| Title |
A consistency evaluation of signal-to-noise ratio in the quality assessment of human brain magnetic resonance images
|
|---|---|
| Published in |
BMC Medical Imaging, May 2018
|
| DOI | 10.1186/s12880-018-0256-6 |
| Pubmed ID | |
| Authors |
Shaode Yu, Guangzhe Dai, Zhaoyang Wang, Leida Li, Xinhua Wei, Yaoqin Xie |
| Abstract |
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. In 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. 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 SNRwm correlates strongly with BIQI, BLIINDS-II and BRISQUE in T2* (r p ≥ 0.78), BRISQUE and NIQE in T1 (r p ≥ 0.77), BLIINDS-II in T2 (r p ≥ 0.68) and BRISQUE and NIQE in T1C (r p ≥ 0.62) weighted MR images, while SNRcsf correlates strongly with BLIINDS-II in T2* (r p ≥ 0.63) and in T2 (r p ≥ 0.64) weighted MR images. 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. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 52 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 9 | 17% |
| Student > Ph. D. Student | 7 | 13% |
| Student > Bachelor | 6 | 12% |
| Student > Master | 4 | 8% |
| Professor | 3 | 6% |
| Other | 5 | 10% |
| Unknown | 18 | 35% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 10 | 19% |
| Medicine and Dentistry | 10 | 19% |
| Computer Science | 2 | 4% |
| Chemistry | 2 | 4% |
| Mathematics | 2 | 4% |
| Other | 3 | 6% |
| Unknown | 23 | 44% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 May 2018.
All research outputs
#15,522,480
of 23,070,218 outputs
Outputs from BMC Medical Imaging
#270
of 607 outputs
Outputs of similar age
#208,627
of 327,764 outputs
Outputs of similar age from BMC Medical Imaging
#3
of 8 outputs
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