Anca Croitor Sava

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. Copyright

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. Copyright

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

Corrections to “Unsupervised Nosologic Imaging for Glioma Diagnosis”

In the above-named work [ibid., vol. 60, no. 6, pp. 1760–1465, Jun. 2013], the first authors affiliation should have read: Y. Li is with the School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731 China, and also with the Department of Electrical Engineering and IBBT-Future Health Department, Katholieke Universiteit Leuven, Leuven 3001, Belgium (e-mail: yuqianli@ uestc.edu.cn). The sixth author Y. Pis affiliation should have read: Y. Pi is with the School of Electronic Engineering, University of Electronic Science and Technology of China.

Quantifying brain tumor tissue abundance in HR-MAS spectra using non-negative blind source separation techniques

Given high‐resolution magic angle spinning (HR‐MAS) spectra from several glial tumor subjects, our goal is to differentiate between tumor tissue types by separating the different sources that contribute to the profile of each spectrum. Blind source separation techniques are applied for obtaining characteristic profiles for necrosis, highly cellular tumor and border tumor tissue and providing the contribution (abundance) of each of these tumor tissue types to the profile of each spectrum. The problem is formulated as a non‐negative source separation problem. Non‐negative matrix factorization, convex analysis of non‐negative sources and non‐negative independent component analysis methods are considered. The results are in agreement with the pathology obtained by the histopathological examination that succeeded the HR‐MAS measurements. Furthermore, an analysis to verify to which extent the dimension of the input space, the input features and the number of sources to be extracted from the HR‐MAS data could influence the performance of the source separation is presented. Copyright

Adaptive Alternating Minimization for Fitting Magnetic Resonance Spectroscopic Imaging Signals

In this paper we discuss the problem of modeling Magnetic Resonance Spectroscopic Imaging (MRSI) signals, in the aim of estimating metabolite concentration over a region of the brain. To this end, we formulate nonconvex optimization problems and focus on appropriate constraints and starting values for the model parameters. Furthermore, we explore the applicability of spatial smoothness for the nonlinear model parameters across the MRSI grid. In order to simultaneously fit all signals in the grid and to impose spatial constraints, an adaptive alternating nonlinear least squares algorithm is proposed. This method is shown to be much more reliable than independently fitting each signal in the grid.