Adaptive anisotropic gaussian filtering to reduce acquisition time in cardiac diffusion tensor imaging

Abstract

Purpose Diffusion tensor imaging (DTI) is used to quantify myocardial-fiber-orientation based on helical-angles (HA). Accurate HA measurements require multiple excitations (NEX) and/or several diffusion encoding directions (DED). Increasing NEX and/or DED increases acquisition time (TA). In this study, we reduce TA by implementing 3D adaptive anisotropic Gaussian filter (AAGF) on DTI data in ex-vivo normal and infarcted porcine hearts. Methods DTI was performed on ex-vivo hearts (9-healthy, 3-myocardial infarction (MI)) with several combinations of DED and NEX. AAGF, mean (AVF) and median filters (MF) were applied on primary eigenvectors prior to HA estimation. The performance of AAGF was compared against AVF and MF. Root mean square error (RMSE), concordance correlation-coefficients and Bland-Altman's technique was used to determine optimal combination of DED and NEX that generated best HA maps in least possible TA. Lastly, effect of AAGF on MI model was evaluated. Results RMSE in HA of AAGF was lower compared to AVF or MF. Post AAGF filtering fewer DED and NEX were required to achieve HA maps with similar integrity as those obtained from higher NEX and/or DED. Alterations caused in HA orientation in MI model were preserved post-filtering. Conclusion Our results demonstrate that AAGF reduces TA without affecting the integrity of myocardial microstructure.