A Fast and Efficient Method for Compressing fMRI Data Sets

Theis, Fabian J.; Tanaka, Toshihisa

doi:10.1007/11550907_122

Cited by 5 publications

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nevertheless, computation time is still an issue, as evidence by the fact that practitioners have developed data structures optimized for 3D fields (e.g., Theis and Tanaka, 2005) and schemes for parallel processing (e.g., Zhao et al, 2007;Wilde et al, 2009). Computational firepower is necessary because over the course of an fMRI study, the number of hypothesis tests conducted and thresholds found easily reaches into the hundreds.…”

Section: Applicationsmentioning

confidence: 99%

Estimating thresholding levels for random fields via Euler characteristics

Adler,

Bartz,

Kou

et al. 2017

Preprint

View full text Add to dashboard Cite

We introduce Lipschitz-Killing curvature (LKC) regression, a new method to produce (1 − α) thresholds for signal detection in random fields that does not require knowledge of the spatial correlation structure. The idea is to fit observed empirical Euler characteristics to the Gaussian kinematic formula via generalized least squares, which quickly and easily provides statistical estimates of the LKCscomplex topological quantities that can be extremely challenging to compute, both theoretically and numerically. With these estimates, we can then make use of a powerful parametric approximation via Euler characteristics for Gaussian random fields to generate accurate (1 − α) thresholds and p-values. The main features of our proposed LKC regression method are easy implementation, conceptual simplicity, and facilitated diagnostics, which we demonstrate in a variety of simulations and applications.

show abstract

Section: Applicationsmentioning

confidence: 99%

Estimating thresholding levels for random fields via Euler characteristics

Adler,

Bartz,

Kou

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

Lossless compression for 3-D MRI data using reversible KLT

Yodchanan

2008

2008 International Conference on Audio, Language and Image Processing

View full text Add to dashboard Cite

A Fast Predictive Lossless Coder for fMRI Data Sets

Tanaka

Murakami

Theis

2006

2006 International Conference on Image Processing

Self Cite

View full text Add to dashboard Cite

We present a novel lossless compression algorithm which compresses sequences of three-dimensional (3D) volumes collected during a functional magnetic resonance imaging (fMRI) experiment. The large data sets involved in this popular biomedical application necessitate fast and efficient compression methods. We propose to use 3D prediction, temporal decorrelation and entropy coding with context modeling for encoding the fMRI scans after preprocessing with the region-of-interest (ROI) masking. The proposed algorithm is conceptually simple and can achieve fast implementation and efficient coding performance. We illustrate computer simulations to show advantages over conventional coding methods.

show abstract

A Fast and Efficient Method for Compressing fMRI Data Sets

Cited by 5 publications

References 7 publications

Estimating thresholding levels for random fields via Euler characteristics

Estimating thresholding levels for random fields via Euler characteristics

Lossless compression for 3-D MRI data using reversible KLT

A Fast Predictive Lossless Coder for fMRI Data Sets

Contact Info

Product

Resources

About