Browsing by Author "Lundholm, Lukas"

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DEVELOPMENT, VALIDATION AND APPLICATION OF A METHOD FOR DETERMINATION OF METABOLITE CONCENTRATIONS WITH PRECLINICAL MAGNETIC RESONANCE SPECTROSCOPY
(2021-05-10) Lundholm, Lukas; University of Gothenburg/Institute of Clinical Sciences; Göteborgs universitet/Institutionen för kliniska vetenskaper
Background: Information on the metabolic content in tissue has diagnostic and prognostic value when examining for example cancer and diseases of the brain. MR spectroscopy is a non-invasive method that allows quantification of metabolite concentrations in vivo, without the use of ionizing radiation, which makes the method highly attractive for both research and clinical applications. However, specialized software is required for generation of so called basis sets, which consist of information on the individual metabolites that are under investigation, and which are required for quantification. Furthermore, method- and MR vendor-specific information must be provided as the basis sets are being generated in order to yield reliable quantification results. A software for generation of basis sets was recently developed at the University of Gothenburg and validated for a preclinical MR system in a previous master thesis project. However, a standardized method for calculation of metabolite concentrations in vivo in the preclinical setting has not yet been developed. Therefore, the purpose of this work was to adapt, validate and apply a method for non-invasive quantification of metabolites from in vivo MR spectroscopy at the preclinical facility at the University of Gothenburg. Method: The software, implemented in MATLAB and previously developed to simulate basis sets for the clinical MR system, was programmatically adapted to import pulse sequence parameters from the preclinical MR system. Validation of the adapted MATLAB software was done by MR spectroscopy measurements on a phantom solution with known concentrations of certain metabolites, followed by metabolite quantification using the LCModel software. Two in vivo experiments were performed to assess the applicability of the method in the preclinical setting: one on the healthy mouse brain and one on a mouse model of human cancer. The point resolved spectroscopy (PRESS) pulse sequence was used for all measurements and simulations.3 Results: The adaption of the software to the preclinical MR system was successful, resulting in simulated basis sets that could be well fitted to the spectra measured in the validation which, in turn, resulted in accurate determination of metabolite concentrations in the phantom. The in vivo experiments resulted in metabolic profiles of the cancer model and the healthy mouse brain that were in agreement with what has been found in previous studies. The method developed in this work will thus enable metabolite quantification of existing and future MR spectroscopy studies at the preclinical MR facility at the University of Gothenburg.
Diffusion MRI for tumor microstructure imaging using VERDICT modeling
(2025-05-20) Lundholm, Lukas
VERDICT is a method which uses a mathematical model that provides estimates of microstructural tumor tissue parameters based on diffusion-weighted MRI data. It is a promising imaging method for non-invasive in vivo evaluation of whole-tumor tissue. However, model assumptions may introduce systematic errors in parameter estimates. The aim of this thesis was to assess the use of VERDICT for tumor tissue evaluation and investigate the impact of model assumptions on parameter estimates, as well as to develop and evaluate methods addressing accuracy issues related to some of these assumptions. The standard clinical approach for evaluating tumor treatment response is by measuring changes in gross tumor volume. However, such changes can be slow, and methods sensitive to microstructural changes may detect response earlier. Paper I investigates the use of VERDICT parameters for radiation treatment response assessment and shows that early parameter changes correlate with treatment outcome. Histological analysis remains the gold standard for assessing tumor microstructure, but tumor heterogeneity limits biopsy representativeness. Paper II explores the use of VERDICT for whole-tumor tissue classification as a potential complement to histology. The work shows that multidimensional cluster analysis of VERDICT parameters enables classification of distinct tumor tissue types. Model assumptions can introduce systematic errors in parameter estimates. Paper III investigates the effect of assumptions related to extracellular–extravascular diffu-sion and presents a Monte Carlo-based model which explicitly accounts for diffusion time dependence. Paper IV investigates the impact of including compartment-specific T2 relaxation in the model, in contrast to uniform T2 relaxation across compartments as assumed in conventional VERDICT. These works show that model assumptions can significantly influence parameter estimates and present methods to mitigate their effects. In conclusion, the results of this thesis highlight the importance of accurate model assumptions in VERDICT, and demonstrate the model’s potential for non-invasive, whole-tumor evaluation of tumor tissue in various applications.