Trehalose as quantitative biomarker for in vivo diagnosis and treatment follow-up in cryptococcomas.

Brain lesions caused by Cryptococcus neoformans or C. gattii (cryptococcomas) are typically difficult to diagnose correctly and treat effectively, but rapid differential diagnosis and treatment initiation are crucial for good outcomes. In previous studies, cultured cryptococcal isolates and ex vivo lesion material contained high concentrations of the virulence factor and fungal metabolite trehalose. Here, we studied the in vivo metabolic profile of cryptococcomas in the brain using magnetic resonance spectroscopy (MRS) and assessed the relationship between trehalose concentration, fungal burden, and treatment response in order to validate its suitability as marker for early and noninvasive diagnosis and its potential to monitor treatment in vivo. We investigated the metabolites present in early and late stage cryptococcomas using in vivo 1H MRS in a murine model and evaluated changes in trehalose concentrations induced by disease progression and antifungal treatment. Animal data were compared to 1H and 13C MR spectra of Cryptococcus cultures and in vivo data from 2 patients with cryptococcomas in the brain. In vivo MRS allowed the noninvasive detection of high concentrations of trehalose in cryptococcomas and showed a comparable metabolic profile of cryptococcomas in the murine model and human cases. Trehalose concentrations correlated strongly with the fungal burden. Treatment studies in cultures and animal models showed that trehalose concentrations decrease following exposure to effective antifungal therapy. Although further cases need to be studied for clinical validation, this translational study indicates that the noninvasive MRS-based detection of trehalose is a promising marker for diagnosis and therapeutic follow-up of cryptococcomas.

Hierarchical non-negative matrix factorization applied to three-dimensional 3 T MRSI data for automatic tissue characterization of the prostate.

In this study non-negative matrix factorization (NMF) was hierarchically applied to simulated and in vivo three-dimensional 3 T MRSI data of the prostate to extract patterns for tumour and benign tissue and to visualize their spatial distribution. Our studies show that the hierarchical scheme provides more reliable tissue patterns than those obtained by performing only one NMF level. We compared the performance of three different NMF implementations in terms of pattern detection accuracy and efficiency when embedded into the same kind of hierarchical scheme. The simulation and in vivo results show that the three implementations perform similarly, although one of them is more robust and better pinpoints the most aggressive tumour voxel(s) in the dataset. Furthermore, they are able to detect tumour and benign tissue patterns even in spectra with lipid artefacts. Copyright © 2016 John Wiley & Sons, Ltd.

Hierarchical non-negative matrix factorization (hNMF): a tissue pattern differentiation method for glioblastoma multiforme diagnosis using MRSI.

MRSI has shown potential in the diagnosis and prognosis of glioblastoma multiforme (GBM) brain tumors, but its use is limited by difficult data interpretation. When the analyzed MRSI data present more than two tissue patterns, conventional non-negative matrix factorization (NMF) implementation may lead to a non-robust estimation. The aim of this article is to introduce an effective approach for the differentiation of GBM tissue patterns using MRSI data. A hierarchical non-negative matrix factorization (hNMF) method that can blindly separate the most important spectral sources in short-TE ¹H MRSI data is proposed. This algorithm consists of several levels of NMF, where only two tissue patterns are computed at each level. The method is demonstrated on both simulated and in vivo short-TE ¹H MRSI data in patients with GBM. For the in vivo study, the accuracy of the recovered spectral sources was validated using expert knowledge. Results show that hNMF is able to accurately estimate the three tissue patterns present in the tumoral and peritumoral area of a GBM, i.e. normal, tumor and necrosis, thus providing additional useful information that can help in the diagnosis of GBM. Moreover, the hNMF results can be displayed as easily interpretable maps showing the contribution of each tissue pattern to each voxel.

Unsupervised nosologic imaging for glioma diagnosis.

In this letter a novel approach to create nosologic images of the brain using magnetic resonance spectroscopic imaging (MRSI) data in an unsupervised way is presented. Different tissue patterns are identified from the MRSI data using nonnegative matrix factorization and are then coded as different primary colors (i.e. red, green, and blue) in an RGB image, so that mixed tissue regions are automatically visualized as mixtures of primary colors. The approach is useful in assisting glioma diagnosis, where several tissue patterns such as normal, tumor, and necrotic tissue can be present in the same voxel/spectrum. Error-maps based on linear least squares estimation are computed for each nosologic image to provide additional reliability information, which may help clinicians in decision making. Tests on in vivo MRSI data show the potential of this new approach.

Exploiting spatial information to estimate metabolite levels in two-dimensional MRSI of heterogeneous brain lesions.

MRSI provides MR spectra from multiple adjacent voxels within a body volume represented as a two- or three-dimensional matrix, allowing the measurement of the distribution of metabolites over this volume. The spectra of these voxels are usually analyzed one by one, without exploiting their spatial context. In this article, we present an advanced metabolite quantification method for MRSI data, in which the available spatial information is considered. A nonlinear least-squares algorithm is proposed in which prior knowledge is included in the form of proximity constraints on the spectral parameters within a grid and optimized starting values. A penalty term that promotes a spatially smooth spectral parameter map is added to the fitting algorithm. This method is adaptive, in the sense that several sweeps through the grid are performed and each solution may tune some hyperparameters at run-time. Simulation studies of MRSI data showed significantly improved metabolite estimates after the inclusion of spatial information. Improved metabolite maps were also demonstrated by applying the method to in vivo MRSI data. Overlapping peaks or peaks of compounds present at low concentration can be better quantified with the proposed method than with single-voxel approaches. The new approach compares favorably against the multivoxel approach embedded in the well-known quantification software LCModel.