Estimating intravoxel fiber architecture using constrained compressed sensing combined with multitensor adaptive smoothing

Abstract

In diffusion magnetic resonance imaging (dMRI), the accuracy of fiber tracking and analysis depends directly on that of intravoxel fiber architecture reconstruction. Several methods have been proposed that estimate intravoxel fiber architecture using low angular resolution acquisitions owing to their shorter acquisition time and relatively low bvalues. But these methods are highly sensitive to noise. We propose an approach to estimating intravoxel fiber architecture in low angular resolution dMRI. The method consists in using a constrained compressed sensing (CCS) method, also known as crossing fiber angular resolution of intravoxel architecture (CFARI) technique, in combination with multitensor adaptive smoothing in which a diffusion-weighted (DW) image smoothing scheme is constructed according to the properties of the multitensor field estimated using CFARI. The results on synthetic, physical phantom and real brain DW images show that the proposed method is able to better resolve fiber architectures while correctly preserving image edge information, which provides a new tool for investigating the microstructures of biological tissues and for fiber tractography.

Brain networks and their relevance for stroke rehabilitation

“…This motivated novel developments based on i) higher angular estimation of fibre orientation by constrained spherical deconvolution (Tournier et al, 2004) optimized tractography (see below) and ii) a more complex modelling of microstructure. Latter includes the usage of multi-tensor models (Malcolm et al, 2010, Chu et al, 2015 or diffusion kurtosis imaging (Jensen et al, 2005a), a model free fitting measuring deviation from a Gaussian distributed signal, which has been used for clinical prediction studies evaluating e.g., the corticospinal tract in stroke (Hui et al, 2012, Spampinato et al, 2017. A very promising approach, especially for the usage in clinical research, is the compartment model framework.…”

Structure Prior Constrained Estimation of Human Cardiac Diffusion Tensors

“…The criterion for selecting the best value was that the mean E1 error be as small as possible. The six indices (E1, E2, HA, TA, FA, and MD) were calculated from the DTs estimated using the four different methods (IRLLS, LPCA, VF, > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 6 and SPC) on the human cardiac data acquired with different numbers (12,30, and 64) of DGDs, and compared to the GT.…”

“…The real cardiac DW data was taken from five ex-vivo human hearts. The data was acquired on a Siemens Avanto 1.5T MR scanner, using echo planar imaging sequence with one image without diffusion weighting and three different numbers (12,30, and 64) of DGDs. Specially, the acquisition with 64 DGDs was repeated 20 times.…”

“…Since these DT estimators are sensitive to noise, it is often convenient to adopt denoising techniques for improving the DT estimation. Recently, a number of denoising methods [30], [31], [40], [41], [32]- [39] [32], [33] methods. Additionally, a principal component analysis based method [42] was also used to process in vivo cardiac DW images [28].…”

MRI-based Measurement of Structural and Functional Connectivity in Health and Disease

“…Furthermore, the tensor model assumes a Gaussian distribution of diffusion, whereas the complex tissue of the brain restricts water diffusion and causes deviation from a Gaussian diffusion displacement distribution (Jensen et al, 2005). Therefore, novel and more complex models have been developed such as multi-tensor models (Chu et al, 2015), diffusion kurtosis imaging (Jensen et al, 2005) (i.e. to measure deviation from the Gaussiandistributed signal), compartment models like CHARMED (Assaf and Basser, 2005) and NODDI (Zhang et al, 2012) (i.e.…”