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<jats:title>Abstract</jats:title> <jats:p> <jats:italic>Purpose.</jats:italic> To investigate indirect radiation-induced changes in airways as precursors to atelectasis post radiation therapy (RT). <jats:italic>Methods.</jats:italic> Three Wisconsin Miniature Swine (WMS<jats:sup> <jats:monospace>TM</jats:monospace> </jats:sup>) underwent a research course of 60 Gy in 5 fractions delivered to a targeted airway/vessel in the inferior left lung. The right lung received a max point dose &lt;5 Gy. Airway segmentation was performed on the pre- and three months post-RT maximum inhale phase of the four-dimensional (4D) computed tomography (CT) scans. Changes in luminal area (Ai) and square root of wall area (<jats:inline-formula> <jats:tex-math> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msqrt> <mml:mrow> <mml:mi mathvariant="italic">WA</mml:mi> </mml:mrow> </mml:msqrt> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://cfn-live-content-bucket-iop-org.s3.amazonaws.com/journals/2057-1976/7/6/065039/revision2/bpexac3197ieqn1.gif?AWSAccessKeyId=AKIAYDKQL6LTV7YY2HIK&amp;Expires=1636110352&amp;Signature=NkNpL1%2FMBZMawYli7Qy1qiTQuNw%3D" xlink:type="simple" /> </jats:inline-formula>) for each airway were investigated. Changes in ventilation were assessed using the Jacobian ratio and were measured in three different regions: the inferior left lung &lt;5 Gy (ILL), the superior left lung &lt;5 Gy (SLL), and the contralateral right lung &lt;5 Gy (RL). <jats:italic>Results.</jats:italic> Airways (n = 25) in the right lung for all swine showed no significant changes (p = 0.48) in Ai post-RT compared to pre-RT. Airways (n = 28) in the left lung of all swine were found to have a significant decrease (p &lt; 0.001) in Ai post-RT compared to pre-RT, correlated (Pearson R = −0.97) with airway dose. Additionally, <jats:inline-formula> <jats:tex-math> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msqrt> <mml:mrow> <mml:mi mathvariant="italic">WA</mml:mi> </mml:mrow> </mml:msqrt> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://cfn-live-content-bucket-iop-org.s3.amazonaws.com/journals/2057-1976/7/6/065039/revision2/bpexac3197ieqn2.gif?AWSAccessKeyId=AKIAYDKQL6LTV7YY2HIK&amp;Expires=1636110352&amp;Signature=KZZ%2FKrbc8wtDr7e51RvJouZMI%2FA%3D" xlink:type="simple" /> </jats:inline-formula> decreased significantly (p &lt; 0.001) with airway dose. Lastly, the Jacobian ratio of the ILL (0.883) was lower than that of the SLL (0.932) and the RL (0.955). <jats:italic>Conclusions.</jats:italic> This work shows that for the swine analyzed, there were significant correlations between Ai and <jats:inline-formula> <jats:tex-math> </jats:tex-math> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msqrt> <mml:mrow> <mml:mi mathvariant="italic">WA</mml:mi> </mml:mrow> </mml:msqrt> </mml:math> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://cfn-live-content-bucket-iop-org.s3.amazonaws.com/journals/2057-1976/7/6/065039/revision2/bpexac3197ieqn3.gif?AWSAccessKeyId=AKIAYDKQL6LTV7YY2HIK&amp;Expires=1636110352&amp;Signature=4pJkUd4kS4SyvCZE7U0JNaxTZ%2F4%3D" xlink:type="simple" /> </jats:inline-formula> change with radiation dose. Additionally, there was a decrease in lung function in the regions of the lung supplied by the irradiated airways compared to the regions supplied by unirradiated airways. These results support the hypothesis that airway dose should be considered during treatment planning in order to potentially preserve functional lung and reduce lung toxicities.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Kilovoltage cone-beam computed tomography (CBCT)-based image-guided radiation therapy (IGRT) is used for daily delivery of radiation therapy, especially for stereotactic body radiation therapy (SBRT), which imposes particularly high demands for setup accuracy. The clinical applications of CBCTs are constrained, however, by poor soft tissue contrast, image artifacts, and instability of Hounsfield unit (HU) values. Here, we propose a new deep learning-based method to generate synthetic CTs (sCT) from thoracic CBCTs. A deep-learning model which integrates histogram matching (HM) into a cycle-consistent adversarial network (Cycle-GAN) framework, called HM-Cycle-GAN, was trained to learn mapping between thoracic CBCTs and paired planning CTs. Perceptual supervision was adopted to minimize blurring of tissue interfaces. An informative maximizing loss was calculated by feeding CBCT into the HM-Cycle-GAN to evaluate the image histogram matching between the planning CTs and the sCTs. The proposed algorithm was evaluated using data from 20 SBRT patients who each received 5 fractions and therefore 5 thoracic CBCTs. To reduce the effect of anatomy mismatch, original CBCT images were pre-processed via deformable image registrations with the planning CT before being used in model training and result assessment. We used planning CTs as ground truth for the derived sCTs from the correspondent co-registered CBCTs. The mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and normalized cross-correlation (NCC) indices were adapted as evaluation metrics of the proposed algorithm. Assessments were done using Cycle-GAN as the benchmark. The average MAE, PSNR, and NCC of the sCTs generated by our method were 66.2 HU, 30.3 dB, and 0.95, respectively, over all CBCT fractions. Superior image quality and reduced noise and artifact severity were seen using the proposed method compared to the results from the standard Cycle-GAN method. Our method could therefore improve the accuracy of IGRT and corrected CBCTs could help improve online adaptive RT by offering better contouring accuracy and dose calculation.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>In interior computed tomography (CT), the x-ray beam is collimated to a limited field-of-view (FOV) (e.g. the volume of the heart) to decrease exposure to adjacent organs, but the resulting image has a severe truncation artifact when reconstructed with traditional filtered back-projection (FBP) type algorithms. In some examinations, such as cardiac or dentomaxillofacial imaging, interior CT could be used to achieve further dose reductions. In this work, we describe a deep learning (DL) method to obtain artifact-free images from interior CT angiography. Our method employs the Pix2Pix generative adversarial network (GAN) in a two-stage process: (1) An extended sinogram is computed from a truncated sinogram with one GAN model, and (2) the FBP reconstruction obtained from that extended sinogram is used as an input to another GAN model that improves the quality of the interior reconstruction. Our double GAN (DGAN) model was trained with 10 000 truncated sinograms simulated from real computed tomography angiography slice images. Truncated sinograms (input) were used with original slice images (target) in training to yield an improved reconstruction (output). DGAN performance was compared with the adaptive de-truncation method, total variation regularization, and two reference DL methods: FBPConvNet, and U-Net-based sinogram extension (ES-UNet). Our DGAN method and ES-UNet yielded the best root-mean-squared error (RMSE) (0.03 ± 0.01), and structural similarity index (SSIM) (0.92 ± 0.02) values, and reference DL methods also yielded good results. Furthermore, we performed an extended FOV analysis by increasing the reconstruction area by 10% and 20%. In both cases, the DGAN approach yielded best results at RMSE (0.03 ± 0.01 and 0.04 ± 0.01 for the 10% and 20% cases, respectively), peak signal-to-noise ratio (PSNR) (30.5 ± 2.6 dB and 28.6 ± 2.6 dB), and SSIM (0.90 ± 0.02 and 0.87 ± 0.02). In conclusion, our method was able to not only reconstruct the interior region with improved image quality, but also extend the reconstructed FOV by 20%.</jats:p>