Pre-excitation gradients for eddy current nulled convex optimized diffusion encoding (Pre-ENCODE)

doi:10.1002/mrm.30068

2024-03-19

PRE

DOI：10.1002/mrm.30068

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PurposeTo evaluate the use of pre-excitation gradients for eddy current-nulled convex optimized diffusion encoding (Pre-ENCODE) to mitigate eddy current-induced image distortions in diffusion-weighted MRI (DWI).MethodsDWI sequences using monopolar (MONO), ENCODE, and Pre-ENCODE were evaluated in terms of the minimum achievable echo time (TE min$$ {}_{\mathrm{min}} $$) and eddy current-induced image distortions using simulations, phantom experiments, and in vivo DWI in volunteers (N=6$$ N=6 $$).ResultsPre-ENCODE provided a shorter TE min$$ {}_{\mathrm{min}} $$ than MONO (71.0 +/-$$ \pm $$ 17.7ms vs. 77.6 +/-$$ \pm $$ 22.9ms) and ENCODE (71.0 +/-$$ \pm $$ 17.7ms vs. 86.2 +/-$$ \pm $$ 14.2ms) in 100%$$ \% $$ of the simulated cases for a commercial 3T MRI system with b-values ranging from 500 to 3000 s/mm 2$$ {}<^>2 $$ and in-plane spatial resolutions ranging from 1.0 to 3.0mm 2$$ {}<^>2 $$. Image distortion was estimated by intravoxel signal variance between diffusion encoding directions near the phantom edges and was significantly lower with Pre-ENCODE than with MONO (10.1%$$ \% $$ vs. 22.7%$$ \% $$, p=6-5$$ p={6}<^>{-5} $$) and comparable to ENCODE (10.1%$$ \% $$ vs. 10.4%$$ \% $$, p=0.12$$ p=0.12 $$). In vivo measurements of apparent diffusion coefficients were similar in global brain pixels (0.37 [0.28,1.45]x10-3$$ \times 1{0}<^>{-3} $$ mm 2$$ {}<^>2 $$/s vs. 0.38 [0.28,1.45]x10-3$$ \times 1{0}<^>{-3} $$mm 2$$ {}<^>2 $$/s, p=0.25$$ p=0.25 $$) and increased in edge brain pixels (0.80 [0.17,1.49]x10-3$$ \times 1{0}<^>{-3} $$ mm 2$$ {}<^>2 $$/s vs. 0.70 [0.18,1.48]x10-3$$ \times 1{0}<^>{-3} $$mm 2$$ {}<^>2 $$/s, p=0.02$$ p=0.02 $$) for MONO compared to Pre-ENCODE.ConclusionPre-ENCODE mitigated eddy current-induced image distortions for diffusion imaging with a shorter TE min$$ {}_{\mathrm{min}} $$ than MONO and ENCODE.

Keyword:

diffusion

eddy currents

time-optimal