SU-F-I-52: An Approach to Estimate Noise in Patient Image by Computing the Minimal Difference in Neighborhoods.

PURPOSE: To quantitatively evaluate and compare the accuracy of two advanced methods that can estimate the level of noise per voxel in patient images. These noise estimation methods show promises in: 1) assuring the performance of imaging systems and algorithms, 2) guiding image processing tasks for clinical and research applications, i.e. by optimization of the parameters, and 3) quantifying patient image quality and assisting image quality improvements.

METHODS: We conducted an experiment of 34 repeated MRI scans (TrueFISP sequence) of a swine head in order to obtain a ground truth noise dataset. Two published noise estimation methods were implemented in this study: 1) Minimal Difference in Neighborhoods (MDiN) and 2) high-pass MDiN. Noise estimation accuracies of two methods were quantitatively measured using the ground truth data and patient MRI images with added Rician noise.

RESULTS: The experimental results with both swine head images and patient images showed that the MDiN method is more accurate. The high-pass MDiN method is slightly less but still sufficiently accurate. The MDiN method could be obtained within a 90% accuracy when tested on the ground-truth dataset.

CONCLUSION: We verified the performance of two efficient methods to automatically estimate per voxel noise levels in patient images. Our results suggest that these methods could be confidently used to assist and guide clinical and research applications that require such noise information. Senior Author Dr. Deshan Yang received research funding form ViewRay and Varian.