Brain tumor evaluation and segmentation by in vivo proton spectroscopy and relaxometry

Abstract

A new methodology has been developed for the evaluation and segmentation of brain tumors using information obtained by different magnetic resonance techniques such as in vivo proton magnetic resonance spectroscopy (1HMRS) and relaxometry. In vivo 1HMRS may be used as a preoperative technique that allows noninvasive monitoring of metabolites to identify the different tissue types present in the lesion (active tumor, necrotic tissue, edema, and normal or non-affected tissue). Spatial resolution for treatment consideration may be improved by using 1HMRS combined or fused with images obtained by relaxometry which exhibit excellent spatial resolution. Some segmentation schemes are presented and discussed. The results show that segmentation performed in this way efficiently determines the spatial localization of the tumor both qualitatively and quantitatively. It provides appropriate information for therapy planning and application of therapies such as radiosurgery or radiotherapy and future control of patient evolution.

A Quasi-Analytical Method for Relaxation Rate Distribution Determination of T<inf>2</inf>-Weighted MRI in Brain

“…The equally spaced sampling of the magnetization decay allows for a polynomial representation of each data point according to (3). If it is assumed [3], [4], that at most 3 tissues are present in each voxel; normal or unaffected tissue, lesion tissue and cerebrospinal fluid, CSF, a set of non linear polynomial equations [6], [7] can be written:…”

“…Tests performed with synthetic data composed of linear combinations of up to 3 exponential functions demonstrated that solutions of equation (16) subjected to conditions (5) always yielded the right number of exponentials. For the simulations, a set of 3 relaxation rates was chosen according to those expected for tumoral lesions in brain [3], [4], i.e., 2 s -1 for necrosis or CSF, 7 s -1 for tumoral tissue and 12 s -1 for normal or unaffected tissue. The results of the simulation assuming that each relaxation rate exhibits a Gaussian distribution around its mean value are shown in Figure 1.…”

“…Relaxation rates used for the simulations were 2, 7, 12 s -1 , corresponding respectively to liquid or necrosis, tumoral tissue and normal or unaffected tissue [3], [4].…”

“…HE determination of relaxation rate distributions for T 2 -weighted Carr-Purcell-Meiboom-Gill (CPMG) MRI has been previously used [1], [2], [3] for tissue classification and tumor segmentation, particularly for obtaining nosologic maps of tumoral lesions in brain [4]. Those efforts relay on the application of simulated annealing and Metropolis algorithm to perform an inverse Laplace transform on relaxation data and whose details are discussed elsewhere [1], [3].…”

“…Those efforts relay on the application of simulated annealing and Metropolis algorithm to perform an inverse Laplace transform on relaxation data and whose details are discussed elsewhere [1], [3]. Even though these methods are extremely precise and robust for the determination of relaxation rate distributions, they are also extremely slow and require some adjustments and side processes to be applicable on a patient basis.…”