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A fast PC algorithm with reversed-order pruning and a parallelization strategy

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arXiv 2021年

作者： Zhang, Kai Tian, Chao Johnson, Todd Zhang, Kun Jiang, Xiaoqian The School of Biomedical Informatics UT Health Science Center at Houston HoustonTX77030 United States The Department of Electrical and Computer Engineering Texas A&M University College StationTX77843 United States The Department of Philosophy Carnegie Mellon University PittsburghPA15213 United States

The PC algorithm is the state-of-the-art algorithm for causal structure discovery on observational data. It can be computationally expensive in the worst case due to the conditional independence tests are performed in an exhaustive-searching manner. This makes the algorithm computationally intractable when the task contains several hundred or thousand nodes, particularly when the true underlying causal graph is dense. We propose a critical observation that the conditional set rendering two nodes independent is non-unique, and including certain redundant nodes do not sacrifice result accuracy. Based on this finding, the innovations of our work are two-folds. First, we innovate on a reserve order linkage pruning PC algorithm which significantly increases the algorithm's efficiency. Second, we propose a parallel computing strategy for statistical independence tests by leveraging tensor computation, which brings further speedup. We also prove the proposed algorithm does not induce statistical power loss under mild graph and data dimensionality assumptions. Experimental results show that the single-threaded version of the proposed algorithm can achieve a 6-fold speedup compared to the PC algorithm on a dense 95-node graph, and the parallel version can make a 825-fold speed-up. We also provide proof that the proposed algorithm is consistent under the same set of conditions with conventional PC algorithm. © 2021, CC BY.

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Scalable quality control on processing of large diffusion-weighted and structural magnetic resonance imaging datasets

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arXiv 2024年

作者： Kim, Michael E. Gao, Chenyu Ramadass, Karthik Kanakaraj, Praitayini Newlin, Nancy R. Rudravaram, Gaurav Schilling, Kurt G. Dewey, Blake E. Bennett, David A. O'Bryant, Sid Barber, Robert C. Archer, Derek Hohman, Timothy J. Bao, Shunxing Li, Zhiyuan Landman, Bennett A. Khairi, Nazirah Mohd Vanderbilt University Department of Computer Science NashvilleTN United States Vanderbilt University Department of Electrical and Computer Engineering NashvilleTN United States Vanderbilt University Medical Center Department of Radiology and Radiological Sciences NashvilleTN United States Department of Neurology Johns Hopkins University School of Medicine BaltimoreMD United States Rush Alzheimer’s Disease Center Rush University Medical Center ChicagoIL United States Institute for Translational Research University of North Texas Health Science Center Fort WorthTX United States Department of Family Medicine University of North Texas Health Science Center Fort WorthTX United States Vanderbilt University Medical Center Vanderbilt Memory and Alzheimer’s Center NashvilleTN United States Vanderbilt University Medical Center Vanderbilt Genetics Institute NashvilleTN United States Vanderbilt University Department of Biomedical Engineering NashvilleTN United States Vanderbilt University Institute of Imaging Science NashvilleTN United States

Proper quality control (QC) is time consuming when working with large-scale medical imaging datasets, yet necessary, as poor-quality data can lead to erroneous conclusions or poorly trained machine learning models. Most efforts to reduce data QC time rely on outlier detection, which cannot capture every instance of algorithm failure. Thus, there is a need to visually inspect every output of data processing pipelines in a scalable manner. We design a QC pipeline that allows for low time cost and effort across a team setting for a large database of diffusion-weighted and structural magnetic resonance images. Our proposed method satisfies the following design criteria: 1.) a consistent way to perform and manage quality control across a team of researchers, 2.) quick visualization of preprocessed data that minimizes the effort and time spent on the QC process without compromising the condition/caliber of the QC, and 3.) a way to aggregate QC results across pipelines and datasets that can be easily shared. In addition to meeting these design criteria, we also provide information on what a "successful" output should be and common occurrences of algorithm failures for various processing pipelines. Our method reduces the time spent on QC by a factor of over 20 when compared to naïvely opening outputs in an image viewer and demonstrate how it can facilitate aggregation and sharing of QC results within a team. While researchers must spend time on robust visual QC of data, there are mechanisms by which the process can be streamlined and efficient. © 2024, CC BY-NC-ND.

关键词： Diffusion tensor imaging

High-resolution agent-based modeling of COVID-19 spreading in a small town

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arXiv 2021年

作者： Truszkowska, Agnieszka Behring, Brandon Hasanyan, Jalil Zino, Lorenzo Butail, Sachit Caroppo, Emanuele Jiang, Zhong-Ping Rizzo, Alessandro Porfiri, Maurizio Department of Mechanical and Aerospace Engineering Department of Biomedical Engineering New York University Tandon School of Engineering BrooklynNY11201 United States Faculty of Science and Engineering University of Groningen Groningen9747 AG Netherlands Department of Mechanical Engineering Northern Illinois University DeKalbIL60115 United States Mental Health Department Local Health Unit ROMA 2 Rome00174 Italy University Research Center He.R.A Università Cattolica del Sacro Cuore Rome00168 Italy Department of Electrical and Computer Engineering New York University Tandon School of Engineering 370 Jay Street BrooklynNY11201 United States Department of Electronics and Telecommunications Politecnico di Torino Turin10129 Italy Office of Innovation New York University Tandon School of Engineering BrooklynNY11201 United States Department of Biomedical Engineering New York University Tandon School of Engineering BrooklynNY11201 United States Center for Urban Science and Progress Tandon School of Engineering New York University 370 Jay Street BrooklynNY11201 United States

Amid the ongoing COVID-19 pandemic, public health authorities and the general population are striving to achieve a balance between safety and normalcy. Ever changing conditions call for the development of theory and simulation tools to finely describe multiple strata of society while supporting the evaluation of "what-if" scenarios. Particularly important is to assess the effectiveness of potential testing approaches and vaccination strategies. Here, an agent-based modeling platform is proposed to simulate the spreading of COVID-19 in small towns and cities, with a single-individual resolution. The platform is validated on real data from New Rochelle, NY —one of the first outbreaks registered in the United States. Supported by expert knowledge and informed by reported data, the model incorporates detailed elements of the spreading within a statistically realistic population. Along with pertinent functionality such as testing, treatment, and vaccination options, the model accounts for the burden of other illnesses with symptoms similar to COVID-19. Unique to the model is the possibility to explore different testing approaches —in hospitals or drive-through facilities— and vaccination strategies that could prioritize vulnerable groups. Decision making by public authorities could benefit from the model, for its fine-grain resolution, open-source nature, and wide range of features. Copyright © 2021, The Authors. All rights reserved.

关键词： Autonomous agents

Artificial intelligence-based image enhancement in PET imaging: Noise reduction and resolution enhancement

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arXiv 2021年

作者： Liu, Juan Malekzadeh, Masoud Mirian, Niloufar Song, Tzu-An Liu, Chi Dutta, Joyita Department of Radiology and Biomedical Imaging Yale School of Medicine New HavenCT United States Department of Electrical and Computer Engineering University of Massachusetts Lowell LowellMA United States Gordon Center for Medical Imaging Massachusetts General Hospital Harvard Medical School BostonMA United States

High noise and low spatial resolution are two key confounding factors that limit the qualitative and quantitative accuracy of PET images. AI models for image denoising and deblurring are becoming increasingly popular for post-reconstruction enhancement of PET images. We present here a detailed review of recent efforts for AI-based PET image enhancement with a focus on network architectures, data types, loss functions, and evaluation metrics. We also highlight emerging areas in this field that are quickly gaining popularity, identify barriers to large-scale adoption of AI models for PET image enhancement, and discuss future directions. © 2021, CC BY.

关键词： Image enhancement

NeSVoR: Implicit Neural Representation for Slice-to-Volume Reconstruction in MRI

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TechRxiv

TechRxiv 2022年

作者： Xu, Junshen Moyer, Daniel Gagoski, Borjan Iglesias, Juan Eugenio Grant, P. Ellen Golland, Polina Adalsteinsson, Elfar Department of Electrical Engineering and Computer Science MIT CambridgeMA United States MIT CambridgeMA United States Fetal-Neonatal Neuroimaging and Developmental Science Center Boston Children’s Hospital BostonMA United States Harvard Medical School BostonMA United States The Center for Medical Image Computing UCL London United Kingdom The Martinos Center for Biomedical Imaging Harvard Medical School BostonMA United States

Reconstructing 3D MR volumes from multiple motion-corrupted stacks of 2D slices has shown promise in imaging of moving subjects, e.g., fetal MRI. However, existing slice-to-volume reconstruction methods are time-consuming, especially when a high-resolution volume is desired. Moreover, they are still vulnerable to severe subject motion and when image artifacts are present in acquired slices. In this work, we present NeSVoR, a resolution-agnostic slice-to-volume reconstruction method, which models the underlying volume as a continuous function of spatial coordinates with implicit neural representation. To improve robustness to subject motion and other image artifacts, we adopt a continuous and comprehensive slice acquisition model that takes into account rigid inter-slice motion, point spread function, and bias fields. NeSVoR also estimates pixel-wise and slice-wise variances of image noise and enables removal of outliers during reconstruction and visualization of uncertainty. Extensive experiments are performed on both simulated and in vivo data to evaluate the proposed method. Results show that NeSVoR achieves state-of-the-art reconstruction quality while providing two to ten-fold acceleration in reconstruction times over the state-of-the-art algorithms. © 2022, CC BY.

关键词： Magnetic resonance imaging

Radon Cumulative Distribution Transform Subspace Modeling for Image Classification

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arXiv 2020年

作者： Shifat-E-Rabbi, Mohammad Yin, Xuwang Rubaiyat, Abu Hasnat Mohammad Li, Shiying Kolouri, Soheil Aldroubi, Akram Nichols, Jonathan M. Rohde, Gustavo K. The Department of Biomedical Engineering University of Virginia CharlottesvilleVA22908 United States The Department of Electrical and Computer Engineering University of Virginia CharlottesvilleVA22904 United States The HRL Laboratories LLC MalibuCA90265 United States The Department of Mathematics Vanderbilt University NashvilleTN37212 United States The U.S. Naval Research Laboratory WashingtonDC20375 United States The Department of Biomedical Engineering The Department of Electrical and Computer Engineering University of Virginia CharlottesvilleVA22908 United States

We present a new supervised image classification method applicable to a broad class of image deformation models. The method makes use of the previously described Radon Cumulative Distribution Transform (R-CDT) for image data, whose mathematical properties are exploited to express the image data in a form that is more suitable for machine learning. While certain operations such as translation, scaling, and higher-order transformations are challenging to model in native image space, we show the R-CDT can capture some of these variations and thus render the associated image classification problems easier to solve. The method - utilizing a nearest-subspace algorithm in R-CDT space - is simple to implement, non-iterative, has no hyper-parameters to tune, is computationally efficient, label efficient, and provides competitive accuracies to state-of-the-art neural networks for many types of classification problems. In addition to the test accuracy performances, we show improvements (with respect to neural network-based methods) in terms of computational efficiency (it can be implemented without the use of GPUs), number of training samples needed for training, as well as out-of-distribution generalization. The Python code for reproducing our results is available at [1]. Copyright © 2020, The Authors. All rights reserved.

关键词： Radon

A new smart-cropping pipeline for prostate segmentation using deep learning networks

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arXiv 2021年

作者： Zaridis, Dimitrios G. Mylona, Eugenia Tachos, Nikolaos S. Marias, Kostas Papanikolaou, Nikolaos Tsiknakis, Manolis Fotiadis, Dimitrios I. The Dept. of Biomedical Research FORTH-IMBB Ioannina Greece The Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering University of Ioannina IoanninaGR 45110 Greece The Institute of Computer Science FORTH Heraklion Greece The Dept. of Electrical and Computer Engineering Hellenic Mediterranean University Greece The Centre for the Unknown Champalimaud Foundation Clinical Computational Imaging Group Lisbon Portugal Crete Heraklion Greece The Dept. of Biomedical Research Institute of Molecular Biology and Biotechnology FORTH Ioannina Greece The Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina IoanninaGR 45110 Greece

Prostate segmentation from magnetic resonance imaging (MRI) is a challenging task. In recent years, several network architectures have been proposed to automate this process and alleviate the burden of manual annotation. Although the performance of these models has achieved promising results, there is still room for improvement before these models can be used safely and effectively in clinical practice. One of the major challenges in prostate MR image segmentation is the presence of class imbalance in the image labels where the background pixels dominate over the prostate. In the present work we propose a DL-based pipeline for cropping the region around the prostate from MRI images to produce a more balanced distribution of the foreground pixels (prostate) and the background pixels and improve segmentation accuracy. The effect of DL-cropping for improving the segmentation performance compared to standard center-cropping is assessed using five popular DL networks for prostate segmentation, namely U-net, U-net+, Res Unet++, Bridge U-net and Dense U-net. The proposed smart-cropping outperformed the standard center cropping in terms of segmentation accuracy for all the evaluated prostate segmentation networks. In terms of Dice score, the highest improvement was achieved for the Unet+ and ResU-net++ architectures corresponding to 8.9% and 8%, respectively. © 2021, CC BY.

关键词： Image segmentation

A Myocardial T1-Mapping Framework with Recurrent and U-Net Convolutional Neural Networks

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A Myocardial T1-Mapping Framework with Recurrent and U-Net C...

IEEE International Symposium on biomedical Imaging

作者： Haris Jeelani Yang Yang Ruixi Zhou Christopher M. Kramer Michael Salerno Daniel S. Weller Electrical and Computer Engineering University of Virginia Charlottesville USA Radiology Icahn School of Medicine at Mount Sinai New York USA Biomedical Engineering University of Virginia Charlottesville USA Medicine Radiology University of Virginia Charlottesville USA

ISBN: (数字)9781538693308

ISBN: (纸本)9781538693315

Noise and aliasing artifacts arise in various accelerated cardiac magnetic resonance (CMR) imaging applications. In accelerated myocardial T1-mapping, the traditional three-parameter based nonlinear regression may not provide accurate estimates due to sensitivity to noise. A deep neural network-based framework is proposed to address this issue. The DeepT1 framework consists of recurrent and U-net convolution networks to produce a single output map from the noisy and incomplete measurements. The results show that DeepT1 provides noise-robust estimates compared to the traditional pixel-wise three parameter fitting.

关键词： Image reconstruction Fitting Myocardium Convolution Acceleration Time series analysis Training

A comparison of deep learning convolution neural networks for liver segmentation in radial turbo spin echo images

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arXiv 2020年

作者： Umapathy, Lavanya Keerthivasan, Mahesh Bharath Galons, Jean-Phillipe Unger, Wyatt Martin, Diego Altbach, Maria I. Bilgin, Ali Department of Electrical and Computer Engineering University of Arizona TucsonAZ United States Department of Medical Imaging University of Arizona TucsonAZ United States Department of Biomedical Engineering University of Arizona TucsonAZ United States

Motion-robust 2D Radial Turbo Spin Echo (RADTSE) pulse sequence can provide a high-resolution composite image, T2-weighted images at multiple echo times (TEs), and a quantitative T2 map, all from a single k-space acquisition. In this work, we use a deep-learning convolutional neural network (CNN) for the segmentation of liver in abdominal RADTSE images. A modified UNET architecture with generalized dice loss objective function was implemented. Three 2D CNNs were trained, one for each image type obtained from the RADTSE sequence. On evaluating the performance of the CNNs on the validation set, we found that CNNs trained on TE images or the T2 maps had higher average dice scores than the composite images. This, in turn, implies that the information regarding T2 variation in tissues aids in improving the segmentation performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks