Optimized Maxwell Compensation in Asymmetric Diffusion Encoding Gradient Waveform Design for Tensor‐Valued Diffusion Encoding

Motivation: Asymmetric diffusion encoding gradient waveforms are critical in tensor-valued diffusion encoding for higher diffusion encoding efficiency but can cause non-negligible concomitant gradients. The concomitant gradients can be mitigated by modeling and constraining the Maxwell term (Mindex) in a predefined numerical optimization framework. Goal(s): To improve Maxwell compensation's accuracy by refining the Mindex estimation model. Approach: The Mindex estimation model is refined by incorporating the influence of linear interpolation between the initially optimized waveforms and the final diffusion encoding waveforms. Results: The new Mindex estimation model shows more accurate Mindex estimation and better Maxwell compensation for the final diffusion gradient waveforms. Impact: A new estimation model of the Maxwell term has been proposed to better compensate for the concomitant gradients in the final asymmetric diffusion encoding gradient waveform, which is promising to further improve the accuracy of diffusion measures.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2025-09-16
DOI
https://doi.org/10.58530/2025/4073
Primary Topic
Electromagnetic Simulation and Numerical Methods
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Optimized Maxwell Compensation in Asymmetric Diffusion Encoding Gradient Waveform Design for Tensor‐Valued Diffusion Encoding

Jennifer A. McNab, Erpeng Dai, Guanhua Wang, Gaohong Wu
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Electromagnetic Simulation and Numerical Methods
article

Optimized Maxwell Compensation in Asymmetric Diffusion Encoding Gradient Waveform Design for Tensor‐Valued Diffusion Encoding

Jennifer A. McNab, Erpeng Dai, Guanhua Wang, Gaohong Wu
article en

Abstract

Motivation: Asymmetric diffusion encoding gradient waveforms are critical in tensor-valued diffusion encoding for higher diffusion encoding efficiency but can cause non-negligible concomitant gradients. The concomitant gradients can be mitigated by modeling and constraining the Maxwell term (Mindex) in a predefined numerical optimization framework. Goal(s): To improve Maxwell compensation's accuracy by refining the Mindex estimation model. Approach: The Mindex estimation model is refined by incorporating the influence of linear interpolation between the initially optimized waveforms and the final diffusion encoding waveforms. Results: The new Mindex estimation model shows more accurate Mindex estimation and better Maxwell compensation for the final diffusion gradient waveforms. Impact: A new estimation model of the Maxwell term has been proposed to better compensate for the concomitant gradients in the final asymmetric diffusion encoding gradient waveform, which is promising to further improve the accuracy of diffusion measures.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 76%
Electromagnetic Simulation and Numerical Methods
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.