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No-reference image quality assessment (NR-IQA) methods objectively predict medical image quality without a reference. This survey reviews NR-IQA for magnetic resonance imaging (MRI), detailing methods, protocols, and future trends for accurate quality prediction.

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Image Processing

Background:

  • No-reference image quality assessment (NR-IQA) is crucial for medical imaging due to the absence of reference images in many acquisition systems.
  • NR-IQA methods objectively predict image perceptual quality, supporting diagnostic accuracy and treatment planning.
  • Magnetic resonance imaging (MRI) presents unique challenges for NR-IQA due to long acquisition times and susceptibility to various quality-degrading factors.

Purpose of the Study:

  • To present a comprehensive survey of recently developed NR-IQA methods specifically for assessing MR images.
  • To characterize popular NR-IQA methods by their approaches to MR image description and quality prediction model creation.
  • To review evaluation protocols and benchmark databases used for NR-IQA methods in the context of MRI.

Main Methods:

  • Review of typical distortions affecting MR image quality.
  • Characterization of popular NR-IQA methods, focusing on feature extraction and quality modeling for MR images.
  • Analysis of established protocols and benchmark datasets for evaluating NR-IQA performance on MR images.

Main Results:

  • Identification and categorization of current NR-IQA techniques applicable to MRI.
  • Evaluation of the strengths and weaknesses of different NR-IQA approaches in describing MR image characteristics.
  • Summary of common practices and datasets used for benchmarking NR-IQA methods in medical imaging.

Conclusions:

  • NR-IQA plays a vital role in ensuring the reliability of MR images for clinical use.
  • Understanding current NR-IQA methods, their evaluation, and limitations is essential for future advancements.
  • Future research should focus on developing more accurate and robust NR-IQA models tailored to the complexities of MRI.