The main tissue parameters targeted by MR tomography include, among others, relaxation times T1 and T2. This paper focuses on the computation of the relaxation time T2 measured with the Spin Echo method, where the sensing coil of the tomograph provides a multi-echo signal. The maxima of these echoes must be interleaved with an exponential function, and the T2 relaxation can be determined directly from the exponential waveform. As this procedure needs to be repeated for each pixel of the scanned tissue, the processing of large images then becomes very intensive. For example, given the common resolution of 256×256 with 20 slices and five echoes at different times TE, it is necessary to reconstruct 1.3∙106 exponential functions. At present, such computation performed on a regular PC may last even several minutes. This paper introduces the results provided by accelerated computation based on parallelization and carried out with a graphics card. By using the simple method of linear regression, we obtain a processing time of less than 36 ms. Another effective option consists in the Levenberg-Marquardt algorithm, which enables us to reconstruct the same image in 96 ms. This period is at least 900 times shorter than that achievable with professional software. In this context, the paper also comprises an analysis of the results provided by the above-discussed techniques.
Dynamic contrast enhanced MRI (DCE-MRI) and dynamic susceptibility contrast MRI (DSC-MRI) are perfusion imaging techniques used mainly for clinical and preclinical measurement of vessel permeability and capillary blood flow, respectively. It is advantageous to apply both methods to exploit their complementary information about the perfusion status of the tissue. We propose a novel acquisition method that combines advantages of the current simultaneous and sequential acquisition. The proposed method consists of a DCE-MRI acquisition interrupted by DSC-MRI acquisition. A new method for processing of the DCE-MRI data is proposed which takes the interleaved acquisition into account. Analysis of both the DCE- and DSC-MRI data is reformulated so that they are approximated by the same pharmacokinetic model (constrained distributed capillary adiabatic tissue homogeneity model). This provides a straightforward evaluation of the methodology as some of the estimated DCE- and DSC-MRI perfusion parameters should be identical. Evaluation on synthetic data showed an acceptable precision and no apparent bias introduced by the interleaved character of the DCE-MRI acquisition. Intravascular perfusion parameters obtained from clinical glioma data showed a fairly high correlation of blood flow estimates from DCE- and DSC-MRI, however, an unknown scaling factor was still present mainly because of the tissue-specific relaxivity. The results show validity of the proposed acquisition method. They also indicate that simultaneous processing of both DCE- and DSC-MRI data with joint estimation of some perfusion parameters (included in both DCE- and DSC-MRI) might be possible to increase the reliability of the DCE- and DSC-MRI methods alone.