A consistency evaluation of signal-to-noise ratio in the quality assessment of human brain magnetic resonance images

BACKGROUND: Quality assessment of medical images is highly related to the quality assurance, image interpretation and decision making. As to magnetic resonance (MR) images, signal-to-noise ratio (SNR) is routinely used as a quality indicator, while little knowledge is known of its consistency regarding different observers. METHODS: In total, 192, 88, 76 and 55 brain images are acquired using T(2)(*), T(1), T(2) and contrast-enhanced T(1) (T(1)C) weighted MR imaging sequences, respectively. To each imaging protocol, the consistency of SNR measurement is verified between and within two observers, and white matter (WM) and cerebral spinal fluid (CSF) are alternately used as the tissue region of interest (TOI) for SNR measurement. The procedure is repeated on another day within 30 days. At first, overlapped voxels in TOIs are quantified with Dice index. Then, test-retest reliability is assessed in terms of intra-class correlation coefficient (ICC). After that, four models (BIQI, BLIINDS-II, BRISQUE and NIQE) primarily used for the quality assessment of natural images are borrowed to predict the quality of MR images. And in the end, the correlation between SNR values and predicted results is analyzed. RESULTS: To the same TOI in each MR imaging sequence, less than 6% voxels are overlapped between manual delineations. In the quality estimation of MR images, statistical analysis indicates no significant difference between observers (Wilcoxon rank sum test, p(w) ≥ 0.11; paired-sample t test, p(p) ≥ 0.26), and good to very good intra- and inter-observer reliability are found (ICC, p(icc) ≥ 0.74). Furthermore, Pearson correlation coefficient (r(p)) suggests that SNR(wm) correlates strongly with BIQI, BLIINDS-II and BRISQUE in T(2)(*) (r(p) ≥ 0.78), BRISQUE and NIQE in T(1) (r(p) ≥ 0.77), BLIINDS-II in T(2) (r(p) ≥ 0.68) and BRISQUE and NIQE in T(1)C (r(p) ≥ 0.62) weighted MR images, while SNR(csf) correlates strongly with BLIINDS-II in T(2)(*) (r(p) ≥ 0.63) and in T(2) (r(p) ≥ 0.64) weighted MR images. CONCLUSIONS: The consistency of SNR measurement is validated regarding various observers and MR imaging protocols. When SNR measurement performs as the quality indicator of MR images, BRISQUE and BLIINDS-II can be conditionally used for the automated quality estimation of human brain MR images.