Linear-fitting-based similarity coefficient map for tissue dissimilarity analysis in -w magnetic resonance imaging

Yu, Shao De; Wu, Shi Bin; Wang, Hao Yu; Wei, Xin Hua; Chen, Xin; Pan, Wanlong; Hu, Jiani; Xie, Yao

doi:10.1088/1674-1056/24/12/128711

Cited by 7 publications

(1 citation statement)

References 17 publications

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“…The flip angle is 15 o , FOV is 220 × 220 mm 2 , slice thickness is 3.0 mm and the resultant image matrix is 384 × 384. Note that the original purpose of multi-echo T 2 * weighted image acquisition is toward tissue dissimilarity analysis [ 12 ]. T 1 , T 2 and T 1 C weighted images are acquired using spin echo protocol with different TR and TE pairs (535 ms and 8 ms; 3500 ms and 105 ms; 650 ms and 9 ms).…”

Section: Methodsmentioning

confidence: 99%

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

Dai

Wang

et al. 2018

BMC Med Imaging

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BackgroundQuality 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.MethodsIn total, 192, 88, 76 and 55 brain images are acquired using T2*, T1, T2 and contrast-enhanced T1 (T1C) 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.ResultsTo 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, pw ≥ 0.11; paired-sample t test, pp ≥ 0.26), and good to very good intra- and inter-observer reliability are found (ICC, picc ≥ 0.74). Furthermore, Pearson correlation coefficient (rp) suggests that SNRwm correlates strongly with BIQI, BLIINDS-II and BRISQUE in T2* (rp ≥ 0.78), BRISQUE and NIQE in T1 (rp ≥ 0.77), BLIINDS-II in T2 (rp ≥ 0.68) and BRISQUE and NIQE in T1C (rp ≥ 0.62) weighted MR images, while SNRcsf correlates strongly with BLIINDS-II in T2* (rp ≥ 0.63) and in T2 (rp ≥ 0.64) weighted MR images.ConclusionsThe 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.

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Section: Methodsmentioning

confidence: 99%