In the past, all pMRI techniques required the sensitivity profile to be estimated either explicitly (SENSE [1], SMASH [2]) or implicitly (GRAPPA [3,4], SPIRiT [5]). All the aforementioned check FAQ methods assume that the sensitivity maps are smooth and hence have a compact support in the Fourier domain. Thus, while acquiring the MRI scan, the centre Inhibitors,Modulators,Libraries of the K-space is densely sampled from which the sensitivity map is either explicitly estimated (SENSE or SMASH) or the interpolation weights (dependent on the sensitivity maps) are estimated (GRAPPA, SPIRiT). Unfortunately joint estimation of sensitivity maps (or related interpolation weights) is an ill-posed problem.All the aforementioned pMRI reconstruction methods proceed in two stages��(i) In the calibration stage, the sensitivity maps or the interpolation weights are estimated; (ii) Based on these estimates, the image is reconstructed in the reconstruction stage.

The reconstruction accuracy of the images is sensitive to the accuracy of the calibration stage. The calibration in turn depends on the choice of certain parameters, e.g., the window size��size of the central K-space region that has been Inhibitors,Modulators,Libraries fully sampled (for all the aforementioned methods) and the kernel size for estimating the interpolation weights (for Inhibitors,Modulators,Libraries GRAPPA and SPIRiT). These parameters are manually tuned and the best results are reported. The GRAPPA Inhibitors,Modulators,Libraries formulation has been studied in detail, and there is a study which claims to offer insights regarding the choice of GRAPPA reconstruction parameters [6]; however for other techniques such as SPIRiT and CS SENSE, there are no detailed studies on parameter tuning.

In this work, we improve upon our previous work on calibration free reconstruction (see Section 2.2). Our method reconstructs Brefeldin_A each of the different multi-coil images, which are then combined by the sum-of-squares approach (used in GRAPPA and SPIRiT). We compare our method with state-of-the-art parallel MRI reconstruction methods; two of these are calibrated techniques��CS SENSE [7] and SPIRiT and the other two are calibration free methods��DCS and SAKE. Our proposed method outperforms all of them.Mathematically the sensitivity encoding of MR images is a modulation operation where the signal (image) is modulated by the sensitivity function (map) of the coils. All the aforesaid studies are based on the assumption the sensitivity map is smooth.

Moreover the design on the receiver coils selleckchem ensure that there sensitivity does not vanish anywhere, i.e., there is no portion of the sensitivity map that has zeroes. This is to ensure that each of the coils collects information about the entire object under scan and no portion of the object is ��invisible�� to any of the coils. The sensitivity maps can thus be represented as smooth functions without any singularities. When this assumption holds, the sensitivity maps will not affect the location of the singularities/discontinuities/edges in the image.