Enhancing the target visibility with synthetic target specific digitally reconstructed radiograph for intrafraction motion monitoring: A proof-of-concept study Journal Article
| Authors: | Fu, Y.; Fan, Q.; Cai, W.; Li, F.; He, X.; Cuaron, J.; Cervino, L.; Moran, J. M.; Li, T.; Li, X. |
| Article Title: | Enhancing the target visibility with synthetic target specific digitally reconstructed radiograph for intrafraction motion monitoring: A proof-of-concept study |
| Abstract: | Background: Intrafraction motion monitoring in External Beam Radiation Therapy (EBRT) is usually accomplished by establishing a correlation between the tumor and the surrogates such as an external infrared reflector, implanted fiducial markers, or patient skin surface. These techniques either have unstable surrogate-tumor correlation or are invasive. Markerless real-time onboard imaging is a noninvasive alternative that directly images the target motion. However, the low target visibility due to overlapping tissues along the X-ray projection path makes tumor tracking challenging. Purpose: To enhance the target visibility in projection images, a patient-specific model was trained to synthesize the Target Specific Digitally Reconstructed Radiograph (TS-DRR). Methods: Patient-specific models were built using a conditional Generative Adversarial Network (cGAN) to map the onboard projection images to TS-DRR. The standard Pix2Pix network was adopted as our cGAN model. We synthesized the TS-DRR based on the onboard projection images using phantom and patient studies for spine tumors and lung tumors. Using previously acquired CT images, we generated DRR and its corresponding TS-DRR to train the network. For data augmentation, random translations were applied to the CT volume when generating the training images. For the spine, separate models were trained for an anthropomorphic phantom and a patient treated with paraspinal stereotactic body radiation therapy (SBRT). For lung, separate models were trained for a phantom with a spherical tumor insert and a patient treated with free-breathing SBRT. The models were tested using Intrafraction Review Images (IMR) for the spine and CBCT projection images for the lung. The performance of the models was validated using phantom studies with known couch shifts for the spine and known tumor deformation for the lung. Results: Both the patient and phantom studies showed that the proposed method can effectively enhance the target visibility of the projection images by mapping them into synthetic TS-DRR (sTS-DRR). For the spine phantom with known shifts of 1 mm, 2 mm, 3 mm, and 4 mm, the absolute mean errors for tumor tracking were 0.11 ± 0.05 mm in the x direction and 0.25 ± 0.08 mm in the y direction. For the lung phantom with known tumor motion of 1.8 mm, 5.8 mm, and 9 mm superiorly, the absolute mean errors for the registration between the sTS-DRR and ground truth are 0.1 ± 0.3 mm in both the x and y directions. Compared to the projection images, the sTS-DRR has increased the image correlation with the ground truth by around 83% and increased the structural similarity index measure with the ground truth by around 75% for the lung phantom. Conclusions: The sTS-DRR can greatly enhance the target visibility in the onboard projection images for both the spine and lung tumors. The proposed method could be used to improve the markerless tumor tracking accuracy for EBRT. © 2023 American Association of Physicists in Medicine. |
| Keywords: | adult; controlled study; cancer radiotherapy; sensitivity and specificity; tumor localization; image analysis; lung neoplasms; radiotherapy; patient monitoring; diagnostic imaging; prediction; lung tumor; abdomen; image enhancement; computerized tomography; tumors; image quality; lung; radiography; external beam radiotherapy; phantoms, imaging; image processing; stereotactic body radiation therapy; motion; biological organs; image reconstruction; image registration; cone beam computed tomography; breathing pattern; spine tumor; cone-beam computed tomography; phantoms; external beam radiation therapy; ground truth; procedures; proof of concept; visibility; lung malformation; spine tumors; intrafraction motion; tumor tracking; humans; human; article; anatomic landmark; deep learning; digitally reconstructed radiographs; generative adversarial networks; markerless; cervical vertebra; projection image; patient-specific modeling |
| Journal Title: | Medical Physics |
| Volume: | 50 |
| Issue: | 12 |
| ISSN: | 0094-2405 |
| Publisher: | American Association of Physicists in Medicine |
| Date Published: | 2023-12-01 |
| Start Page: | 7791 |
| End Page: | 7805 |
| Language: | English |
| DOI: | 10.1002/mp.16580 |
| PUBMED: | 37399367 |
| PROVIDER: | scopus |
| PMCID: | PMC11313213 |
| DOI/URL: | |
| Notes: | The MSK Cancer Center Support Grant (P30 CA008748) is acknowledged in the PubMed record and PDF. Corresponding MSK author Yabo Fu -- Source: Scopus |
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