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Interventional MRI with sparse sampling using union-of-subspaces

S. Derin Babacan, Fan Lam, Xi Peng, Minh N. Do, Zhi-Pei Liang
2012 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI)  
Index Terms Interventional MRI; fast imaging; sparse sampling; union of subspaces NIH Public Access  ...  A significant problem in interventional magnetic resonance imaging is limited imaging speed. This paper addresses this problem using a new signal model known as union-of-subspaces.  ...  Keyhole techniques [6] acquire an initial high resolution reference image before the interventional procedure, which is then linearly combined with the sparse k-space samples acquired during the interventional  ... 
doi:10.1109/isbi.2012.6235547 pmid:25152538 pmcid:PMC4138723 dblp:conf/isbi/BabacanLPDL12 fatcat:qmwr376vmja4lj47gtxa45rbwi

Joint Diagnosis and Conversion Time Prediction of Progressive Mild Cognitive Impairment (pMCI) Using Low-Rank Subspace Clustering and Matrix Completion [chapter]

Kim-Han Thung, Pew-Thian Yap, Ehsan Adeli-M, Dinggang Shen
2015 Lecture Notes in Computer Science  
We assume that the data reside in a space formed by a union of several low-dimensional subspaces and that similar MCI conditions reside in similar subspaces.  ...  Therefore, we first use incomplete low-rank representation (ILRR) and spectral clustering to cluster the data according to their representative low-rank subspaces.  ...  LRR assumes that data samples are approximately (i.e., the data are noisy) drawn from a union of multiple subspaces and aims to cluster the samples into their respective subspaces and at the same time  ... 
doi:10.1007/978-3-319-24574-4_63 pmid:27054201 pmcid:PMC4820009 fatcat:nsuakkemwzfb7hczvu3glluafu

Research on the Development of Localized Music Curriculum System Based on the Theory of Multiple Intelligences

Dezhi Yu, Tianzhuo Gong, Sheng Bin
2022 Scientific Programming  
The experimental results show that, compared with the parameter estimation without preprocessing, the parameter estimation with preprocessing is more accurate, the accuracy of the improved algorithm reaches  ...  In the process of simulation experiment, the optimal solution of each factor matrix was designed, the source music signal was reconstructed, and the sufficiently sparse source music signal was separated  ...  Acknowledgments is work was supported by Department of musicology, Harbin Normal University.  ... 
doi:10.1155/2022/9167229 fatcat:tjnxgsohrrdbfpufcjstsonrsq

Image Reconstruction: From Sparsity to Data-adaptive Methods and Machine Learning [article]

Saiprasad Ravishankar, Jong Chul Ye, Jeffrey A. Fessler
2019 arXiv   pre-print
A third type of methods has been designed to accommodate modified data acquisition methods, such as reduced sampling in MRI and CT to reduce scan time or radiation dose.  ...  A fourth type of methods replaces mathematically designed models of signals and systems with data-driven or adaptive models inspired by the field of machine learning.  ...  Because different signals may be approximated with different subsets of dictionary columns, the model is viewed as a union of subspaces model [82] .  ... 
arXiv:1904.02816v2 fatcat:ehahzrib2ff3dl5yl6pa7xpf24

Recursive Recovery of Sparse Signal Sequences From Compressive Measurements: A Review

Namrata Vaswani, Jinchun Zhan
2016 IEEE Transactions on Signal Processing  
An important class of applications where this problem occurs is dynamic projection imaging, e.g., dynamic magnetic resonance imaging (MRI) for real-time medical applications such as interventional radiology  ...  In this article, we review the literature on design and analysis of recursive algorithms for reconstructing a time sequence of sparse signals from compressive measurements.  ...  Shorter scan times along with online reconstruction can potentially enable real-time 3 imaging of fast changing physiological phenomena, thus making many interventional MRI applications such as MRI-guided  ... 
doi:10.1109/tsp.2016.2539138 fatcat:7aacki7zbvddjbp575fej3gulq

From Compressed-Sensing to Artificial Intelligence-Based Cardiac MRI Reconstruction

Aurélien Bustin, Niccolo Fuin, René M. Botnar, Claudia Prieto
2020 Frontiers in Cardiovascular Medicine  
However, CMR suffers from long acquisition times due to the need of obtaining images with high temporal and spatial resolution, different contrasts, and/or whole-heart coverage.  ...  Unlike analytical methods for which the reconstruction problem is explicitly defined into the optimization process, machine learning techniques make use of large data sets to learn the key reconstruction  ...  The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health.  ... 
doi:10.3389/fcvm.2020.00017 pmid:32158767 pmcid:PMC7051921 fatcat:xbge626jhzeulfjlssdlqnalze

Fast high-resolution metabolic imaging of acute stroke with 3D magnetic resonance spectroscopy

Yao Li, Tianyao Wang, Tianxiao Zhang, Zengping Lin, Yudu Li, Rong Guo, Yibo Zhao, Ziyu Meng, Jun Liu, Xin Yu, Zhi-Pei Liang, Parashkev Nachev
2020 Brain  
Serial structural and perfusion MRI was used to define detailed spatial maps of tissue-level outcomes against which high-resolution metabolic changes were evaluated.  ...  In an 8-min scan, we simultaneously obtained 3D maps of N-acetylaspartate and lactate at a nominal spatial resolution of 2.0 × 3.0 × 3.0 mm3 with near whole-brain coverage from a cohort of 18 patients  ...  Funding This study was supported by National Natural Science Foundation of China (81871083 and 61671292), and Shanghai Jiao Tong University Scientific and Technological Innovation Funds (2019QYA12).  ... 
doi:10.1093/brain/awaa264 pmid:33141145 fatcat:ckcddw6zwjfg7a75cjkifag5he

Segmentation of the Prostatic Gland and the Intraprostatic Lesions on Multiparametic MRI Using Mask R-CNN

Zhenzhen Dai, Eric Carver, Chang Liu, Joon Lee, Aharon Feldman, Weiwei Zong, Milan Pantelic, Mohamed Elshaikh, Ning Wen
2020 Advances in Radiation Oncology  
Prostate gland segmentation was performed using T2-weighted images (T2WIs), although IL segmentation was performed using T2WIs and coregistered apparent diffusion coefficient maps with prostate patches  ...  When trained with patients from both cohorts, the values were as follows: DSC of 0.64 ± 0.11, 0.56 ± 0.15, and 0.46 ± 0.15; Sens. of 0.57 ± 0.23, 0.50 ± 0.28, and 0.33 ± 0.17; and Spec. of 0.980 ± 0.009  ...  Stacked independent subspace analysis þ sparse label DSC Z 86.7% AE 2.2% 30 T2WIs Vincent et al 24 Prostate segmentation AAM DSC Z 0.88 AE 0.03 MICCAI 2012 Promise12 challenge Klein et al 25  ... 
doi:10.1016/j.adro.2020.01.005 pmid:32529143 pmcid:PMC7280293 fatcat:zjt6fuv32jds7lxhww37w6yf64

Optimizing Preprocessing and Analysis Pipelines for Single-Subject fMRI: 2. Interactions with ICA, PCA, Task Contrast and Inter-Subject Heterogeneity

Nathan W. Churchill, Grigori Yourganov, Anita Oder, Fred Tam, Simon J. Graham, Stephen C. Strother, Xi-Nian Zuo
2012 PLoS ONE  
We also tested subspace estimation using Principal Component Analysis (PCA), and Independent Component Analysis (ICA).  ...  Two levels of cognitive contrast were examined in an fMRI adaptation of the Trail-Making Test, with data from young, healthy adults.  ...  Tracing was performed with an MRI-compatible writing tablet and stylus [41] , with subjects monitoring their performance on a projection screen.  ... 
doi:10.1371/journal.pone.0031147 pmid:22383999 pmcid:PMC3288007 fatcat:toggdubh35axlndovxxqjsispi

The Classification of Patient Semantical Records and Medical Images

Wang Yue Dong, Wang Na
2021 Journal of Biomedical and Sustainable Healthcare Applications  
The normalcy or irregularity of a particular picture might be used to make medical judgments.  ...  Extracting characteristics may be done in a number of ways. Computational and numerical modifications are used in these techniques.  ...  The resultant pictures can't be handled like conventional scalar representations, spawning novel sub of MIC. Dispersion MRI, cognitive MRI, and other types of MRI are only a few samples.  ... 
doi:10.53759/0088/jbsha202101009 fatcat:6hqzgtrv7jcqbls3uasm5th5i4

Catalyzing Clinical Diagnostic Pipelines Through Volumetric Medical Image Segmentation Using Deep Neural Networks: Past, Present, Future [article]

Teofilo E. Zosa
2021 arXiv   pre-print
deep learning architectures with historical background and the elucidation of the current trajectory of volumetric medical image segmentation research.  ...  novel approaches developed in response to those challenges, concluding with the proposal of future directions in the field.  ...  This version led to 13% greater performance than one trained using multinomial logistic loss with sample weighting.  ... 
arXiv:2103.14969v2 fatcat:ikxjpikwrneb3ijt6acerkdobu

Data Harmonisation for Information Fusion in Digital Healthcare: A State-of-the-Art Systematic Review, Meta-Analysis and Future Research Directions [article]

Yang Nan, Javier Del Ser, Simon Walsh, Carola Schönlieb, Michael Roberts, Ian Selby, Kit Howard, John Owen, Jon Neville, Julien Guiot, Benoit Ernst, Ana Pastor (+14 others)
2022 arXiv   pre-print
Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research.  ...  Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired  ...  With the defined MNN pairs, a GAN model was used to produce the cohortinvariant samples.  ... 
arXiv:2201.06505v1 fatcat:fdb7l52kmbecvbz3styi52ttkm

Localization of Distributed EEG Sources in the Context of Epilepsy: A Simulation Study

H. Becker, L. Albera, P. Comon, R. Gribonval, F. Wendling, I. Merlet
2016 IRBM  
of the sources.  ...  The objective of this paper consists in comparing the performance of eight representative algorithms taking into account recently developed methods.  ...  Conflicts of interest None.  ... 
doi:10.1016/j.irbm.2016.04.001 fatcat:bj4vz3b3avdf3farnoqonfxpha

Data Harmonisation for Information Fusion in Digital Healthcare: A State-of-the-Art Systematic Review, Meta-Analysis and Future Research Directions

Yang Nan, Javier Del Ser, Simon Walsh, Carola Schönlieb, Michael Roberts, Ian Selby, Kit Howard, John Owen, Jon Neville, Julien Guiot, Benoit Ernst, Ana Pastor (+14 others)
2022 Information Fusion  
Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research.  ...  Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired  ...  With the defined MNN pairs, a GAN model was used to produce the cohort-invariant samples.  ... 
doi:10.1016/j.inffus.2022.01.001 pmid:35664012 pmcid:PMC8878813 fatcat:57zns35robfzxg5qojnyvntcyy

Computer-Assisted Analysis of Biomedical Images [article]

Leonardo Rundo
2021 arXiv   pre-print
In conclusion, the ultimate goal of these research studies is to gain clinically and biologically useful insights that can guide differential diagnosis and therapies, leading towards biomedical data integration  ...  in mind the clinical feasibility of the developed solutions.  ...  of promising subspaces.  ... 
arXiv:2106.04381v1 fatcat:osqiyd3sbja3zgrby7bf4eljfm
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