Denoising and spatial resolution enhancement of 4D flow MRI using proper orthogonal decomposition and lasso regularization.

4D-Flow MRI has emerged as a powerful tool to non-invasively image blood velocity profiles in the human cardio-vascular system. However, it is plagued by issues such as velocity aliasing, phase offsets, acquisition noise, and low spatial and temporal resolution. In imaging small blood vessel malformations such as intra-cranial aneurysms, the spatial resolution of 4D-Flow is often inadequate to resolve fine flow features. In this paper, we address the problem of low spatial resolution and noise by combining 4D-Flow MRI and patient specific computational fluid dynamics using Least Absolute Shrinkage and Selection Operator. Extensive experiments using numerical phantoms of two actual intra-cranial aneurysms geometries show the applicability of the proposed method in recovering the flow profile. Comparisons with the state-of-the-art denoising methods for 4D-Flow show lower error metrics. This method can enable more accurate computation of flow derived patho-physiological parameters such as wall shear stresses, pressure gradients, and viscous dissipation.

Curvelet Transform-based volume fusion for correcting signal loss artifacts in Time-of-Flight Magnetic Resonance Angiography data.

Flow fields in cerebral aneurysms can be measured in vivo with phase-contrast MRI (4D Flow MRI), providing 3D anatomical magnitude images as well as 3-directional velocities through the cardiac cycle. The low spatial resolution of the 4D Flow MRI data, however, requires the images to be co-registered with higher resolution angiographic data for better segmentation of the blood vessel geometries to adequately quantify relevant flow descriptors such as wall shear stress or flow residence time. Time-of-Flight Magnetic Resonance Angiography (TOF MRA) is a non-invasive technique for visualizing blood vessels without the need to administer contrast agent. Instead TOF uses the blood flow-related enhancement of unsaturated spins entering into an imaging slice as means to generate contrast between the stationary tissue and the moving blood. Because of the higher resolutions, TOF data are often used to assist with the segmentation process needed for the flow analysis and Computational Fluid Dynamics (CFD) modeling. However, presence of slow moving and recirculating blood flow such as in brain aneurysms, especially regions where the blood flow is not perpendicular to the image plane, causes signal loss in these regions. In this work a 3D Curvelet Transform-based image fusion approach is proposed for signal loss artifact reduction of TOF volume data. Experiments show the superiority of the proposed approach in comparison to other multi-resolution 3D Wavelet-based image fusion methodologies. The proposed approach can further facilitate model-based fluid analysis and pre/post-operative treatment of patients with brain aneurysms.

Slanted support window-based stereo matching for surface reconstruction of microscopic samples.

This work is to address the limitations of 2D Scanning Electron Microscopy (SEM) micrographs in providing 3D topographical information necessary for various types of analysis in biological and biomedical sciences as well as mechanical and material engineering by investigating modern stereo vision methodologies for 3D surface reconstruction of microscopic samples. To achieve this, micrograph pairs of the microscopic samples are acquired by utilizing an SEM equipped with motor controlled specimen stage capable of precise translational, rotational movements and tilting of the specimen stage. After pre-processing of the micrographs by SIFT feature detection/description followed by RANSAC for matching outlier removal and stereo rectification, a dense stereo matching methodology is utilized which takes advantage of slanted support window formulation for sub-pixel accuracy stereo matching of the input images. This results in a dense disparity map which is used to determine the true depth/elevation of individual surface points. This is a major improvement in comparison to previous matching methodologies which require additional post-processing refinement steps to reduce the negative effects of discrete disparity assignment or the blurring artifacts in near the edge regions. The provided results are great representatives of the superior performance of the slanted support window assumption employed here for surface reconstruction of microscopic samples.

Merging computational fluid dynamics and 4D Flow MRI using proper orthogonal decomposition and ridge regression.

Time resolved phase-contrast magnetic resonance imaging 4D-PCMR (also called 4D Flow MRI) data while capable of non-invasively measuring blood velocities, can be affected by acquisition noise, flow artifacts, and resolution limits. In this paper, we present a novel method for merging 4D Flow MRI with computational fluid dynamics (CFD) to address these limitations and to reconstruct de-noised, divergence-free high-resolution flow-fields. Proper orthogonal decomposition (POD) is used to construct the orthonormal basis of the local sampling of the space of all possible solutions to the flow equations both at the low-resolution level of the 4D Flow MRI grid and the high-level resolution of the CFD mesh. Low-resolution, de-noised flow is obtained by projecting in vivo 4D Flow MRI data onto the low-resolution basis vectors. Ridge regression is then used to reconstruct high-resolution de-noised divergence-free solution. The effects of 4D Flow MRI grid resolution, and noise levels on the resulting velocity fields are further investigated. A numerical phantom of the flow through a cerebral aneurysm was used to compare the results obtained using the POD method with those obtained with the state-of-the-art de-noising methods. At the 4D Flow MRI grid resolution, the POD method was shown to preserve the small flow structures better than the other methods, while eliminating noise. Furthermore, the method was shown to successfully reconstruct details at the CFD mesh resolution not discernible at the 4D Flow MRI grid resolution. This method will improve the accuracy of the clinically relevant flow-derived parameters, such as pressure gradients and wall shear stresses, computed from in vivo 4D Flow MRI data.