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Novel data-driven subtypes and stages of brain atrophy in the ALS-FTD spectrum

Translational Neurodegeneration 2023年第1期12卷 50-69页

作者： Ting Shen Jacob W.Vogel Jeffrey Duda Jeffrey S.Phillips Philip ACook James Gee Lauren Elman Colin Quinn Defne A.Amado Michael Baer Lauren Massimo Murray Grossman David J.Irwin Corey T.McMillan Penn Frontotemporal Degeneration Center Department of NeurologyPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPA 19104USA Department of Clinical Sciences SciLifeLabLund University22242 LundSweden Penn Image Computing and Science Lab(PICSL) Department of RadiologyPerelman School of MedicineUniversity of Penn-sylvaniaPhiladelphiaPA 19104USA Department of Neurology Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPA 19104USA Digital Neuropathology Laboratory Department of NeurologyPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPA 19104USA.

Background TDP-43 proteinopathies represent a spectrum of neurological disorders,anchored clinically on either end by amyotrophic lateral sclerosis(ALS)and frontotemporal degeneration(FTD).The ALS-FTD spectrum exhibits a diverse range of clinical presentations with overlapping phenotypes,highlighting its *** study was aimed to use disease progression modeling to identify novel data-driven spatial and temporal subtypes of brain atrophy and its progression in the ALS-FTD *** We used a data-driven procedure to identify 13 anatomic clusters of brain volume for 57 behavioral variant FTD(bvFTD;with either autopsy-confirmed TDP-43 or TDP-43 proteinopathy-associated genetic variants),103 ALS,and 47 ALS-FTD patients with likely TDP-43.A Subtype and Stage Inference(SuStaIn)model was trained to identify subtypes of individuals along the ALS-FTD spectrum with distinct brain atrophy patterns,and we related subtypes and stages to clinical,genetic,and neuropathological features of *** SuStaIn identified three novel subtypes:two disease subtypes with predominant brain atrophy in either prefrontal/somatomotor regions or limbic-related regions,and a normal-appearing group without obvious brain *** limbic-predominant subtype tended to present with more impaired cognition,higher frequencies of pathogenic variants in TBK1 and TARDBP genes,and a higher proportion of TDP-43 types B,E and *** contrast,the prefrontal/somatomotor-predominant subtype had higher frequencies of pathogenic variants in C9orf72 and GRN genes and higher proportion of TDP-43 type *** normal-appearing brain group showed higher frequency of ALS relative to ALS-FTD and bvFTD patients,higher cognitive capacity,higher proportion of lower motor neuron onset,milder motor symptoms,and lower frequencies of genetic pathogenic *** overall SuStaIn stages also correlated with evidence for clinical progression including longer disease duration,higher King’s stage,and cog

关键词： Amyotrophic lateral sclerosis Frontotemporal degeneration Disease heterogeneity SuStaIn model

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4D foetal cardiac ultrasound image detection based on deep learning with weakly supervised localisation for rapid diagnosis of evolving hypoplastic left heart syndrome

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1485-1499页

作者： Gang Wang Weisheng Li Mingliang Zhou Haobo Zhu Guang Yang Choon Hwai Yap Chongqing Key Laborotory of Image Rocognition Chongqing University of Posts and TelecommunicationsChongqingChina School of Computing and Data Engineering NingboTech UniversityNingboChina Department of Bioengineering Imperial College LondonLondonUK Key Laboratory of Cyberspace Big Data Intelligent Security(Chongqing University of Posts and Telecommunications) Ministry of EducationChongqingChina School of Computer Science Chongqing UniversityChongqingChina University of Oxford OxfordUK Cardiovascular Research Centre Royal Brompton HospitalLondonUK National Heart and Lung Institute Imperial College LondonLondonUK

Hypoplastic left heart syndrome(HLHS)is a rare,complex,and incredibly foetal congenital heart *** decrease neonatal mortality,evolving HLHS(eHLHS)in pregnant women should be critically diagnosed as soon as ***,diagnosis is currently heavily dependent on skilled medical professionals using foetal cardiac ultrasound images,making it difficult to rapidly and easily examine for this ***,the authors propose a cost-effective deep learning framework for rapid diagnosis of eHLHS(RDeH),which we have named ***,the framework implements a coarseto-fine two-stage detection approach,with a structure classification network for 4D human foetal cardiac ultrasound images from various spatial and temporal domains,and a fine detection module with weakly-supervised localisation for high-precision nidus localisation and physician *** experiments extensively compare the authors’network with other state-of-the-art methods on a 4D human foetal cardiac ultrasound image dataset and show two main benefits:(1)it achieved superior average accuracy of 99.37%on three categories of foetal ultrasound images from different cases;(2)it demonstrates visually fine detection performance with weakly supervised *** framework could be used to accelerate the diagnosis of eHLHS,and hence significantly lessen reliance on experienced medical physicians.

关键词： 4D deep learning fetal cardiac ultrasound hypoplastic left heart syndrome weakly-supervised learning

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Neural Ordinary Differential Equation based Sequential image Registration for Dynamic Characterization

arXiv

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arXiv 2024年

作者： Wu, Yifan Dong, Mengjin Jena, Rohit Qin, Chen Gee, James C. Penn Image Computing and Science Laboratory University of Pennsylvania PhiladelphiaPA19104 United States Department of Electrical and Electronic Engineering and I-X Imperial College London United Kingdom

Deformable image registration (DIR) is crucial in medical image analysis, enabling the exploration of biological dynamics such as organ motions and longitudinal changes in imaging. Leveraging Neural Ordinary Differential Equations (ODE) for registration, this extension work discusses how this framework can aid in the characterization of sequential biological processes. Utilizing the Neural ODE’s ability to model state derivatives with neural networks, our Neural Ordinary Differential Equation Optimization-based (NODEO) framework considers voxels as particles within a dynamic system, defining deformation fields through the integration of neural differential equations. This method learns dynamics directly from data, bypassing the need for physical priors, making it exceptionally suitable for medical scenarios where such priors are unavailable or inapplicable. Consequently, the framework can discern underlying dynamics and use sequence data to regularize the transformation trajectory. We evaluated our framework on two clinical datasets: one for cardiac motion tracking and another for longitudinal brain MRI analysis. Demonstrating its efficacy in both 2D and 3D imaging scenarios, our framework offers flexibility and model agnosticism, capable of managing image sequences and facilitating label propagation throughout these sequences. This study provides a comprehensive understanding of how the Neural ODE-based framework uniquely benefits the image registration challenge. Copyright © 2024, The Authors. All rights reserved.

关键词： Metadata

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Automated deep learning segmentation of high-resolution 7 T postmortem MRI for quantitative analysis of structure-pathology correlations in neurodegenerative diseases

arXiv

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arXiv 2023年

作者： Khandelwal, Pulkit Duong, Michael Tran Sadaghiani, Shokufeh Lim, Sydney Denning, Amanda Chung, Eunice Ravikumar, Sadhana Arezoumandan, Sanaz Peterson, Claire Bedard, Madigan Capp, Noah Ittyerah, Ranjit Migdal, Elyse Choi, Grace Kopp, Emily Loja, Bridget Hasan, Eusha Li, Jiacheng Bahena, Alejandra Prabhakaran, Karthik Mizsei, Gabor Gabrielyan, Marianna Schuck, Theresa Trotman, Winifred Robinson, John Ohm, Daniel T. Lee, Edward B. Trojanowski, John Q. McMillan, Corey Grossman, Murray Irwin, David J. Detre, John A. Dylan Tisdall, M. Das, Sandhitsu R. Wisse, Laura E.M. Wolk, David A. Yushkevich, Paul A. Department of Bioengineering University of Pennsylvania Philadelphia United States Penn Image Computing and Science Laboratory University of Pennsylvania Philadelphia United States Department of Neurology University of Pennsylvania Philadelphia United States Department of Radiology University of Pennsylvania Philadelphia United States Department of Pathology and Laboratory Medicine University of Pennsylvania PhiladelphiaPA United States Department of Diagnostic Radiology Lund University Lund Sweden

Postmortem MRI allows brain anatomy to be examined at high resolution and to link pathology measures with morphometric measurements. However, automated segmentation methods for brain mapping in postmortem MRI are not well developed, primarily due to limited availability of labeled datasets, and heterogeneity in scanner hardware and acquisition protocols. In this work, we present a high resolution of 135 postmortem human brain tissue specimens imaged at 0.3 mm3 isotropic using a T2w sequence on a 7T whole-body MRI scanner. We developed a deep learning pipeline to segment the cortical mantle by benchmarking the performance of nine deep neural architectures, followed by post-hoc topological correction. We then segment four subcortical structures (caudate, putamen, globus pallidus, and thalamus), white matter hyperintensities, and the normal appearing white matter. We show generalizing capabilities across whole brain hemispheres in different specimens, and also on unseen images acquired at 0.28 mm3 and 0.16 mm3 isotropic T2*w FLASH sequence at 7T. We then compute localized cortical thickness and volumetric measurements across key regions, and link them with semi-quantitative neuropathological ratings. Our code, Jupyter notebooks, and the containerized executables are publicly available at the project webpage. © 2023, CC BY-NC-SA.

关键词： image segmentation

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Mutually-Constrained Cross-Sectional and Longitudinal Non-Negative Matrix Factorization: Application to Modeling Brain Aging Trajectories

Mutually-Constrained Cross-Sectional and Longitudinal Non-Ne...

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IEEE International Symposium on Biomedical Imaging

作者： Ioanna Skampardoni Junhao Wen Erus Guray Haochang Shou Konstantina Nikita Christos Davatzikos Centre for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Laboratory of AI and Biomedical Science (LABS) Stevens Neuroimaging and Informatics Institute Keck School of Medicine of USC University of Southern California Los Angeles CA USA Department of Biostatistics Epidemiology & Informatics Penn Statistics in Imaging and Visualization Center University of Pennsylvania Philadelphia PA USA

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Brain aging is a multifaceted and highly heterogeneous process accompanied by several pathologies. Here, we propose a method for dissecting the heterogeneity of neuropathologic processes occurring with aging using machine learning and leveraging information from cross-sectional and longitudinal data. Specifically, we hypothesize that the heterogeneity observed in brain aging can be captured by a set of patterns consistent with longitudinal trajectories of brain change, the latter directly capturing evolving neuropathologic processes on an individual basis. Applying the method to structural magnetic resonance imaging data from the BLSA study, we derived five distinct, reproducible, and clinically informative components of neuroanatomical brain change, highlighting the method’s potential as a tool for precision medicine.

关键词： Pathology Magnetic resonance imaging Precision medicine Sociology Aging Hazards Trajectory

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Domain Generalizer: A few-shot meta learning framework for domain generalization in medical imaging

arXiv

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arXiv 2020年

作者： Khandelwal, Pulkit Yushkevich, Paul Department of Bioengineering University of Pennsylvania PhiladelphiaPA United States Penn Image Computing and Science Laboratory Department of Radiology University of Pennsylvania PhiladelphiaPA United States

Deep learning models perform best when tested on target (test) data domains whose distribution is similar to the set of source (train) domains. However, model generalization can be hindered when there is significant difference in the underlying statistics between the target and source domains. In this work, we adapt a domain generalization method based on a model-agnostic meta-learning framework [1] to biomedical imaging. The method learns a domain-agnostic feature representation to improve generalization of models to the unseen test distribution. The method can be used for any imaging task, as it does not depend on the underlying model architecture. We validate the approach through a computed tomography (CT) vertebrae segmentation task across healthy and pathological cases on three datasets. Next, we employ few-shot learning, i.e. training the generalized model using very few examples from the unseen domain, to quickly adapt the model to new unseen data distribution. Our results suggest that the method could help generalize models across different medical centers, image acquisition protocols, anatomies, different regions in a given scan, healthy and diseased populations across varied imaging modalities. Copyright © 2020, The Authors. All rights reserved.

关键词： Computerized tomography

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Enhanced generative adversarial network for 3D brain MRI super-resolution

Enhanced generative adversarial network for 3D brain MRI sup...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Jiancong Wang Yuhua Chen Yifan Wu Jianbo Shi James Gee Penn Image Computing and Science Laboratory University of Pennsylvania Philadelphia PA USA Department of Bioengineering University of California Los Angeles CA USA Department of Computer and Information Science University of Pennsylvania Philadelphia PA USA

ISBN: (数字)9781728165530

ISBN: (纸本)9781728165547

Single image super-resolution (SISR) reconstruction for magnetic resonance imaging (MRI) has generated significant interest because of its potential to not only speed up imaging but to improve quantitative processing and analysis of available image data. Generative Adversarial Networks (GAN) have proven to perform well in image recovery tasks. In this work, we followed the GAN framework and developed a generator coupled with discriminator to tackle the task of 3D SISR on T1 brain MRI images. We developed a novel 3D memory-efficient residual-dense block generator (MRDG) that achieves state-of-the-art performance in terms of SSIM (Structural Similarity), PSNR (Peak Signal to Noise Ratio) and NRMSE (Normalized Root Mean Squared Error) metrics. We also designed a pyramid pooling discriminator (PPD) to recover details on different size scales simultaneously. Finally, we introduced model blending, a simple and computational efficient method to balance between image and texture quality in the final output, to the task of SISR on 3D images.

关键词： Three-dimensional displays Gallium nitride Training Generative adversarial networks Spatial resolution Magnetic resonance imaging

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tau-neurodegeneration mismatch from inter-modality image translation using deep learning

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Alzheimer's & Dementia 2023年第S10期19卷

作者： Xueying Lyu Pulkit Khandelwal Michael Tran Duong Jiancong Wang Long Xie Laura EM Wisse Paul A. Yushkevich Sandhitsu R. Das David A. Wolk University of Pennsylvania Philadelphia PA USA Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA Department of Diagnostic Radiology Clinical Sciences Lund University Lund Sweden

Background The relationship between tau neurofibrillary tangles (T) and neurodegeneration (N) may offer clues to potential presence of comorbidities, as well as relative resilience and vulnerability to Alzheimer’s disease (AD) pathology. We have previously developed measures of tau PET (T) and cortical thickness (N) mismatch based on linear model residuals. However, the underlying relationships between T and N are likely complex and non-linear. Moreover, predicting cortical thickness based on local tau level may miss non-local contributions of tauopathy. Here we investigate T-N mismatch using a deep-learning 3D image translation neural network to estimate synthetic maps of cortical thickness based on tau PET images. Deviation between the synthetic and actual cortical thickness serves as a metric of T-N mismatch. Method We derived 3D cortical thickness from T1-MRI and SUVR from 18F-flortaucipir tracer uptake to represent N and T maps respectively. We predicted cortical thickness maps from tau SUVR by training a 3D U-Net (Ronneberger et al. 2015) to learn the relationship between paired cortical tau SUVR and thickness maps from 70 symptomatic patients from ADNI. Approximately 70% of the training set was A+. We used regional standardized mean absolute error between predicted and actual thickness across 104 bilateral gray matter regions of interest as T-N mismatch for clustering in a separate independent sample of 194 A+ symptomatic patients. Result The voxel-wise mean absolute error of thickness translation on 194 tested patients was 0.54 mm. We obtained six T-N data-driven clusters (Figure 2). The group with lowest reconstruction error was defined as canonical – meaning that the degree of neurodegeneration was accurately predicted by tau PET. There were three groups with lower thickness than predicted, which were denoted as temporal-limbic (TL) vulnerable, posterior vulnerable and diffuse vulnerable based on their spatial patterns. Additionally, two groups with greate

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Correction: Baseline structural MRI and plasma biomarkers predict longitudinal structural atrophy and cognitive decline in early Alzheimer's disease

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Alzheimer's research & therapy 2024年第1期16卷 11页

作者： Long Xie Sandhitsu R Das Laura E M Wisse Ranjit Ittyerah Robin de Flores Leslie M Shaw Paul A Yushkevich David A Wolk Penn Image Computing and Science Laboratory (PICSL) Department of Radiology University of Pennsylvania 3700 Hamilton Walk Suite D600 Richards Building 6 Floor Philadelphia PA 19104 USA. Long.Xie@uphs.upenn.edu. Penn Image Computing and Science Laboratory (PICSL) Department of Radiology University of Pennsylvania 3700 Hamilton Walk Suite D600 Richards Building 6 Floor Philadelphia PA 19104 USA. Penn Memory Center University of Pennsylvania Philadelphia PA USA. Department of Diagnostic Radiology Lund University Lund Sweden. Department of Pathology and Laboratory Medicine University of Pennsylvania Philadelphia PA USA.

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Aging and Alzheimer’s Disease Have Dissociable Effects on Local and Regional Medial Temporal Lobe Connectivity

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Alzheimer's & Dementia 2023年第S10期19卷

作者： Stanislau Hrybouski Sandhitsu R. Das Long Xie Laura EM Wisse Melissa Kelley Jacqueline Lane Monica Sherin Michael DiCalogero Ilya M. Nasrallah John A. Detre Paul A. Yushkevich David A. Wolk University of Pennsylvania Philadelphia PA USA Penn Image Computing and Science Laboratory (PICSL) University of Pennsylvania Philadelphia PA USA Lund University Lund Sweden Department of Radiology University of Pennsylvania Philadelphia PA USA

Background The extent to which pathological processes in aging and Alzheimer’s disease (AD) relate to functional disruption of the medial temporal lobe (MTL)-dependent brain networks is poorly understood. To address this knowledge gap, we examined functional connectivity (FC) alterations between anterior and posterior regions of the MTL and in MTL-associated functional communities – the Anterior-Temporal (AT) and Posterior-Medial (PM) networks – in normal agers, individuals with preclinical AD, and patients with Mild Cognitive Impairment or mild dementia due to AD. Method In this cross-sectional study, we analyzed data from 179 individuals from the Aging Brain Cohort study of the penn ADRC. Detailed information about participants is provided in Table 1. For intra-MTL FC comparisons, the MTL subregions were segmented using the automated segmentation of hippocampal subfields-T1 (ASHS-T1) pipeline (Fig. 1a). When modeling the MTL’s interactions with the rest of the cortex, we employed four MTL ROIs (left/right × anterior/posterior) derived from an ex vivo atlas of tau accumulation in the MTL. Our functional datasets were preprocessed using a customized fMRIprep pipeline. Sparse network estimations and modularity-based consensus clustering were used to reconstruct the AT and PM network systems (Fig. 1b). Age effect analyses and group comparisons along the AD continuum were performed using the General Linear Model within the network-based statistical framework. Result The preclinical stage of AD was characterized by increased FC between the perirhinal cortex and other regions of the MTL, as well as between the anterior MTL and its direct neighbors in the AT network (Fig. 1c-d). This effect was not present in symptomatic AD. Instead, symptomatic patients displayed reduced hippocampal and intra-PM connectivity. For normal aging, our results led to three main conclusions (for visuals, see Fig. 2). First, intra-network connectivity of both the AT and PM networks decreases wi

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