A framework for quality control and parameter optimization in diffusion tensor imaging: theoretical analysis and validation.
In this communication, a theoretical framework for quality control and parameter optimization in diffusion tensor imaging (DTI) is presented and validated. The approach is based on the analytical error propagation of the mean diffusivity (D(av)) obtained directly from the diffusion-weighted data acquired using rotationally invariant and uniformly distributed icosahedral encoding schemes. The error propagation of a recently described and validated cylindrical tensor model is further extrapolated to the spherical tensor case (diffusion anisotropy approximately 0) to relate analytically the precision error in fractional tensor anisotropy (FA) with the mean diffusion-to-noise ratio (DNR). The approach provided simple analytical and empirical quality control measures for optimization of diffusion parameter space in an isotropic medium that can be tested using widely available water phantoms.
Hasan
Khader M
KM
Department of Diagnostic and Interventional Imaging, University of Texas Health Science Center at Houston Medical School, Houston, TX 77030, USA. Khader.M.Hasan@uth.tmc.edu
D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D003198:Computer Simulation; D038524:Diffusion Magnetic Resonance Imaging / Q000295:instrumentation / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008959:Models, Neurological; D019047:Phantoms, Imaging; D011785:Quality Assurance, Health Care / Q000379:methods; D011786:Quality Control; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity