactive controllers that attempt to respond to the various events the car encounters. Yet, achieving a truly safe and reliable autonomous system necessitates anticipating such events and planning the correct actions in advance to avoid undesirable behavior. Recent advances in deep learning have shown remarkable performance in predicting future frames from video sequences. However, most of these approaches can only handle a few moving elements in the scene and perform poorly when the camera is in motion. This is mainly due to the difficulty of disentangling camera intrinsic motion from object-dependent motion. In this work, we equip autonomous cars with an object-oriented next-frame predictor that leverages Transformer architecture to extract, for each moving object in the scene, a spatial transformation applied to the object to predict its configuration in the next frame. Static elements of the scene are then used to estimate camera intrinsic motion, which is applied to the background to predict how it will be viewed in the next frame. Notably, our approach significantly reduces the complexity typically associated with such models by requiring the estimation of only 14 parameters per moving object, independent of image resolution. We have validated the generalization capabilities of our model through training on simulated datasets and testing on real-world datasets. The results indicate that our model not only outperforms existing models trained solely on real data but also exhibits superior resilience to occlusions and incomplete data in the input sequences. These findings underscore the potential of our model to significantly improve the predictive analytics capabilities of autonomous driving systems, thereby enhancing their safety and reliability in dynamic

关键词： Next frame prediction moving camera image reconstruction deep learning projective and affine transforms Next frame prediction moving camera image reconstruction deep learning projective and affine transforms

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Multiobjective optimization guided by image quality index for limited-angle CT image reconstruction

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JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2024年第5期32卷 1209-1237页

作者： He, Yu Wang, Chengxiang Yu, Wei Wang, Jiaxi Chongqing Normal Univ Sch Math Sci Chongqing 401331 Peoples R China Hubei Univ Sci & Technol Xianning Med Coll Sch Biomed Engn & Imaging Xianning 437100 Peoples R China Hubei Univ Sci & Technol Key Lab Optoeletron & Intelligent Control Xianning Peoples R China Chengdu Univ Coll Comp Sci Chengdu Peoples R China

BACKGROUND: Due to the incomplete projection data collected by limited-angle computed tomography (CT), severe artifacts are present in the reconstructed image. Classical regularization methods such as total variation (TV) minimization, t0 minimization, are unable to suppress artifacts at the edges perfectly. Most existing regularization methods are single-objective optimization approaches, stemming from scalarization methods for multiobjective optimization problems (MOP). OBJECTIVE: To further suppress the artifacts and effectively preserve the edge structures of the reconstructed image. METHOD: This study presents a multiobjective optimization model incorporates both data fidelity term and t0-norm of the image gradient as objective functions. It employs an iterative approach different from traditional scalarization methods, using the maximization of structural similarity (SSIM) values to guide optimization rather than minimizing the objective *** iterative method involves two steps, firstly, simultaneous algebraic reconstruction technique (SART) optimizes the data fidelity term using SSIM and the Simulated Annealing (SA) algorithm for guidance. The degradation solution is accepted in the form of probability, and guided image filtering (GIF) is introduced to further preserve the image edge when the degradation solution is rejected. Secondly, the result from the first step is integrated into the second objective function as a constraint, we use t0 minimization to optimize t0-norm of the image gradient, and the SSIM, SA algorithm and GIF are introduced to guide optimization process by improving SSIM value like the first step. RESULTS: With visual inspection, the peak signal-to-noise ratio (PSNR), root mean square error (RMSE), and SSIM values indicate that our approach outperforms other traditional methods. CONCLUSIONS: The experiments demonstrate the effectiveness of our method and its superiority over other classical methods in artifact suppression and edge

关键词： CT reconstruction limited-angle CT image quality assessment multiobjective optimization

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SR-CurvANN: Advancing 3D surface reconstruction through curvature-aware neural networks

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COMPUTERS & GRAPHICS-UK 2025年 130卷

作者： Hernandez-Bautista, Marina Melero, Francisco J. Univ Granada Dept Comp Sci & Artificial Intelligence Granada 18071 Spain Univ Granada Dept Software Engn Granada 18071 Spain Andalusian Res Inst Data Sci & Computat Intelligen Jaen Spain

incomplete or missing data in three-dimensional (3D) models can lead to erroneous or flawed renderings, limiting their usefulness in applications such as visualization, geometric computation, and 3D printing. Conventional surface-repair techniques often fail to infer complex geometric details in missing areas. Neural networks successfully address hole-filling tasks in 2D images using inpainting techniques. The combination of surface reconstruction algorithms, guided by the model's curvature properties and the creativity of neural networks in the inpainting processes, should provide realistic results in the hole completion task. In this paper, we propose a novel method entitled SR-CurvANN (Surface reconstruction Based on Curvature-Aware Neural Networks) that incorporates neural network-based 2D inpainting to effectively reconstruct 3D surfaces. We train the neural networks with images that represent planar representations of the curvature at vertices of hundreds of 3D models. Once the missing areas have been inferred, a coarse-to-fine surface deformation process ensures that the surface fits the reconstructed curvature image. Our proposal makes it possible to learn and generalize patterns from a wide variety of training 3D models, generating comprehensive inpainted curvature images and surfaces. Experiments conducted on 959 models with several holes have demonstrated that SR-CurvANN excels in the shape completion process, filling holes with a remarkable level of realism and precision.

关键词： Shape completion Curvature representation Parametrization Inpainting

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Model-based iterative reconstruction for direct imaging with point spread function encoded echo planar MRI

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MAGNETIC RESONANCE IMAGING 2024年 109卷 189-202页

作者： Meyer, Nolan K. In, Myung-Ho Black, David F. Campeau, Norbert G. Welker, Kirk M. Huston iii, John Halverson, Maria A. Bernstein, Matt A. Trzasko, Joshua D. Mayo Clin Dept Radiol 200 First St SW Rochester MN 55905 USA

Background: Echo planar imaging (EPI) is a fast measurement technique commonly used in magnetic resonance imaging (MRI), but is highly sensitive to measurement non-idealities in reconstruction. Point spread function (PSF)-encoded EPI is a multi-shot strategy which alleviates distortion, but acquisition of encodings suitable for direct distortion-free imaging prolongs scan time. In this work, a model-based iterative reconstruction (MBIR) framework is introduced for direct imaging with PSF-EPI to improve image quality and acceleration potential. Methods: An MBIR platform was developed for accelerated PSF-EPI. The reconstruction utilizes a subspace representation, is regularized to promote local low-rankedness (LLR), and uses variable splitting for efficient iteration. Comparisons were made against standard reconstructions from prospectively accelerated PSF-EPI data and with retrospective subsampling. Exploring aggressive partial Fourier acceleration of the PSF-encoding dimension, additional comparisons were made against an extension of Homodyne to direct PSF-EPI in numerical experiments. A neuroradiologists' assessment was completed comparing images reconstructed with MBIR from retrospectively truncated data directly against images obtained with standard reconstructions from nontruncated datasets. Results: image quality results were consistently superior for MBIR relative to standard and Homodyne reconstructions. As the MBIR signal model and reconstruction allow for arbitrary sampling of the PSF space, random sampling of the PSF-encoding dimension was also demonstrated, with quantitative assessments indicating best performance achieved through nonuniform PSF sampling combined with partial Fourier. With retrospective subsampling, MBIR reconstructs high-quality images from sub-minute scan datasets. MBIR was shown to be superior in a neuroradiologists' assessment with respect to three of five performance criteria, with equivalence for the remaining two. Conclusions: A no

关键词： image reconstruction Echo planar imaging Point spread function Low-rank

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A low-rank tensor reconstruction and denoising method for enhancing CNN performance

A low-rank tensor reconstruction and denoising method for en...

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Southwest Symposium on image Analysis and Interpretation (SSIAI)

作者： Harikumar, Rohin Minkoff, Susan E. Lou, Yifei Univ Texas Dallas Dept Math Sci Richardson TX 75083 USA Univ N Carolina Dept Math Chapel Hill NC 27515 USA

ISBN: (纸本)9798350360110

Neural network training data is often corrupted by equipment malfunction or noise leading to red blurry and incomplete data. This paper proposes a combination of a reconstruction technique and a neural network to deal with data corruption in a machine vision task. Specifically, we consider minimizing the tensor nuclear norm for low-rank data completion and denoising and demonstrate the method's effectiveness using a convolutional neural network (CNN) for image classification. We conduct classification experiments on 3 datasets, showing consistently that training on reconstructed images achieves improved accuracy ranging from 7-25% over training using corrupted data.

关键词： Low-rank tensor completion tensor nuclear norm convolutional neural network alternating direction method of multipliers image classification

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A Novel NMF Guided for Hyperspectral Unmixing from incomplete and Noisy data

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷 1页

作者： Dong, Le Lu, Xiaoqiang Liu, Ganchao Yuan, Yuan Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Peoples R China Chinese Acad Sci Xian Inst Opt & Precis Mech Xian 710119 Shannxi Peoples R China

The nonnegative matrix factorization (NMF)-combined spatial-spectral information has been widely applied in the unmixing of hyperspectral images (HSIs). However, how to select the appropriate similarity pixels and explore the spatial information and how to adapt the unmixing algorithm to complex data are both great challenges. In this article, we propose a novel unmixing method named spatial-spectral neighborhood preserving NMF (SSNPNMF) for incomplete and noisy HSI data. First, a spatial-spectral kernel regularizer is introduced to preprocess the HSI, which can reduce noise and complete missing elements. Second, a distance metric SSD based on spatial-spectral information is designed to select similar pixels in the image. Subsequently, the spatial-spectral relationship of the selected first k similar pixels is used to reconstruct the image and obtain the reconstruction matrix. Finally, the reconstruction matrix is used to constrain the abundances and improve the unmixing performance. Experimental results on synthetic data and Cuprite data indicate that SSNPNMF has a more effective unmixing performance compared with the state-of-the-art methods.

关键词： image reconstruction Hyperspectral imaging Noise measurement Gaussian noise Interference Stability analysis Sensors Hyperspectral unmixing (HU) image reconstruction nonnegative matrix factorization (NMF) spatial-spectral information

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Compound Weakly Supervised Clustering

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IEEE TRANSACTIONS ON image PROCESSING 2024年 33卷 957-971页

作者： Tao, Hong Jiang, Jiacheng Hou, Chenping Luo, Tingjin Fan, Ruidong Zhang, Jing Natl Univ Def Technol NUDT Coll Sci Changsha 410073 Hunan Peoples R China

Clustering is a fundamental and important step in many image processing tasks, such as face recognition and image segmentation. The performance of clustering can be largely enhanced if relevant weak supervision information is appropriately exploited. To achieve this goal, in this paper, we propose the Compound Weakly Supervised Clustering (CSWC) method. Concretely, CSWC incorporates two types of widely available and easily accessed weak supervision information from the label and feature aspects, respectively. To be specific, at the label level, the pairwise constraints are utilized as a kind of typical weak label supervision information. At the feature level, the partial instances collected from multiple perspectives have internal consistency and they are regarded as weak structure supervision information. To achieve a more confident clustering partition, we learn a unified graph with its similarity matrix to incorporate the above two types of weak supervision. On one hand, this similarity matrix is constructed by self-expression across the partial instances collected from multiple perspectives. On the other hand, the pairwise constraints, i.e., must-links and cannot-links, are considered by formulating a regularizer on the similarity matrix. Finally, the clustering results can be directly obtained according to the learned graph, without performing additional clustering techniques. Besides evaluating CSWC on 7 benchmark datasets, we also apply it to the application of face clustering in video data since it has vast application potentiality. Experimental results demonstrate the effectiveness of our algorithm in both incorporating compound weak supervision and identifying faces in real applications.

关键词： Face recognition Compounds image reconstruction Clustering methods Clustering algorithms Task analysis Laplace equations Compound weak supervision constrained clustering incomplete view learning

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Satellite image Inpainting With Edge-Conditional Expectation Attention

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 10830-10845页

作者： Zhou, Dazhi Chen, Yanjun Zhang, Yuhong Niu, Jing Harbin Normal Univ Sch Math Sci Harbin 150025 Peoples R China Harbin Normal Univ Sch Geog Sci Harbin 150025 Peoples R China

Satellite images often suffer from data loss and corruption due to various factors, including sensor malfunctions and atmospheric interference, leading to incomplete and degraded imagery. In satellite images, long-range dependencies are particularly significant due to irregular and widely distributed geomorphological edges, such as rivers, mountains, and urban structures. Traditional convolutional neural network-based inpainting methods face challenges due to their fixed receptive fields and parameter sharing, limiting their ability to effectively capture long-range dependencies and differentiate between corrupted and uncorrupted areas. To address these limitations, we propose a deep learning approach based on an edge-conditional expectation attention module, which conditions the attention mechanism on edge information to enhance the model's focus on high-frequency edge details. This enables the network to capture critical structures within the image better. In addition, we apply Chebyshev's inequality within the attention mechanism to constrain the expectation of attention outputs, reducing excessive deviations and stabilizing the reconstruction process. Experimental results demonstrate that our approach outperforms several state-of-the-art methods in restoring missing regions and reconnecting geomorphological features.

关键词： image edge detection Satellite images Transformers Feature extraction Remote sensing Earth Computational modeling Convolutional neural networks Artificial satellites image restoration Chebyshev adjustment edge extraction image inpainting remote sensing transformer

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reconstruction of Mixed Traffic Systems at Micro and Macro Scales

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IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE 2025年第1期17卷 97-99页

作者： Wang, Yanbing Fernandez, Fernando Garcia Li, Zhixiong Arizona State Univ Tempe AZ 85281 USA Univ Carlos III Madrid Madrid Spain Yonsei Univ Seoul South Korea

Understanding complex traffic dynamics, characterized by the interplay between vehicle automation technologies, human drivers, and diverse transportation modes, presents significant challenges for transportation research. The integration of autonomous vehicles with human-driven vehicles introduces new dynamics into traffic flow, necessitating a comprehensive understanding of their coexistence. Additionally, the heterogeneity of various vehicle classes further complicate traffic patterns. Advancements in traffic data collection, particularly camera-based trajectory data, provide a wealth of information for analyzing microscopic details and macroscopic trends in traffic behavior. However, challenges in modeling and estimation techniques, such as accuracy, robustness and scalability need to be addressed. Furthermore, data quality issues need to be tackled in a systematic manner. This dissertation addresses the overarching question of developing comprehensive and efficient methods to understand complex traffic dynamics. It solves a series of reconstruction problems, involving parameter estimation, state estimation, and data reconciliation. Parameter estimation focuses on inferring unknown parameters of microscopic traffic models from on-board vehicle sensor data. State estimation involves estimating current or past states of macroscopic traffic dynamics using fixed-location measurements. data reconciliation processes incomplete and noisy video tracking data while adhering to dynamical constraints. The integration of these tools provides insights into complex traffic patterns at various scales.

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