TY - GEN
T1 - Feasibility of planning-CT-free rapid workflow for stereotactic body radiotherapy
T2 - Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling
AU - Hooshangnejad, Hamed
AU - Ding, Kai
N1 - Publisher Copyright:
© 2022 SPIE.
PY - 2022
Y1 - 2022
N2 - Purpose: Radiotherapy (RT) is widely used for cancer management. The standard RT clinical workflow imposes substantial burdens on patients. Multiple image acquisitions for diagnosis and RT planning increase the travel, cost, and wait time before actual RT treatment, which is critical for cancer patients. Diagnostic CT (dCT) is shown to be suitable for RT planning, however different planning CT (pCT) acquisition setup (e.g., table curvature) and motion management procedure (e.g., deep inspiration/active breath control) makes it infeasible. In this study, we present the feasibility of a fully automatic image adaptation method to omit the need for pCT and expedite the treatment course at institutions where on-table adaptive radiotherapy is available. Methods: We designed a 3D convolutional neural network (3DCNN) to perform the dCT to pCT image adaptation. An automatic table removal algorithm was developed to isolate the patient's body without altering the curvature. The patient body isolated dCT and pCT pair were, then, deformably registered (rCT). The Network was trained on pCTs and the 3D displacement fields (DF). We evaluated the performance of our method using the root averaged sum of squared differences (RASSD), and body contour similarity using Dice similarity coefficient (DSC) and Hausdorff distance (HD). Results: The generated DFs were applied to dCT to generate the synthetic pCTs (sCT). For test cases, the difference of RASSDs, DSCs, and HD between (rCT-vs-pCT) and (sCT-vs-pCT) were 6 HU, 0.02 and 0.3 mm, respectively. Conclusion: A novel 3DCNN based method was presented to adapt the dCT to pCT, omitting the need for acquiring multiple scans before treatment delivery, reducing the cost and length of the RT treatment pathway.
AB - Purpose: Radiotherapy (RT) is widely used for cancer management. The standard RT clinical workflow imposes substantial burdens on patients. Multiple image acquisitions for diagnosis and RT planning increase the travel, cost, and wait time before actual RT treatment, which is critical for cancer patients. Diagnostic CT (dCT) is shown to be suitable for RT planning, however different planning CT (pCT) acquisition setup (e.g., table curvature) and motion management procedure (e.g., deep inspiration/active breath control) makes it infeasible. In this study, we present the feasibility of a fully automatic image adaptation method to omit the need for pCT and expedite the treatment course at institutions where on-table adaptive radiotherapy is available. Methods: We designed a 3D convolutional neural network (3DCNN) to perform the dCT to pCT image adaptation. An automatic table removal algorithm was developed to isolate the patient's body without altering the curvature. The patient body isolated dCT and pCT pair were, then, deformably registered (rCT). The Network was trained on pCTs and the 3D displacement fields (DF). We evaluated the performance of our method using the root averaged sum of squared differences (RASSD), and body contour similarity using Dice similarity coefficient (DSC) and Hausdorff distance (HD). Results: The generated DFs were applied to dCT to generate the synthetic pCTs (sCT). For test cases, the difference of RASSDs, DSCs, and HD between (rCT-vs-pCT) and (sCT-vs-pCT) were 6 HU, 0.02 and 0.3 mm, respectively. Conclusion: A novel 3DCNN based method was presented to adapt the dCT to pCT, omitting the need for acquiring multiple scans before treatment delivery, reducing the cost and length of the RT treatment pathway.
KW - Computed Tomography Synthesis
KW - Convolutional Neural Network
KW - Deep Learning
KW - Rapid Radiation Therapy Workflow
UR - http://www.scopus.com/inward/record.url?scp=85128253280&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85128253280&partnerID=8YFLogxK
U2 - 10.1117/12.2611484
DO - 10.1117/12.2611484
M3 - Conference contribution
AN - SCOPUS:85128253280
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2022
A2 - Linte, Cristian A.
A2 - Siewerdsen, Jeffrey H.
PB - SPIE
Y2 - 21 March 2022 through 27 March 2022
ER -