Minimizing macrovessel signal in cerebral perfusion imaging using independent component analysis

Robust [Formula: see text] Approaches to Computing the Geometric Median and Principal and Independent Components

Robust measures are introduced for methods to determine statistically uncorrelated or also statistically independent components spanning data measured in a way that does not permit direct separation of these underlying components. Because of the nonlinear nature of the proposed methods, iterative methods are presented for the optimization of merit functions, and local convergence of these methods is proved. Illustrative examples are presented to demonstrate the benefits of the robust approaches, including an application to the processing of dynamic medical imaging.

Time-optimized high-resolution readout-segmented diffusion tensor imaging

Readout-segmented echo planar imaging with 2D navigator-based reacquisition is an uprising technique enabling the sampling of high-resolution diffusion images with reduced susceptibility artifacts. However, low signal from the small voxels and long scan times hamper the clinical applicability. Therefore, we introduce a regularization algorithm based on total variation that is applied directly on the entire diffusion tensor. The spatially varying regularization parameter is determined automatically dependent on spatial variations in signal-to-noise ratio thus, avoiding over- or under-regularization. Information about the noise distribution in the diffusion tensor is extracted from the diffusion weighted images by means of complex independent component analysis. Moreover, the combination of those features enables processing of the diffusion data absolutely user independent. Tractography from in vivo data and from a software phantom demonstrate the advantage of the spatially varying regularization compared to un-regularized data with respect to parameters relevant for fiber-tracking such as Mean Fiber Length, Track Count, Volume and Voxel Count. Specifically, for in vivo data findings suggest that tractography results from the regularized diffusion tensor based on one measurement (16 min) generates results comparable to the un-regularized data with three averages (48 min). This significant reduction in scan time renders high resolution (1×1×2.5 mm³) diffusion tensor imaging of the entire brain applicable in a clinical context.

The 39 steps: evading error and deciphering the secrets for accurate dynamic susceptibility contrast MRI

Dynamic susceptibility contrast (DSC) MRI is the most commonly used MRI method to assess cerebral perfusion and other related haemodynamic parameters. Although the technique is well established and used routinely in clinical centres, there are still many problems that impede accurate perfusion quantification. In this review article, we present 39 steps which guide the reader through the theoretical principles, practical decisions, potential problems, current limitations and latest advances in DSC-MRI. The 39 steps span the collection, analysis and interpretation of DSC-MRI data, expounding issues and possibilities relating to the contrast agent, the acquisition of DSC-MRI data, data pre-processing, the contrast concentration-time course, the arterial input function, deconvolution, common perfusion parameters, post-processing possibilities, patient studies, absolute versus relative quantification and automated analysis methods.

Gradient echo plural contrast imaging--signal model and derived contrasts: T2*, T1, phase, SWI, T1f, FST2*and T2*-SWI

Gradient Echo Plural Contrast Imaging (GEPCI) is a post processing technique that, based on a widely available multiple gradient echo sequence, allows simultaneous generation of naturally co-registered images with various contrasts: T1 weighted, R2*=1/T2* maps and frequency (f) maps. Herein, we present results demonstrating the capability of GEPCI technique to generate image sets with additional contrast characteristics obtained by combing the information from these three basic contrast maps. Specifically, we report its ability to generate GEPCI-susceptibility weighted images (GEPCI-SWI) with improved SWI contrast that is free of T1 weighting and RF inhomogeneities; GEPCI-SWI-like images with the contrast similar to original SWI; T1f images that offer superior GM/WM matter contrast obtained by combining the GEPCI T1 and frequency map data; Fluid Suppressed T2* (FST2*) images that utilize GEPCI T1 data to suppress CSF signal in T2* maps and provide contrast similar to FLAIR T2 weighted images; and T2*-SWI images that combine SWI contrast with quantitative T2* map and offer advantages of visualizing venous structure with hyperintense T2* lesions (e.g. MS lesions). To analyze GEPCI images we use an improved algorithm for combining data from multi-channel RF coils and a method for unwrapping phase/frequency maps that takes advantage of the information on phase evolution as a function of gradient echo time in GEPCI echo train.

Separation of perfusion signals from diffusion-weighted image series enabled by independent component analysis

BACKGROUND AND PURPOSE

An important task in diagnostic imaging of acute ischemic stroke is to identify the so-called diffusion-perfusion mismatch area. We aimed to investigate the possibility of facilitating the identification process by combining independent component analysis (ICA) and diffusion-weighted MRI (DWI), with the expectation that this would eliminate the need for additional perfusion imaging to delineate perfusion lesion.

METHODS

Simulations were performed to confirm the utility of an intuitively determined sequence of 14 b-factors ranging from 0 to 1,000 seconds/mm(2) for ICA separation of perfusion lesion. Corresponding DWI data from 2 stroke patients, 1 in the acute and 1 in the subacute phase, were decomposed into independent component (IC) maps, and their b-dependent amplitude decay profiles were subjected to multiexponential fitting.

RESULTS

Low-perfusion areas were successfully delineated on IC maps in both patients. Comparison with the areas of diffusion lesion identifiable on relatively high b-factor images in the DWI data, for example, those at b= 1,000 seconds/mm(2) , allowed the mismatch to be identified.

CONCLUSION

This study demonstrates that combining ICA and DWI enables noninvasive mapping of sluggish perfusion provided an appropriate b-sequence is applied, and that it thereby facilitates the identification of diffusion-perfusion mismatch.

Automated macrovessel artifact correction in dynamic susceptibility contrast magnetic resonance imaging using independent component analysis

Dynamic susceptibility contrast-MRI is the most commonly used functional MRI-based method for studying changes in cerebral perfusion. However, several studies indicated a systematic overestimation of perfusion parameters compared with other imaging modalities related to the high sensitivity of dynamic susceptibility contrast-MRI for blood flow in large vessels. In this study, we therefore suggest an improved, automated, robust, and efficient method allowing for generating hemodynamic parameter maps where signal influence from large vessels is minimized. Based on independent component analysis, this fully automated approach corrects dynamic susceptibility contrast-MRI data without any user interaction, thus making a clinical applicability possible. The accuracy of the proposed method was tested in 10 patients with cerebrovascular disease. Application of our correction algorithm resulted in a significant reduction of the effect of macrovessel signal on hemodynamic parameters like the cerebral blood flow and the cerebral blood volume compared with uncorrected data. As desired, our method specifically corrected for macrovessel artifacts in cortical grey matter tissue, leaving white matter tissue parameters largely unaffected. This may increase sensitivity and reliability of detecting perfusion abnormalities in patient groups, in particular with regard to stroke and other cerebrovascular disorders.

Deconvolution for DCE-MRI using an exponential approximation basis

A deconvolution approach for dynamic contrast enhanced magnetic resonance imaging using an approximation basis of exponential functions constrained to be non-negative and non-increasing is developed and compared with widely used methods. Monotonicity in an exponential basis is implemented in terms of a newly derived condition which is a considerable generalization over a previous condition that implies complete monotonicity. Since the constraints imply a bound on the total variation, a well known staircasing effect may result with other approximation bases, but an exponential basis is shown to resist staircasing. In addition to the choice of approximation basis, further regularization is implemented in terms of the number of basis functions and the distribution of their parameters. The exponential approach is applied to dynamic contrast enhanced magnetic resonance imaging data to determine physiological parameters pixelwise to visualize a cerebral tumor, and the results are compared favorably with those of the standard truncated singular value decomposition approach. In particular, kernels estimated with constrained exponentials are free of oscillations and staircasing, and the images of estimated kernel parameters are sharper than those obtained by truncated singular value decomposition.