Convex optimized diffusion encoding (CODE) gradient waveforms for minimum echo time and bulk motion-compensated diffusion-weighted MRI

Abstract

Purpose: To evaluate convex optimized diffusion encoding (CODE) gradient waveforms for minimum echo time and bulk motion-compensated diffusion-weighted imaging (DWI).

Methods: Diffusion-encoding gradient waveforms were designed for a range of b-values and spatial resolutions with and without motion compensation using the CODE framework. CODE, first moment (M₁ ) nulled CODE-M₁ , and first and second moment (M₂ ) nulled CODE-M₁ M₂ were used to acquire neuro, liver, and cardiac ADC maps in healthy subjects (n=10) that were compared respectively to monopolar (MONO), BIPOLAR (M₁ = 0), and motion-compensated (MOCO, M₁ + M₂ = 0) diffusion encoding.

Results: CODE significantly improved the SNR of neuro ADC maps compared with MONO (19.5 ± 2.5 versus 14.5 ± 1.9). CODE-M₁ liver ADCs were significantly lower (1.3 ± 0.1 versus 1.8 ± 0.3 × 10^-3 mm² /s, ie, less motion corrupted) and more spatially uniform (6% versus 55% ROI difference) than MONO and had higher SNR than BIPOLAR (SNR = 14.9 ± 5.3 versus 8.0 ± 3.1). CODE-M₁ M₂ cardiac ADCs were significantly lower than MONO (1.9 ± 0.6 versus 3.8 ± 0.3 x10^-3 mm² /s) throughout the cardiac cycle and had higher SNR than MOCO at systole (9.1 ± 3.9 versus 7.0 ± 2.6) while reporting similar ADCs (1.5 ± 0.2 versus 1.4 ± 0.6 × 10^-3 mm² /s).

Conclusions: CODE significantly improved SNR for ADC mapping in the brain, liver and heart, and significantly improved DWI bulk motion robustness in the liver and heart. Magn Reson Med 77:717-729, 2017. © 2016 International Society for Magnetic Resonance in Medicine.