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Magnetic Resonance Imaging (MRI) and Ventilation Perfusion Scans are two radiological investigations that offer detailed diagnostic images of the body, particularly lung structures.
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Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
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A deep learning method for producing ventilation images from 4DCT: First comparison with technegas SPECT ventilation.

Zhiqiang Liu1, Junjie Miao1, Peng Huang1

  • 1National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 17 Panjiayuannanli, Chaoyang District, Beijing, 100021, China.

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|December 29, 2019
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A novel deep learning (DL) method accurately generates four-dimensional computed tomography (4DCT) ventilation imaging, outperforming traditional HU and JAC methods. This advanced ventilation imaging aids in lung cancer radiotherapy planning and response assessment.

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4DCT ventilation imagingSPECT validationdeep learning

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Area of Science:

  • Medical Imaging
  • Radiotherapy
  • Artificial Intelligence

Background:

  • Accurate ventilation imaging is crucial for lung cancer radiotherapy.
  • Current methods like 4DCT-based CTVI have limitations in precision.
  • Comparing CTVI with SPECT-VI is essential for validation.

Purpose of the Study:

  • To develop a deep learning (DL) method for 4DCT ventilation imaging.
  • To evaluate the accuracy of DL-based ventilation imaging against SPECT-VI.
  • To compare DL method performance with HU and JAC methods.

Main Methods:

  • Developed a DL model (U-net) using 4DCT data from 50 lung/esophagus cancer patients.
  • Correlated 4DCT features with SPECT-VI using two input datasets (10-phase and 2-phase).
  • Validated DL method against HU and JAC methods using Spearman correlation and Dice Similarity Coefficient (DSC).

Main Results:

  • DL method showed significantly higher correlation (Spearman rs ≈ 0.73) with SPECT-VI compared to HU (rs ≈ 0.22) and JAC (rs ≈ -0.09).
  • DL method achieved superior spatial overlap (DSC ≈ 0.73) with SPECT-VI functional regions versus HU (DSC ≈ 0.45) and JAC (DSC ≈ 0.33).
  • The DL method demonstrated a statistically significant improvement (P < 10-7) over traditional methods.

Conclusions:

  • A DL method for CTVI was successfully developed and validated against SPECT-VI.
  • DL-derived CTVI offers significantly improved accuracy over HU and JAC methods.
  • This enhanced CTVI is valuable for lung functional avoidance radiotherapy and treatment response modeling.