A maximum-likelihood method to estimate a single ADC value of lesions using diffusion MRI

PURPOSE

Design a statistically rigorous procedure to estimate a single apparent diffusion coefficient (ADC) of lesion from the mean lesion signal intensity in diffusion MRI.

THEORY AND METHODS

A rigorous maximum-likelihood technique that incorporated the statistics of the mean lesion intensity and accounted for lesion heterogeneity was derived to estimate the ADC value. Performance evaluation included comparison with the conventionally used linear-regression and a statistically rigorous state-of-the-art ADC-map technique using realistic and clinically relevant simulation studies conducted with assistance of patient data for homogeneous and heterogeneous lesion models.

RESULTS

The proposed technique outperformed the linear-regression and ADC-map approaches over a large spectrum of signal-to-noise ratio, ADC, lesion size, image-misalignment parameters, including at no image misalignment, and different amounts of lesion heterogeneity. The method was also superior at different sets of b values and in studies from specific patient-image-derived data. The technique took less than a second to execute.

CONCLUSIONS

A rigorous, computationally fast, easy-to-implement, and convenient-to-use maximum-likelihood technique was proposed to estimate a single ADC value of the lesion. Results provide strong evidence in support of the method. Magn Reson Med 76:1919-1931, 2016. © 2016 International Society for Magnetic Resonance in Medicine.

A Maximum-Likelihood Approach for ADC Estimation of Lesions in Visceral Organs

Accurate estimation of the apparent diffusion coefficient (ADC) of lesions in diffusion-weighted magnetic resonance imaging (DWMRI) is important to predict and monitor anti-cancer therapy response. The task of ADC estimation of lesions is complicated due to noise in the image, different variances in signal strengths at different b values and other random phenomena. In organs that have visceral motion, due to motion across scans, estimating the ADC becomes even more complex. To get rid of inaccuracies due to motion, only a single ADC value of the lesion is estimated, conventionally using a linear-regression (LR) approach. The LR approach is based on an inaccurate noise model and also suffers from other deficiencies. In this paper, we propose an easy-to-implement and computationally-fast maximum-likelihood (ML) method to estimate the ADC value of heterogeneous lesions in visceral organs. The proposed method takes into account the Rician distribution of noise in DWMRI. In the process, we also derive the statistical model for the measured mean signal intensity in DWMRI. We show using Monte-Carlo simulations that that the proposed method is more accurate than the LR method.