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Related Concept Videos

Radiological Investigation II: MRI and Ventilation Perfusion Scan01:30

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Description
Magnetic Resonance Imaging (MRI) and Ventilation Perfusion Scans are two radiological investigations that offer detailed diagnostic images of the body, particularly lung structures.
MRI
MRI uses magnetic fields and radiofrequency signals to distinguish between normal and abnormal tissues. This technology provides a more detailed diagnostic image than CT scans, enabling it to characterize pulmonary nodules, stage bronchogenic carcinoma, and evaluate inflammatory activity in...
236

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Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
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Deriving Pulmonary Ventilation Images From Clinical 4D-CBCT Using a Deep Learning-Based Model.

Zhiqiang Liu1, Yuan Tian1, Junjie Miao1

  • 1National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College, Department of Radiation Oncology, Beijing, China.

Frontiers in Oncology
|May 19, 2022
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Summary
This summary is machine-generated.

A new deep learning (DL) method accurately derives ventilation images from 4D cone-beam computed tomography (CBCT-VI) without deformable image registration (DIR). This approach shows significant improvement in correlation and similarity compared to traditional methods for monitoring lung function during radiotherapy.

Keywords:
4D-CBCTdeep learningfunctional imagingimage-guided radiotherapypulmonary ventilation imaging

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Area of Science:

  • Medical Imaging
  • Radiotherapy Physics
  • Computational Biology

Background:

  • Accurate measurement of lung ventilation is crucial for radiotherapy planning.
  • Current methods using 4D cone-beam computed tomography (CBCT) rely on deformable image registration (DIR), which can introduce inaccuracies.
  • Developing DIR-independent methods for ventilation imaging is essential for improving treatment precision.

Purpose of the Study:

  • To propose and validate a novel deep learning (DL) method for deriving ventilation images from 4D-CBCT (CBCT-VI) that bypasses the need for DIR.
  • To compare the accuracy of the proposed DL-based CBCT-VI with traditional DIR-dependent methods.
  • To assess the potential of DL-derived CBCT-VI for monitoring dynamic lung function changes during radiotherapy.

Main Methods:

  • A DL model was developed to generate CBCT-VI using 4D-CBCT data from 28 lung or esophagus cancer patients.
  • The DL model was trained and evaluated using sevenfold cross-validation, with inputs including either 10 phases or 2 phases (peak-exhalation/inhalation) of 4D-CBCT.
  • Performance was compared against density-change-based and Jacobian-based DIR methods using voxel-wise Spearman's correlation and Dice Similarity Coefficient (DSC) against SPECT-VI.
  • Statistical analysis was performed using a one-factor ANONA model.

Main Results:

  • The DL method achieved significantly higher correlation (0.65 ± 0.13/0.65 ± 0.15) and Dice Similarity Coefficient (0.59 ± 0.08/0.58 ± 0.09) compared to DIR-dependent methods (correlation ~0.02, DSC ~0.34).
  • The DL-derived CBCT-VI demonstrated the strongest correlation and highest similarity with the gold-standard SPECT-VI.
  • Results indicate that using fewer phases (2 vs. 10) did not significantly impact the performance of the DL model.

Conclusions:

  • The proposed DL method offers a significant improvement in the accuracy of ventilation image generation from 4D-CBCT.
  • This DIR-independent approach holds promise for accurate, real-time monitoring of lung function during radiotherapy.
  • The CBCT-VI derived using DL can potentially enhance personalized radiotherapy treatment planning and delivery.