PURPOSE: Cardiac diffusion tensor imaging using EPI readout is prone to image distortions in the presence of field inhomogeneities. In this work, a framework to analyze and correct image distortions in cardiac diffusion tensor imaging is presented. METHODS: A multi-coil reconstruction framework was implemented to enable field map-based off-resonance correction. Numerical simulations were used to examine reconstruction performance for EPI phase-encode directions blip up-down and down-up for different degrees of off-resonance gradients and varying field map resolution. The impact of coil encoding was analyzed using the g-factor and normalized RMSE. Finally, the proposed method was tested on free-breathing in vivo cardiac diffusion tensor imaging data acquired in healthy subjects at 3 Tesla. RESULTS: Depending on the local field map gradient strength and polarity and the selected phase-encode direction, field inhomogeneities lead to either local spatial compression or stretching with standard image reconstruction. Although spatial compression results in loss of image resolution upon field map-based reconstruction, spatial stretching can be recovered once multiple receive coils are utilized. Multi-coil reconstruction was found to reduce the normalized RMSE from 34.3% to 8.1% for image compression, and 33.6% to 1.8% for image stretching, with resulting average g-factors 14.7 ± 2.9 and 1.2 ± 0.1, respectively. In vivo, multi-coil field map-based reconstruction yielded improved alignment of angle maps with anatomical cine data. CONCLUSION: Multi-coil, field map-based image reconstruction for echo-planar cardiac diffusion tensor imaging allows accurate image reconstruction provided that the phase-encode direction and polarity is chosen to principally align with the direction and polarity of the prominent gradients of field inhomogeneities.
PURPOSE: Cardiac diffusion tensor imaging using EPI readout is prone to image distortions in the presence of field inhomogeneities. In this work, a framework to analyze and correct image distortions in cardiac diffusion tensor imaging is presented. METHODS: A multi-coil reconstruction framework was implemented to enable field map-based off-resonance correction. Numerical simulations were used to examine reconstruction performance for EPI phase-encode directions blip up-down and down-up for different degrees of off-resonance gradients and varying field map resolution. The impact of coil encoding was analyzed using the g-factor and normalized RMSE. Finally, the proposed method was tested on free-breathing in vivo cardiac diffusion tensor imaging data acquired in healthy subjects at 3 Tesla. RESULTS: Depending on the local field map gradient strength and polarity and the selected phase-encode direction, field inhomogeneities lead to either local spatial compression or stretching with standard image reconstruction. Although spatial compression results in loss of image resolution upon field map-based reconstruction, spatial stretching can be recovered once multiple receive coils are utilized. Multi-coil reconstruction was found to reduce the normalized RMSE from 34.3% to 8.1% for image compression, and 33.6% to 1.8% for image stretching, with resulting average g-factors 14.7 ± 2.9 and 1.2 ± 0.1, respectively. In vivo, multi-coil field map-based reconstruction yielded improved alignment of angle maps with anatomical cine data. CONCLUSION: Multi-coil, field map-based image reconstruction for echo-planar cardiac diffusion tensor imaging allows accurate image reconstruction provided that the phase-encode direction and polarity is chosen to principally align with the direction and polarity of the prominent gradients of field inhomogeneities.
Authors: Jaume Coll-Font; Shi Chen; Robert Eder; Yiling Fang; Qiao Joyce Han; Maaike van den Boomen; David E Sosnovik; Choukri Mekkaoui; Christopher T Nguyen Journal: Magn Reson Med Date: 2021-08-13 Impact factor: 4.668