Abstract #4802

Status of on-scanner gradient nonlinearity bias correction for extension to diffusion kurtosis imaging in multi-vendor setting

Thomas L Chenevert¹, Ramesh Paudyal², Alfonso Lema-Dopico², Luca Marinelli³, Nastaren Abad³, Lisa J Wilmes⁴, Li Wen⁴, Omar Darwish⁵, Debosmita Biswas⁶, Ramin Jafari⁷, Johannes M Peters⁸, Nola Hylton⁴, Savannah C Partridge⁶, Amita Shukla-Dave⁹, and Dariya Malyarenko¹

¹Radiology, University of Michigan, Ann Arbor, MI, United States, ²Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States, ³Technology & Innovation Center, GE HealthCare, Niskayuna, NY, United States, ⁴Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States, ⁵Siemens Healthineers AG, Erlangen, Germany, ⁶Radiology, University of Washington, Seattle, WA, United States, ⁷Philips Healthcare, New York, NY, United States, ⁸Clinical Science, Philips MR, Best, Netherlands, ⁹Medical Physics and Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States

Synopsis

Keywords: DWI/DTI/DKI, System Imperfections: Measurement & Correction

Motivation: Clinical oncology imaging trials utilize non-Gaussian diffusion kurtosis imaging (DKI) metrics to improve diagnostic accuracy and therapy response monitoring.

Goal(s): Our ongoing multi-institution project is testing the extension of vendor-provided on-scanner implementations of correction for gradient non-linearity (GNL) bias in Gaussian apparent diffusion coefficient (ADC) for DKI applications.

Approach: Previously developed formalism for correction of multiplicative GNL bias in DKI parameters is applied using b-value maps or via corrected diffusion weighted images (DWI).

Results: This work evaluates feasibility of using on-scanner generated multi-b DWI outputs for DKI GNL correction across three MRI platforms in a quantitative phantom with non-Gaussian diffusion materials.

Impact: Feasibility of diffusion kurtosis parameter correction for gradient nonlinearity bias using vendor-provided on-scanner outputs is tested across three MR vendor systems in a quantitative non-Gaussian diffusion phantom. Correction integration with other DWI enhancements is found necessary for the best performance.

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