Magnetic Resonance Spectroscopy

K.U.Leuven

Tuesday, November 29th 2011 from 9am until 8pm

Arenbergkasteel, Kasteelpark Arenberg 1, 3001 Leuven - Belgium,
Auditorium 01.07

09.00 - 09.30

Prof. dr. ir. D. Graveron - Demilly

Université Claude Bernard LYON 1, France

Towards non-invasive in vivo assessement of metabolic content: the jMRUI software package for medical MRS

09.30 - 10.00

Prof. dr. D. van Ormondt

Delft University of Technology, The Netherlands

Semi-parametric estimation in MRS without searching in function space

10.00 - 10.30

Coffee break

10.30 - 12.30

Maria Isabel Osorio Garcia

Advanced signal processing for Magnetic Resonance Spectroscopy

- PhD Defense - See details

Jury

Prof. dr. ir. P. Van Houtte, president
Prof. dr. ir. S. Van Huffel, promotor
Prof. dr. U. Himmelreich, copromotor
Dr. D. Sima, copromotor
Prof. dr. M. Hubert
Prof. dr. ir. D. Vandermeulen
Prof. dr. ir. R. Pintelon (VUB)
Prof. dr. ir. D. Graveron-Demilly (INSA Lyon)
Prof. dr. D. van Ormondt (TU Delft)

Abstract

Assertive diagnosis of cancer, Alzheimer's disease, epilepsy and other metabolic diseases is essential to provide patients with the adequate treatment. Recently, different invasive and non-invasive techniques have been developed for this purpose, nevertheless, due to their harmless properties the non-invasive techniques have gained more value. A well-known non-invasive technique is Magnetic Resonance Spectroscopic Imaging (MRSI), which provides spectra (metabolite peaks) and images (anatomical structures) of the examined tissue.

In MRS, molecules containing certain excitable nuclei, such as 1H, provide the metabolite information. As a consequence, the MR spectra obtained contain peaks corresponding to different metabolites which are finally the biomarkers of diseases. In order to estimate the concentration of these metabolites, the amplitudes are computed and compared against normal values in order to establish the diagnosis. The method to obtain such amplitudes is also called quantification and its accuracy is essential for a correct diagnosis.

Quantification of MRS signals is affected by a relatively low signal-to-noise ratio (SNR), a residual water resonance, lineshape distortions, overlapping resonances and underlying macromolecules and lipids affecting their baseline. Our ultimate goal was, therefore, based on the development of signal processing tools and algorithms to improve the quantification of MRS signals. For this, we proposed a heuristic method to improve the residual water filtering using Hankel Singular Value Decomposition (HSVD). Additionally, a method for lineshape estimation of distorted MR spectra was developed and evaluated in simulated, in vitro and in vivo signals. Besides, the baseline approach was implemented via a parametric modelling method based on prior knowledge acquired from a set of in vivo macromolecular signals (measured via inversion recovery), which aims at avoiding long measurements and allowing a flexible set of baseline components. Finally, analysis of quantification results aimed towards an automatic evaluation of the residual, thereby benefiting MRS spectral analysis in the clinical environment.

12.30 - 14.00

Lunch break

14.00 - 14.30

Prof. dr. A. Heerschap

Radboud University Medical Centre Nijmegen, The Netherlands

Towards MR spectroscopy in routine clinical practice

14.30 - 15.00

Prof. dr. B. Celda

Universitat de Valencia, Spain

Metabolic profiles in biomedicine and clinical research

15.00 - 15.30

Coffee break

15.30 - 17.30

Anca R. Croitor Sava

Signal processing and classification for Magnetic Resonance Spectroscopy with clinical applications

- PhD Defense - See details

Jury

Prof. dr. ir. Carlo Vandecasteele, president
Prof. dr. ir. S. Van Huffel, promotor
Prof. dr. D.M. Sima, co-promoter
Prof. dr. U. Himmelreich
Prof. dr. ir. D. Vandermeulen
Prof. dr. A.M. De Meyer
Prof. dr. A. Heerschap (RUMCN Nijmegen)
Prof. dr. B. Celda (UVEG Valencia)
Prof. dr. D. Graveron-Demilly (INSA Lyon)

Abstract

Over the past decades, Magnetic Resonance Imaging (MRI) has taken a leading role in the study of human body and it is widely used in clinical diagnosis. In vivo and ex vivo Magnetic Resonance Spectroscopic (MRS) techniques can additionally provide valuable metabolic information as compared to MRI and are gaining more clinical interest. The analysis of MRS data is a complex procedure and requires several preprocessing steps aiming to improve the quality of the data and to extract the most relevant features before any classification algorithm can be successfully applied.

In this thesis a new approach to quantify magnetic resonance spectroscopic imaging (MRSI) data and therefore to obtain improved metabolite estimates is proposed. Then, an important part is focusing on improving the diagnosis of glial brain tumors which are characterized by an extensive heterogeneity since various intratumoral histopathological properties such as viable tumor cells, necrotic tissue and infiltration with normal tissue can be identified in the tumor region. For a reliable diagnosis of the glial tumor type and grade this thesis proposes a first screening between these intratumoral histopathological properties. To this aim, cluster analysis and several blind source separation methods are tested on ex vivo HR-MAS and in vivo MRSI data. Moreover, several approaches to fuse multimodal information coming from MRI, MRSI and HR-MAS for the classification of glial brain tumors are considered.

MRS techniques are nowadays successfully considered for the analysis of body fluids. A pilot research to study the amniotic fluid from fetuses with congenital diaphragmatic hernia using high resolution MRS spectroscopy is proposed.

17.30 - 20.00

Reception