Ventilation from four-dimensional computed tomography: density versus Jacobian methods

Abstract

Two calculation methods to produce ventilation images from four-dimensional computed tomography (4DCT) acquired without added contrast have been reported. We reported a method to obtain ventilation images using deformable image registration (DIR) and the underlying CT density information. A second method performs the ventilation image calculation from the DIR result alone, using the Jacobian determinant of the deformation field to estimate the local volume changes resulting from ventilation. For each of these two approaches, there are variations on their implementation. In this study, two implementations of the Jacobian-based methodology are evaluated, as well as a single density change-based model for calculating the physiologic specific ventilation from 4DCT. In clinical practice, (99m)Tc-labeled aerosol single photon emission computed tomography (SPECT) is the standard method used to obtain ventilation images in patients. In this study, the distributions of ventilation obtained from the CT-based ventilation image calculation methods are compared with those obtained from the clinical standard SPECT ventilation imaging. Seven patients with 4DCT imaging and standard (99m)Tc-labeled aerosol SPECT/CT ventilation imaging obtained on the same day as part of a prospective validation study were selected. The results of this work demonstrate the equivalence of the Jacobian-based methodologies for quantifying the specific ventilation on a voxel scale. Additionally, we found that both Jacobian- and density-change-based methods correlate well with global measurements of the resting tidal volume. Finally, correlation with the clinical SPECT was assessed using the Dice similarity coefficient, which showed statistically higher (p-value < 10(-4)) correlation between density-change-based specific ventilation and the clinical reference than did either Jacobian-based implementation.

CT ventilation functional image-based IMRT treatment plans are comparable to SPECT ventilation functional image-based plans

“…CT ventilation imaging has the potential for widespread clinical implementation, as 4D-CT is routinely acquired for treatment planning at many centers [14] and ventilation computation only involves image processing/analysis without extra scans to patients. Additionally, CT ventilation imaging has a shorter scan time, higher spatial resolution (the exact resolution is unknown), lower cost, and/or greater availability than other modalities.Validation studies for CT ventilation imaging have been focused on cross-modality image comparisons [15][16][17][18][19][20][21][22]. For example, studies with mechanically ventilated sheep have demonstrated strong correlations between CT ventilation and xenon-CT ventilation [15,16].…”

“…Validation studies for CT ventilation imaging have been focused on cross-modality image comparisons [15][16][17][18][19][20][21][22]. For example, studies with mechanically ventilated sheep have demonstrated strong correlations between CT ventilation and xenon-CT ventilation [15,16].…”

Using 4DCT‐ventilation to characterize lung function changes for pediatric patients getting thoracic radiotherapy

“…Each patient's pre and post‐treatment 4DCT scan was used to calculate 4DCT‐ventilation maps using previously described methods 1, 2, 9. The lungs are first segmented on the 0% (Inhale) and 50% (Exhale) phases of the 4DCT data set.…”

Evaluation of the ΔV 4D CT ventilation calculation method usingin vivoxenon CT ventilation data and comparison to other methods

“…The Jacobian is a mathematical method where volume change is estimated using the first derivative of the deformation field 13 , 24 , 32 , 33 . Local volume change of the lung is calculated using the Jacobian of the transformation that maps the end expiration phase of 4D CT image to the end inspiration phase.…”

“…Because of the different metrics between the two ventilation imaging modalities, namely density difference for the XeCT versus volume change for the ΔV method, the two ventilation distributions were converted to the relative percentile distribution (32) first and then the Dice similarity coefficient (DSC) (36) was applied to calculate the similarity between the two ventilation volumes. When volumes A and B are compared, DSC is calculated as,$DSCfalse(A,Bfalse)=\frac{2\times \left|A\cap B\right|}{\left|A\right|+\left|B\right|}$The values of DSC index range between 1.0 and 0.0.…”