Biomedical subjects

T Laudadio

Publications and source records attributed to T Laudadio.

3 recordsLinked to original sources

PubMed2006

Fast nosologic imaging of the brain.

Magnetic resonance spectroscopic imaging (MRSI) provides information about the spatial metabolic heterogeneity of an organ in the human body. In this way, MRSI can be used to detect tissue regions with abnormal metabolism, e.g. tumor tissue. The main drawback of MRSI in clinical practice is that the analysis of the data requires a lot of expertise from the radiologists. In this article, we present an automatic method that assigns each voxel of a spectroscopic image of the brain to a histopathological class. The method is based on Canonical Correlation Analysis (CCA), which has recently been shown to be a robust technique for tissue typing. In CCA, the spectral as well as the spatial information about the voxel is used to assign it to a class. This has advantages over other methods that only use spectral information since histopathological classes are normally covering neighbouring voxels. In this paper, a new CCA-based method is introduced in which MRSI and MR imaging information is integrated. The performance of tissue typing is compared for CCA applied to the whole MR spectra and to sets of features obtained from the spectra. Tests on simulated and in vivo MRSI data show that the new method is very accurate in terms of classification and segmentation. The results also show the advantage of combining spectroscopic and imaging data.

Algorithms↗

PubMed2004

Subspace-based MRS data quantitation of multiplets using prior knowledge.

Accurate quantitation of Magnetic Resonance Spectroscopy (MRS) signals is an essential step before converting the estimated signal parameters, such as frequencies, damping factors, and amplitudes, into biochemical quantities (concentration, pH). Several subspace-based parameter estimators have been developed for this task, which are efficient and accurate time-domain algorithms. However, they suffer from a serious drawback: they allow only a limited inclusion of prior knowledge which is important for accuracy and resolution. In this paper, a new method is presented: KNOB-SVD and its improved variant KNOB-TLS. KNOB-SVD is a recently proposed method, based on the Singular Value Decomposition (SVD), which allows the use of more prior knowledge about the signal parameters than previously published subspace-based methods. We compare its performance in terms of robustness and accuracy with the performance of three commonly used methods for signal parameter estimation: HTLS, a subspace-based method which does not allow any inclusion of prior knowledge, except for the model order; HTLSPK(Delta fd(eq)), a subspace-based method obtained by incorporating in HTLS the prior information that the frequency differences between doublet components are known and the damping factors are equal; and AMARES, an interactive maximum likelihood method that allows the inclusion of a variety of prior knowledge. Extensive simulation and in vivo studies, using (31)P as well as proton MRS signals, show that the new method outperforms HTLS and HTLSPK(Delta fd(eq)) in robustness, accuracy, and resolution, and that it provides parameter estimates comparable to the AMARES ones.

Journal Article↗

PubMed2002

Improved Lanczos algorithms for blackbox MRS data quantitation.

Magnetic resonance spectroscopy (MRS) has been shown to be a potentially important medical diagnostic tool. The success of MRS depends on the quantitative data analysis, i.e., the interpretation of the signal in terms of relevant physical parameters, such as frequencies, decay constants, and amplitudes. A variety of time-domain algorithms to extract parameters have been developed. On the one hand, there are so-called blackbox methods. Minimal user interaction and limited incorporation of prior knowledge are inherent to this type of method. On the other hand, interactive methods exist that are iterative, require user involvement, and allow inclusion of prior knowledge. We focus on blackbox methods. The computationally most intensive part of these blackbox methods is the computation of the singular value decomposition (SVD) of a Hankel matrix. Our goal is to reduce the needed computational time without affecting the accuracy of the parameters of interest. To this end, algorithms based on the Lanczos method are suitable because the main computation at each step, a matrix-vector product, can be efficiently performed by means of the fast Fourier transform exploiting the structure of the involved matrix. We compare the performance in terms of accuracy and efficiency of four algorithms: the classical SVD algorithm based on the QR decomposition, the Lanczos algorithm, the Lanczos algorithm with partial reorthogonalization, and the implicitly restarted Lanczos algorithm. Extensive simulation studies show that the latter two algorithms perform best.

Algorithms↗