Paulo Quintero, Can Wu, Hao Zhang, Ricardo Otazo, Laura Cervino, Wendy Harris
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<b>Methods.</b>Deformable image registration (DIR) is performed between one 4D-MRI reference phase and all other phases, and principal-component-analysis (PCA) is implemented on their respective deformation vectors. By sampling 1,000 times the PCA eigenvalues and applying the new deformations over a reference CT, 1,000 digital reconstructed radiographs (DRRs) were generated to train a convolutional neural network (CNN) to predict their respective eigenvalues. The method was implemented and tested using a digital phantom (XCAT) and an MRI-compatible phantom (ZEUS) with five DRR angles (0°, 45°, 90°, 135°, 180°). Seven motion scenarios were tested. For model performance, mean absolute error (MAE) and root mean square error (RMSE) were reported. Image quality was evaluated with structure similarity index (SSIM) and normalized RMSE (nRMSE), and target-volume variations were evaluated with volumetric dice coefficient (VDC) and Hausdorff-distance (HD).
<b>Results.</b>The model performance across the evaluated angles were MAE<sub>(XCAT, ZEUS)</sub>=(0.053±0.003, 0.094±0.003), and RMSE<sub>(XCAT, ZEUS)</sub>=(0.054±0.007, 0.103±0.002). Similarly, SSIM<sub>(XCAT, ZEUS)</sub>=(0.994±0.001, 0.96±0.02), and nRMSE<sub>(XCAT, ZEUS)</sub>=(0.13±0.01, 0.17±0.03). For all motion scenarios for XCAT and ZEUS, SSIM were 0.98±0.01 and 0.84±0.02, nRMSE were 0.14±0.01 and 0.27±0.02, VDC were 0.98±0.01 and 0.90±0.01, and HD were 0.24±0.02 mm and 2.3±0.8 mm, respectively, averaged across all angles. Finally, SSIM, nRMSE, VDC and HU values for ZEUS using the deformed images as ground truth, presented an improvement of 13%, 28%, 4%, and 76%, respectively.
<b>Conclusion</b>. Results from a digital and physical phantom demonstrate a novel approach to generate real-time 3D synthetic MRI from onboard kV images on a conventional LINAC for intra-fraction monitoring in abdominal radiotherapy.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3000,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physics in medicine and biology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1088/1361-6560/adbeb5","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Introduction.Real-time 2D-kV-triggered images used to evaluate intra-fraction motion during abdominal radiotherapy only provides 2D information with poor soft-tissue contrast. The main goal of this research is to evaluate a novel method that generates synthetic 3D-MRI from single 2D-kV images for online motion monitoring in abdominal radiotherapy.
Methods.Deformable image registration (DIR) is performed between one 4D-MRI reference phase and all other phases, and principal-component-analysis (PCA) is implemented on their respective deformation vectors. By sampling 1,000 times the PCA eigenvalues and applying the new deformations over a reference CT, 1,000 digital reconstructed radiographs (DRRs) were generated to train a convolutional neural network (CNN) to predict their respective eigenvalues. The method was implemented and tested using a digital phantom (XCAT) and an MRI-compatible phantom (ZEUS) with five DRR angles (0°, 45°, 90°, 135°, 180°). Seven motion scenarios were tested. For model performance, mean absolute error (MAE) and root mean square error (RMSE) were reported. Image quality was evaluated with structure similarity index (SSIM) and normalized RMSE (nRMSE), and target-volume variations were evaluated with volumetric dice coefficient (VDC) and Hausdorff-distance (HD).
Results.The model performance across the evaluated angles were MAE(XCAT, ZEUS)=(0.053±0.003, 0.094±0.003), and RMSE(XCAT, ZEUS)=(0.054±0.007, 0.103±0.002). Similarly, SSIM(XCAT, ZEUS)=(0.994±0.001, 0.96±0.02), and nRMSE(XCAT, ZEUS)=(0.13±0.01, 0.17±0.03). For all motion scenarios for XCAT and ZEUS, SSIM were 0.98±0.01 and 0.84±0.02, nRMSE were 0.14±0.01 and 0.27±0.02, VDC were 0.98±0.01 and 0.90±0.01, and HD were 0.24±0.02 mm and 2.3±0.8 mm, respectively, averaged across all angles. Finally, SSIM, nRMSE, VDC and HU values for ZEUS using the deformed images as ground truth, presented an improvement of 13%, 28%, 4%, and 76%, respectively.
Conclusion. Results from a digital and physical phantom demonstrate a novel approach to generate real-time 3D synthetic MRI from onboard kV images on a conventional LINAC for intra-fraction monitoring in abdominal radiotherapy.
期刊介绍:
The development and application of theoretical, computational and experimental physics to medicine, physiology and biology. Topics covered are: therapy physics (including ionizing and non-ionizing radiation); biomedical imaging (e.g. x-ray, magnetic resonance, ultrasound, optical and nuclear imaging); image-guided interventions; image reconstruction and analysis (including kinetic modelling); artificial intelligence in biomedical physics and analysis; nanoparticles in imaging and therapy; radiobiology; radiation protection and patient dose monitoring; radiation dosimetry