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RAIDER: Rapid, anatomy-independent, deep learning-based PDFF and R*2 estimation using magnitude-only signals

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arXiv 2024年

作者： Bray, Timothy J.P. Minore, Giulio V. Bainbridge, Alan Dwyer-Hemmings, Louis Taylor, Stuart A. Hall-Craggs, Margaret A. Zhang, Hui Centre for Medical Imaging University College London London United Kingdom Department of Imaging University College London Hospital London United Kingdom Department of Computer Science Centre for Medical Image Computing University College London London United Kingdom Department of Medical Physics University College London Hospital London United Kingdom

Purpose: There has been substantial recent interest in magnitude-based fitting methods for estimating proton density fat fraction (PDFF) and R*2 from chemical shift-encoded MRI data, since these methods can still be used when complexbased methods fail or when phase data are inaccessible or unreliable, and may also be used as a final processing step with complex-based methods. However, conventional fitting techniques are computationally expensive. Deep learning (DL)-based methods promise to accelerate parameter estimation, but previous attempts have used convolutional neural networks (CNNs), which are limited by training requirements and poor generalizability ('anatomy-dependence'). To address these limitations, we propose RAIDER, a voxelwise method for rapid, anatomy-independent deep learning-based PDFF and R*2 estimation using multiecho magnitude-data. Theory and Methods: RAIDER uses two multilayer perceptrons, each trained separately with simulated single-voxel multi-echo magnitude signals, to estimate PDFF and R*2 . The use of two networks, each with restricted training distribution, solves the problem of degeneracy during training. During inference, the solution from one of the two networks is chosen based on likelihood. Performance and speed are investigated in a series of simulation experiments, in phantoms and in vivo. Results: RAIDER is 285-1450 times faster than conventional magnitude fitting, taking 1.4-2.3s per slice rather than 8-56minutes, and offers performance similar to conventional fitting. It produces accurate PDFF measurements in phantoms and in vivo images with different anatomies, despite having been trained only on simulation data. Conclusion: RAIDER can substantially accelerate magnitude-based PDFF and R*2 estimation, whilst avoiding intrinsic limitations of CNN-based methods. © 2024, CC BY-NC-ND.

关键词： Chemical shift

Automating Theory of Mind Assessment with a LLaMA-3-Powered Chatbot: Enhancing Faux Pas Detection in Autism 14

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Automating Theory of Mind Assessment with a LLaMA-3-Powered ...

14th International Conference on computer and Knowledge Engineering, ICCKE 2024

作者： Fallah, Avisa Nazari, Mohammad Ali Keramati, Ali Mirfazeli, Fatemeh Sadat School of Mathematics Statistics and Computer Science University of Tehran Tehran Iran National Brain Centre Iran University of Medical Sciences Tehran Iran Department of Neuroscience Advanced Technologies in Medicine Iran University of Medical Sciences Image Brain Institute Tehran Iran School of Electrical and Computer Engineering University of Tehran Tehran Iran Mental Health Research Center Department of Psychiatry School of Medicine Psychosocial Health Research Institute Advanced Technologies in Medicine National Brain Centre Iran University of Medical Sciences Tehran Iran

ISBN: (纸本)9798331511272

The Theory of Mind (ToM) is critical for social interactions, enabling individuals to understand and predict others’ mental states. Deficits in ToM, particularly in detecting faux pas, are common among individuals with Autism Spectrum Disorder (ASD). Traditional methods for assessing ToM, such as the Faux Pas Recognition Test (FPRT), require significant human involvement, which can limit accessibility and scalability. This study aims to explore how large language model (LLM)powered chatbots, specifically utilizing the LLaMA-3 model, can enhance the assessment of ToM by automating and improving the administration of the FPRT. The methodology involved creating a dual-phase process where the chatbot first assesses user responses and then provides rehabilitative guidance to correct misunderstandings. Additionally, the system employs a scoring mechanism combining automated evaluation with human oversight to ensure accuracy and consistency, with system prompts crafted based on chain-of-thought prompting to enhance the depth and reliability of the assessments. Our findings demonstrate the effectiveness of LLM-powered chatbots in delivering dynamic and supportive ToM assessments, suggesting its potential for broader application for both artificial intelligence and psychological practices. ©2024 IEEE.

关键词： Chain-of-thought Faux Pas Recognition Test Interactive Chatbot Language Models Theory of Mind

Tackling Hallucination from Conditional Models for medical image Reconstruction with DynamicDPS

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arXiv 2025年

作者： Kim, Seunghoi Tregidgo, Henry F.J. Figini, Matteo Jin, Chen Joshi, Sarang Alexander, Daniel C. Centre for Medical Image Computing University College London United Kingdom Department of Medical Physics and Biomedical Engineering University College London United Kingdom Department of Computer Science University College London United Kingdom The University of Utah United States Centre for AI DS&AI AstraZeneca United Kingdom

Hallucinations are spurious structures not present in the ground truth, posing a critical challenge in medical image reconstruction, especially for data-driven conditional models. We hypothesize that combining an unconditional diffusion model with data consistency, trained on a diverse dataset, can reduce these hallucinations. Based on this, we propose DynamicDPS, a diffusion-based framework that integrates conditional and unconditional diffusion models to enhance low-quality medical images while systematically reducing hallucinations. Our approach first generates an initial reconstruction using a conditional model, then refines it with an adaptive diffusion-based inverse problem solver. DynamicDPS skips early stage in the reverse process by selecting an optimal starting time point per sample and applies Wolfe’s line search for adaptive step sizes, improving both efficiency and image fidelity. Using diffusion priors and data consistency, our method effectively reduces hallucinations from any conditional model output. We validate its effectiveness in image Quality Transfer for low-field MRI enhancement. Extensive evaluations on synthetic and real MR scans, including a downstream task for tissue volume estimation, show that DynamicDPS reduces hallucinations, improving relative volume estimation by over 15% for critical tissues while using only 5% of the sampling steps required by baseline diffusion models. As a model-agnostic and fine-tuning-free approach, DynamicDPS offers a robust solution for hallucination reduction in medical imaging. The code will be made publicly available upon publication. Copyright © 2025, The Authors. All rights reserved.

关键词： Inverse problems

Pre-Processing Investigation for Brain Abnormality Detection and Analysis Through MRI of Brain 1

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Pre-Processing Investigation for Brain Abnormality Detection...

1st IEEE International Conference on Innovations in High Speed Communication and Signal Processing, IHCSP 2023

作者： Srivastava, Satyajee Arfat, Mohd. Pachar, Sunita Krishna Yellapragada, Rama Jyotiyana, Priya School of Computing University of Engineering and Technology Uttarakhand Roorkee247667 India Jamia Hamdard Medical Imaging Technology Department of Para Medical Sciences New Delhi India GLA UNIVERSITY IBM Department Mathura India Koneru Lakshmaiah Education Foundation Green Fields Department of Computer Science and Engineering Guntur District A.P India Jaipur Engineering College and Research Centre Computer Science and Engineering India

ISBN: (纸本)9798350345957

Both men and women face equal risk from conditions such brain tumours, strokes, blood clots, and multiple sclerosis (MS). The most frequent and prevalent of these brain illnesses is a brain tumour. The prognosis and therapy for a brain lesion depend on how quickly and precisely it is diagnosed. However, neuroradiology experts are the only ones qualified to make the diagnosis, as it is extremely difficult. Each medical report from an imaging investigation must be reviewed and confirmed by at least two physicians. If there's a problem, it could mean a biopsy or even surgery. This study describes a fully automated procedure for removing artefacts and the skull from magnetic resonance (MR) pictures of the brain by utilising computational geometry, wavelet decomposition, and thresholding. The intermediate step of correct preprocessing of MR images is thresholding using the standard deviation method. For various MR images, the suggested preprocessing approach achieves a high degree of accuracy with a small error rate. This preliminary processing is employed to lessen the likelihood of erroneous detection. As a result, the accuracy with which diseases are diagnosed using brain MRI by a computer system is improved. © 2023 IEEE.

关键词： Magnetic resonance

Adaptive particle swarm optimization model for resource leveling

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Evolving Systems 2023年第4期14卷 593-604页

作者： Lin, Jerry Chun-Wei Lv, Qing Yu, Dehu Srivastava, Gautam Chen, Chun-Hao School of Information and Control Engineering Qingdao University of Technology Qingdao China Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway School of Civil Engineering Qingdao University of Technology Qingdao China School of Civil Engineering Shandong Jianzhu University Jinan China Department of Math and Computer Science Brandon University Brandon Canada Research Centre for Interneural Computing China Medical University Taichung Taiwan Department of Information and Finance Management National Taipei University of Technology Taipei Taiwan

In the process of engineering project construction, the balanced allocation of resources has an important impact on the purchase of actual materials, the progress of the site construction and the arrangement of temporary facilities. Although there have been many studies on the use of evolutionary computation (EC) to solve the fixed duration of the resource-leveling problem, the optimization effect of standard EC to solve the resource-balancing problem in large-scale network design planning is reduced due to the phenomenon of premature convergence of PSO and the complexity of large-scale network planning. Therefore, in this paper, an adaptive dissipative particle swarm optimization (ADPSO) algorithm is used to solve the resource balancing optimization problem for different network-plans scales, and the feasibility of the developed model is verified using four different scaled network plans (databases). At the same time, a solution to the dynamic resource-leveling problem with optimization for a fixed duration is proposed. The results show that the proposed models are suitable for solving the problem of resource-leveling with a fixed duration of optimization of different scale network plans, and their accuracy and stability are significantly improved compared to the state-of-the-art approaches. Moreover, the proposed models are accurate and suitable for combining the calculation method of network planning time parameters with the ADPSO algorithm to realize dynamic resource balancing optimization. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Particle swarm optimization (PSO)

Bayesian inference of a new Mallows model for characterising symptom sequences applied in primary progressive aphasia

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arXiv 2023年

作者： Taylor, Beatrice Shand, Cameron Hardy, Chris J.D. Oxtoby, Neil P. UCL Centre for Medical Image Computing Department of Computer Science University College London United Kingdom UCL Queen Square Institute of Neurology Dementia Research Centre University College London United Kingdom

Machine learning models offer the potential to understand diverse datasets in a data-driven way, powering insights into individual disease experiences and ensuring equitable healthcare. In this study, we explore Bayesian inference for characterising symptom sequences, and the associated modelling challenges. We adapted the Mallows model to account for partial rankings and right-censored data, employing custom MCMC fitting. Our evaluation, encompassing synthetic data and a primary progressive aphasia dataset, highlights the model's efficacy in revealing mean orderings and estimating ranking variance. This holds the potential to enhance clinical comprehension of symptom occurrence. However, our work encounters limitations concerning model scalability and smalldataset sizes. © 2023, CC BY.

关键词： Inference engines

Enhancing Spatiotemporal Disease Progression Models via Latent Diffusion and Prior Knowledge

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arXiv 2024年

作者： Puglisi, Lemuel Alexander, Daniel C. Ravì, Daniele Dept. of Math and Computer Science University of Catania Italy Centre for Medical Image Computing University College London United Kingdom School of Physics Engineering and CS University of Hertfordshire United Kingdom

In this work, we introduce Brain Latent Progression (BrLP), a novel spatiotemporal disease progression model based on latent diffusion. BrLP is designed to predict the evolution of diseases at the individual level on 3D brain MRIs. Existing deep generative models developed for this task are primarily data-driven and face challenges in learning disease progressions. BrLP addresses these challenges by incorporating prior knowledge from disease models to enhance the accuracy of predictions. To implement this, we propose to integrate an auxiliary model that infers volumetric changes in various brain regions. Additionally, we introduce Latent Average Stabilization (LAS), a novel technique to improve spatiotemporal consistency of the predicted progression. BrLP is trained and evaluated on a large dataset comprising 11,730 T1-weighted brain MRIs from 2,805 subjects, collected from three publicly available, longitudinal Alzheimer’s Disease (AD) studies. In our experiments, we compare the MRI scans generated by BrLP with the actual follow-up MRIs available from the subjects, in both cross-sectional and longitudinal settings. BrLP demonstrates significant improvements over existing methods, with an increase of 22% in volumetric accuracy across AD-related brain regions and 43% in image similarity to the ground-truth scans. The ability of BrLP to generate conditioned 3D scans at the subject level, along with the novelty of integrating prior knowledge to enhance accuracy, represents a significant advancement in disease progression modeling, opening new avenues for precision medicine. The code of BrLP is available at the following link: https://***/LemuelPuglisi/BrLP. © 2024, CC BY.

关键词： Brain

Enhancing Multi-Label ECG Classification via Task-Guided Lead Correlations in Internet of medical Things

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IEEE Internet of Things Journal 2025年第12期12卷 20544-20555页

作者： Yuan, Xiaoyan Wang, Wei Chen, Junxin Fang, Kai Bashir, Ali Kashif Mondal, Tapas Hu, Xiping Deen, M. Jamal Beijing Institute of Technology School of Medical Technology Beijing100081 China Shenzhen MSU-BIT University Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Artificial Intelligence Research Institute Guangdong Shenzhen518172 China Dalian University of Technology School of Software Dalian116621 China Zhejiang A and F University College of Mathematics and Computer Science Hangzhou311300 China Manchester Metropolitan University Department of Computing and Mathematics United Kingdom Chitkara University Institute of Engineering and Technology Centre for Research Impact and Outcome Chitkara University Punjab Rajpura140401 India McMaster University Faculty of Health Science and the Department of Pediatrics Hamilton Canada McMaster University McMaster School of Biomedical Engineering Hamilton Canada McMaster University Electrical and Computer Engineering Department Hamilton Canada

With the rise of the Internet of Things (IoT), wearable devices have enabled real-time health monitoring, particularly through physiological signals like electrocardiograms (ECG). The standard 12-lead ECG records the electrical activity of the heart from multiple perspectives, providing valuable insights into cardiac health. However, existing 12-lead ECG analysis methods often treat leads as channel-level arrangements or rely on spatial adjacency to predefine lead connections, limiting their ability to capture the complex spatial and functional relationships between leads fully. To address this limitation, we propose TGLLNet, a task-driven model that automatically learns inter-lead relationships to improve multi-label ECG classification. TGLLNet adaptively learns lead connectivity patterns and relational strengths, enhancing ECG representation and improving model generalizability across tasks. Specifically, TGLLNet employs a Temporal Graph Construction (ETGC) module to convert ECG signals into temporal graphs and uses a Residual Pyramid Graph Convolution (RPG) module for multi-level graph embeddings, utilizing a Graph Convolutional Network (GCN) with independently learnable adjacency matrices. Combined with a Temporal Context Convolution (TCC) module, TGLLNet captures spatio-temporal dependencies, significantly improving ECG representation. Experimental results on 7 tasks from PTB-XL and CPSC2018 datasets demonstrate that TGLLNet outperforms existing methods, showing superior generalizability across different tasks. Our code is available at https://***/rosemary333/TGLLnet. © 2014 IEEE.

关键词： Graph embeddings

Decoupling semantic similarity from spatial alignment for neural networks 24

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Decoupling semantic similarity from spatial alignment for ne...

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Tassilo Wald Constantin Ulrich Gregor Köhler David Zimmerer Stefan Denner Michael Baumgartner Fabian Isensee Priyank Jaini Klaus H. Maier-Hein Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany and Helmholtz Imaging DKFZ Heidelberg Germany and Faculty of Mathematics and Computer Science University of Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany and Medical Faculty Heidelberg University of Heidelberg Germany and National Center for Tumor Diseases (NCT) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany and Faculty of Mathematics and Computer Science University of Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany and Helmholtz Imaging DKFZ Heidelberg Germany Google Deepmind Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany and Helmholtz Imaging DKFZ Heidelberg Germany and Faculty of Mathematics and Computer Science University of Heidelberg Germany and Medical Faculty Heidelberg University of Heidelberg Germany and Pattern Analysis and Learning Group Department of Radiation Oncology

ISBN: (纸本)9798331314385

What representation do deep neural networks learn? How similar are images to each other for neural networks? Despite the overwhelming success of deep learning methods key questions about their internal workings still remain largely unanswered, due to their internal high dimensionality and complexity. To address this, one approach is to measure the similarity of activation responses to various inputs. Representational Similarity Matrices (RSMs) distill this similarity into scalar values for each input pair. These matrices encapsulate the entire similarity structure of a system, indicating which input leads to similar responses. While the similarity between images is ambiguous, we argue that the spatial location of semantic objects does neither influence human perception nor deep learning classifiers. Thus this should be reflected in the definition of similarity between image responses for computer vision systems. Revisiting the established similarity calculations for RSMs we expose their sensitivity to spatial alignment. In this paper, we propose to solve this through semantic RSMs, which are invariant to spatial permutation. We measure semantic similarity between input responses by formulating it as a set-matching problem. Further, we quantify the superiority of semantic RSMs over spatio-semantic RSMs through image retrieval and by comparing the similarity between representations to the similarity between predicted class probabilities.

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