Abstract #1705

Convex Optimized Diffusion Encoding (CODE) with Partial Fourier Imaging for EPI Diffusion Weighted Imaging

Matthew J. Middione¹, Tyler E. Cork^1,2, Michael Loecher¹, Tim Sprenger³, Arnaud Guidon⁴, Shreyas S. Vasanawala¹, and Daniel B. Ennis¹

¹Department of Radiology, Stanford University, Stanford, CA, United States, ²Department of Bioengineering, Stanford University, Stanford, CA, United States, ³GE Healthcare, Munich, Germany, ⁴GE Healthcare, Boston, MA, United States

Synopsis

Diffusion weighted imaging typically uses monopolar (MONO) diffusion gradient waveforms, which may have sequence dead time that extends the TE and reduces the SNR. Partial Fourier (PF) imaging is routinely used in DWI to shorten the TE (improving SNR), but can lead to image blurring. Convex Optimized Diffusion Encoding (CODE) is a constrained optimization technique that designs time-optimal diffusion encoding gradient waveforms without sequence dead time. CODE produces a shorter TE than MONO, which leads to increased SNR. Herein, we show that CODE without PF has increased SNR and reduced image blurring compared to MONO with PF=6/8.

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