Results 31 to 40 of about 38,801 (275)

Simultaneous use of Individual and Joint Regularization Terms in Compressive Sensing: Joint Reconstruction of Multi-Channel Multi-Contrast MRI Acquisitions [PDF]

, 2019
Purpose: A time-efficient strategy to acquire high-quality multi-contrast images is to reconstruct undersampled data with joint regularization terms that leverage common information across contrasts.
Güngör, Alper, Güven, H. Emre, Kilic, Toygan, Kopanoglu, Emre, Oguz, Kader K., Saritas, Emine Ulku, Çukur, Tolga   +6 more
core   +4 more sources

Utilizing logistic regression to compare risk factors in disease modeling with imbalanced data: a case study in vitamin D and cancer incidence

Frontiers in Oncology, 2023
Imbalanced data, a common challenge encountered in statistical analyses of clinical trial datasets and disease modeling, refers to the scenario where one class significantly outnumbers the other in a binary classification problem. This imbalance can lead
Mohammad Meysami, Vijay Kumar, McKayah Pugh, Samuel Thomas Lowery, Shantanu Sur, Sumona Mondal, James M. Greene   +6 more
doaj   +1 more source

Optimal placement of a limited number of observations for period searches [PDF]

, 2006
Robotic telescopes present the opportunity for the sparse temporal placement of observations when period searching. We address the best way to place a limited number of observations to cover the dynamic range of frequencies required by an observer.
A. Allan, Bode, Chaplin, Deeming, E. S. Saunders, Horne, Littlefair, Lomb, Mould, Nather, Scargle, Schwarzenberg-Czerny, T. Naylor   +12 more
core   +2 more sources

FlowVN Trained on a Single Dataset Enables Rapid Reconstruction of Highly Accelerated 4D Flow MRI Across Multiple Sites. [PDF]

Magn Reson Med
ABSTRACT Purpose The aim of this study is to evaluate a deep variational network, FlowVN, for the reconstruction of heavily undersampled 4D Flow MRI across multiple sites. Methods FlowVN was trained on fully sampled 4D Flow MRI datasets of healthy volunteers from one site. The model was tested on retrospective undersampled data (R = 6–22) of six normal
Qazi SA, Bianchessi T, Viola F, Trenti C, Ylipää E, Ebbers T, Dyverfeldt P.   +6 more
europepmc   +2 more sources

A systematic study of the class imbalance problem in convolutional neural networks

, 2018
In this study, we systematically investigate the impact of class imbalance on classification performance of convolutional neural networks (CNNs) and compare frequently used methods to address the issue.
Buda, Mateusz, Maki, Atsuto, Mazurowski, Maciej A.   +2 more
core   +1 more source

Uncalibrated Distortions vs Undersampling

Vision Research, 1996
In a recent paper of ours [Hess & Field (1993). Vision Research, 33, 2663-2670], we claim that there was a predictable relationship between position errors and contrast errors for an undersampled system. In this paper we re-state our main points. We feel that the response to that paper by Levi and Klein in the accompanying article does not require us ...
FIELD, DAVID J, HESS, ROBERT F
openaire   +2 more sources

Phase-contrast with interleaved undersampled projections [PDF]

Magnetic Resonance in Medicine, 2000
MR phase-contrast techniques provide velocity-sensitive angiograms and quantitative flow measurements but require long scan times. Recently it has been shown that undersampled projection reconstruction can acquire higher resolution per unit time than Fourier techniques with acceptable artifacts when used in contrast-enhanced MR angiography ...
A V, Barger, D C, Peters, W F, Block, K K, Vigen, F R, Korosec, T M, Grist, C A, Mistretta   +6 more
openaire   +2 more sources

Beyond Nyquist: A Comparative Analysis of 3D Deep Learning Models Enhancing MRI Resolution

Journal of Imaging
High-spatial resolution MRI produces abundant structural information, enabling highly accurate clinical diagnosis and image-guided therapeutics. However, the acquisition of high-spatial resolution MRI data typically can come at the expense of less ...
Soumick Chatterjee, Alessandro Sciarra, Max Dünnwald, Anitha Bhat Talagini Ashoka, Mayura Gurjar Cheepinahalli Vasudeva, Shudarsan Saravanan, Venkatesh Thirugnana Sambandham, Pavan Tummala, Steffen Oeltze-Jafra, Oliver Speck, Andreas Nürnberger   +10 more
doaj   +1 more source

Quantifying admissible undersampling for sparsity-exploiting iterative image reconstruction in X-ray CT

, 2012
Iterative image reconstruction (IIR) with sparsity-exploiting methods, such as total variation (TV) minimization, investigated in compressive sensing (CS) claim potentially large reductions in sampling requirements.
Jørgensen, Jakob H., Pan, Xiaochuan, Sidky, Emil Y.   +2 more
core   +1 more source

Dynamic Compressive Sensing of Time-Varying Signals via Approximate Message Passing [PDF]

, 2013
In this work the dynamic compressive sensing (CS) problem of recovering sparse, correlated, time-varying signals from sub-Nyquist, non-adaptive, linear measurements is explored from a Bayesian perspective.
Justin Ziniel, Philip Schniter, Senior Member, Student Member   +3 more
core   +2 more sources