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

PurposeTo evaluate convex optimized diffusion encoding (CODE) gradient waveforms for minimum echo time and bulk motion-compensated diffusion-weighted imaging (DWI). MethodsDiffusion-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-M1M2 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. ResultsCODE significantly improved the SNR of neuro ADC maps compared with MONO (19.52.5 versus 14.5 +/- 1.9). CODE-M-1 liver ADCs were significantly lower (1.3 +/- 0.1 versus 1.8 +/- 0.3 x 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-M1M2 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 x 10(-3) mm(2)/s). ConclusionsCODE 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. (c) 2016 International Society for Magnetic Resonance in Medicine