Reproducibility of Four-dimensional Computed Tomography-based Lung Ventilation Imaging

Rationale and Objectives

A novel ventilation imaging method based on four-dimensional (4D) computed tomography (CT) has been applied to the field of radiation oncology. Understanding its reproducibility is a prerequisite for clinical applications. The purpose of this study was to quantify the reproducibility of 4D CT ventilation imaging over different days and the same session.

Materials and Methods

Two ventilation images were created from repeat 4D CT scans acquired over the average time frames of 15 days for 6 lung cancer patients and 5 minutes for another 6 patients. The reproducibility was quantified using the voxel-based Spearman rank correlation coefficients for all lung voxels and Dice similarity coefficients (DSC) for the spatial overlap of segmented high-, moderate-, and low-functional lung volumes. Furthermore, the relationship between the variation in abdominal motion range as a measure of the depth of breathing and variation in ventilation was evaluated using linear regression.

Results

The voxel-based correlation between the two ventilation images was moderate on average (0.50 ± 0.15). The DSCs were also moderate for the high- (0.60 ± 0.08), moderate- (0.46 ± 0.06), and low-functional lung (0.58 ± 0.09). No patients demonstrated strong correlations. The relationship between the motion range variation and ventilation variation was found to be moderate and significant.

Conclusions

We investigated the reproducibility of 4D CT ventilation imaging over the time frames of 15 days and 5 minutes and found that it was only moderately reproducible. Respiratory variation during 4D CT scans was found to deteriorate the reproducibility. Improvement of 4D CT imaging is necessary to increase the reproducibility of 4D CT ventilation imaging.

In recent years, there have been significant advances in molecular and functional imaging as applied to the field of radiation oncology. The most important applications include treatment planning, especially for dose painting (ie, boosting dose to active tumor subregions) and functional avoidance (ie, sparing dose from high-functional subregions of normal tissues) . The other important application is the assessment of tumor response to treatment and radiation-induced normal tissue injury . In lung cancer radiotherapy, lung ventilation or perfusion imaging with single photon emission computed tomography (SPECT) and magnetic resonance (MR) have been used in such applications. However, SPECT and MR imaging suffer from drawbacks such as low resolution, high cost, long scan time, and limited availability.

Lung ventilation images can be created by a novel technique based on four-dimensional (4D) computed tomography (CT) . The 4D CT-derived ventilation can be considered “free” information for lung cancer radiotherapy patients, because 4D CT scans are currently in routine use for treatment planning purposes at many centers (42.3%) and ventilation computation involves only image processing and analysis. Moreover, 4D CT ventilation imaging has higher resolution, lower cost, shorter scan time, and/or higher availability compared to SPECT or MR imaging. In the literature, there have been several applications of 4D CT ventilation imaging to functional avoidance and assessment of radiation-induced ventilation changes . Four-dimensional CT ventilation images have been found to vary widely with deformable image registration (DIR) algorithms and ventilation metrics , indicating the need for careful validation. Nevertheless, little validation has been performed to date. Several investigators evaluated the physiologic accuracy of 4D CT ventilation imaging by comparing with xenon CT ventilation imaging for animal subjects and found reasonable correlations . Major drawbacks of these studies include limited axial coverage (∼3 cm). Castillo et al and Yamamoto et al compared 4D CT and SPECT ventilation images for thoracic cancer patients. Although they observed some regional agreements, the correlations were low overall, which was at least in part due to central airway depositions of the SPECT radiotracer (ie, technetium-99m-labeled diethylenetriamine pentaacetate) aerosols. Positive data on human subjects include significantly lower 4D CT ventilation in emphysematous regions (ie, known low-signal regions) than in nonemphysematous regions (ie, known high-signal regions) for 12 lung cancer patients as demonstrated by Yamamoto et al . More recently, Castillo et al compared 4D CT ventilation and SPECT perfusion images for 10 lung cancer patients and demonstrated strong correlations in functional defect regions distal to airway obstruction because of gross tumor . These results indicate the potential for a high physiologic accuracy of 4D CT ventilation imaging. Given the lack of data showing strong correlations with ground truth ventilation imaging for human subjects, however, further studies are necessary. In addition, the reproducibility should also be investigated, which is particularly important for longitudinal studies such as the assessment of radiation-induced ventilation changes. Recently, Du et al investigated the reproducibility of 4D CT ventilation imaging using repeat 4D CT scans acquired over less than 10 minutes for three anesthetized, mechanically ventilated sheep and nine lung cancer patients . They found high reproducibility for sheep, but relatively poor reproducibility for patients.

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Material and methods

Patients

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4D CT Ventilation Imaging

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J(x,y,z)=∣∣∣∣∣∣∣1+∂ux(x,y,z)∂x∂uy(x,y,z)∂x∂uz(x,y,z)∂x∂ux(x,y,z)∂y1+∂uy(x,y,z)∂y∂uz(x,y,z)∂y∂ux(x,y,z)∂z∂uy(x,y,z)∂z1+∂uz(x,y,z)∂z∣∣∣∣∣∣∣, J

(

)

∂

(

)

∂

(

)

∂

(

)

∂

(

)

∂

(

)

∂

(

)

∂

(

)

∂

(

)

∂

(

)

∂

which represents the differential contraction or expansion at point (x,y,z) (

) . The volume of each exhale voxel deformed into the inhale phase (Volvoxelin) (

Vol

voxel

) can be estimated by

Volvoxelin(x,y,z)=Volvoxelex(x,y,z)⋅J(x,y,z), Vol

voxel

(

)

Vol

voxel

(

)

⋅

(

)

where Volvoxelex Vol

voxel is the exhale voxel volume. In this study, the ventilation metric (V) (

) was defined as exhale-to-inhale volume change and can be expressed as

V(x,y,z)=Volvoxelin(x,y,z)−Volvoxelex(x,y,z)=Volvoxelex⋅{J(x,y,z)−1}. V

(

)

Vol

voxel

(

)

−

Vol

voxel

(

)

Vol

voxel

⋅

{

(

)

−

}

A value of zero corresponds to local volume preservation. A negative value represents local contraction, and a positive value represents local expansion. Thus 4D CT ventilation images at the peak-exhale phase were created for the first (V1st) (

) and second (V2nd) (

) scans. Further details on each step of 4D CT ventilation imaging have been described elsewhere .

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Quantification of the Tidal Volume

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Volair(x,y,z)=−HU(x,y,z)1000Volvoxel(x,y,z), Vol

air

(

)

−

(

)

1000

Vol

voxel

(

)

where HU HU is the Hounsfield unit value . Note that the air and tissue densities were assumed to be −1000 and 0 HU, respectively. Second, the air volume in the peak-exhale lung was subtracted from that of the peak-inhale lung to determine a tidal volume.

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Quantification of the Reproducibility of 4D CT Ventilation Imaging

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DSC=2⋅FLV1st∩FLV2ndFLV1st+FLV2nd, D

⋅

∩

where FLV F

V is the segmented high-, moderate-, or low-functional lung volume.

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V(x,y,z)=Volvoxelex{J(x,y,z)−1}V¯¯¯. V

(

)

Vol

voxel

{

(

)

−

}

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Regression Analysis for the Relationship between the Abdominal Motion Range Variation and 4D CT Ventilation Variation

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ΔV(x,y,z)=V2nd(x,y,z)−V1st(x,y,z). Δ

(

)

(

)

−

(

)

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Δm(x,y,z)=m2nd(x,y,z)m2nd¯¯¯¯¯¯¯−m1st(x,y,z)m1st¯¯¯¯¯¯¯. Δ

(

)

(

)

−

(

)

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Vm(x,y,z)=V(x,y,z)m(x,y,z). V

(

)

(

)

(

)

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Results

Patients

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Table 1

Characteristics of Patients and Repeat 4D CT Scans for 12 Patients

Patient Scan Gender Age (y) Histology and Stage Time Frame AV Biofeedback Used? Arm Position Immobilization Device Used? Peak-exhale Lung (Air) Volume (cm 3 ) Peak-inhale Lung (Air) Volume (cm 3 ) Tidal Volume (cm 3 ) Average Abdominal Motion Range (cm)Different-day cohort 1 First Male 78 NSCLC, stage I 13 days Yes Up Yes 4742 (3898) 5439 (4546) 648 1.25 Second Yes Down No 3863 (2912) 4936 (3706) 794 1.28 2 First Male 66 NSCLC, stage III 12 days Yes Up Yes 3681 (2889) 4055 (3241) 352 1.02 Second Yes Down No 3615 (1955) 4349 (2367) 412 1.37 3 First Male 62 NSCLC, stage III 9 days No Up Yes 5896 (4727) 6386 (5176) 449 0.64 Second No Down No 5537 (3897) 6532 (4481) 584 1.04 4 First Male 82 NSCLC, stage IV 8 days Yes Up Yes 7812 (6694) 8130 (6983) 289 0.43 Second Yes Up Yes 8436 (4993) 9021 (5492) 499 0.51 5 First Male 67 NSCLC, stage IV 30 days Yes Up Yes 5021 (4019) 5735 (4710) 691 0.97 Second Yes Up Yes 4748 (3307) 5655 (4108) 800 0.97 6 First Male 77 NSCLC, stage II 15 days No Up Yes 6622 (5663) 7126 (6142) 479 0.95 Second No Down No 6145 (4771) 6624 (5211) 441 0.95Same-session Cohort 1 First Male 60 NSCLC, stage IV 5 min No Down Yes 3470 (2579) 3712 (2869) 290 0.65 Second Yes Down Yes 3508 (2579) 3837 (2853) 275 0.64 2 First Female 74 NSCLC, stage I 6 min Yes Down Yes 2837 (1887) 3430 (2445) 558 0.66 Second No Down Yes 2645 (1765) 3356 (2369) 604 0.65 3 First Male 61 NSCLC, stage I 5 min No Up Yes 4382 (3448) 4711 (3719) 272 0.59 Second Yes Up Yes 4564 (3404) 4971 (3705) 301 0.57 4 First Female 57 Metastatic tumor 5 min Yes Up Yes 1418 (854) 2004 (1397) 543 0.95 Second No Up Yes 1375 (822) 1786 (1240) 418 0.62 5 First Male 59 NSCLC, stage III 5 min No Up Yes 4257 (3428) 4860 (3993) 566 0.74 Second Yes Up Yes 4839 (3343) 5020 (3859) 516 0.36 6 First Female 67 NSCLC, stage III 4 min No Up Yes 2467 (1884) 2879 (2266) 382 0.66 Second Yes Up Yes 2575 (1823) 2953 (2187) 364 0.70

4D, four-dimensional; AV, audiovisual; CT, computed tomography; NSCLC, non–small-cell lung cancer.

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Reproducibility of 4D CT Ventilation Imaging

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Table 2

Voxel-based Spearman Correlation Coefficients (All Voxels within the Lung) and Dice Similarity Coefficients (Segmented Functional Lung Volumes) between the First and Second Four-dimensional Computed Tomography Ventilation Images for 12 Patients

Patient Voxel-based Correlation Dice Similarity Coefficient High Moderate LowDifferent-day cohort 1 0.50 0.57 0.43 0.56 2 0.65 0.66 0.44 0.65 3 0.38 0.56 0.40 0.47 4 0.45 0.56 0.46 0.56 5 0.53 0.60 0.44 0.54 6 0.36 0.54 0.44 0.53 Mean ± SD 0.48 ± 0.11 0.58 ± 0.05 0.44 ± 0.02 0.55 ± 0.06Same-session cohort 1 0.75 0.72 0.59 0.76 2 0.66 0.64 0.47 0.67 3 0.42 0.60 0.49 0.57 4 0.69 0.74 0.54 0.67 5 0.34 0.49 0.38 0.52 6 0.31 0.51 0.46 0.51 Mean ± SD 0.53 ± 0.20 0.62 ± 0.11 0.49 ± 0.07 0.62 ± 0.10All patients Mean ± SD 0.50 ± 0.15 0.60 ± 0.08 0.46 ± 0.06 0.58 ± 0.09

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Relationship between the Abdominal Motion Range Variation and 4D CT Ventilation Variation

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Table 3

Slopes, r 2 , and P Values of the Linear Regression Models for the Relationship between the Abdominal Motion Range Variation and Four-dimensional Computed Tomography Ventilation Variation for 12 Patients

Patient Slope_r_ 2 P ValueDifferent-day cohort 1 0.88 0.81 <.01 2 0.70 0.90 <.01 3 0.48 0.29 .06 4 0.30 0.23 .03 5 0.55 0.35 .30 6 −0.04 0.01 .83Same-session cohort 1 0.33 0.07 .44 2 1.11 0.85 <.01 3 0.17 0.02 .65 4 1.03 0.83 <.01 5 0.35 0.28 <.01 6 0.46 0.41 .01All patients 0.26 0.42 <.01

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Discussion

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Conclusions

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Acknowledgment

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Reproducibility of Four-dimensional Computed Tomography-based Lung Ventilation Imaging

Rationale and Objectives

Materials and Methods

Results

Conclusions

Material and methods

Patients

4D CT Ventilation Imaging

Quantification of the Tidal Volume

Quantification of the Reproducibility of 4D CT Ventilation Imaging

Regression Analysis for the Relationship between the Abdominal Motion Range Variation and 4D CT Ventilation Variation

Results

Patients

Reproducibility of 4D CT Ventilation Imaging

Relationship between the Abdominal Motion Range Variation and 4D CT Ventilation Variation

Discussion

Conclusions

Acknowledgment

References

Further Reading

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