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arXiv 2025年

作者： Puglisi, Lemuel Alexander, Daniel C. Ravì, Daniele Dept. of Math and Computer Science University of Catania Viale Andrea Doria 6 Catania Italy Centre for Medical Image Computing University College London 90 High Holborn London United Kingdom MIFT Department University of Messina Viale Ferdinando Stagno d’Alcontres 31 Messina Italy

The growing availability of longitudinal Magnetic Resonance Imaging (MRI) datasets has facilitated Artificial Intelligence (AI)-driven modeling of disease progression, making it possible to predict future medical scans for individual patients. However, despite significant advancements in AI, current methods continue to face challenges including achieving patient-specific individualization, ensuring spatiotemporal consistency, efficiently utilizing longitudinal data, and managing the substantial memory demands of 3D scans. To address these challenges, we propose Brain Latent Progression (BrLP), a novel spatiotemporal model designed to predict individual-level disease progression in 3D brain MRIs. The key contributions in BrLP are fourfold: (i) it operates in a small latent space, mitigating the computational challenges posed by high-dimensional imaging data;(ii) it explicitly integrates subject metadata to enhance the individualization of predictions;(iii) it incorporates prior knowledge of disease dynamics through an auxiliary model, facilitating the integration of longitudinal data;and (iv) it introduces the Latent Average Stabilization (LAS) algorithm, which (a) enforces spatiotemporal consistency in the predicted progression at inference time and (b) allows us to derive a measure of the uncertainty for the prediction. We train and evaluate BrLP on 11,730 T1-weighted (T1w) brain MRIs from 2,805 subjects and validate its generalizability on an external test set comprising 2,257 MRIs from 962 subjects. Our experiments compare BrLP-generated MRI scans with real follow-up MRIs, demonstrating state-of-the-art accuracy compared to existing methods. The code is publicly available at: https://***/LemuelPuglisi/BrLP. © 2025, CC BY.

关键词： Magnetic resonance imaging

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Developing undevelopable surfaces using particle systems

Developing undevelopable surfaces using particle systems

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作者： Weiler, Florian Plath, Jan MeVis Research Center for Medical Image Computing Bremen Germany Universität Bremen Dept. of Mathematics/Computer Science Bremen Germany

ISBN: (纸本)9783902661210

This paper presents a new approach for calculating a development of an undevelopable surface using a particle system. An undevelopable surface is any surface that cannot be flattened into a map without stretching, tearing, or compressing itself, as for example the surface of a sphere. While geometry based algorithms for solving this problem usually introduce large distortions to the development, the technique presented here allows calculating a development with minimal distortion. The technique was developed to enable automatic calculation of 3d-models of made to measure shoes based solely on the CAD-/CAM-data used for construction and production. It is embedded into a web-based process-chain which aims at reducing the costs for manufacturing custom-sized shoes, and thus supporting traditional shoe-makers. Copyright © 2007 IFAC.

关键词： computer aided design

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A fast and robust graph-based approach for boundary estimation of fiber bundles relying on fractional anisotropy maps

A fast and robust graph-based approach for boundary estimati...

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2010 20th International Conference on Pattern Recognition, ICPR 2010

作者： Bauer, M.H.A. Egger, J. O'Donnell, T. Barbieri, S. Klein, J. Freisleben, B. Hahn, H.-K. Nimsky, C. Dept. of Neurosurgery University of Marburg Germany Dept. of Math. and Computer Science University of Marburg Germany Dept. of Imaging and Visualization Siemens Corporate Research Princeton United States Institute for Medical Image Computing Fraunhofer MEVIS Bremen Germany

ISBN: (纸本)9780769541099

In this paper, a fast and robust graph-based approach for boundary estimation of fiber bundles derived from Diffusion Tensor Imaging (DTI) is presented. DTI is a non-invasive imaging technique that allows the estimation of the location of white matter tracts based on measurements of water diffusion properties. Depending on DTI data, the fiber bundle boundary can be determined to gain information about eloquent structures, which is of major interest for neurosurgery. DTI in combination with tracking algorithms allows the estimation of position and course of fiber tracts in the human brain. The presented method uses these tracking results as the starting point for a graph-based approach. The overall method starts by computing the fiber bundle centerline between two user-defined regions of interests (ROIs). This centerline determines the planes that are used for creating a directed graph. Then, the mincut of the graph is calculated, creating an optimal boundary of the fiber bundle. © 2010 IEEE.

关键词： Diffusion tensor imaging

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Computational diffusion MRI: MICCAI workshop, Munich, Germany,october 9th, 2015

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Workshop on Computational Diffusion MRI, MICCAI 2015

作者： Fuster, Andrea Ghosh, Aurobrata Kaden, Enrico Rathi, Yogesh Reisert, Marco Dept. of Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands Centre for Medical Image Computing University College London London United Kingdom Brigham and Women’s Hospital Harvard Medical School BostonMA United States Department of Radiology University Medical Center Freiburg Germany

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Blockchain-based federated learning with checksums to increase security in Internet of Things solutions

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Journal of Ambient Intelligence and Humanized computing 2023年第5期14卷 4685-4694页

作者： Prokop, Katarzyna Polap, Dawid Srivastava, Gautam Lin, Jerry Chun-Wei Faculty of Applied Mathematics Silesian University of Technology Kaszubska 23 Gliwice44-100 Poland Dept. of Math and Computer Science Brandon University BrandonMBR7A 6A9 Canada Research Centre for Interneural Computing China Medical University Taichung Taiwan Western Norway University of Applied Sciences Bergen Norway Dept. of Computer Science and Math Lebanese American University Beirut1102 Lebanon

Federated learning is becoming a practical solution for machine learning (ML) in industry. This is due to the possibility of implementing artificial intelligence (AI) systems and training its models on private data sets. However, this is not an ideal solution as it is possible to manipulate or even intercept the model during its transmission between the server and workers. In this paper, we propose a solution to ensure the security of the model transmitted between units in FL. The given model is encrypted with AES, DES, RSA algorithms, then a checksum is determined. This checksum with a private key is stored as a transaction on a blockchain. In the case of sending the model and its modification, the recipient can easily verify whether it is correct. The proposed solution has been described, tested, and compared to indicate its advantages and disadvantages. Conducted experiments were based on analyzing the communication time between participants, the accuracy of machine learning models, and attack detection. In terms of attack detection on the blockchain, we reached 81% thanks to the checksum mechanism. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Blockchain

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Computational Diffusion MRI and Brain Connectivity 1

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丛书名： Mathematics and Visualization

1000年

作者： Thomas Schultz Gemma Nedjati-Gilani Eleftheria Panagiotaki Archana Venkataraman Lauren O'Donnell

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A generative model of realistic brain cells with application to numerical simulation of diffusionweighted MR signal

arXiv

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arXiv 2018年

作者： Palombo, Marco Alexander, Daniel C. Zhang, Hui Centre for Medical Image Computing and Dept of Computer Science University College London London United Kingdom

In this work, we introduce a novel computational framework that we developed to use numerical simulations to investigate the complexity of brain tissue at a microscopic level with a detail never realised before. Directly inspired by the advances in computational neuroscience for modelling brain cells, we propose a generative model that enables us to simulate molecular diffusion within realistic digitalised brain cells, such as neurons and glia, in a completely controlled and flexible fashion. We validate our new approach by showing an excellent match between the morphology and simulated DWMR signal of the generated digital model of brain cells and those of digital reconstruction of real brain cells from available open-access databases. We demonstrate the versatility and potentiality of the framework by showing a select set of examples of relevance for the DW-MR community. Further development is ongoing, which will support even more realistic conditions like dense packing of numerous 3D complex cell structures and varying cell surface permeability. Copyright © 2018, The Authors. All rights reserved.

关键词： Cell membranes

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DEVELOPING UNDEVELOPABLE SURFACES USING PARTICLE SYSTEMS

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IFAC Proceedings Volumes 2007年第1期40卷 286-291页

作者： Florian Weiler Jan Plath MeVis Research Center for Medical Image Computing Bremen Germany formerly Universität Bremen Dept. of Mathematics/Computer Science Bremen Germany Universität Bremen Dept. of Mathematics/Computer Science Bremen Germany

关键词： computer-aided design computer-aided manufacturing Computational Geometry Process-automation

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Incomplete nutation diffusion imaging: An ultrafast, single-scan approach for diffusion mapping

arXiv

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arXiv 2019年

作者： Ianuş, Andrada Shemesh, Noam Champalimaud Neuroscience Programme Champalimaud Centre for the Unknown Lisbon Portugal Centre for Medical Image Computing Dept. of Computer Science University College London London United Kingdom

Purpose: Diffusion Magnetic Resonance Imaging (dMRI) is confounded by its long acquisition duration, thereby thwarting the detection of rapid microstructural changes, especially when diffusivity variations are accompanied by rapid changes in T2. The purpose of the present study is to accelerate dMRI to a single scan acquisition, and to enable a more accurate estimation of diffusivity as function of time. Methods: A general methodology termed Incomplete Initial Nutation Diffusion Imaging (INDI) capturing two diffusion contrasts in a single shot, is presented. INDI creates a longitudinal magnetization reservoir that facilitates the successive acquisition of two images separated by only a few milliseconds. INDI's theory is presented, followed by proof-of-concept ex- and in-vivo experiments at 16.4 T and 9.4 T. Results: Mean diffusivities (MDs) extracted from INDI were comparable with Diffusion Tensor Imaging (DTI) and the two-shot IDE in the water phantom. As expected in the brain tissues, DTI provided lower MD than UF-IDE and IDE, but IDE and UF-IDE were in excellent agreement. Simulations are presented for identifying the regimes where INDI is most beneficial. Conclusions: INDI accelerates dMRI acquisition to single-shot mode, which can be of great importance for mapping dynamic microstructural properties in-vivo without T2 bias. Copyright © 2019, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Computational Diffusion MRI 1

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丛书名： Mathematics and Visualization

1000年

作者： Andrea Fuster Aurobrata Ghosh Enrico Kaden Yogesh Rathi Marco Reisert

ISBN: (数字)9783319285887

ISBN: (纸本)9783319285863;9783319803814

Over the last decade interest in diffusion MRI has exploded. The technique provides unique insights into the microstructure of living tissue and enables in-vivo connectivity mapping of the brain. Computational techniques are key to the continued success and development of diffusion MRI and to its widespread transfer into clinical practice. New processing methods are essential for addressing issues at each stage of the diffusion MRI pipeline: acquisition, reconstruction, modeling and model fitting, image processing, fiber tracking, connectivity mapping, visualization, group studies and inference.

关键词： Visualization Computational Biology/Bioinformatics Computational science and Engineering Simulation and Modeling image Processing and computer Vision Statistics for Life sciences, Medicine, Health sciences

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