Nikiforaki Katerina, Karatzanis Ioannis, Dovrou Aikaterini, Bobowicz Maciej, Gwozdziewicz Katarzyna, Díaz Oliver, Tsiknakis Manolis, Fotiadis Dimitrios I, Lekadir Karim, Marias Kostas
Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas (FORTH), 70013 Heraklion, Greece.
School of Medicine, University of Crete, 71003 Heraklion, Greece.
J Imaging. 2024 May 9;10(5):115. doi: 10.3390/jimaging10050115.
Image quality assessment of magnetic resonance imaging (MRI) data is an important factor not only for conventional diagnosis and protocol optimization but also for fairness, trustworthiness, and robustness of artificial intelligence (AI) applications, especially on large heterogeneous datasets. Information on image quality in multi-centric studies is important to complement the contribution profile from each data node along with quantity information, especially when large variability is expected, and certain acceptance criteria apply. The main goal of this work was to present a tool enabling users to assess image quality based on both subjective criteria as well as objective image quality metrics used to support the decision on image quality based on evidence. The evaluation can be performed on both conventional and dynamic MRI acquisition protocols, while the latter is also checked longitudinally across dynamic series. The assessment provides an overall image quality score and information on the types of artifacts and degrading factors as well as a number of objective metrics for automated evaluation across series (BRISQUE score, Total Variation, PSNR, SSIM, FSIM, MS-SSIM). Moreover, the user can define specific regions of interest (ROIs) to calculate the regional signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), thus individualizing the quality output to specific use cases, such as tissue-specific contrast or regional noise quantification.
磁共振成像（MRI）数据的图像质量评估不仅是传统诊断和方案优化的重要因素，对于人工智能（AI）应用的公平性、可信度和稳健性而言也是如此，尤其是在大型异构数据集上。在多中心研究中，关于图像质量的信息对于补充每个数据节点的贡献概况以及数量信息非常重要，特别是当预期存在较大变异性且适用某些验收标准时。这项工作的主要目标是提供一种工具，使用户能够基于主观标准以及用于基于证据支持图像质量决策的客观图像质量指标来评估图像质量。该评估可以在传统和动态MRI采集协议上进行，而对于后者，还会在动态序列中进行纵向检查。该评估提供总体图像质量评分、关于伪影类型和降解因素的信息，以及用于跨序列自动评估的一些客观指标（BRISQUE评分、总变差、峰值信噪比、结构相似性指数、特征相似性指数、多尺度结构相似性指数）。此外，用户可以定义特定的感兴趣区域（ROI）来计算区域信噪比（SNR）和对比噪声比（CNR），从而将质量输出个性化以适用于特定用例，例如组织特异性对比度或区域噪声量化。
