Synthetic pulmonary perfusion images from 4DCT for functional avoidance using deep learning

Porter, Evan; Myziuk, Nicholas; Quinn, Thomas J.; Lozano, D A; Peterson, Avery; Quach, Duyen M; Siddiqui, Z.A.; Guerrero, Thomas

doi:10.1088/1361-6560/ac16ec

Cited by 8 publications

(15 citation statements)

References 45 publications

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“…Meanwhile, the results from this and our previous study were also consistent with the work of Porter et al. ( 19 ) on synthetic pulmonary perfusion images from 4D-CT, and a Spearman correlation of 0.7 between 4D-CT- and SPECT-based perfusion images was reported. In this study, to reduce the influence of image noise, all the ventilation images finally went through a smoothing with a 9 × 9 × 9 box median filter.…”

Section: Discussionsupporting

confidence: 93%

Deriving Pulmonary Ventilation Images From Clinical 4D-CBCT Using a Deep Learning-Based Model

et al. 2022

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PurposeThe current algorithms for measuring ventilation images from 4D cone-beam computed tomography (CBCT) are affected by the accuracy of deformable image registration (DIR). This study proposes a new deep learning (DL) method that does not rely on DIR to derive ventilation images from 4D-CBCT (CBCT-VI), which was validated with the gold-standard single-photon emission-computed tomography ventilation image (SPECT-VI).Materials and MethodsThis study consists of 4D-CBCT and 99mTc-Technegas SPECT/CT scans of 28 esophagus or lung cancer patients. The scans were rigidly registered for each patient. Using these data, CBCT-VI was derived using a deep learning-based model. Two types of model input data are studied, namely, (a) 10 phases of 4D-CBCT and (b) two phases of peak-exhalation and peak-inhalation of 4D-CBCT. A sevenfold cross-validation was applied to train and evaluate the model. The DIR-dependent methods (density-change-based and Jacobian-based methods) were used to measure the CBCT-VIs for comparison. The correlation was calculated between each CBCT-VI and SPECT-VI using voxel-wise Spearman’s correlation. The ventilation images were divided into high, medium, and low functional lung regions. The similarity of different functional lung regions between SPECT-VI and each CBCT-VI was evaluated using the dice similarity coefficient (DSC). One-factor ANONA model was used for statistical analysis of the averaged DSC for the different methods of generating ventilation images.ResultsThe correlation values were 0.02 ± 0.10, 0.02 ± 0.09, and 0.65 ± 0.13/0.65 ± 0.15, and the averaged DSC values were 0.34 ± 0.04, 0.34 ± 0.03, and 0.59 ± 0.08/0.58 ± 0.09 for the density change, Jacobian, and deep learning methods, respectively. The strongest correlation and the highest similarity with SPECT-VI were observed for the deep learning method compared to the density change and Jacobian methods.ConclusionThe results showed that the deep learning method improved the accuracy of correlation and similarity significantly, and the derived CBCT-VIs have the potential to monitor the lung function dynamic changes during radiotherapy.

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Section: Discussionsupporting

confidence: 93%

Deriving Pulmonary Ventilation Images From Clinical 4D-CBCT Using a Deep Learning-Based Model

et al. 2022

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“…An asymmetrical structural similarity index measure (aSSIM) was used as the secondary loss function for the generator, which was based on the work of Porter et al. who used an asymmetrical mean absolute error (AMAE) for predicting perfusion defects 29 . Gerard et al.…”

Section: Methodsmentioning

confidence: 99%

“…15 Currently, functional lung imaging and research are based on physical properties of the image such as calculating local CT density changes 16 or calculating regional volume changes using the Jacobian from deformable image registration (DIR). 17 Recently, there have been works published that use machine learning to predict radiation-induced pneumonitis, [18][19][20][21] derive ventilation [22][23][24][25][26] and perfusion maps [27][28][29][30] , and perform segmentation [31][32][33][34] and DIR. [35][36][37][38] However, no work to date has investigated using machine learning to model local pulmonary ventilation change following RT.…”

Section: Introductionmentioning

confidence: 99%

Predicting pulmonary ventilation damage after radiation therapy for nonsmall cell lung cancer using a ResNet generative adversarial network

et al. 2023

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Background: Functional lung avoidance radiation therapy (RT) is a technique being investigated to preferentially avoid specific regions of the lung that are predicted to be more susceptible to radiation-induced damage. Reducing the dose delivered to high functioning regions may reduce the occurrence radiationinduced lung injuries (RILIs) and toxicities. However, in order to develop effective lung function-sparing plans, accurate predictions of post-RT ventilation change are needed to determine which regions of the lung should be spared. Purpose: To predict pulmonary ventilation change following RT for nonsmall cell lung cancer using machine learning. Methods: A conditional generative adversarial network (cGAN) was developed with data from 82 human subjects enrolled in a randomized clinical trial approved by the institution's IRB to predict post-RT pulmonary ventilation change. The inputs to the network were the pre-RT pulmonary ventilation map and radiation dose distribution. The loss function was a combination of the binary cross-entropy loss and an asymmetrical structural similarity index measure (aSSIM) function designed to increase penalization of underprediction of ventilation damage. Network performance was evaluated against a previously developed polynomial regression model using a paired sample t-test for comparison. Evaluation was performed using eight-fold crossvalidation. Results: From the eight-fold cross-validation, we found that relative to the polynomial model, the cGAN model significantly improved predicting regions of ventilation damage following radiotherapy based on true positive rate (TPR), 0.14±0.15 to 0.72±0.21, and Dice similarity coefficient (DSC), 0.19±0.16 to 0.46±0.14, but significantly declined in true negative rate, 0.97±0.05 to 0.62±0.21, and accuracy, 0.79±0.08 to 0.65±0.14. Additionally, the average true positive volume increased from 104±119 cc in the POLY model to 565±332 cc in the cGAN model, and the average false negative volume decreased from 654±361 cc in the POLY model to 193±163 cc in the cGAN model. Conclusions:The proposed cGAN model demonstrated significant improvement in TPR and DSC. The higher sensitivity of the cGAN model can improve the clinical utility of functional lung avoidance RT by identifying larger volumesThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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“…20 4DCT perfusion images have been synthesized using a deep learning approach with MAA-SPECT as the nuclear medicine ground-truth. 21 Deep learning has been implemented to generate CTVIs from 4DCT with DIR-based CTVIs as the reference ground-truth. 22 Deep learning has also been successfully used to produce CTVIs from 4DCT with Technegas SPECT as the nuclear medicine ground-truth, which showed a higher degree of correlation compared with DIR-based CTVIs.…”

Section: Introductionmentioning

confidence: 99%

“…A recent systematic review has found that deep learning applied to functional lung imaging is a relatively small field with good opportunities for further research 20 . 4DCT perfusion images have been synthesized using a deep learning approach with MAA‐SPECT as the nuclear medicine ground‐truth 21 . Deep learning has been implemented to generate CTVIs from 4DCT with DIR‐based CTVIs as the reference ground‐truth 22 .…”

Section: Introductionmentioning

confidence: 99%

Investigating the use of machine learning to generate ventilation images from CT scans

et al. 2022

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Background Radiotherapy treatment planning incorporating ventilation imaging can reduce the incidence of radiation‐induced lung injury. The gold‐standard of ventilation imaging, using nuclear medicine, has limitations with respect to availability and cost. Purpose An alternative type of ventilation imaging to nuclear medicine uses 4DCT (or breath‐hold CT [BHCT] pair) with deformable image registration (DIR) and a ventilation metric to produce a CT ventilation image (CTVI). The purpose of this study is to investigate the application of machine learning as an alternative to DIR‐based methods when producing CTVIs. Methods A patient dataset of 15 inhale and exhale BHCTs and Galligas PET ventilation images were used to train and test a 2D U‐Net style convolutional neural network. The neural network established relationships between axial input BHCT image pairs and axial labeled Galligas PET images and was evaluated using eightfold cross‐validation. Once trained, the neural network could produce a CTVI from an input BHCT image pair. The CTVIs produced by the neural network were qualitatively assessed visually and quantitatively compared to a Galligas PET ventilation image using a Spearman correlation and Dice similarity coefficient (DSC). The DSC measured the spatial overlap between three segmented equal lung volumes by ventilation (high, medium, and low functioning lung [LFL]). Results The mean Spearman correlation between the CTVIs and the Galligas PET ventilation images was 0.58 ± 0.14. The mean DSC over high, medium, and LFL between the CTVIs and Galligas PET ventilation images was 0.55 ± 0.06. Visually, a systematic overprediction of ventilation within the lung was observed in the CTVIs with respect to the Galligas PET ventilation images, with jagged regions of ventilation in the sagittal and coronal planes. Conclusions A convolutional neural network was developed that could produce a CTVI from a BHCT image pair, which was then compared with a Galligas PET ventilation image. The performance of this machine learning method was comparable to previous benchmark studies investigating a DIR‐based CTVI, warranting future development, and investigation of applying machine learning to a CTVI.

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Synthetic pulmonary perfusion images from 4DCT for functional avoidance using deep learning

Cited by 8 publications

References 45 publications

Deriving Pulmonary Ventilation Images From Clinical 4D-CBCT Using a Deep Learning-Based Model

Deriving Pulmonary Ventilation Images From Clinical 4D-CBCT Using a Deep Learning-Based Model

Predicting pulmonary ventilation damage after radiation therapy for nonsmall cell lung cancer using a ResNet generative adversarial network

Investigating the use of machine learning to generate ventilation images from CT scans

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