  This paper presents modification of the TwIST algorithm for Compressive
Sensing MRI images reconstruction. Compressive Sensing is new approach in
signal processing whose basic idea is recovering signal form small set of
available samples. The application of the Compressive Sensing in biomedical
imaging has found great importance. It allows significant lowering of the
acquisition time, and therefore, save the patient from the negative impact of
the MR apparatus. TwIST is commonly used algorithm for 2D signals
reconstruction using Compressive Sensing principle. It is based on the Total
Variation minimization. Standard version of the TwIST uses masked 2D Discrete
Fourier Transform coefficients as Compressive Sensing measurements. In this
paper, different masks and different transformation domains for coefficients
selection are tested. Certain percent of the measurements is used from the
mask, as well as small number of coefficients outside the mask. Comparative
analysis using 2D DFT and 2D DCT coefficients, with different mask shapes is
performed. The theory is proved with experimental results.
