Joint groupwise registration and ADC estimation in the liver using a B-value weighted metric

Abstract

The proposed algorithm is able to effectively deal with both the presence of motion and the geometric distortions, increasing accuracy and reproducibility in diffusion parameters estimation.

Robust estimation of the apparent diffusion coefficient invariant to acquisition noise and physiological motion

“…In addition, despite their elegant formulation, computational needs associated to MRF approaches are high. With respect to the latter, in [17], we showed a better performance of our scheme.…”

“…Therefore, we have made use of the method we have previously proposed in [17], a methodology to jointly estimate the ADC map and register DW-MRI sequences acquired in different breath hold states. The processing pipeline has been depicted in Fig.…”

“…(4) as data fidelity term; this function is based on Eq. (1) as the forward model, which should match the measured DW magnitude data as defined in [17]:…”

“…In [17], we have previously presented a novel light methodology for joint 55 ADC estimation and groupwise registration of diffusion weighted images in the liver. However, despite its good performance, the method did not take into account any possible bias introduced by the noise present in the data.…”

“…The purpose of this paper is to define a complete framework that allows the ADC estimation from DW-MRI data taking into account two preeminent confounding factors: the motion of the patient and the physiological noise present 100 in the data. To that end, the work is grounded on a methodology for joint ADC estimation and groupwise registration that we have previously presented in [17]. Although that scheme has shown to be able to successfully cope with the movement of the patient, it did not take into account any model for the noise.…”