Compression of fMRI data using wavelet tensor train decomposition

The application of the Wavelet Tensor Train (WTT) decomposition to the compression of functional magnetic resonance imaging (fMRI) data is considered. Contrary to the classical wavelet transforms, the WTT decomposition is an algebraic technique for the construction of adaptive wavelet transforms, but it requires to store filters for each data array. The WTT method of compressing realistic fMRI data is compared with Daubechies wavelet transforms. The numerical results show that the WTT transform can be successfully used to compress lossy data.