Nowadays, MRSI represents a powerful non-invasive diagnostic tool. The ability of Magnetic Resonance Spectroscopy to detect metabolites is already very useful in daily radiologic practice since it provides significant biochemical information on the molecules of the organism under investigation. MRSI data can also be exploited in tissue segmentation techniques, which play a crucial role in many biomedical applications, such as the quantification of tissue volumes, localization of possible pathologies, improvement of pre-surgical diagnosis, and optimization of the surgical approach, therapy planning, etc. A variety of methods are available in the literature. They are often used in combination in order to solve different segmentation problems.They can be divided into several categories: thresholding techniques (1), region growing techniques (2), clustering techniques (3), Markov random field models (4), classifiers (5), artificial neural networks (6), etc. In this article, a fast and reliable tissue segmentation technique, based on a statistical method called Canonical Correlation Analysis (CCA), is proposed. This method is the multivariate variant of the ordinary correlation analysis and has already been successfully applied to functional Magnetic Resonance Imaging data in order to map sensor, motor, and cognitive functions to specific areas in the brain (7). Here CCA is adapted for MRSI data processing in order to detect possible homogeneous tissue regions, such as tumor regions, characterizing the considered sample. The goal is achieved by combining the spectral-spatial information provided by the MRSI data set and a signal subspace that models the spectrum of a characteristic tissue type, which may be present in the organ under investigation and, therefore, needs to be detected. More precisely, CCA quantifies the relationship between two sets of variables, magnitude spectra of the measured data and signal subspace, by means of correlation coefficients. These coefficients are afterward exploited in order to construct nosologic images (8) in which all the detected tissues are visualized. Such images can be easily interpreted by radiologists and physicians and, along with clinical and radiologic information, can improve the accuracy of the diagnosis.Extensive studies, performed on simulated as well as in vivo prostate MRSI data, were carried out in order to explore the properties of the proposed method. Moreover, the performance of CCA and ordinary correlation analysis was compared. The aforementioned studies show that CCA significantly outperforms ordinary correlation analysis in terms of accuracy and robustness.The article is organized as follows. In the Theory section, the basic definition of CCA is introduced and 3 possible implementations are outlined. In the Methods section, the application of CCA to MRSI data is described and the set up for the simulation studies and the acquisition environment of the in vivo studies is defined. In the Results and Discussion section, the results of the simulation and in viv...
This paper compares two spectral processing methods for obtaining quantitative measures from in vivo prostate spectra, evaluates their effectiveness, and discusses the necessary modifications for accurate results. A frequency domain analysis (FDA) method based on peak integration was compared with a time domain fitting (TDF) method, a model-based nonlinear least squares fitting algorithm. The accuracy of both methods at estimating the choline + creatine + polyamines to citrate ratio (CCP:C) was tested using Monte Carlo simulations, empirical phantom MRSI data and in vivo MRSI data. The paper discusses the different approaches employed to achieve the quantification of the overlapping choline, creatine and polyamine resonances. Monte Carlo simulations showed induced biases on the estimated CCP:C ratios. Both methods were successful in identifying tumor tissue, provided that the CCP:C ratio was greater than a given (normal) threshold. Both methods predicted the same voxel condition in 94% of the in vivo voxels (68 out of 72). Both TDF and FDA methods had the ability to identify malignant voxels in an artifact-free case study using the estimated CCP:C ratio. Comparing the ratios estimated by the TDF and the FDA, the methods predicted the same spectrum type in 17 out of 18 voxels of the in vivo case study (94.4%).
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