2026-04-14T17:29:49Zhttps://uvadoc.uva.es/oai/request

oai:uvadoc.uva.es:10324/313752021-07-06T08:34:16Zcom_10324_1191com_10324_931com_10324_894col_10324_1381

Peña Nogales, Óscar Zhang, Yuxin Luis García, Rodrigo de Aja Fernández, Santiago H. Holmes, James Hernando, Diego 2018 Diffusion-Weighted MRI often suffers from signal attenuation due to long TE, sensitivity to physiological motion, and dephasing due to concomitant gradients (CGs). These challenges complicate image interpretation and may introduce bias in quantitative diffusion measurements. Motion moment-nulled diffusion-weighting gradients have been proposed to compensate motion, however, they frequently result in high TE and suffer from CG effects. In this work, the Optimal Diffusion-weighting Gradient waveform Design method that overcomes limitations of state-of-the-art waveforms is revisited and validated in phantom and in-vivo experiments. These diffusion-weighting gradient waveforms reduce the TE and increase the SNR of state-of-the-art waveforms without and with CG-nulling. application/pdf http://uvadoc.uva.es/handle/10324/31375 eng Optimal Diffusion-weighting Gradient Waveform Design (ODGD): Formulation and Experimental Validation info:eu-repo/semantics/conferenceObject TEXT UVaDOC. Repositorio Documental de la Universidad de Valladolid Hispana