Dr. Yuxi Pang Profile

Dr. Yuxi Pang

at Univ of Michigan

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Proceedings Article | 10 March 2020 Presentation + Paper

Numerical DWI phantoms to optimize accuracy and precision of quantitative parametric maps for non-Gaussian diffusion

Dariya Malyarenko, Yuxi Pang, Ghoncheh Amouzandeh, Thomas Chenevert

Proceedings Volume 11313, 113130W (2020) https://doi.org/10.1117/12.2549412

KEYWORDS: Signal to noise ratio, Diffusion weighted imaging, Error analysis, Kidney, Brain, Tumors, Numerical modeling

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Clinical applications of quantitative diffusion-weighted imaging (qDWI) require confidence intervals for derived diffusion parameters to aid differentiation of technical errors from tissue characteristics. This study outlines practical procedures to evaluate precision (uncertainty) and accuracy (bias) of parametric maps derived for non-Gaussian diffusion models using numerical DWI phantoms (digital reference objects (DROs)) generated for advanced qDWI clinical trial protocols. The generated DROs include simulated acquisition noise, DICOM scaling and clinically-relevant qDWI parameter ranges for perfusion-fraction intra-voxel incoherent motion, kurtosis and stretched exponential diffusion models. Evaluation of fit accuracy and precision is illustrated for unsupervised linear least-squares (LLS) versus nonlinear minimization (NLM) algorithms ignorant of noise. DRO application examples are shown for calibration of noise-induced uncertainty with a physical DWI phantom, retrospective model fidelity analysis for brain tumor data and prospective error mapping in renal tissue. Physical DWI phantom analysis confirms adequate DRO noise model and realistic predicted fit errors. Detection of step-like bias activation in fit parameter space, consistent with modeldependent signal truncation at the noise floor, is proposed based on differences in NLM versus LLS fit maps. For all diffusion models, noise-induced bias in diffusivity is anti-correlated to bias in the model-specific (non-diffusivity) parameter. For confident assessment of fit parameter precision, bias minimization through b-range constraints and bdependent averaging is advised prior to fit uncertainty mapping. For low bias, higher precision is achieved by LLS versus NLM, and for diffusivity versus model parameter. The described procedures allow efficient qDWI protocol optimization toward reduced acquisition and model-dependent errors.

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