A Deep Learning Method for Producing Ventilation Images from 4DCT: First Comparison with Technegas SPECT Ventilation.

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Abstract

The purpose of this study is to develop a deep learning (DL) method for producing four-dimensional computed tomography (4DCT) ventilation imaging and to evaluate the accuracy of the DL-based ventilation imaging against SPECT ventilation imaging (SPECT-VI). The performance of the DL-based method is assessed by comparing with density change- and Jacobian-based (HU and JAC) methods.
Fifty patients with esophagus or lung cancer who underwent thoracic radiotherapy were enrolled in this study. For each patient, 4DCT scans paired with 99mTc-Technegas SPECT/CT were acquired before the first radiotherapy treatment. 4DCT and SPECT/CT were first rigidly registered using MIMvista and converted to data matrix using MATLAB, and then transferred to a DL model based on U-net for correlating 4DCT features and SPECT-VI. Two forms of 4DCT dataset ((1) ten phases and (2) two phases of peak-exhalation and peak-inhalation) as input are studied. Tenfold cross-validation procedure was used to evaluate the performance of the DL model. For comparative evaluation, HU and JAC methodologies are used to calculate specific ventilation imaging based on 4DCT (CTVI) for each patient. The voxel-wise Spearman’s correlation was evaluated over the whole lung between each of CTVI and corresponding SPECT-VI. The SPECT-VI and produced CTVIs were segmented into high, median and low functional lung (HFL, MFL and LFL) regions. The spatial overlap of corresponding HFL, MFL and LFL for each CTVI against SPECT-VI was also evaluated by using the dice similarity coefficient (DSC). The averaged DSC of functional lung regions was calculated and statistically analyzed with a one-factor ANONA model among different methods.
The voxel-wise Spearman rs values were (0.22±0.31), (-0.09±0.18) and (0.73±0.16)/(0.71±0.17) for the CTVIHU , CTVIJAC and CTVIDL(1) /CTVIDL(2) . These results showed the DL method yielded the strongest correlation with SPECT-VI. Using the DSC as the spatial overlap metric, we found that the CTVIHU , CTVIJAC and CTVIDL(1) /CTVIDL(2) methods achieved averaged DSC values for all patients to be (0.45±0.08), (0.33±0.04) and (0.73±0.09)/(0.71±0.09), respectively. The results demonstrated that the DL method yielded the highest similarity with SPECT-VI with the prominently significant difference (p-value < 10-7 ).
This study developed a DL method for producing CTVI and performed a validation against SPECT-VI. The results demonstrated that DL method can derive CTVI with greatly improved accuracy in comparison to HU and JAC methods. The produced ventilation images can be more accurate and useful for lung functional avoidance radiotherapy and treatment response modeling.
© 2019 American Association of Physicists in Medicine.