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

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 (M 1 ) nulled CODE‐M 1 , and first and second moment (M 2 ) nulled CODE‐M 1 M 2 were used to acquire neuro, liver, and cardiac ADC maps in healthy subjects (n=10) that were compared respectively to monopolar (MONO), BIPOLAR (M 1 = 0), and motion‐compensated (MOCO, M 1 + M 2 = 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‐M 1 liver ADCs were significantly lower (1.3 ± 0.1 versus 1.8 ± 0.3 × 10 −3 mm 2 /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‐M 1 M 2 cardiac ADCs were significantly lower than MONO (1.9 ± 0.6 versus 3.8 ± 0.3 x10 −3 mm 2 /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 mm 2 /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.