Eric Aliotta1,2, Holden H Wu1,2, Daniel B Ennis1,2. 1. Department of Radiological Sciences, University of California, Los Angeles, California, USA. 2. Biomedical Physics Interdepartmental Program, University of California, Los Angeles, California, USA.
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.
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.
Authors: Óscar Peña-Nogales; Yuxin Zhang; Xiaoke Wang; Rodrigo de Luis-Garcia; Santiago Aja-Fernández; James H Holmes; Diego Hernando Journal: Magn Reson Med Date: 2018-11-05 Impact factor: 4.668
Authors: Ilya A Verzhbinsky; Luigi E Perotti; Kevin Moulin; Tyler E Cork; Michael Loecher; Daniel B Ennis Journal: IEEE Trans Med Imaging Date: 2019-08-08 Impact factor: 10.048
Authors: Luigi E Perotti; Patrick Magrath; Ilya A Verzhbinsky; Eric Aliotta; Kévin Moulin; Daniel B Ennis Journal: Funct Imaging Model Heart Date: 2017-05-23