The "hidden noise" problem in MR image reconstruction.

ABSTRACT:

Purpose

The performance of modern image reconstruction methods is commonly judged using quantitative error metrics like root mean squared-error and the structural similarity index, which are calculated by comparing reconstructed images against fully sampled reference data. In practice, the reference data will contain noise and is not a true gold standard. In this work, we demonstrate that the "hidden noise" present in reference data can substantially confound standard approaches for ranking different image reconstruction results.

Methods

Using both experimental and simulated k-space data and several different image reconstruction techniques, we examined whether there was correlation between performance metrics obtained with typical noisy reference data versus those obtained

SUBMITTER: Wang J 

PROVIDER: S-EPMC11209803 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature