4D-CT Lung Ventilation Images Vary with 4D-CT Sorting Techniques 1. Innovation/impact Our long-term goal is to reduce pulmonary toxicity and improve survival for lung cancer patients through 4D-CT ventilation imaging biomarker-based personalized radiotherapy, which optimizes dose according to risk of toxicity before the completion of treatment. 4D-CT ventilation imaging biomarker-based personalized radiotherapy is estimated to allow toxicity reduction by 4%, corresponding to 5,000 patients/year in the US.* 2. Rationale In lung cancer radiotherapy, existing models to predict pulmonary toxicity (e.g., radiation pneumonitis) that occurs in 5-20% have been inconsistent and inconclusive (Marks et al., IJROBP 2010), limiting investigations into adequate dose to achieve high tumor control. Novel lung ventilation imaging based on 4D-CT has potential as a biomarker for pneumonitis, in which there is histologically impaired gas exchange and lung compliance (Travis et al., IJROBP 1977). 4D-CT ventilation imaging is based on: (1) acquisition of 4D-CT scans; (2) spatial mapping of different respiratory phases of 4D-CT images using deformable image registration (DIR); (3) quantification of regional volume change. Only weak correlations with ground-truth ventilation images have been reported for human subjects (Castillo et al., PMB 2010). A previous work has demonstrated that 4D-CT ventilation images vary widely with DIR algorithms and metrics of volume changes (Yamamoto et al., Med Phys 2011). The current 4D-CT technique with phase-based sorting results in artifacts at an alarmingly high frequency (90%) (Yamamoto et al., IJROBP 2008), which may introduce another variations into ventilation calculations. The purpose of this study was to quantify the variability of 4D-CT ventilation imaging to 4D-CT sorting techniques. 3. Key Results Variability of 4D-CT ventilation imaging to 4D-CT sorting techniques

Anterior

Posterior

(normalized by overall mean)

Peak-exhale4D-CT

Phase-based sorting Anatomic similarity-based sorting

(a) (b)

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Ventilation normalized by overall mean

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Phase-based sortingDisplacement- &anatomic similarity-based sorting

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phaseV(normalized by overall mean)

anatV

phaseV

anatV

 Figure 1. (a) Comparison of peak-exhale 4D-CT images sorted by: (1) phase; (2) anatomic similarity and abdominal displacement (Johnston et al., Med Phys 2011), and resulting ventilation images (Vphase and anatV ) for patient 4, indicating a moderate voxel-based

correlation of 0.69. Marked artifacts were observed in 4D-CT images sorted by phase (red arrows), which were reduced in those sorted by anatomic similarity and displacement. A number of unexpected negative ventilation values were observed in Vphase (green arrows),

which were reduced in anatV . (b) Comparison of ventilation histograms for Vphase and anatV . The percentage of negative ventilation

values was 24% for Vphase , which was significantly larger than 5% for anatV . Note that ventilation is normalized by the overall mean.

                                                            * Based on: (1) a preliminary study showing that 4D-CT ventilation image-based treatment planning reduced the mean dose to functional lung regions by 2 Gy on average (Yamamoto et al., IJROBP 2011); (2) published toxicity data as a function of the mean lung dose (Marks et al., IJROBP 2010); (3) 56% (ASTRO Fact Sheet) of 222,520 new lung cancer patients/year (Jemal et al., CA Cancer J Clin 2010) are treated by radiotherapy.

Table 1. Voxel-based Spearman rank correlation coefficients for all lung voxels and Dice similarity coefficients for segmented low-functional lung regions with equal volumes between Vphase and anatV for five patients.

Patient Voxel-based correlation

Dice similarity coefficient

1 0.68 0.67 2 0.57 0.57 3 0.75 0.70 4 0.69 0.64 5 0.77 0.67

Relationship between abdominal motion range variation and 4D-CT ventilation variation (determined by subtracting motion range or ventilation of phase-based sorting from that of anatomic similarity-based sorting)

Abdominal motion range variation

Ventilation variation

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Abdominal motion range variation

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 Figure 2. (a) Comparison of the images of abdominal motion range variation and 4D-CT ventilation variation for patient 4, indicating a significant, strong linear relationship (R2=0.88, p<0.01). (b) Abdominal motion range variation vs. ventilation variation for the same patient. Points represent the average ventilation variation within motion range variation bins. Error bars represent the 95% confidence interval for the average. Table 2. Slopes, R2 and p-values of the linear regression models for the relationship between the abdominal motion range variation and 4D-CT ventilation variation for five patients.

Patient Slope R2 p-value 1 0.38 0.96 <0.01 2 0.25 0.36 0.16 3 0.69 0.81 0.04 4 1.48 0.88 <0.01 5 1.42 0.94 <0.01

Correlation between 4D-CT ventilation and SPECT ventilation images (assumed ground-truth)

SPECT ventilation4D-CT ventilationphaseV anatV

 Figure 3. Comparison of the two 4D-CT ventilation (Vphase and anatV ) and SPECT ventilation images for patient 4, indicating that anatV

showed higher correlations with SPECT in lower regions than Vphase (red arrows). Note that the ventilation images are shown with a

scale from the 10th percentile to the 90th percentile values.