Stroke infarct volume estimation in fixed tissue: Comparison of diffusion kurtosis imaging to diffusion weighted imaging and histology in a rodent MCAO model.

Diffusion kurtosis imaging (DKI) is a new promising MRI technique with microstructural sensitivity superior to conventional diffusion tensor (DTI) based methods. In stroke, considerable mismatch exists between the infarct lesion outline obtained from the two methods, kurtosis and diffusion tensor derived metrics. We aim to investigate if this mismatch can be examined in fixed tissue. Our investigation is based on estimates of mean diffusivity (MD) and mean (of the) kurtosis tensor (MKT) obtained using recent fast DKI methods requiring only 19 images. At 24 hours post stroke, rat brains were fixed and prepared. The infarct was clearly visible in both MD and MKT maps. The MKT lesion volume was roughly 31% larger than the MD lesion volume. Subsequent histological analysis (hematoxylin) revealed similar lesion volumes to MD. Our study shows that structural components underlying the MD/MKT mismatch can be investigated in fixed tissue and therefore allows a more direct comparison between lesion volumes from MRI and histology. Additionally, the larger MKT infarct lesion indicates that MKT do provide increased sensitivity to microstructural changes in the lesion area compared to MD.

On the design of filters for Fourier and oSVD-based deconvolution in bolus tracking perfusion MRI.

OBJECT: Bolus tracking perfusion evaluation relies on the deconvolution of a tracers concentration time-courses in an arterial and a tissue voxel following the tracer kinetic model. The object of this work is to propose a method to design a data-driven Tikhonov regularization filter in the Fourier domain and to compare it to the singular value decomposition (SVD)-based approaches using the mathematical equivalence of Fourier and circular SVD (oSVD). MATERIALS AND METHODS: The adaptive filter is designed using Tikhonov regularization that depends on only one parameter. Using a simulation, such an optimal parameter that minimizes the sum of statistical and systematic error is determined as a function of the first moment difference between the tissue and the arterial curve and the contrast to noise ratios of the input data (CNR( a ) in arteries and CNR( t ) in tissue). The performance of the method is evaluated and compared to oSVD in simulations and measured data. RESULTS: The proposed method yields a smaller flow underestimation especially for high flows when compared to the oSVD approach with constant threshold. However, this improvement comes to the price of an increased uncertainty of the flow values. The translation of the Tikhonov regularization parameter to an adaptive oSVD-threshold is in good agreement with the literature. CONCLUSION: The proposed method is a comprehensive approach for the design of data-driven filters that can be easily adapted to specific needs.

Analysis of partial volume effects on arterial input functions using gradient echo: a simulation study.

Absolute blood flow and blood volume measurements using perfusion weighted MRI require an accurately measured arterial input function (AIF). Because of limited spatial resolution of MR images, AIF voxels cannot be placed completely within a feeding artery. We present a two-compartment model of an AIF voxel including the relaxation properties of blood and tissue. Artery orientations parallel and perpendicular to the main magnetic field were investigated and AIF voxels were modeled to either include or be situated close to a large artery. The impact of partial volume effects on quantitative perfusion metrics was investigated for the gradient echo pulse sequence at 1.5 T and 3.0 T. It is shown that the tissue contribution broadens and introduces fluctuations in the AIF. Furthermore, partial volume effects bias perfusion metrics in a nonlinear fashion, compromising quantitative perfusion estimates and profoundly effecting local AIF selection.