Dataset Information

High-resolution in vivo MR-STAT using a matrix-free and parallelized reconstruction algorithm.

ABSTRACT: MR-STAT is a recently proposed framework that allows the reconstruction of multiple quantitative parameter maps from a single short scan by performing spatial localisation and parameter estimation on the time-domain data simultaneously, without relying on the fast Fourier transform (FFT). To do this at high resolution, specialized algorithms are required to solve the underlying large-scale nonlinear optimisation problem. We propose a matrix-free and parallelized inexact Gauss-Newton based reconstruction algorithm for this purpose. The proposed algorithm is implemented on a high-performance computing cluster and is demonstrated to be able to generate high-resolution (1?mm? × ?1?mm in-plane resolution) quantitative parameter maps in simulation, phantom, and in vivo brain experiments. Reconstructed T1 and T2 values for the gel phantoms are in agreement with results from gold standard measurements and, for the in vivo experiments, the quantitative values show good agreement with literature values. In all experiments, short pulse sequences with robust Cartesian sampling are used, for which MR fingerprinting reconstructions are shown to fail.

SUBMITTER: van der Heide O

PROVIDER: S-EPMC7079175 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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High-resolution in vivo MR-STAT using a matrix-free and parallelized reconstruction algorithm.

van der Heide Oscar O Sbrizzi Alessandro A Luijten Peter R PR van den Berg Cornelis A T CAT

NMR in biomedicine 20200127 4

MR-STAT is a recently proposed framework that allows the reconstruction of multiple quantitative parameter maps from a single short scan by performing spatial localisation and parameter estimation on the time-domain data simultaneously, without relying on the fast Fourier transform (FFT). To do this at high resolution, specialized algorithms are required to solve the underlying large-scale nonlinear optimisation problem. We propose a matrix-free and parallelized inexact Gauss-Newton based recons ...[more]

PMID: 31985134

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Project description:PurposeTo evaluate the capability of P947, a magnetic resonance (MR) imaging contrast agent that molecularly targets matrix metalloproteinases (MMPs), to aid detection and imaging of MMPs in atherosclerotic lesions in vivo; its specificity compared with that of P1135; expression and distribution of MMPs in atherosclerotic vessels; and in vivo distribution and molecular localization of fluorescent europium (Eu) P947.Materials and methodsThe Animal Care and Use Committee approved all experiments. P947 was synthesized by attaching a gadolinium chelate (1,4,7,10-tetraazacyclododecane-N,N',N'',N'''-tetraacetic acid) to a peptide that specifically binds MMPs. Scrambled form of P947 (P1135) was synthesized by replacing the targeting moiety of P947 with a scrambled peptide lacking the ability to bind MMPs. P947, P1135, and gadoterate meglumine were injected into atherosclerotic apolipoprotein E-deficient and wild-type mice. The aortic MR imaging enhancement produced by the contrast agents was measured at different times and was compared by using one-way analysis of variance. MMP expression was investigated in the aortas by using MMP immunostaining and in situ MMP zymography. A fluorescent form of P947 (Eu-P947) was synthesized to compare the in vivo distribution of the contrast agent (Eu-P947) with specific MMP immunofluorescent staining.ResultsMMP-targeted P947 facilitated a 93% increase (P < .001) in MR image signal intensity (contrast-to-noise ratio [CNR], 17.7 compared with 7.7; P < .001) of atherosclerotic lesions in vivo. Nontargeted P1135 (scrambled P947) provided 33% MR image enhancement (CNR, 10.8), whereas gadoterate meglumine provided 5% (CNR, 6.9). Confocal laser scanning microscopy demonstrated colocalization between fluorescent Eu-P947 and MMPs in atherosclerotic plaques. Eu-P947 was particularly present in the fibrous cap region of plaques.ConclusionP947 improved MR imaging for atherosclerosis through MMP-specific targeting. The results were validated and provide support for further assessment of P947 as a potential tool for the identification of unstable atherosclerosis.

Project description:The recent advent of tensor tomography techniques has enabled tomographic investigations of the 3D nanostructure organization of biological and material science samples. These techniques extended the concept of conventional X-ray tomography by reconstructing not only a scalar value such as the attenuation coefficient per voxel, but also a set of parameters that capture the local anisotropy of nanostructures within every voxel of the sample. Tensor tomography data sets are intrinsically large as each pixel of a conventional X-ray projection is substituted by a scattering pattern, and projections have to be recorded at different sample angular orientations with several tilts of the rotation axis with respect to the X-ray propagation direction. Currently available reconstruction approaches for such large data sets are computationally expensive. Here, a novel, fast reconstruction algorithm, named iterative reconstruction tensor tomography (IRTT), is presented to simplify and accelerate tensor tomography reconstructions. IRTT is based on a second-rank tensor model to describe the anisotropy of the nanostructure in every voxel and on an iterative error backpropagation reconstruction algorithm to achieve high convergence speed. The feasibility and accuracy of IRTT are demonstrated by reconstructing the nanostructure anisotropy of three samples: a carbon fiber knot, a human bone trabecula specimen and a fixed mouse brain. Results and reconstruction speed were compared with those obtained by the small-angle scattering tensor tomography (SASTT) reconstruction method introduced by Liebi et al. [Nature (2015), 527, 349-352]. The principal orientation of the nanostructure within each voxel revealed a high level of agreement between the two methods. Yet, for identical data sets and computer hardware used, IRTT was shown to be more than an order of magnitude faster. IRTT was found to yield robust results, it does not require prior knowledge of the sample for initializing parameters, and can be used in cases where simple anisotropy metrics are sufficient, i.e. the tensor approximation adequately captures the level of anisotropy and the dominant orientation within a voxel. In addition, by greatly accelerating the reconstruction, IRTT is particularly suitable for handling large tomographic data sets of samples with internal structure or as a real-time analysis tool during the experiment for online feedback during data acquisition. Alternatively, the IRTT results might be used as an initial guess for models capturing a higher complexity of structural anisotropy such as spherical harmonics based SASTT in Liebi et al. (2015), improving both overall convergence speed and robustness of the reconstruction.

Dataset Information

High-resolution in vivo MR-STAT using a matrix-free and parallelized reconstruction algorithm.

Publications

High-resolution in vivo MR-STAT using a matrix-free and parallelized reconstruction algorithm.

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