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A machine-learning framework for automatic reference-free quality assessment in MRI.

T Küstner1, S Gatidis2, A Liebgott3

  • 1Institute of Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany; Section on Experimental Radiology, University of Tübingen, Germany.

Magnetic Resonance Imaging
|July 24, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a new machine learning framework for automated magnetic resonance (MR) image quality assessment. The reference-free system accurately evaluates image quality post-acquisition, aiding diagnostics and quality control.

Keywords:
Deep learningImage quality assessmentMachine-learningMagnetic resonance imagingNon-reference/blind

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Magnetic resonance (MR) imaging generates vast datasets requiring manual quality checks, which are time-consuming and costly.
  • Image artifacts can compromise diagnostic accuracy and post-processing, necessitating automated quality assessment methods.
  • Reference-based quality assessment is often infeasible due to the lack of standard reference images.

Purpose of the Study:

  • To develop and evaluate a novel, reference-free machine learning framework for automated MR image quality assessment.
  • To reduce the manual effort in quality assessment through active learning and an efficient blinded reading platform.
  • To investigate the performance of different image features and classifiers for MR image quality estimation.

Main Methods:

  • A machine learning-based, reference-free framework was developed for MR image quality assessment.
  • The framework was trained on human observer-derived labels using an active learning strategy and a blinded reading platform.
  • Image features and classifiers, including support-vector machines and deep neural networks, were evaluated on a cohort of 250 patients.

Main Results:

  • The proposed framework achieved a high test accuracy of 93.7% in estimating MR image quality on a 5-point Likert scale.
  • The system demonstrated effective quality estimation without relying on reference images.
  • Active learning and the blinded reading platform significantly reduced the effort required for human observer labeling.

Conclusions:

  • The developed MR image quality assessment framework provides accurate and efficient automated quality estimation.
  • This system can be implemented for prospective quality assurance, including automatic acquisition adaptation and guided scanner operation.
  • It also serves as a valuable tool for retrospective quality assessment, supporting diagnostic decisions and quality control in cohort studies.