Brain tumor classification of virtual NMR voxels based on realistic blood vessel-induced spin dephasing using support vector machines.

Remodeling of tissue microvasculature commonly promotes neoplastic growth; however, there is no imaging modality in oncology yet that noninvasively quantifies microvascular changes in clinical routine. Although blood capillaries cannot be resolved in typical magnetic resonance imaging (MRI) measurements, their geometry and distribution influence the integral nuclear magnetic resonance (NMR) signal from each macroscopic MRI voxel. We have numerically simulated the expected transverse relaxation in NMR voxels with different dimensions based on the realistic microvasculature in healthy and tumor-bearing mouse brains (U87 and GL261 glioblastoma). The 3D capillary structure in entire, undissected brains was acquired using light sheet fluorescence microscopy to produce large datasets of the highly resolved cerebrovasculature. Using this data, we trained support vector machines to classify virtual NMR voxels with different dimensions based on the simulated spin dephasing accountable to field inhomogeneities caused by the underlying vasculature. In prediction tests with previously blinded virtual voxels from healthy brain tissue and GL261 tumors, stable classification accuracies above 95% were reached. Our results indicate that high classification accuracies can be stably attained with achievable training set sizes and that larger MRI voxels facilitated increasingly successful classifications, even with small training datasets. We were able to prove that, theoretically, the transverse relaxation process can be harnessed to learn endogenous contrasts for single voxel tissue type classifications on tailored MRI acquisitions. If translatable to experimental MRI, this may augment diagnostic imaging in oncology with automated voxel-by-voxel signal interpretation to detect vascular pathologies.

Gibbs point field model quantifies disorder in microvasculature of U87-glioblastoma.

Microvascular proliferation in glioblastoma multiforme is a biological key mechanism to facilitate tumor growth and infiltration and a main target for treatment interventions. The vascular architecture can be obtained by Single Plane Illumination Microscopy (SPIM) to evaluate vascular heterogeneity in tumorous tissue. We make use of the Gibbs point field model to quantify the order of regularity in capillary distributions found in the U87 glioblastoma model in a murine model and to compare tumorous and healthy brain tissue. A single model parameter Γ was assigned that is linked to tissue-specific vascular topology through Monte-Carlo simulations. Distributions of the model parameter Γ differ significantly between glioblastoma tissue with mean 〈ΓG〉=2.1±0.4, as compared to healthy brain tissue with mean 〈ΓH〉=4.9±0.4, suggesting that the average Γ-value allows for tissue differentiation. These results may be used for diagnostic magnetic resonance imaging, where it has been shown recently that Γ is linked to tissue-inherent relaxation parameters.

Improved compressed sensing reconstruction for [Formula: see text]F magnetic resonance imaging.

OBJECTIVE: In magnetic resonance imaging (MRI), compressed sensing (CS) enables the reconstruction of undersampled sparse data sets. Thus, partial acquisition of the underlying k-space data is sufficient, which significantly reduces measurement time. While 19F MRI data sets are spatially sparse, they often suffer from low SNR. This can lead to artifacts in CS reconstructions that reduce the image quality. We present a method to improve the image quality of undersampled, reconstructed CS data sets. MATERIALS AND METHODS: Two resampling strategies in combination with CS reconstructions are presented. Numerical simulations are performed for low-SNR spatially sparse data obtained from 19F chemical-shift imaging measurements. Different parameter settings for undersampling factors and SNR values are tested and the error is quantified in terms of the root-mean-square error. RESULTS: An improvement in overall image quality compared to conventional CS reconstructions was observed for both strategies. Specifically spike artifacts in the background were suppressed, while the changes in signal pixels remained small. DISCUSSION: The proposed methods improve the quality of CS reconstructions. Furthermore, because resampling is applied during post-processing, no additional measurement time is required. This allows easy incorporation into existing protocols and application to already measured data.

Voxel-size dependent quantitative susceptibility mapping of blood vessel networks: A simulation study.

Quantitative susceptibility mapping provides a measure for the local susceptibility within a voxel in magnetic resonance imaging (MRI). So far, theoretical and numerical studies focus on the assumption of a constant susceptibility inside each MR voxel. For blood vessel networks, however, susceptibility differences between blood and surrounding tissue occur on a much smaller length scale than the typical voxel size in routine MRI. In this work, the dependency of the quantitative susceptibility value on vessel size and voxel size is analyzed.

Vessel radius mapping in an extended model of transverse relaxation.

OBJECTIVES: Spin dephasing of the local magnetization in blood vessel networks can be described in the static dephasing regime (where diffusion effects may be ignored) by the established model of Yablonskiy and Haacke. However, for small capillary radii, diffusion phenomena for spin-bearing particles are not negligible. MATERIAL AND METHODS: In this work, we include diffusion effects for a set of randomly distributed capillaries and provide analytical expressions for the transverse relaxation times T2* and T2 in the strong collision approximation and the Gaussian approximation that relate MR signal properties with microstructural parameters such as the mean local capillary radius. RESULTS: Theoretical results are numerically validated with random walk simulations and are used to calculate capillary radius distribution maps for glioblastoma mouse brains at 9.4 T. For representative tumor regions, the capillary maps reveal a relative increase of mean radius for tumor tissue towards healthy brain tissue of [Formula: see text] (p < 0.001). CONCLUSION: The presented method may be used to quantify angiogenesis or the effects of antiangiogenic therapy in tumors whose growth is associated with significant microvascular changes.