Original research
Comparison of heterogeneity quantification algorithms for brain SPECT perfusion images
1 Laboratoire d’Informatique, de Traitement de l’Information et des Systemes (EA-LITIS 4108), QUANT. I. F. (Quantification en Imagerie Fonctionnelle, Faculty of Medicine, Rouen University, Saint Etienne du Rouvray, 76801, France
2 Department of Nuclear Medicine, Henri Becquerel Center and Rouen University Hospital, Rouen, 76000, France
3 Unite Mixte de Recherche – Centre National de la Recherche Scientifique (UMR-CNRS 6085), Raphael Salem Mathematics Laboratory, Saint Etienne du Rouvray, 76801, France
4 Henri Becquerel Center, Nuclear Medicine Department, 1 rue d'Amiens, Rouen, 76000, France
EJNMMI Research 2012, 2:40 doi:10.1186/2191-219X-2-40
Published: 20 July 2012Abstract
Background
Several algorithms from the literature were compared with the original random walk (RW) algorithm for brain perfusion heterogeneity quantification purposes. Algorithms are compared on a set of 210 brain single photon emission computed tomography (SPECT) simulations and 40 patient exams.
Methods
Five algorithms were tested on numerical phantoms. The numerical anthropomorphic Zubal head phantom was used to generate 42 (6 × 7) different brain SPECT simulations. Seven diffuse cortical heterogeneity levels were simulated with an adjustable Gaussian noise function and six focal perfusion defect levels with temporoparietal (TP) defects. The phantoms were successively projected and smoothed with Gaussian kernel with full width at half maximum (FWHM = 5 mm), and Poisson noise was added to the 64 projections. For each simulation, 5 Poisson noise realizations were performed yielding a total of 210 datasets. The SPECT images were reconstructed using filtered black projection (Hamming filter: α = 0.5).
The five algorithms or measures tested were the following: the coefficient of variation, the entropy and local entropy, fractal dimension (FD) (box counting and Fourier power spectrum methods), the gray-level co-occurrence matrix (GLCM), and the new RW.
The heterogeneity discrimination power was obtained with a linear regression for each algorithm. This regression line is a mean function of the measure of heterogeneity compared to the different diffuse heterogeneity and focal defect levels generated in the phantoms. A greater slope denotes a larger separation between the levels of diffuse heterogeneity.
The five algorithms were computed using 40 99mTc-ethyl-cysteinate-dimer (ECD) SPECT images of patients referred for memory impairment. Scans were blindly ranked by two physicians according to the level of heterogeneity, and a consensus was obtained. The rankings obtained by the algorithms were compared with the physicians' consensus ranking.
Results
The GLCM method (slope = 58.5), the fractal dimension (35.9), and the RW method (31.6) can differentiate the different levels of diffuse heterogeneity. The GLCM contrast parameter method is not influenced by a focal defect contrary to the FD and RW methods. A significant correlation was found between the RW method and the physicians' classification (r = 0.86; F = 137; p < 0.0001).
Conclusions
The GLCM method can quantify the different levels of diffuse heterogeneity in brain-simulated SPECT images without an influence from the focal cortical defects. However, GLCM classification was not correlated with the physicians' classification (Rho = −0.099). The RW method was significantly correlated with the physicians' heterogeneity perception but is influenced by the existence of a focal defect.
