Four-dimensional computed tomography pulmonary ventilation images vary with deformable image registration algorithms and metrics

Tokihiro Yamamoto, Sven Kabus, Tobias Klinder, Jens Von Berg, Cristian Lorenz, Billy W. Loo, Paul J. Keall

Research output: Contribution to journalArticle

44 Citations (Scopus)

Abstract

Purpose: A novel pulmonary ventilation imaging technique based on four-dimensional (4D) CT has advantages over existing techniques and could be used for functional avoidance in radiotherapy. There are various deformable image registration (DIR) algorithms and two classes of ventilation metric that can be used for 4D-CT ventilation imaging, each yielding different images. The purpose of this study was to quantify the variability of the 4D-CT ventilation to DIR algorithms and metrics. Methods: 4D-CT ventilation images were created for 12 patients using different combinations of two DIR algorithms, volumetric (DIRvol) and surface-based (DIRsur), yielding two displacement vector fields (DVFs) per patient (DVFvol and DVFsur), and two metrics, Hounsfield unit (HU) change (VHU) and Jacobian determinant of deformation (VJac), yielding four ventilation image sets (V HU vol, V HU sur, V Jac vol, and V Jac sur). First, DVFvol and DVFsur were compared visually and quantitatively to the length of 3D displacement vector difference. Second, four ventilation images were compared based on voxel-based Spearman's rank correlation coefficients and coefficients of variation as a measure of spatial heterogeneity. V HU vol was chosen as the reference for the comparison. Results: The mean length of 3D vector difference between DVFvol and DVFsur was 2.0±1.1 mm on average, which was smaller than the voxel dimension of the image set and the variations. Visually, the reference V HU vol demonstrated similar regional distributions with V HU sur; the reference, however, was markedly different from V Jac vol and V Jac sur. The correlation coefficients of V HU vol with V HU sur, V Jac vol, and V Jac sur were 0.77±0.06, 0.25±0.06, and 0.15±0.07, respectively, indicating that the metric introduced larger variations in the ventilation images than the DIR algorithm. The spatial heterogeneities for V HU vol, V HU sur, V Jac vol, and V Jac sur were 1.8±1.6, 1.8±1.5 (p=0.85), 0.6±0.2 (p=0.02), and 0.7±0.2 (p=0.03), respectively, also demonstrating that the metric introduced larger variations. Conclusions: 4D-CT pulmonary ventilation images vary widely with DIR algorithms and metrics. Careful physiologic validation to determine the appropriate DIR algorithm and metric is needed prior to its applications.

Original languageEnglish (US)
Pages (from-to)1348-1358
Number of pages11
JournalMedical Physics
Volume38
Issue number3
DOIs
StatePublished - Mar 2011
Externally publishedYes

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Four-Dimensional Computed Tomography
Pulmonary Ventilation
Ventilation
Nonparametric Statistics
Radiotherapy

Keywords

  • deformable image registration
  • four-dimensional (4D) CT
  • functional imaging
  • lung

ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging

Cite this

Four-dimensional computed tomography pulmonary ventilation images vary with deformable image registration algorithms and metrics. / Yamamoto, Tokihiro; Kabus, Sven; Klinder, Tobias; Von Berg, Jens; Lorenz, Cristian; Loo, Billy W.; Keall, Paul J.

In: Medical Physics, Vol. 38, No. 3, 03.2011, p. 1348-1358.

Research output: Contribution to journalArticle

Yamamoto, Tokihiro ; Kabus, Sven ; Klinder, Tobias ; Von Berg, Jens ; Lorenz, Cristian ; Loo, Billy W. ; Keall, Paul J. / Four-dimensional computed tomography pulmonary ventilation images vary with deformable image registration algorithms and metrics. In: Medical Physics. 2011 ; Vol. 38, No. 3. pp. 1348-1358.
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abstract = "Purpose: A novel pulmonary ventilation imaging technique based on four-dimensional (4D) CT has advantages over existing techniques and could be used for functional avoidance in radiotherapy. There are various deformable image registration (DIR) algorithms and two classes of ventilation metric that can be used for 4D-CT ventilation imaging, each yielding different images. The purpose of this study was to quantify the variability of the 4D-CT ventilation to DIR algorithms and metrics. Methods: 4D-CT ventilation images were created for 12 patients using different combinations of two DIR algorithms, volumetric (DIRvol) and surface-based (DIRsur), yielding two displacement vector fields (DVFs) per patient (DVFvol and DVFsur), and two metrics, Hounsfield unit (HU) change (VHU) and Jacobian determinant of deformation (VJac), yielding four ventilation image sets (V HU vol, V HU sur, V Jac vol, and V Jac sur). First, DVFvol and DVFsur were compared visually and quantitatively to the length of 3D displacement vector difference. Second, four ventilation images were compared based on voxel-based Spearman's rank correlation coefficients and coefficients of variation as a measure of spatial heterogeneity. V HU vol was chosen as the reference for the comparison. Results: The mean length of 3D vector difference between DVFvol and DVFsur was 2.0±1.1 mm on average, which was smaller than the voxel dimension of the image set and the variations. Visually, the reference V HU vol demonstrated similar regional distributions with V HU sur; the reference, however, was markedly different from V Jac vol and V Jac sur. The correlation coefficients of V HU vol with V HU sur, V Jac vol, and V Jac sur were 0.77±0.06, 0.25±0.06, and 0.15±0.07, respectively, indicating that the metric introduced larger variations in the ventilation images than the DIR algorithm. The spatial heterogeneities for V HU vol, V HU sur, V Jac vol, and V Jac sur were 1.8±1.6, 1.8±1.5 (p=0.85), 0.6±0.2 (p=0.02), and 0.7±0.2 (p=0.03), respectively, also demonstrating that the metric introduced larger variations. Conclusions: 4D-CT pulmonary ventilation images vary widely with DIR algorithms and metrics. Careful physiologic validation to determine the appropriate DIR algorithm and metric is needed prior to its applications.",
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AU - Kabus, Sven

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AU - Von Berg, Jens

AU - Lorenz, Cristian

AU - Loo, Billy W.

AU - Keall, Paul J.

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N2 - Purpose: A novel pulmonary ventilation imaging technique based on four-dimensional (4D) CT has advantages over existing techniques and could be used for functional avoidance in radiotherapy. There are various deformable image registration (DIR) algorithms and two classes of ventilation metric that can be used for 4D-CT ventilation imaging, each yielding different images. The purpose of this study was to quantify the variability of the 4D-CT ventilation to DIR algorithms and metrics. Methods: 4D-CT ventilation images were created for 12 patients using different combinations of two DIR algorithms, volumetric (DIRvol) and surface-based (DIRsur), yielding two displacement vector fields (DVFs) per patient (DVFvol and DVFsur), and two metrics, Hounsfield unit (HU) change (VHU) and Jacobian determinant of deformation (VJac), yielding four ventilation image sets (V HU vol, V HU sur, V Jac vol, and V Jac sur). First, DVFvol and DVFsur were compared visually and quantitatively to the length of 3D displacement vector difference. Second, four ventilation images were compared based on voxel-based Spearman's rank correlation coefficients and coefficients of variation as a measure of spatial heterogeneity. V HU vol was chosen as the reference for the comparison. Results: The mean length of 3D vector difference between DVFvol and DVFsur was 2.0±1.1 mm on average, which was smaller than the voxel dimension of the image set and the variations. Visually, the reference V HU vol demonstrated similar regional distributions with V HU sur; the reference, however, was markedly different from V Jac vol and V Jac sur. The correlation coefficients of V HU vol with V HU sur, V Jac vol, and V Jac sur were 0.77±0.06, 0.25±0.06, and 0.15±0.07, respectively, indicating that the metric introduced larger variations in the ventilation images than the DIR algorithm. The spatial heterogeneities for V HU vol, V HU sur, V Jac vol, and V Jac sur were 1.8±1.6, 1.8±1.5 (p=0.85), 0.6±0.2 (p=0.02), and 0.7±0.2 (p=0.03), respectively, also demonstrating that the metric introduced larger variations. Conclusions: 4D-CT pulmonary ventilation images vary widely with DIR algorithms and metrics. Careful physiologic validation to determine the appropriate DIR algorithm and metric is needed prior to its applications.

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