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Deep learning-driven multi-view multi-task image quality assessment method for chest CT image.

Jialin Su1, Meifang Li2,3, Yongping Lin4

  • 1School of Optoelectronic and Communication Engineering, Xiamen University of Technology, Xiamen, 361024, China.

Biomedical Engineering Online
|December 7, 2023
PubMed
Summary
This summary is machine-generated.

A new deep learning method automates chest CT image quality assessment, improving diagnostic accuracy. This multi-view, multi-task approach enhances efficiency and reduces human error in evaluating image suitability for patient condition assessment.

Keywords:
Chest computed tomography imagesDeep learningImage quality assessmentMulti-taskMulti-view

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Radiology

Background:

  • Chest computed tomography (CT) image quality is critical for accurate diagnoses.
  • Manual image quality assessment is time-consuming and prone to human error.
  • Automated assessment is needed to improve efficiency and reliability.

Purpose of the Study:

  • To develop and validate a deep learning (DL)-driven multi-view multi-task image quality assessment (M[Formula: see text]IQA) method.
  • To assess the suitability of chest CT images for patient condition evaluation.
  • To automate the pre-diagnostic image quality assessment process.

Main Methods:

  • Utilized retrospective chest CT images from 327 patients (1613 for training/validation, 41 for testing).
  • Developed a DL-driven M[Formula: see text]IQA method using a multi-view fusion strategy across coronal, axial, and sagittal planes.
  • Incorporated four specific algorithms for evaluating inspiration, position, radiation protection, and artifacts.

Main Results:

  • The M[Formula: see text]IQA method achieved 87% precision, 93% sensitivity, 69% specificity, and a 0.90 F1-score on the test set.
  • Ablation and comparative studies confirmed the effectiveness of the proposed algorithms.
  • Demonstrated strong generalization performance across various image quality assessment tasks.

Conclusions:

  • A DL-driven M[Formula: see text]IQA method with four novel algorithms was successfully developed and validated.
  • The method shows significant promise for automating chest CT image quality assessment.
  • The study confirms the effectiveness of the proposed algorithms and the overall method.