Adaptive Iterative T2 Mapping with Maximum Pearson Correlation in the Presence of Noise

Stephan William Anderson¹, Jorge A. Soto¹, Osamu Sakai¹, Hernan Jara¹

Purpose: To develop a T2 qMRI algorithm such that the number of echoes used for semi-logarithmic regression is adaptively and iteratively determined on a pixel-by-pixel basis for maximum pixelwise Pearson correlation. Methods: The adaptive iterative T2 algorithm was programmed and used to process CPMG images of mouse liver specimen at 11.7T. Results: T2 maps were generated that provide high anatomical detail as well as high Pearson correlation coefficients across the field of view. Processing time for 16 slices was 90s. Conclusion: Adaptive iterative T2 mapping with short processing times is feasible. This algorithm could be useful for monitoring subtle T2 changes caused by disease in animal models and also for processing in vivo CPMG data.