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This work is motivated by a study of a population of multiple sclerosis (MS) patients using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to identify active brain lesions. At each visit, a contrast agent is administered…

Applications · Statistics 2013-06-25 Haochang Shou , Russell T. Shinohara , Han Liu , Daniel S. Reich , Ciprian M. Crainiceanu

We propose a novel T2 relaxation data analysis method which we have named spectrum analysis for multiple exponentials via experimental condition oriented simulation (SAME-ECOS). SAME-ECOS, which was developed based on a combination of…

Multiexponential modeling of relaxation or diffusion MR signal decays is a popular approach for estimating and spatially mapping different microstructural tissue compartments. While this approach can be quite powerful, it is also limited by…

Image and Video Processing · Electrical Eng. & Systems 2019-05-10 Daeun Kim , Jessica L. Wisnowski , Christopher T. Nguyen , Justin P. Haldar

Recovering the T2 distribution from multi-echo T2 magnetic resonance (MR) signals is challenging but has high potential as it provides biomarkers characterizing the tissue micro-structure, such as the myelin water fraction (MWF). In this…

While the multiexponential nature of T2 decays measured in vivo is well known, characterizing T2 decays by a single time constant is still very useful when differentiating among structures and pathologies in MRI images. A novel, robust,…

General Physics · Physics 2008-03-21 Keith S Cover

It is frequently of interest to jointly analyze multiple sequences of multiple tests in order to identify simultaneous signals, defined as features tested in multiple studies whose test statistics are non-null in each. In many problems,…

Methodology · Statistics 2019-01-16 Sihai Dave Zhao , Yet Tien Nguyen

We consider a multiple hypothesis testing problem in a sensor network over the joint spatio-temporal domain. The sensor network is modeled as a graph, with each vertex representing a sensor and a signal over time associated with each…

Signal Processing · Electrical Eng. & Systems 2025-01-23 Xingchao Jian , Martin Gölz , Feng Ji , Wee Peng Tay , Abdelhak M. Zoubir

Continuous and multimodal stress detection has been performed recently through wearable devices and machine learning algorithms. However, a well-known and important challenge of working on physiological signals recorded by conventional…

Machine Learning · Computer Science 2021-07-30 Arman Iranfar , Adriana Arza , David Atienza

A longstanding open problem in condensed matter physics is whether or not a strongly disordered interacting insulator can be mapped to a system of effectively non-interacting localized excitations. We investigate this issue on the…

Strongly Correlated Electrons · Physics 2020-05-22 Fahad Mahmood , Dipanjan Chaudhuri , Sarang Gopalakrishnan , Rahul Nandkishore , N. P. Armitage

In many applications, we are given access to noisy modulo samples of a smooth function with the goal being to robustly unwrap the samples, i.e., to estimate the original samples of the function. In a recent work, Cucuringu and Tyagi…

Statistics Theory · Mathematics 2021-12-06 Hemant Tyagi

A signal recovery problem is considered, where the same binary testing problem is posed over multiple, independent data streams. The goal is to identify all signals, i.e., streams where the alternative hypothesis is correct, and noises,…

Methodology · Statistics 2022-11-08 Yiming Xing , Georgios Fellouris

Identifying signals that replicate across multiple studies is essential for establishing robust scientific evidence, yet existing methods for high-dimensional replicability analysis either rely on restrictive modeling assumptions, are…

Methodology · Statistics 2026-03-05 Haochen Lei , Yan Li , Hongyuan Cao

A model-based reconstruction technique for accelerated T2 mapping with improved accuracy is proposed using undersampled Cartesian spin-echo MRI data. The technique employs an advanced signal model for T2 relaxation that accounts for…

Medical Physics · Physics 2015-03-03 Tilman J. Sumpf , Andreas Petrovic , Martin Uecker , Florian Knoll , Jens Frahm

We consider a model where a signal (discrete or continuous) is observed with an additive Gaussian noise process. The signal is issued from a linear combination of a finite but increasing number of translated features. The features are…

Statistics Theory · Mathematics 2024-07-23 Cristina Butucea , Jean-François Delmas , Anne Dutfoy , Clément Hardy

We propose a methodology for testing linear hypothesis in high-dimensional linear models. The proposed test does not impose any restriction on the size of the model, i.e. model sparsity or the loading vector representing the hypothesis.…

Methodology · Statistics 2019-07-09 Yinchu Zhu , Jelena Bradic

Text-to-Image (T2I) models have advanced rapidly, yet they remain vulnerable to semantic leakage, the unintended transfer of semantically related features between distinct entities. Existing mitigation strategies are often…

Computer Vision and Pattern Recognition · Computer Science 2025-10-20 Mor Ventura , Michael Toker , Or Patashnik , Yonatan Belinkov , Roi Reichart

In this paper we propose a computationally efficient multiple hypothesis testing procedure for persistent homology. The computational efficiency of our procedure is based on the observation that one can empirically simulate a null…

Computational Geometry · Computer Science 2022-08-29 Mikael Vejdemo-Johansson , Sayan Mukherjee

Multicomponent T2-mapping using a gradient and spin-echo (GraSE) acquisition has become standard for myelin water imaging at 3T. Higher magnetic field strengths promise SNR benefits but face specific absorption rate limits and shortened T2…

Medical Physics · Physics 2021-05-14 Vanessa Wiggermann , Alex MacKay , Alexander Rauscher , Gunther Helms

In compressed sensing the goal is to recover a signal from as few as possible noisy, linear measurements. The general assumption is that the signal has only a few non-zero entries. The recovery can be performed by multiple different…

Information Theory · Computer Science 2020-10-07 Hendrik Bernd Petersen , Bubacarr Bah , Peter Jung

Purpose: Training auto-encoders on simulated signal evolution and inserting the decoder into the forward model improves reconstructions through more compact, Bloch-equation-based representations of signal in comparison to linear subspaces.…

Signal Processing · Electrical Eng. & Systems 2023-05-02 Yamin Arefeen , Junshen Xu , Molin Zhang , Zijing Dong , Fuyixue Wang , Jacob White , Berkin Bilgic , Elfar Adalsteinsson
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