Unsupervised Nosologic Imaging for Glioma Diagnosis

Abstract

In this letter a novel approach to create nosologic images of the brain using magnetic resonance spectroscopic imaging (MRSI) data in an unsupervised way is presented. Different tissue patterns are identified from the MRSI data using nonnegative matrix factorization and are then coded as different primary colors (i.e. red, green, and blue) in an RGB image, so that mixed tissue regions are automatically visualized as mixtures of primary colors. The approach is useful in assisting glioma diagnosis, where several tissue patterns such as normal, tumor, and necrotic tissue can be present in the same voxel/spectrum. Error-maps based on linear least squares estimation are computed for each nosologic image to provide additional reliability information, which may help clinicians in decision making. Tests on in vivo MRSI data show the potential of this new approach.

Data Analysis and Tissue Type Assignment for Glioblastoma Multiforme

“…The MRSI protocol had the same imaging parameters as in our previous work [15, 16]. All the MRSI data were acquired at the University Hospital of Leuven (UZ Leuven, Belgium) on a 3 T MR scanner (Achieva, Philips, Best, The Netherlands).…”

“…Written informed consent was obtained from all patients before their participation in the study. Data preprocessing was done as in our previous papers [15, 16] using the in-house software SPID [19]. …”

“…Unlike the supervised classification methods, which labels each voxel based on large training sets [5–10], tissue typing for NMF tissue differentiation is not usually considered [12, 13, 16]. Recently, a tissue typing method was carried out by simply exploring which tissue contributes most to the voxel [14].…”

“…Such an approach ignored the voxels with intensively mixed tissues, that is, the different tissues contributing fairly equal. We tried to integrate the distribution information of each pure tissue in one image by encoding each of them as a color channel [16]. The obtained images, known as nosologic images, showed the spatial distribution of all tissue types.…”

Step-adaptive approximate least squares

“…Examples of such areas are localization [1,2] and positioning [3], robotics [4] and sensor registration [5], power and battery applications [6,7], biomedical applications [8,9], or image processing [10,11]. For the linear LS problem the following system model is assumed:…”