High-resolution extracellular pH imaging of liver cancer with multiparametric MR using Deep Image Prior

Abstract

Noninvasive extracellular pH (pH_e) mapping with Biosensor Imaging of Redundant Deviation in Shifts (BIRDS) using MR spectroscopic imaging (MRSI) has been demonstrated on 3T clinical MR scanners at 8 $\times 8\times 10$ mm³ spatial resolution and applied to study various liver cancer treatments. Although pH_e imaging at higher resolution can be achieved by extending the acquisition time, a postprocessing method to increase the resolution is preferable, to minimize the duration spent by the subject in the MR scanner. In this work, we propose to improve the spatial resolution of pH_e mapping with BIRDS by incorporating anatomical information in the form of multiparametric MRI and using an unsupervised deep-learning technique, Deep Image Prior (DIP). Specifically, we used high-resolution $T_1$ , $T_2$ , and diffusion-weighted imaging (DWI) MR images of rabbits with VX2 liver tumors as inputs to a U-Net architecture to provide anatomical information. U-Net parameters were optimized to minimize the difference between the output super-resolution image and the experimentally acquired low-resolution pH_e image using the mean-absolute error. In this way, the super-resolution pH_e image would be consistent with both anatomical MR images and the low-resolution pH_e measurement from the scanner. The method was developed based on data from 49 rabbits implanted with VX2 liver tumors. For evaluation, we also acquired high-resolution pH_e images from two rabbits, which were used as ground truth. The results indicate a good match between the spatial characteristics of the super-resolution images and the high-resolution ground truth, supported by the low pixelwise absolute error.

Keywords: Deep Image Prior; MRSI; extracellular pH imaging; liver cancer; multiparametric MRI; unsupervised deep learning.