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Pan S, Abouei E, Peng J, Qian J, Wynne JF, Wang T, Chang CW, Roper J, Nye JA, Mao H, Yang X. Full-dose whole-body PET synthesis from low-dose PET using high-efficiency denoising diffusion probabilistic model: PET consistency model. Med Phys 2024; 51:5468-5478. [PMID: 38588512 PMCID: PMC11321936 DOI: 10.1002/mp.17068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Revised: 03/26/2024] [Accepted: 03/26/2024] [Indexed: 04/10/2024] Open
Abstract
PURPOSE Positron Emission Tomography (PET) has been a commonly used imaging modality in broad clinical applications. One of the most important tradeoffs in PET imaging is between image quality and radiation dose: high image quality comes with high radiation exposure. Improving image quality is desirable for all clinical applications while minimizing radiation exposure is needed to reduce risk to patients. METHODS We introduce PET Consistency Model (PET-CM), an efficient diffusion-based method for generating high-quality full-dose PET images from low-dose PET images. It employs a two-step process, adding Gaussian noise to full-dose PET images in the forward diffusion, and then denoising them using a PET Shifted-window Vision Transformer (PET-VIT) network in the reverse diffusion. The PET-VIT network learns a consistency function that enables direct denoising of Gaussian noise into clean full-dose PET images. PET-CM achieves state-of-the-art image quality while requiring significantly less computation time than other methods. Evaluation with normalized mean absolute error (NMAE), peak signal-to-noise ratio (PSNR), multi-scale structure similarity index (SSIM), normalized cross-correlation (NCC), and clinical evaluation including Human Ranking Score (HRS) and Standardized Uptake Value (SUV) Error analysis shows its superiority in synthesizing full-dose PET images from low-dose inputs. RESULTS In experiments comparing eighth-dose to full-dose images, PET-CM demonstrated impressive performance with NMAE of 1.278 ± 0.122%, PSNR of 33.783 ± 0.824 dB, SSIM of 0.964 ± 0.009, NCC of 0.968 ± 0.011, HRS of 4.543, and SUV Error of 0.255 ± 0.318%, with an average generation time of 62 s per patient. This is a significant improvement compared to the state-of-the-art diffusion-based model with PET-CM reaching this result 12× faster. Similarly, in the quarter-dose to full-dose image experiments, PET-CM delivered competitive outcomes, achieving an NMAE of 0.973 ± 0.066%, PSNR of 36.172 ± 0.801 dB, SSIM of 0.984 ± 0.004, NCC of 0.990 ± 0.005, HRS of 4.428, and SUV Error of 0.151 ± 0.192% using the same generation process, which underlining its high quantitative and clinical precision in both denoising scenario. CONCLUSIONS We propose PET-CM, the first efficient diffusion-model-based method, for estimating full-dose PET images from low-dose images. PET-CM provides comparable quality to the state-of-the-art diffusion model with higher efficiency. By utilizing this approach, it becomes possible to maintain high-quality PET images suitable for clinical use while mitigating the risks associated with radiation. The code is availble at https://github.com/shaoyanpan/Full-dose-Whole-body-PET-Synthesis-from-Low-dose-PET-Using-Consistency-Model.
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Affiliation(s)
- Shaoyan Pan
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
- Department of Biomedical Informatics, Emory University, Atlanta, GA 30322, USA
| | - Elham Abouei
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
| | - Junbo Peng
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
| | - Joshua Qian
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
| | - Jacob F Wynne
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
| | - Tonghe Wang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Chih-Wei Chang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
| | - Justin Roper
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
| | - Jonathon A Nye
- Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Hui Mao
- Department of Radiology and Imaging Science, and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30322, USA
- Department of Biomedical Informatics, Emory University, Atlanta, GA 30322, USA
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SHIRATO H. Biomedical advances and future prospects of high-precision three-dimensional radiotherapy and four-dimensional radiotherapy. PROCEEDINGS OF THE JAPAN ACADEMY. SERIES B, PHYSICAL AND BIOLOGICAL SCIENCES 2023; 99:389-426. [PMID: 37821390 PMCID: PMC10749389 DOI: 10.2183/pjab.99.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 09/13/2023] [Indexed: 10/13/2023]
Abstract
Biomedical advances of external-beam radiotherapy (EBRT) with improvements in physical accuracy are reviewed. High-precision (±1 mm) three-dimensional radiotherapy (3DRT) can utilize respective therapeutic open doors in the tumor control probability curve and in the normal tissue complication probability curve instead of the one single therapeutic window in two-dimensional EBRT. High-precision 3DRT achieved higher tumor control and probable survival rates for patients with small peripheral lung and liver cancers. Four-dimensional radiotherapy (4DRT), which can reduce uncertainties in 3DRT due to organ motion by real-time (every 0.1-1 s) tumor-tracking and immediate (0.1-1 s) irradiation, have achieved reduced adverse effects for prostate and pancreatic tumors near the digestive tract and with similar or better tumor control. Particle beam therapy improved tumor control and probable survival for patients with large liver tumors. The clinical outcomes of locally advanced or multiple tumors located near serial-type organs can theoretically be improved further by integrating the 4DRT concept with particle beams.
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Affiliation(s)
- Hiroki SHIRATO
- Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
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Bosetti DG, Angrisani A, Castronovo FM, Pesce GA, Zilli T. Magnetic resonance imaging-guided stereotactic body radiotherapy for prostate cancer: more than a simple "MIRAGE"? Transl Cancer Res 2023; 12:2454-2457. [PMID: 37969364 PMCID: PMC10643941 DOI: 10.21037/tcr-23-611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Accepted: 09/18/2023] [Indexed: 11/17/2023]
Affiliation(s)
- Davide Giovanni Bosetti
- Department of Radiation Oncology, Oncology Institute of Southern Switzerland, EOC, Bellinzona, Switzerland
| | - Antonio Angrisani
- Department of Radiation Oncology, Oncology Institute of Southern Switzerland, EOC, Bellinzona, Switzerland
| | - Francesco Mosè Castronovo
- Department of Radiation Oncology, Oncology Institute of Southern Switzerland, EOC, Bellinzona, Switzerland
| | - Gianfranco Angelo Pesce
- Department of Radiation Oncology, Oncology Institute of Southern Switzerland, EOC, Bellinzona, Switzerland
| | - Thomas Zilli
- Department of Radiation Oncology, Oncology Institute of Southern Switzerland, EOC, Bellinzona, Switzerland
- Facoltà di Scienze Biomediche, Università della Svizzera Italiana, Lugano, Switzerland
- Faculty of Medicine, University of Geneva, Geneva, Switzerland
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He X, Cai W, Li F, Fan Q, Zhang P, Cuaron JJ, Cerviño LI, Moran JM, Li X, Li T. Patient specific prior cross attention for kV decomposition in paraspinal motion tracking. Med Phys 2023; 50:5343-5353. [PMID: 37538040 PMCID: PMC11167561 DOI: 10.1002/mp.16644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 06/20/2023] [Accepted: 06/28/2023] [Indexed: 08/05/2023] Open
Abstract
BACKGROUND X-ray image quality is critical for accurate intrafraction motion tracking in radiation therapy. PURPOSE This study aims to develop a deep-learning algorithm to improve kV image contrast by decomposing the image into bony and soft tissue components. In particular, we designed a priori attention mechanism in the neural network framework for optimal decomposition. We show that a patient-specific prior cross-attention (PCAT) mechanism can boost the performance of kV image decomposition. We demonstrate its use in paraspinal SBRT motion tracking with online kV imaging. METHODS Online 2D kV projections were acquired during paraspinal SBRT for patient motion monitoring. The patient-specific prior images were generated by randomly shifting and rotating spine-only DRR created from the setup CBCT, simulating potential motions. The latent features of the prior images were incorporated into the PCAT using multi-head cross attention. The neural network aimed to learn to selectively amplify the transmission of the projection image features that correlate with features of the priori. The PCAT network structure consisted of (1) a dual-branch generator that separates the spine and soft tissue component of the kV projection image and (2) a dual-function discriminator (DFD) that provides the realness score of the predicted images. For supervision, we used a loss combining mean absolute error loss, discriminator loss, perceptual loss, total variation, and mean squared error loss for soft tissues. The proposed PCAT approach was benchmarked against previous work using the ResNet generative adversarial network (ResNetGAN) without prior information. RESULTS The trained PCAT had improved performance in effectively retaining and preserving the spine structure and texture information while suppressing the soft tissues from the kV projection images. The decomposed spine-only x-ray images had the submillimeter matching accuracy at all beam angles. The decomposed spine-only x-ray significantly reduced the maximum errors to 0.44 mm (<2 pixels) in comparison to 0.92 mm (∼4 pixels) of ResNetGAN. The PCAT decomposed spine images also had higher PSNR and SSIM (p-value < 0.001). CONCLUSION The PCAT selectively learned the important latent features by incorporating the patient-specific prior knowledge into the deep learning algorithm, significantly improving the robustness of the kV projection image decomposition, and leading to improved motion tracking accuracy in paraspinal SBRT.
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Affiliation(s)
- Xiuxiu He
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Weixing Cai
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Feifei Li
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Qiyong Fan
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Pengpeng Zhang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - John J. Cuaron
- Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Laura I. Cerviño
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Jean M. Moran
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Xiang Li
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Tianfang Li
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
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Adachi T, Nakamura M, Iramina H, Matsumoto K, Ishihara Y, Tachibana H, Kurokawa S, Cho S, Tanaka K, Fukumoto K, Nishiyama T, Kito S, Mizowaki T. Identification of reproducible radiomic features from on-board volumetric images: A multi-institutional phantom study. Med Phys 2023; 50:5585-5596. [PMID: 36932977 DOI: 10.1002/mp.16376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 02/25/2023] [Accepted: 02/27/2023] [Indexed: 03/19/2023] Open
Abstract
BACKGROUND Radiomics analysis using on-board volumetric images has attracted research attention as a method for predicting prognosis during treatment; however, the lack of standardization is still one of the main concerns. PURPOSE This study investigated the factors that influence the reproducibility of radiomic features extracted from on-board volumetric images using an anthropomorphic radiomics phantom. Furthermore, a phantom experiment was conducted with different treatment machines from multiple institutions as external validation to identify reproducible radiomic features. METHODS The phantom was designed to be 35 × 20 × 20 cm with eight types of heterogeneous spheres (⌀ = 1, 2, and 3 cm). On-board volumetric images were acquired using 15 treatment machines from eight institutions. Of these, kilovoltage cone-beam computed tomography (kV-CBCT) image data acquired from four treatment machines at one institution were used as an internal evaluation dataset to explore the reproducibility of radiomic features. The remaining image data, including kV-CBCT, megavoltage-CBCT (MV-CBCT), and megavoltage computed tomography (MV-CT) provided by seven different institutions (11 treatment machines), were used as an external validation dataset. A total of 1,302 radiomic features, including 18 first-order, 75 texture, 465 (i.e., 93 × 5) Laplacian of Gaussian (LoG) filter-based, and 744 (i.e., 93 × 8) wavelet filter-based features, were extracted within the spheres. The intraclass correlation coefficient (ICC) was calculated to explore feature repeatability and reproducibility using an internal evaluation dataset. Subsequently, the coefficient of variation (COV) was calculated to validate the feature variability of external institutions. An absolute ICC exceeding 0.85 or COV under 5% was considered indicative of a highly reproducible feature. RESULTS For internal evaluation, ICC analysis showed that the median percentage of radiomic features with high repeatability was 95.2%. The ICC analysis indicated that the median percentages of highly reproducible features for inter-tube current, reconstruction algorithm, and treatment machine were decreased by 20.8%, 29.2%, and 33.3%, respectively. For external validation, the COV analysis showed that the median percentage of reproducible features was 31.5%. A total of 16 features, including nine LoG filter-based and seven wavelet filter-based features, were indicated as highly reproducible features. The gray-level run-length matrix (GLRLM) was classified as containing the most frequent features (N = 8), followed by the gray-level dependence matrix (N = 7) and gray-level co-occurrence matrix (N = 1) features. CONCLUSIONS We developed the standard phantom for radiomics analysis of kV-CBCT, MV-CBCT, and MV-CT images. With this phantom, we revealed that the differences in the treatment machine and image reconstruction algorithm reduce the reproducibility of radiomic features from on-board volumetric images. Specifically, the most reproducible features for external validation were LoG or wavelet filter-based GLRLM features. However, the acceptability of the identified features should be examined in advance at each institution before applying the findings to prognosis prediction.
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Affiliation(s)
- Takanori Adachi
- Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Shogoin, Sakyo-ku, Kyoto, Japan
| | - Mitsuhiro Nakamura
- Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Shogoin, Sakyo-ku, Kyoto, Japan
- Department of Advanced Medical Physics, Graduate School of Medicine, Kyoto University, Shogoin, Sakyo-ku, Kyoto, Japan
| | - Hiraku Iramina
- Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Shogoin, Sakyo-ku, Kyoto, Japan
| | - Kazushige Matsumoto
- Department of Radiology, National Hospital Organization Kyoto Medical Center, Fushimi-ku, Kyoto, Japan
| | - Yoshitomo Ishihara
- Department of Radiation Oncology, Japanese Red Cross Wakayama Medical Center, Wakayama, Japan
| | - Hidenobu Tachibana
- Department of Radiation Oncology, National Cancer Center Hospital East, Kashiwa, Japan
| | - Shogo Kurokawa
- Department of Radiation Oncology, National Cancer Center Hospital East, Kashiwa, Japan
| | - SangYong Cho
- Division of Radiation Oncology, Chiba Cancer Center, Chuo-ku, Chiba, Japan
| | - Kazunori Tanaka
- Department of Radiation Oncology, Kyoto City Hospital, Nakagyo-ku, Kyoto, Japan
| | - Kenta Fukumoto
- Department of Radiation Oncology, Kyoto City Hospital, Nakagyo-ku, Kyoto, Japan
| | - Tomohiro Nishiyama
- Department of Radiation Oncology, Kyoto-Katsura Hospital, Nishikyo-ku, Kyoto, Japan
| | - Satoshi Kito
- Department of Radiotherapy, Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital, Bunkyo-ku, Tokyo, Japan
| | - Takashi Mizowaki
- Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Shogoin, Sakyo-ku, Kyoto, Japan
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Jassar H, Tai A, Chen X, Keiper TD, Paulson E, Lathuilière F, Bériault S, Hébert F, Savard L, Cooper DT, Cloake S, Li XA. Real-time motion monitoring using orthogonal cine MRI during MR-guided adaptive radiation therapy for abdominal tumors on 1.5T MR-Linac. Med Phys 2023; 50:3103-3116. [PMID: 36893292 DOI: 10.1002/mp.16342] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 02/01/2023] [Accepted: 02/24/2023] [Indexed: 03/11/2023] Open
Abstract
BACKGROUND Real-time motion monitoring (RTMM) is necessary for accurate motion management of intrafraction motions during radiation therapy (RT). PURPOSE Building upon a previous study, this work develops and tests an improved RTMM technique based on real-time orthogonal cine magnetic resonance imaging (MRI) acquired during magnetic resonance-guided adaptive RT (MRgART) for abdominal tumors on MR-Linac. METHODS A motion monitoring research package (MMRP) was developed and tested for RTMM based on template rigid registration between beam-on real-time orthogonal cine MRI and pre-beam daily reference 3D-MRI (baseline). The MRI data acquired under free-breathing during the routine MRgART on a 1.5T MR-Linac for 18 patients with abdominal malignancies of 8 liver, 4 adrenal glands (renal fossa), and 6 pancreas cases were used to evaluate the MMRP package. For each patient, a 3D mid-position image derived from an in-house daily 4D-MRI was used to define a target mask or a surrogate sub-region encompassing the target. Additionally, an exploratory case reviewed for an MRI dataset of a healthy volunteer acquired under both free-breathing and deep inspiration breath-hold (DIBH) was used to test how effectively the RTMM using the MMRP can address through-plane motion (TPM). For all cases, the 2D T2/T1-weighted cine MRIs were captured with a temporal resolution of 200 ms interleaved between coronal and sagittal orientations. Manually delineated contours on the cine frames were used as the ground-truth motion. Common visible vessels and segments of target boundaries in proximity to the target were used as anatomical landmarks for reproducible delineations on both the 3D and the cine MRI images. Standard deviation of the error (SDE) between the ground-truth and the measured target motion from the MMRP package were analyzed to evaluate the RTMM accuracy. The maximum target motion (MTM) was measured on the 4D-MRI for all cases during free-breathing. RESULTS The mean (range) centroid motions for the 13 abdominal tumor cases were 7.69 (4.71-11.15), 1.73 (0.81-3.05), and 2.71 (1.45-3.93) mm with an overall accuracy of <2 mm in the superior-inferior (SI), the left-right (LR), and the anterior-posterior (AP) directions, respectively. The mean (range) of the MTM from the 4D-MRI was 7.38 (2-11) mm in the SI direction, smaller than the monitored motion of centroid, demonstrating the importance of the real-time motion capture. For the remaining patient cases, the ground-truth delineation was challenging under free-breathing due to the target deformation and the large TPM in the AP direction, the implant-induced image artifacts, and/or the suboptimal image plane selection. These cases were evaluated based on visual assessment. For the healthy volunteer, the TPM of the target was significant under free-breathing which degraded the RTMM accuracy. RTMM accuracy of <2 mm was achieved under DIBH, indicating DIBH is an effective method to address large TPM. CONCLUSIONS We have successfully developed and tested the use of a template-based registration method for an accurate RTMM of abdominal targets during MRgART on a 1.5T MR-Linac without using injected contrast agents or radio-opaque implants. DIBH may be used to effectively reduce or eliminate TPM of abdominal targets during RTMM.
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Affiliation(s)
- Hassan Jassar
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - An Tai
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Xinfeng Chen
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Timothy D Keiper
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Eric Paulson
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | | | | | | | | | | | | | - X Allen Li
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
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Siva S, Ost P, Ali M. The MIRAGE Trial-Optical Illusion or the Future of Prostate Stereotactic Radiotherapy? JAMA Oncol 2023; 9:373-375. [PMID: 36633862 DOI: 10.1001/jamaoncol.2022.6334] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Affiliation(s)
- Shankar Siva
- Department of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia
| | - Piet Ost
- Department of Radiation Oncology, Iridium Network, Wilrijk, Belgium
- Department of Human Structure and Repair, Ghent University, Ghent, Belgium
| | - Muhammad Ali
- Department of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia
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Cai W, Fan Q, Li F, He X, Zhang P, Cervino L, Li X, Li T. Markerless motion tracking with simultaneous MV and kV imaging in spine SBRT treatment-a feasibility study. Phys Med Biol 2023; 68:10.1088/1361-6560/acae16. [PMID: 36549010 PMCID: PMC9944511 DOI: 10.1088/1361-6560/acae16] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 12/22/2022] [Indexed: 12/24/2022]
Abstract
Objective. Motion tracking with simultaneous MV-kV imaging has distinct advantages over single kV systems. This research is a feasibility study of utilizing this technique for spine stereotactic body radiotherapy (SBRT) through phantom and patient studies.Approach. A clinical spine SBRT plan was developed using 6xFFF beams and nine sliding-window IMRT fields. The plan was delivered to a chest phantom on a linear accelerator. Simultaneous MV-kV image pairs were acquired during beam delivery. KV images were triggered at predefined intervals, and synthetic MV images showing enlarged MLC apertures were created by combining multiple raw MV frames with corrections for scattering and intensity variation. Digitally reconstructed radiograph (DRR) templates were generated using high-resolution CBCT reconstructions (isotropic voxel size (0.243 mm)3) as the reference for 2D-2D matching. 3D shifts were calculated from triangulation of kV-to-DRR and MV-to-DRR registrations. To evaluate tracking accuracy, detected shifts were compared to known phantom shifts as introduced before treatment. The patient study included a T-spine patient and an L-spine patient. Patient datasets were retrospectively analyzed to demonstrate the performance in clinical settings.Main results. The treatment plan was delivered to the phantom in five scenarios: no shift, 2 mm shift in one of the longitudinal, lateral and vertical directions, and 2 mm shift in all the three directions. The calculated 3D shifts agreed well with the actual couch shifts, and overall, the uncertainty of 3D detection is estimated to be 0.3 mm. The patient study revealed that with clinical patient image quality, the calculated 3D motion agreed with the post-treatment cone beam CT. It is feasible to automate both kV-to-DRR and MV-to-DRR registrations using a mutual information-based method, and the difference from manual registration is generally less than 0.3 mm.Significance. The MV-kV imaging-based markerless motion tracking technique was validated through a feasibility study. It is a step forward toward effective motion tracking and accurate delivery for spinal SBRT.
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Affiliation(s)
- Weixing Cai
- Memorial Sloan Kettering Cancer Center, Department of Medical Physics, 1275 York Avenue, New York, NY 10065, United States of America
| | - Qiyong Fan
- Memorial Sloan Kettering Cancer Center, Department of Medical Physics, 1275 York Avenue, New York, NY 10065, United States of America
| | - Feifei Li
- Memorial Sloan Kettering Cancer Center, Department of Medical Physics, 1275 York Avenue, New York, NY 10065, United States of America
| | - Xiuxiu He
- Memorial Sloan Kettering Cancer Center, Department of Medical Physics, 1275 York Avenue, New York, NY 10065, United States of America
| | - Pengpeng Zhang
- Memorial Sloan Kettering Cancer Center, Department of Medical Physics, 1275 York Avenue, New York, NY 10065, United States of America
| | - Laura Cervino
- Memorial Sloan Kettering Cancer Center, Department of Medical Physics, 1275 York Avenue, New York, NY 10065, United States of America
| | - Xiang Li
- Memorial Sloan Kettering Cancer Center, Department of Medical Physics, 1275 York Avenue, New York, NY 10065, United States of America
| | - Tianfang Li
- Memorial Sloan Kettering Cancer Center, Department of Medical Physics, 1275 York Avenue, New York, NY 10065, United States of America
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Terunuma T, Sakae T, Hu Y, Takei H, Moriya S, Okumura T, Sakurai H. Explainability and controllability of patient-specific deep learning with attention-based augmentation for markerless image-guided radiotherapy. Med Phys 2023; 50:480-494. [PMID: 36354286 PMCID: PMC10100026 DOI: 10.1002/mp.16095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 10/27/2022] [Accepted: 10/27/2022] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND We reported the concept of patient-specific deep learning (DL) for real-time markerless tumor segmentation in image-guided radiotherapy (IGRT). The method was aimed to control the attention of convolutional neural networks (CNNs) by artificial differences in co-occurrence probability (CoOCP) in training datasets, that is, focusing CNN attention on soft tissues while ignoring bones. However, the effectiveness of this attention-based data augmentation has not been confirmed by explainable techniques. Furthermore, compared to reasonable ground truths, the feasibility of tumor segmentation in clinical kilovolt (kV) X-ray fluoroscopic (XF) images has not been confirmed. PURPOSE The first aim of this paper was to present evidence that the proposed method provides an explanation and control of DL behavior. The second purpose was to validate the real-time lung tumor segmentation in clinical kV XF images for IGRT. METHODS This retrospective study included 10 patients with lung cancer. Patient-specific and XF angle-specific image pairs comprising digitally reconstructed radiographs (DRRs) and projected-clinical-target-volume (pCTV) images were calculated from four-dimensional computer tomographic data and treatment planning information. The training datasets were primarily augmented by random overlay (RO) and noise injection (NI): RO aims to differentiate positional CoOCP in soft tissues and bones, and NI aims to make a difference in the frequency of occurrence of local and global image features. The CNNs for each patient-and-angle were automatically optimized in the DL training stage to transform the training DRRs into pCTV images. In the inference stage, the trained CNNs transformed the test XF images into pCTV images, thus identifying target positions and shapes. RESULTS The visual analysis of DL attention heatmaps for a test image demonstrated that our method focused CNN attention on soft tissue and global image features rather than bones and local features. The processing time for each patient-and-angle-specific dataset in the training stage was ∼30 min, whereas that in the inference stage was 8 ms/frame. The estimated three-dimensional 95 percentile tracking error, Jaccard index, and Hausdorff distance for 10 patients were 1.3-3.9 mm, 0.85-0.94, and 0.6-4.9 mm, respectively. CONCLUSIONS The proposed attention-based data augmentation with both RO and NI made the CNN behavior more explainable and more controllable. The results obtained demonstrated the feasibility of real-time markerless lung tumor segmentation in kV XF images for IGRT.
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Affiliation(s)
- Toshiyuki Terunuma
- Faculty of Medicine, University of Tsukuba, Tsukuba, Japan.,Proton Medical Research Center, University of Tsukuba Hospital, Tsukuba, Japan
| | - Takeji Sakae
- Faculty of Medicine, University of Tsukuba, Tsukuba, Japan.,Proton Medical Research Center, University of Tsukuba Hospital, Tsukuba, Japan
| | - Yachao Hu
- Proton Medical Research Center, University of Tsukuba Hospital, Tsukuba, Japan.,Center Hospital and Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Hideyuki Takei
- Faculty of Medicine, University of Tsukuba, Tsukuba, Japan.,Proton Medical Research Center, University of Tsukuba Hospital, Tsukuba, Japan
| | - Shunsuke Moriya
- Faculty of Medicine, University of Tsukuba, Tsukuba, Japan.,Proton Medical Research Center, University of Tsukuba Hospital, Tsukuba, Japan
| | - Toshiyuki Okumura
- Faculty of Medicine, University of Tsukuba, Tsukuba, Japan.,Proton Medical Research Center, University of Tsukuba Hospital, Tsukuba, Japan
| | - Hideyuki Sakurai
- Faculty of Medicine, University of Tsukuba, Tsukuba, Japan.,Proton Medical Research Center, University of Tsukuba Hospital, Tsukuba, Japan
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10
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Dosimetric impact of rotational set-up errors in high-risk prostate cancer. POLISH JOURNAL OF MEDICAL PHYSICS AND ENGINEERING 2022. [DOI: 10.2478/pjmpe-2022-0023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Abstract
Introduction: Cone-beam computed tomography (CBCT) provides an excellent solution to quantitative assessment and correction of patient set-up errors during radiotherapy. However, most linear accelerators are equipped with conventional therapy tables that can be moved in three translational directions and perform only yaw rotation. Uncorrected roll and pitch result in rotational set-up errors, particularly when the distance from the isocenter to the target border is large. The aim of this study was to investigate the impact of rotational errors on the dose delivered to the clinical target volume (CTV), the planning target volume (PTV) and organs at risk (OAR).
Material and methods: 30 patients with prostate cancer treated with VMAT technique had daily CBCT scans (840 CBCTs in total) prior to treatment delivery. The rotational errors remaining after on-line correction were retrospectively analysed. The sum plans simulating the dose distribution during the treatment course were calculated for selected patients with significant rotational errors.
Results: The dose delivered to the prostate bed CTV reported in the sum plan was not lower than in the original plan for all selected patients. For four patients from the selected group, the D98% for prostate bed PTV was less than 95%. The V47.88Gy for pelvic lymph nodes PTV was less than 98% for two of the selected patients.
Conclusions: The analysis of the dosimetric parameters showed that the impact of uncorrected rotations is not clinically significant in terms of the dose delivered to OAR and the dose coverage of CTV. However, the PTV dose coverage is correlated with distance away from the isocenter and is smaller than planned.
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11
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Chemical Overview of Gel Dosimetry Systems: A Comprehensive Review. Gels 2022; 8:gels8100663. [PMID: 36286165 PMCID: PMC9601373 DOI: 10.3390/gels8100663] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 10/10/2022] [Accepted: 10/12/2022] [Indexed: 11/17/2022] Open
Abstract
Advances in radiotherapy technology during the last 25 years have significantly improved both dose conformation to tumors and the preservation of healthy tissues, achieving almost real-time feedback by means of high-precision treatments and theranostics. Owing to this, developing high-performance systems capable of coping with the challenging requirements of modern ionizing radiation is a key issue to overcome the limitations of traditional dosimeters. In this regard, a deep understanding of the physicochemical basis of gel dosimetry, as one of the most promising tools for the evaluation of 3D high-spatial-resolution dose distributions, represents the starting point for developing new and innovative systems. This review aims to contribute thorough descriptions of the chemical processes and interactions that condition gel dosimetry outputs, often phenomenologically addressed, and particularly formulations reported since 2017.
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12
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Ferris WS, Chao EH, Smilowitz JB, Kimple RJ, Bayouth JE, Culberson WS. Using 4D dose accumulation to calculate organ-at-risk dose deviations from motion-synchronized liver and lung tomotherapy treatments. J Appl Clin Med Phys 2022; 23:e13627. [PMID: 35486094 PMCID: PMC9278681 DOI: 10.1002/acm2.13627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 03/22/2022] [Accepted: 04/11/2022] [Indexed: 11/06/2022] Open
Abstract
Tracking systems such as Radixact Synchrony change the planned delivery of radiation during treatment to follow the target. This is typically achieved without considering the location changes of organs at risk (OARs). The goal of this work was to develop a novel 4D dose accumulation framework to quantify OAR dose deviations due to the motion and tracked treatment. The framework obtains deformation information and the target motion pattern from a four-dimensional computed tomography dataset. The helical tomotherapy treatment plan is split into 10 plans and motion correction is applied separately to the jaw pattern and multi-leaf collimator (MLC) sinogram for each phase based on the location of the target in each phase. Deformable image registration (DIR) is calculated from each phase to the references phase using a commercial algorithm, and doses are accumulated according to the DIR. The effect of motion synchronization on OAR dose was analyzed for five lung and five liver subjects by comparing planned versus synchrony-accumulated dose. The motion was compensated by an average of 1.6 cm of jaw sway and by an average of 5.7% of leaf openings modified, indicating that most of the motion compensation was from jaw sway and not MLC changes. OAR dose deviations as large as 19 Gy were observed, and for all 10 cases, dose deviations greater than 7 Gy were observed. Target dose remained relatively constant (D95% within 3 Gy), confirming that motion-synchronization achieved the goal of maintaining target dose. Dose deviations provided by the framework can be leveraged during the treatment planning process by identifying cases where OAR doses may change significantly from their planned values with respect to the critical constraints. The framework is specific to synchronized helical tomotherapy treatments, but the OAR dose deviations apply to any real-time tracking technique that does not consider location changes of OARs.
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Affiliation(s)
- William S Ferris
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | | | - Jennifer B Smilowitz
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, USA.,Department of Human Oncology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Randall J Kimple
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, USA.,Department of Human Oncology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, USA.,University of Wisconsin Carbone Cancer Center, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - John E Bayouth
- Department of Human Oncology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Wesley S Culberson
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, USA
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13
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Weidner A, Stengl C, Dinkel F, Dorsch S, Murillo C, Seeber S, Gnirs R, Runz A, Echner G, Karger CP, Jäkel O. An abdominal phantom with anthropomorphic organ motion and multimodal imaging contrast for MR-guided radiotherapy. Phys Med Biol 2022; 67. [PMID: 35081516 DOI: 10.1088/1361-6560/ac4ef8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 01/26/2022] [Indexed: 11/12/2022]
Abstract
Purpose. Improvements in image-guided radiotherapy (IGRT) enable accurate and precise treatment of moving tumors in the abdomen while simultaneously sparing healthy tissue. However, the lack of validation tools for newly developed MR-guided radiotherapy hybrid devices such as the MR-Linac is an open issue. This study presents a custom developed abdominal phantom with respiratory organ motion and multimodal imaging contrast to perform end-to-end tests for IGRT treatment planning scenarios.Methods. The abdominal phantom contains deformable and anatomically shaped liver and kidney models made of Ni-DTPA and KCl-doped agarose mixtures that can be reproducibly positioned within the phantom. Organ models are wrapped in foil to avoid ion exchange with the surrounding agarose and to provide stable T1 and T2 relaxation times as well as HU numbers. Breathing motion is realized by a diaphragm connected to an actuator that is hydraulically controlled via a programmable logic controller. With this system, artificial and patient-specific breathing patterns can be carried out. In 1.5 T magnetic resonance imaging (MRI), diaphragm, liver and kidney motion was measured and compared to the breathing motion of a healthy male volunteer for different breathing amplitudes including shallow, normal and deep breathing.Results. The constructed abdominal phantom demonstrated organ-equivalent intensity values in CT as well as in MRI. T1-weighted (T1w) and T2-weighted (T2w) relaxation times for 1.5 T and CT numbers were 552.9 ms, 48.2 ms and 48.8 HU (liver) as well as 950.42 ms, 79 ms and 28.2 HU (kidney), respectively. These values were stable for more than six months. Extracted breathing motion from a healthy volunteer revealed a liver to diaphragm motion ratio (LDMR) of 64.4% and a kidney to diaphragm motion ratio (KDMR) of 30.7%. Well-comparable values were obtained for the phantom (LDMR: 65.5%, KDMR: 27.5%).Conclusions. The abdominal phantom demonstrated anthropomorphic T1 and T2 relaxation times as well as HU numbers and physiological motion pattern in MRI and CT. This allows for wide use in the validation of IGRT including MRgRT.
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Affiliation(s)
- Artur Weidner
- Division of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg D-69120, Germany.,Faculty of Medicine, University of Heidelberg, Im Neuenheimer Feld 672, Heidelberg D-69120, Germany.,Heidelberg Institute for Radiation Oncology (HIRO), National Center for Radiation Research in Oncology, Im Neuenheimer Feld 280, Heidelberg D-69120, Germany
| | - Christina Stengl
- Division of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg D-69120, Germany.,Faculty of Medicine, University of Heidelberg, Im Neuenheimer Feld 672, Heidelberg D-69120, Germany.,Heidelberg Institute for Radiation Oncology (HIRO), National Center for Radiation Research in Oncology, Im Neuenheimer Feld 280, Heidelberg D-69120, Germany
| | - Fabian Dinkel
- Division of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg D-69120, Germany.,Heidelberg Institute for Radiation Oncology (HIRO), National Center for Radiation Research in Oncology, Im Neuenheimer Feld 280, Heidelberg D-69120, Germany
| | - Stefan Dorsch
- Division of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg D-69120, Germany.,Heidelberg Institute for Radiation Oncology (HIRO), National Center for Radiation Research in Oncology, Im Neuenheimer Feld 280, Heidelberg D-69120, Germany
| | - Carlos Murillo
- Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg D-69120, Germany
| | - Steffen Seeber
- Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg D-69120, Germany
| | - Regula Gnirs
- Division of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg D-69120, Germany
| | - Armin Runz
- Division of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg D-69120, Germany.,Heidelberg Institute for Radiation Oncology (HIRO), National Center for Radiation Research in Oncology, Im Neuenheimer Feld 280, Heidelberg D-69120, Germany
| | - Gernot Echner
- Division of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg D-69120, Germany.,Heidelberg Institute for Radiation Oncology (HIRO), National Center for Radiation Research in Oncology, Im Neuenheimer Feld 280, Heidelberg D-69120, Germany
| | - Christian P Karger
- Division of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg D-69120, Germany.,Heidelberg Institute for Radiation Oncology (HIRO), National Center for Radiation Research in Oncology, Im Neuenheimer Feld 280, Heidelberg D-69120, Germany
| | - Oliver Jäkel
- Division of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg D-69120, Germany.,Heidelberg Institute for Radiation Oncology (HIRO), National Center for Radiation Research in Oncology, Im Neuenheimer Feld 280, Heidelberg D-69120, Germany.,Heidelberg Ion-Beam Therapy Center (HIT), Im Neuenheimer Feld 450, Heidelberg D-69120, Germany
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14
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Zhao W, Shen L, Islam MT, Qin W, Zhang Z, Liang X, Zhang G, Xu S, Li X. Artificial intelligence in image-guided radiotherapy: a review of treatment target localization. Quant Imaging Med Surg 2021; 11:4881-4894. [PMID: 34888196 PMCID: PMC8611462 DOI: 10.21037/qims-21-199] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Accepted: 07/05/2021] [Indexed: 01/06/2023]
Abstract
Modern conformal beam delivery techniques require image-guidance to ensure the prescribed dose to be delivered as planned. Recent advances in artificial intelligence (AI) have greatly augmented our ability to accurately localize the treatment target while sparing the normal tissues. In this paper, we review the applications of AI-based algorithms in image-guided radiotherapy (IGRT), and discuss the indications of these applications to the future of clinical practice of radiotherapy. The benefits, limitations and some important trends in research and development of the AI-based IGRT techniques are also discussed. AI-based IGRT techniques have the potential to monitor tumor motion, reduce treatment uncertainty and improve treatment precision. Particularly, these techniques also allow more healthy tissue to be spared while keeping tumor coverage the same or even better.
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Affiliation(s)
- Wei Zhao
- School of Physics, Beihang University, Beijing, China
| | - Liyue Shen
- Department of Radiation Oncology, Stanford University, Stanford, USA
| | - Md Tauhidul Islam
- Department of Radiation Oncology, Stanford University, Stanford, USA
| | - Wenjian Qin
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zhicheng Zhang
- Department of Radiation Oncology, Stanford University, Stanford, USA
| | - Xiaokun Liang
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Gaolong Zhang
- School of Physics, Beihang University, Beijing, China
| | - Shouping Xu
- Department of Radiation Oncology, PLA General Hospital, Beijing, China
| | - Xiaomeng Li
- Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong, China
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15
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Skouboe S, De Roover R, Gammelmark Muurholm C, Ravkilde T, Crijns W, Hansen R, Depuydt T, Poulsen PR. Six degrees of freedom dynamic motion-including dose reconstruction in a commercial treatment planning system. Med Phys 2021; 48:1427-1435. [PMID: 33415778 DOI: 10.1002/mp.14707] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 11/19/2020] [Accepted: 12/23/2020] [Indexed: 01/06/2023] Open
Abstract
PURPOSE Intrafractional motion during radiotherapy delivery can deteriorate the delivered dose. Dynamic rotational motion of up to 38 degrees has been reported during prostate cancer radiotherapy, but methods to determine the dosimetric consequences of such rotations are lacking. Here, we create and experimentally validate a dose reconstruction method that accounts for dynamic rotations and translations in a commercial treatment planning system (TPS). Interplay effects are quantified by comparing dose reconstructions with dynamic and constant rotations. METHODS The dose reconstruction accumulates the dose in points of interest while the points are moved in six degrees of freedom (6DoF) in a precalculated time-resolved four-dimensional (4D) dose matrix to emulate dynamic motion in a patient. The required 4D dose matrix was generated by splitting the original treatment plan into multiple sub-beams, each representing 0.4 s dose delivery, and recalculating the dose of the split plan in the TPS (Eclipse). The dose accumulation was performed via TPS scripting by querying the dose of each sub-beam in dynamically moving points, allowing dose reconstruction with any dynamic motion. The dose reconstruction was validated with film dosimetry for two prostate dual arc VMAT plans with intra-prostatic lesion boosts. The plans were delivered to a pelvis phantom with internal dynamic rotational motion of a film stack (21 films with 2.5 mm separation). Each plan was delivered without motion and with three prostate motion traces. Motion-including dose reconstruction was performed for each motion experiment using the actual dynamic rotation as well as a constant rotation equal to the mean rotation during the experiment. For each experiment, the 3%/2 mm γ failure rate of the TPS dose reconstruction was calculated with the film measurement being the reference. For each motion experiment, the motion-induced 3%/2 mm γ failure rate was calculated using the static delivery as the reference and compared between film measurements and TPS dose reconstruction. DVH metrics for RT structures fully contained in the film volume were also compared between film and TPS. RESULTS The mean γ failure rate of the TPS dose reconstructions when compared to film doses was 0.8% (two static experiments) and 1.7% (six dynamic experiments). The mean (range) of the motion-induced γ failure rate in film measurements was 35.4% (21.3-59.2%). The TPS dose reconstruction agreed with these experimental γ failure rates with root-mean-square errors of 2.1% (dynamic rotation dose reconstruction) and 17.1% (dose reconstruction assuming constant rotation). By DVH metrics, the mean (range) difference between dose reconstructions with dynamic and constant rotation was 4.3% (-0.3-10.6%) (urethra D 2 % ), -0.6% (-5.6%-2.5%) (urethra D 99 % ), 1.1% (-7.1-7.7%) (GTV D 2 % ), -1.4% (-17.4-7.1%) (GTV D 95 % ), -1.2% (-17.1-5.7%) (GTV D 99 % ), and -0.1% (-3.2-7.6%) (GTV mean dose). Dose reconstructions with dynamic motion revealed large interplay effects (cold and hot spots). CONCLUSIONS A method to perform dose reconstructions for dynamic 6DoF motion in a TPS was developed and experimentally validated. It revealed large differences in dose distribution between dynamic and constant rotations not identifiable through dose reconstructions with constant rotation.
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Affiliation(s)
- Simon Skouboe
- Danish Center for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
| | - Robin De Roover
- Department of Oncology, KU Leuven, Leuven, Belgium.,Department of Radiation Oncology, University Hospitals Leuven, Leuven, Belgium
| | | | - Thomas Ravkilde
- Department of Medical Physics, Aarhus University Hospital, Aarhus, Denmark
| | - Wouter Crijns
- Department of Oncology, KU Leuven, Leuven, Belgium.,Department of Radiation Oncology, University Hospitals Leuven, Leuven, Belgium
| | - Rune Hansen
- Department of Medical Physics, Aarhus University Hospital, Aarhus, Denmark
| | - Tom Depuydt
- Department of Oncology, KU Leuven, Leuven, Belgium.,Department of Radiation Oncology, University Hospitals Leuven, Leuven, Belgium
| | - Per Rugaard Poulsen
- Danish Center for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark.,Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
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16
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Darvish-Molla S, Spurway A, Sattarivand M. Comprehensive characterization of ExacTrac stereoscopic image guidance system using Monte Carlo and Spektr simulations. Phys Med Biol 2020; 65:245029. [PMID: 32392546 DOI: 10.1088/1361-6560/ab91d8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The purpose of this work is to develop accurate computational methods to comprehensively characterize and model the clinical ExacTrac imaging system, which is used as an image guidance system for stereotactic treatment applications. The Spektr toolkit was utilized to simulate the spectral and imaging characterization of the system. Since Spektr only simulates the primary beam (ignoring scatter), a full model of ExacTrac was also developed in Monte Carlo (MC) to characterize the imaging system. To ensure proper performance of both simulation models, Spektr and MC data were compared to the measured spectral and half value layers (HVLs) values. To validate the simulation results, x-ray spectra of the ExacTrac system were measured for various tube potentials using a CdTe spectrometer with multiple added narrow collimators. The raw spectra were calibrated using a 57Co source and corrected for the escape peaks and detector efficiency. HVLs in mm of Al for various energies were measured using a calibrated RaySafe detector. Spektr and MC HVLs were calculated and compared to the measured values. The patient surface dose was calculated for different clinical imaging protocols from the measured air kerma and HVL values following the TG-61 methodology. The x-ray focal spot was measured by slanted edge technique using gafchromic films. ExacTrac imaging system beam profiles were simulated for various energies by MC simulation and the results were benchmarked by experimentally acquired beam profiles using gafchromic films. The effect of 6D IGRT treatment couch on beam hardening, dynamic range of the flat panel detector and scatter effect were determined using both Spektr simulation and experimental measurements. The measured and simulated spectra (of both MC and Spektr) for various kVps were compared and agreed within acceptable error. As another validation, the measured HVLs agreed with the Spektr and MC simulated HVLs on average within 1.0% for all kVps. The maximum and minimum patient surface doses were found to be 1.06 mGy for shoulder (high) and 0.051 mGy for cranial (low) imaging protocols, respectively. The MC simulated beam profiles were well matched with experimental results and replicated the penumbral slopes, the heel effect, and out-of-field regions. Dynamic range of detector (in terms of air kerma at detector surface) was found to be in the range of [6.1 × 10-6, 5.3 × 10-3] mGy. Accurate MC and Spektr models of the ExacTrac image guidance system were successfully developed and benchmarked via experimental validation. While patient surface dose for available imaging protocols were reported in this study, the established MC model may be used to obtain 3D imaging dose distribution for real patient geometries.
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Affiliation(s)
- Sahar Darvish-Molla
- Department of Medical Physics, Juravinski Cancer Centre at Hamilton Health Sciences, Hamilton, ON, Canada. Author to whom any correspondence should be addressed
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17
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Iramina H, Nakamura M, Miyabe Y, Mukumoto N, Ono T, Hirashima H, Mizowaki T. Quantification and correction of the scattered X-rays from a megavoltage photon beam to a linac-mounted kilovoltage imaging subsystem. BJR Open 2020; 2:20190048. [PMID: 33324865 PMCID: PMC7731796 DOI: 10.1259/bjro.20190048] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Revised: 08/24/2020] [Accepted: 08/25/2020] [Indexed: 01/14/2023] Open
Abstract
Objective To quantify and correct megavoltage (MV) scattered X-rays (MV-scatter) on an image acquired using a linac-mounted kilovoltage (kV) imaging subsystem. Methods and materials A linac-mounted flat-panel detector (FPD) was used to acquire an image containing MV-scatter by activating the FPD only during MV beam irradiation. 6-, 10-, and 15 MV with a flattening-filter (FF; 6X-FF, 10X-FF, 15X-FF), and 6- and 10 MV without an FF (6X-FFF, 10X-FFF) were used. The maps were acquired by changing one of the irradiation parameters while the others remained fixed. The mean pixel values of the MV-scatter were normalized to the 6X-FF reference condition (MV-scatter value). An MV-scatter database was constructed using these values. An MV-scatter correction experiment with one full arc image acquisition and two square field sizes (FSs) was conducted. Measurement- and estimation-based corrections were performed using the database. The image contrast was calculated at each angle. Results The MV-scatter increased with a larger FS and dose rate. The MV-scatter value factor varied substantially depending on the FPD position or collimator rotation. The median relative error ranges of the contrast for the image without, and with the measurement- and estimation-based correction were -10.9 to -2.9, and -1.5 to 4.8 and -7.4 to 2.6, respectively, for an FS of 10.0 × 10.0 cm2. Conclusions The MV-scatter was strongly dependent on the FS, dose rate, and FPD position. The MV-scatter correction improved the image contrast. Advances in knowledge The MV-scatters on the TrueBeam linac kV imaging subsystem were quantified with various MV beam parameters, and strongly depended on the fieldsize, dose rate, and flat panel detector position. The MV-scatter correction using the constructed database improved the image quality.
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Affiliation(s)
- Hiraku Iramina
- Department of Radiation Oncology and Image-applied Therapy, Kyoto University Hospital, Kyoto 606-8507, Japan
| | | | - Yuki Miyabe
- Department of Radiation Oncology and Image-applied Therapy, Kyoto University Hospital, Kyoto 606-8507, Japan
| | - Nobutaka Mukumoto
- Department of Radiation Oncology and Image-applied Therapy, Kyoto University Hospital, Kyoto 606-8507, Japan
| | - Tomohiro Ono
- Department of Radiation Oncology and Image-applied Therapy, Kyoto University Hospital, Kyoto 606-8507, Japan
| | - Hideaki Hirashima
- Department of Radiation Oncology and Image-applied Therapy, Kyoto University Hospital, Kyoto 606-8507, Japan
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Li T, Li F, Cai W, Zhang P, Li X. Technical Note: Synthetic treatment beam imaging for motion monitoring during spine SBRT treatments - a phantom study. Med Phys 2020; 48:125-131. [PMID: 33231877 DOI: 10.1002/mp.14618] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Revised: 08/28/2020] [Accepted: 09/17/2020] [Indexed: 12/31/2022] Open
Abstract
PURPOSE One of the biggest challenges in applying megavoltage (MV) treatment beam imaging for monitoring spine motion in stereotactic body radiotherapy (SBRT) is the small beam apertures in the images due to strong beam modulations in IMRT planning. The purpose of this study is to investigate the feasibility of a markerless motion tracking method in spine SBRT delivery using a novel enhanced synthetic treatment beam (ESTB) imaging technique. METHODS Three clinical spine SBRT plans using 6XFFF beams and sliding window IMRT technique were transferred to a thorax phantom and delivered by a TrueBeam machine. Before delivery, the phantom was aligned to the plan isocenter using CBCT setup and verified with a second CBCT, and then, 2 mm shifts were introduced in both the craniocaudal (CC) and the left-right (LR) directions with the couch. During beam delivery, MV images were continuously taken with an electronic portal imaging device (EPID) and automatically grabbed by Varian iTools Capture software with a frame rate of 11.6 Hz. After preprocessing for scatter correction and beam intensity compensation, every 50 frames of MV images were combined to generate a series of ESTB images for each beam. The ESTB images were then registered to the projections of the verification CBCT at the matched beam angles to detect the 2 mm shifts. RESULTS Compared to snapshot MV images, the ESTB images had significantly enlarged fields of view (FOVs) and improved image quality. Based on two-dimensional (2D) rigid registration, the ESTB image to CBCT projection matching showed submillimeter accuracy in detecting motion. Specifically, the root mean square errors in detecting the LR/CC shifts were 0.35/0.28, 0.32/0.35, 0.63/0.44, 0.55/0.51, and 0.69/0.42 mm at gantry angles 180, 160, 140, 120, and 100, respectively. CONCLUSION Our results in the phantom study suggest that ESTB images from a sliding window IMRT plan can be used to detect spine motion, with submillimeter precision in the 2D plane perpendicular to the beam.
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Affiliation(s)
- Tianfang Li
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, USA
| | - Feifei Li
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, USA
| | - Weixing Cai
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, USA
| | - Pengpeng Zhang
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, USA
| | - Xiang Li
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, USA
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Abstract
AIM/OBJECTIVES/BACKGROUND The American College of Radiology (ACR) and the American Society for Radiation Oncology (ASTRO) have jointly developed the following practice parameter for image-guided radiation therapy (IGRT). IGRT is radiation therapy that employs imaging to maximize accuracy and precision throughout the entire process of treatment delivery with the goal of optimizing accuracy and reliability of radiation therapy to the target, while minimizing dose to normal tissues. METHODS The ACR-ASTRO Practice Parameter for IGRT was revised according to the process described on the ACR website ("The Process for Developing ACR Practice Parameters and Technical Standards," www.acr.org/ClinicalResources/Practice-Parametersand-Technical-Standards) by the Committee on Practice Parameters of the ACR Commission on Radiation Oncology in collaboration with the ASTRO. Both societies then reviewed and approved the document. RESULTS This practice parameter is developed to serve as a tool in the appropriate application of IGRT in the care of patients with conditions where radiation therapy is indicated. It addresses clinical implementation of IGRT including personnel qualifications, quality assurance standards, indications, and suggested documentation. CONCLUSIONS This practice parameter is a tool to guide clinical use of IGRT and does not make recommendations on site-specific IGRT directives. It focuses on the best practices and principles to consider when using IGRT effectively, especially with the significant increase in imaging data that is now available with IGRT. The clinical benefit and medical necessity of the imaging modality and frequency of IGRT should be assessed for each patient.
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20
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Image-guided Radiotherapy to Manage Respiratory Motion: Lung and Liver. Clin Oncol (R Coll Radiol) 2020; 32:792-804. [PMID: 33036840 DOI: 10.1016/j.clon.2020.09.008] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 08/26/2020] [Accepted: 09/18/2020] [Indexed: 12/25/2022]
Abstract
Organ motion as a result of respiratory and cardiac motion poses significant challenges for the accurate delivery of radiotherapy to both the thorax and the upper abdomen. Modern imaging techniques during radiotherapy simulation and delivery now permit better quantification of organ motion, which in turn reduces tumour and organ at risk position uncertainty. These imaging advances, coupled with respiratory correlated radiotherapy delivery techniques, have led to the development of a range of approaches to manage respiratory motion. This review summarises the key strategies of image-guided respiratory motion management with a focus on lung and liver radiotherapy.
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21
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Vander Veken L, Dechambre D, Michiels S, Cohilis M, Souris K, Lee JA, Geets X. Improvement of kilovoltage intrafraction monitoring accuracy through gantry angles selection. Biomed Phys Eng Express 2020; 6. [PMID: 35073540 DOI: 10.1088/2057-1976/abb18e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 08/21/2020] [Indexed: 11/11/2022]
Abstract
Kilovoltage intrafraction monitoring (KIM) is a method allowing to precisely infer the tumour trajectory based on cone beam computed tomography (CBCT) 2D-projections. However, its accuracy is deteriorated in the case of highly mobile tumours involving hysteresis. A first adaptation of KIM consisting of a prior amplitude based binning step has been developed in order to minimize the errors of the original model (phase-KIM). In this work, we propose enhanced methods (KIMsub-arc optimand phase-KIMsub-arc optim) to improve the accuracy of KIM and phase-KIM which relies on the selection of the optimal starting CBCT gantry angle. Aiming at demonstrating the interest of our approach, we carried out a simulation study and an experimental study: we compared the accuracy of the conventional versus sub-arc optim methods on simulated realistic tumour motions with amplitudes ranging from 5 to 30 mm in 1 mm increments. The same approach was performed using a lung dynamic phantom generating a 30 mm amplitude sinusoidal motion. The results show that for in-silico simulated motions of 10, 20 and 30 mm amplitude, the three-dimensional root mean square error (3D-RMSE) can be reduced by 0.67 mm, 0.91 mm, 0.94 mm and 0.18 mm, 0.25 mm, 0.28 mm using KIMsub-arc optimand phase-KIMsub-arc optimrespectively. Considering all in-silico simulated trajectories, the percentage of errors larger than 1 mm decreases from 21.9% down to 1.6% for KIM (p < 0.001) and from 6.6% down to 1.2% for phase-KIM (p < 0.001). Experimentally, the 3D-RMSE is lowered by 0.5732 mm for KIM and by 0.1 mm for phase-KIM. The percentage of errors larger than 1 mm falls from 39.7% down to 18.5% for KIM and from 23.2% down to 11.1% for phase-KIM. In conclusion, our method efficiently anticipates CBCT gantry angles associated with a significantly better accuracy by using KIM and phase-KIM.
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Affiliation(s)
- Loïc Vander Veken
- Institut de Recherche Experimentale et Clinique (IREC), Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Université Catholique de Louvain, 1200 Brussels, Belgium
| | - David Dechambre
- Radiotherapy Department, Cliniques Universitaires Saint-Luc, 1200 Brussels, Belgium
| | - Steven Michiels
- Institut de Recherche Experimentale et Clinique (IREC), Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Université Catholique de Louvain, 1200 Brussels, Belgium.,Department of Oncology, Laboratory of Experimental Radiotherapy, Katholieke Universiteit Leuven, 3000 Leuven, Belgium
| | - Marie Cohilis
- Institut de Recherche Experimentale et Clinique (IREC), Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Université Catholique de Louvain, 1200 Brussels, Belgium.,Department of Oncology, Laboratory of Experimental Radiotherapy, Katholieke Universiteit Leuven, 3000 Leuven, Belgium
| | - Kevin Souris
- Institut de Recherche Experimentale et Clinique (IREC), Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Université Catholique de Louvain, 1200 Brussels, Belgium.,Department of Oncology, Laboratory of Experimental Radiotherapy, Katholieke Universiteit Leuven, 3000 Leuven, Belgium
| | - John Aldo Lee
- Institut de Recherche Experimentale et Clinique (IREC), Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Université Catholique de Louvain, 1200 Brussels, Belgium
| | - Xavier Geets
- Institut de Recherche Experimentale et Clinique (IREC), Center of Molecular Imaging, Radiotherapy and Oncology (MIRO), Université Catholique de Louvain, 1200 Brussels, Belgium.,Radiotherapy Department, Cliniques Universitaires Saint-Luc, 1200 Brussels, Belgium
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22
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Xing Y, Zhang Y, Nguyen D, Lin MH, Lu W, Jiang S. Boosting radiotherapy dose calculation accuracy with deep learning. J Appl Clin Med Phys 2020; 21:149-159. [PMID: 32559018 PMCID: PMC7484829 DOI: 10.1002/acm2.12937] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 05/09/2020] [Accepted: 05/11/2020] [Indexed: 12/26/2022] Open
Abstract
In radiotherapy, a trade‐off exists between computational workload/speed and dose calculation accuracy. Calculation methods like pencil‐beam convolution can be much faster than Monte‐Carlo methods, but less accurate. The dose difference, mostly caused by inhomogeneities and electronic disequilibrium, is highly correlated with the dose distribution and the underlying anatomical tissue density. We hypothesize that a conversion scheme can be established to boost low‐accuracy doses to high‐accuracy, using intensity information obtained from computed tomography (CT) images. A deep learning‐driven framework was developed to test the hypothesis by converting between two commercially available dose calculation methods: Anisotropic analytic algorithm (AAA) and Acuros XB (AXB). A hierarchically dense U‐Net model was developed to boost the accuracy of AAA dose toward the AXB level. The network contained multiple layers of varying feature sizes to learn their dose differences, in relationship to CT, both locally and globally. Anisotropic analytic algorithm and AXB doses were calculated in pairs for 120 lung radiotherapy plans covering various treatment techniques, beam energies, tumor locations, and dose levels. For each case, the CT and the AAA dose were used as the input and the AXB dose as the “ground‐truth” output, to train and test the model. The mean squared errors (MSEs) and gamma passing rates (2 mm/2% & 1 mm/1%) were calculated between the boosted AAA doses and the “ground‐truth” AXB doses. The boosted AAA doses demonstrated substantially improved match to the “ground‐truth” AXB doses, with average (± s.d.) gamma passing rate (1 mm/1%) 97.6% (±2.4%) compared to 87.8% (±9.0%) of the original AAA doses. The corresponding average MSE was 0.11(±0.05) vs 0.31(±0.21). Deep learning is able to capture the differences between dose calculation algorithms to boost the low‐accuracy algorithms. By combining a less accurate dose calculation algorithm with a trained deep learning model, dose calculation can potentially achieve both high accuracy and efficiency.
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Affiliation(s)
- Yixun Xing
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - You Zhang
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Dan Nguyen
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Mu-Han Lin
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Weiguo Lu
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Steve Jiang
- Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
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23
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Keall P, Nguyen DT, O'Brien R, Hewson E, Ball H, Poulsen P, Booth J, Greer P, Hunter P, Wilton L, Bromley R, Kipritidis J, Eade T, Kneebone A, Hruby G, Moodie T, Hayden A, Turner S, Arumugam S, Sidhom M, Hardcastle N, Siva S, Tai KH, Gebski V, Martin J. Real-Time Image Guided Ablative Prostate Cancer Radiation Therapy: Results From the TROG 15.01 SPARK Trial. Int J Radiat Oncol Biol Phys 2020; 107:530-538. [PMID: 32234553 DOI: 10.1016/j.ijrobp.2020.03.014] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Revised: 03/09/2020] [Accepted: 03/11/2020] [Indexed: 01/28/2023]
Abstract
PURPOSE Kilovoltage intrafraction monitoring (KIM) is a novel software platform implemented on standard radiation therapy systems and enabling real-time image guided radiation therapy (IGRT). In a multi-institutional prospective trial, we investigated whether real-time IGRT improved the accuracy of the dose patients with prostate cancer received during radiation therapy. METHODS AND MATERIALS Forty-eight patients with prostate cancer were treated with KIM-guided SABR with 36.25 Gy in 5 fractions. During KIM-guided treatment, the prostate motion was corrected for by either beam gating with couch shifts or multileaf collimator tracking. A dose reconstruction method was used to evaluate the dose delivered to the target and organs at risk with and without real-time IGRT. Primary outcome was the effect of real-time IGRT on dose distributions. Secondary outcomes included patient-reported outcomes and toxicity. RESULTS Motion correction occurred in ≥1 treatment for 88% of patients (42 of 48) and 51% of treatments (121 of 235). With real-time IGRT, no treatments had prostate clinical target volume (CTV) D98% dose 5% less than planned. Without real-time IGRT, 13 treatments (5.5%) had prostate CTV D98% doses 5% less than planned. The prostate CTV D98% dose with real-time IGRT was closer to the plan by an average of 1.0% (range, -2.8% to 20.3%). Patient outcomes showed no change in the 12-month patient-reported outcomes compared with baseline and no grade ≥3 genitourinary or gastrointestinal toxicities. CONCLUSIONS Real-time IGRT is clinically effective for prostate cancer SABR.
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Affiliation(s)
- Paul Keall
- ACRF Image X Institute, University of Sydney, Sydney, Australia.
| | - Doan Trang Nguyen
- ACRF Image X Institute, University of Sydney, Sydney, Australia; School of Biomedical Engineering, University of Technology, Sydney, Sydney, Australia
| | - Ricky O'Brien
- ACRF Image X Institute, University of Sydney, Sydney, Australia
| | - Emily Hewson
- ACRF Image X Institute, University of Sydney, Sydney, Australia
| | - Helen Ball
- ACRF Image X Institute, University of Sydney, Sydney, Australia
| | - Per Poulsen
- Department of Oncology and Danish Center for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
| | - Jeremy Booth
- Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, Australia; School of Physics, University of Sydney, Sydney, Australia
| | - Peter Greer
- Department of Radiation Oncology, Calvary Mater Newcastle Hospital, Newcastle, Australia; University of Newcastle, Newcastle, Australia
| | - Perry Hunter
- Department of Radiation Oncology, Calvary Mater Newcastle Hospital, Newcastle, Australia
| | - Lee Wilton
- Department of Radiation Oncology, Calvary Mater Newcastle Hospital, Newcastle, Australia
| | - Regina Bromley
- Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, Australia
| | - John Kipritidis
- Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, Australia
| | - Thomas Eade
- Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, Australia; Northern Clinical School, University of Sydney, Sydney, Australia
| | - Andrew Kneebone
- Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, Australia; Northern Clinical School, University of Sydney, Sydney, Australia
| | - George Hruby
- Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, Australia; Northern Clinical School, University of Sydney, Sydney, Australia
| | - Trevor Moodie
- Crown Princess Mary Cancer Centre, Westmead Hospital, Sydney, Australia
| | - Amy Hayden
- Crown Princess Mary Cancer Centre, Westmead Hospital, Sydney, Australia
| | - Sandra Turner
- Crown Princess Mary Cancer Centre, Westmead Hospital, Sydney, Australia
| | - Sankar Arumugam
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool Hospital, Sydney, Australia
| | - Mark Sidhom
- Liverpool and Macarthur Cancer Therapy Centres, Liverpool Hospital, Sydney, Australia
| | - Nicholas Hardcastle
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Australia; Institute of Medical Physics, University of Sydney, Sydney, Australia
| | - Shankar Siva
- Sir Peter MacCallum Department of Oncology, Peter MacCallum Cancer Centre, University of Melbourne, Australia
| | - Keen-Hun Tai
- Sir Peter MacCallum Department of Oncology, Peter MacCallum Cancer Centre, University of Melbourne, Australia
| | - Val Gebski
- NHMRC Clinical Trials Centre, University of Sydney, Sydney, Australia
| | - Jarad Martin
- Department of Radiation Oncology, Calvary Mater Newcastle Hospital, Newcastle, Australia; University of Newcastle, Newcastle, Australia
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24
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Wang C, Hunt M, Zhang L, Rimner A, Yorke E, Lovelock M, Li X, Li T, Mageras G, Zhang P. Technical Note: 3D localization of lung tumors on cone beam CT projections via a convolutional recurrent neural network. Med Phys 2020; 47:1161-1166. [PMID: 31899807 DOI: 10.1002/mp.14007] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Revised: 12/16/2019] [Accepted: 12/28/2019] [Indexed: 11/07/2022] Open
Abstract
PURPOSE To design a convolutional recurrent neural network (CRNN) that calculates three-dimensional (3D) positions of lung tumors from continuously acquired cone beam computed tomography (CBCT) projections, and facilitates the sorting and reconstruction of 4D-CBCT images. METHOD Under an IRB-approved clinical lung protocol, kilovoltage (kV) projections of the setup CBCT were collected in free-breathing. Concurrently, an electromagnetic signal-guided system recorded motion traces of three transponders implanted in or near the tumor. Convolutional recurrent neural network was designed to utilize a convolutional neural network (CNN) for extracting relevant features of the kV projections around the tumor, followed by a recurrent neural network for analyzing the temporal patterns of the moving features. Convolutional recurrent neural network was trained on the simultaneously collected kV projections and motion traces, subsequently utilized to calculate motion traces solely based on the continuous feed of kV projections. To enhance performance, CRNN was also facilitated by frequent calibrations (e.g., at 10° gantry rotation intervals) derived from cross-correlation-based registrations between kV projections and templates created from the planning 4DCT. Convolutional recurrent neural network was validated on a leave-one-out strategy using data from 11 lung patients, including 5500 kV images. The root-mean-square error between the CRNN and motion traces was calculated to evaluate the localization accuracy. RESULT Three-dimensional displacement around the simulation position shown in the Calypso traces was 3.4 ± 1.7 mm. Using motion traces as ground truth, the 3D localization error of CRNN with calibrations was 1.3 ± 1.4 mm. CRNN had a success rate of 86 ± 8% in determining whether the motion was within a 3D displacement window of 2 mm. The latency was 20 ms when CRNN ran on a high-performance computer cluster. CONCLUSIONS CRNN is able to provide accurate localization of lung tumors with aid from frequent recalibrations using the conventional cross-correlation-based registration approach, and has the potential to remove reliance on the implanted fiducials.
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Affiliation(s)
- Chuang Wang
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, USA
| | - Margie Hunt
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, USA
| | - Lei Zhang
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, USA
| | - Andreas Rimner
- Department of Radiation Oncology, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, USA
| | - Ellen Yorke
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, USA
| | - Michael Lovelock
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, USA
| | - Xiang Li
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, USA
| | - Tianfang Li
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, USA
| | - Gig Mageras
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, USA
| | - Pengpeng Zhang
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, USA
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25
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Wang S, Liu Y, Feng Y, Zhang J, Swinnen J, Li Y, Ni Y. A Review on Curability of Cancers: More Efforts for Novel Therapeutic Options Are Needed. Cancers (Basel) 2019; 11:E1782. [PMID: 31766180 PMCID: PMC6896199 DOI: 10.3390/cancers11111782] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 11/01/2019] [Accepted: 11/04/2019] [Indexed: 02/07/2023] Open
Abstract
Cancer remains a major cause of death globally. Given its relapsing and fatal features, curing cancer seems to be something hardly possible for the majority of patients. In view of the development in cancer therapies, this article summarizes currently available cancer therapeutics and cure potential by cancer type and stage at diagnosis, based on literature and database reviews. Currently common cancer therapeutics include surgery, chemotherapy, radiotherapy, targeted therapy, and immunotherapy. However, treatment with curative intent by these methods are mainly eligible for patients with localized disease or treatment-sensitive cancers and therefore their contributions to cancer curability are relatively limited. The prognosis for cancer patients varies among different cancer types with a five-year relative survival rate (RSR) of more than 80% in thyroid cancer, melanoma, breast cancer, and Hodgkin's lymphoma. The most dismal prognosis is observed in patients with small-cell lung cancer, pancreatic cancer, hepatocellular carcinoma, oesophagal cancer, acute myeloid leukemia, non-small cell lung cancer, and gastric cancer with a five-year RSR ranging between 7% and 28%. The current review is intended to provide a general view about how much we have achieved in curing cancer as regards to different therapies and cancer types. Finally, we propose a small molecule dual-targeting broad-spectrum anticancer strategy called OncoCiDia, in combination with emerging highly sensitive liquid biopsy, with theoretical curative potential for the management of solid malignancies, especially at the micro-cancer stage.
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Affiliation(s)
- Shuncong Wang
- KU Leuven, Campus Gasthuisberg, Faculty of Medicine, 3000 Leuven, Belgium; (S.W.); (Y.L.); (Y.F.); (J.S.)
| | - Yewei Liu
- KU Leuven, Campus Gasthuisberg, Faculty of Medicine, 3000 Leuven, Belgium; (S.W.); (Y.L.); (Y.F.); (J.S.)
| | - Yuanbo Feng
- KU Leuven, Campus Gasthuisberg, Faculty of Medicine, 3000 Leuven, Belgium; (S.W.); (Y.L.); (Y.F.); (J.S.)
| | - Jian Zhang
- Laboratories of Translational Medicine, Jiangsu Province Academy of Traditional Chinese Medicine, Nanjing 210028, China;
| | - Johan Swinnen
- KU Leuven, Campus Gasthuisberg, Faculty of Medicine, 3000 Leuven, Belgium; (S.W.); (Y.L.); (Y.F.); (J.S.)
| | - Yue Li
- Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
| | - Yicheng Ni
- KU Leuven, Campus Gasthuisberg, Faculty of Medicine, 3000 Leuven, Belgium; (S.W.); (Y.L.); (Y.F.); (J.S.)
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Draulans C, De Roover R, van der Heide UA, Haustermans K, Pos F, Smeenk RJ, De Boer H, Depuydt T, Kunze-Busch M, Isebaert S, Kerkmeijer L. Stereotactic body radiation therapy with optional focal lesion ablative microboost in prostate cancer: Topical review and multicenter consensus. Radiother Oncol 2019; 140:131-142. [PMID: 31276989 DOI: 10.1016/j.radonc.2019.06.023] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Revised: 06/13/2019] [Accepted: 06/14/2019] [Indexed: 12/25/2022]
Abstract
Stereotactic body radiotherapy (SBRT) for prostate cancer (PCa) is gaining interest by the recent publication of the first phase III trials on prostate SBRT and the promising results of many other phase II trials. Before long term results became available, the major concern for implementing SBRT in PCa in daily clinical practice was the potential risk of late genitourinary (GU) and gastrointestinal (GI) toxicity. A number of recently published trials, including late outcome and toxicity data, contributed to the growing evidence for implementation of SBRT for PCa in daily clinical practice. However, there exists substantial variability in delivering SBRT for PCa. The aim of this topical review is to present a number of prospective trials and retrospective analyses of SBRT in the treatment of PCa. We focus on the treatment strategies and techniques used in these trials. In addition, recent literature on a simultaneous integrated boost to the tumor lesion, which could create an additional value in the SBRT treatment of PCa, was described. Furthermore, we discuss the multicenter consensus of the FLAME consortium on SBRT for PCa with a focal boost to the macroscopic intraprostatic tumor nodule(s).
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Affiliation(s)
- Cédric Draulans
- Department of Radiation Oncology, University Hospitals Leuven, Belgium; Department of Oncology, KU Leuven, Belgium.
| | - Robin De Roover
- Department of Radiation Oncology, University Hospitals Leuven, Belgium; Department of Oncology, KU Leuven, Belgium.
| | - Uulke A van der Heide
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
| | - Karin Haustermans
- Department of Radiation Oncology, University Hospitals Leuven, Belgium; Department of Oncology, KU Leuven, Belgium.
| | - Floris Pos
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
| | - Robert Jan Smeenk
- Department of Radiation Oncology, Radboud University Medical Centre, Nijmegen, The Netherlands.
| | - Hans De Boer
- Department of Radiation Oncology, University Medical Center, Utrecht, The Netherlands.
| | - Tom Depuydt
- Department of Radiation Oncology, University Hospitals Leuven, Belgium; Department of Oncology, KU Leuven, Belgium.
| | - Martina Kunze-Busch
- Department of Radiation Oncology, Radboud University Medical Centre, Nijmegen, The Netherlands.
| | - Sofie Isebaert
- Department of Radiation Oncology, University Hospitals Leuven, Belgium; Department of Oncology, KU Leuven, Belgium.
| | - Linda Kerkmeijer
- Department of Radiation Oncology, Radboud University Medical Centre, Nijmegen, The Netherlands; Department of Radiation Oncology, University Medical Center, Utrecht, The Netherlands.
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État des lieux de la radiothérapie adaptative en 2019 : de la mise en place à l’utilisation clinique. Cancer Radiother 2019; 23:581-591. [DOI: 10.1016/j.canrad.2019.07.142] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Accepted: 07/12/2019] [Indexed: 12/20/2022]
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Skouboe S, Poulsen PR, Muurholm CG, Worm E, Hansen R, Høyer M, Ravkilde T. Simulated real‐time dose reconstruction for moving tumors in stereotactic liver radiotherapy. Med Phys 2019; 46:4738-4748. [DOI: 10.1002/mp.13792] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Revised: 08/13/2019] [Accepted: 08/19/2019] [Indexed: 12/21/2022] Open
Affiliation(s)
- Simon Skouboe
- Department of Oncology Aarhus University Hospital Aarhus N 8200Denmark
| | - Per Rugaard Poulsen
- Department of Oncology Aarhus University Hospital Aarhus N 8200Denmark
- Danish Center for Particle Therapy Aarhus University Hospital Aarhus N 8200 Denmark
| | | | - Esben Worm
- Department of Medical Physics Aarhus University Hospital Aarhus N 8200Denmark
| | - Rune Hansen
- Department of Medical Physics Aarhus University Hospital Aarhus N 8200Denmark
| | - Morten Høyer
- Danish Center for Particle Therapy Aarhus University Hospital Aarhus N 8200 Denmark
| | - Thomas Ravkilde
- Department of Medical Physics Aarhus University Hospital Aarhus N 8200Denmark
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29
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Bertholet J, Knopf A, Eiben B, McClelland J, Grimwood A, Harris E, Menten M, Poulsen P, Nguyen DT, Keall P, Oelfke U. Real-time intrafraction motion monitoring in external beam radiotherapy. Phys Med Biol 2019; 64:15TR01. [PMID: 31226704 PMCID: PMC7655120 DOI: 10.1088/1361-6560/ab2ba8] [Citation(s) in RCA: 113] [Impact Index Per Article: 22.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Revised: 05/10/2019] [Accepted: 06/21/2019] [Indexed: 12/25/2022]
Abstract
Radiotherapy (RT) aims to deliver a spatially conformal dose of radiation to tumours while maximizing the dose sparing to healthy tissues. However, the internal patient anatomy is constantly moving due to respiratory, cardiac, gastrointestinal and urinary activity. The long term goal of the RT community to 'see what we treat, as we treat' and to act on this information instantaneously has resulted in rapid technological innovation. Specialized treatment machines, such as robotic or gimbal-steered linear accelerators (linac) with in-room imaging suites, have been developed specifically for real-time treatment adaptation. Additional equipment, such as stereoscopic kilovoltage (kV) imaging, ultrasound transducers and electromagnetic transponders, has been developed for intrafraction motion monitoring on conventional linacs. Magnetic resonance imaging (MRI) has been integrated with cobalt treatment units and more recently with linacs. In addition to hardware innovation, software development has played a substantial role in the development of motion monitoring methods based on respiratory motion surrogates and planar kV or Megavoltage (MV) imaging that is available on standard equipped linacs. In this paper, we review and compare the different intrafraction motion monitoring methods proposed in the literature and demonstrated in real-time on clinical data as well as their possible future developments. We then discuss general considerations on validation and quality assurance for clinical implementation. Besides photon RT, particle therapy is increasingly used to treat moving targets. However, transferring motion monitoring technologies from linacs to particle beam lines presents substantial challenges. Lessons learned from the implementation of real-time intrafraction monitoring for photon RT will be used as a basis to discuss the implementation of these methods for particle RT.
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Affiliation(s)
- Jenny Bertholet
- Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS
Foundation Trust, London, United
Kingdom
- Author to whom any correspondence should be
addressed
| | - Antje Knopf
- Department of Radiation Oncology,
University Medical Center
Groningen, University of Groningen, The
Netherlands
| | - Björn Eiben
- Department of Medical Physics and Biomedical
Engineering, Centre for Medical Image Computing, University College London, London,
United Kingdom
| | - Jamie McClelland
- Department of Medical Physics and Biomedical
Engineering, Centre for Medical Image Computing, University College London, London,
United Kingdom
| | - Alexander Grimwood
- Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS
Foundation Trust, London, United
Kingdom
| | - Emma Harris
- Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS
Foundation Trust, London, United
Kingdom
| | - Martin Menten
- Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS
Foundation Trust, London, United
Kingdom
| | - Per Poulsen
- Department of Oncology, Aarhus University Hospital, Aarhus,
Denmark
| | - Doan Trang Nguyen
- ACRF Image X Institute, University of Sydney, Sydney,
Australia
- School of Biomedical Engineering,
University of Technology
Sydney, Sydney, Australia
| | - Paul Keall
- ACRF Image X Institute, University of Sydney, Sydney,
Australia
| | - Uwe Oelfke
- Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS
Foundation Trust, London, United
Kingdom
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Choi CH, Kim JH, Kim JI, Park JM. Comparison of treatment plan quality among MRI-based IMRT with a linac, MRI-based IMRT with tri-Co-60 sources, and VMAT for spine SABR. PLoS One 2019; 14:e0220039. [PMID: 31329641 PMCID: PMC6645671 DOI: 10.1371/journal.pone.0220039] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Accepted: 07/08/2019] [Indexed: 11/18/2022] Open
Abstract
PURPOSE This study compares the plan quality of magnetic-resonance image (MRI)-based intensity modulated radiation therapy (IMRT) using a linac (MR-linac-IMRT), MRI-based IMRT using tri-Co-60 sources (MR-Co-60-IMRT), and volumetric modulated arc therapy (VMAT) for spine stereotactic ablative radiotherapy (SABR). METHODS Twenty patients with thoracic spine metastasis were retrospectively selected for this study. For each patient, the MR-linac-IMRT, MR-Co-60-IMRT, and VMAT plans were generated using an identical CT image set and structures, except for the spinal cord and spinal cord planning organ-at-risk volume (PRV). Those two structures were contoured based on CT image sets for VMAT planning while those were contoured based on MR image sets for MR-linac-IMRT and MR-Co-60-IMRT planning. The initial prescription doses were 18 Gy in a single fraction for every plan in this study. If the tolerance level of the spinal cord was not met, the prescription doses were reduced to meet the tolerance level of the spinal cord. Dose-volumetric parameters of each plan were analyzed. RESULTS The average spinal cord volumes contoured based on the CT and MR images were 3.8±1.6 cm3 and 1.1±1.0 cm3, respectively (p<0.001). For four patients, the prescription doses of VMAT plans were reduced to 16 Gy to satisfy the spinal cord tolerance level. For thirteen patients, the prescription doses of MR-Co-60-IMRT plans were reduced to be less than 16 Gy to meet the spinal cord tolerance level. However, for every MR-linac-IMRT plan, the initial prescription doses of 18 Gy could be delivered to the target volume while satisfying the spinal cord tolerance. The average values of D10%, V10Gy, and V14Gy of the spinal cord PRV consistently indicated that the doses to the spinal cord PRV in the MR-linac-IMRT plans were the lowest among three types of plans in this study (all with p≤0.003). CONCLUSION MR-linac-IMRT appears promising for spine SABR.
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Affiliation(s)
- Chang Heon Choi
- Department of Radiation Oncology, Seoul National University Hospital, Seoul, Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea
- Biomedical Research Institute, Seoul National University Hospital, Seoul, Korea
- Department of Radiation Oncology, Sheikh Khalifa Specialty Hospital, Ras Al Khaimah, United Arab Emirates
| | - Jin Ho Kim
- Department of Radiation Oncology, Seoul National University Hospital, Seoul, Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea
- Biomedical Research Institute, Seoul National University Hospital, Seoul, Korea
| | - Jung-in Kim
- Department of Radiation Oncology, Seoul National University Hospital, Seoul, Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea
- Biomedical Research Institute, Seoul National University Hospital, Seoul, Korea
- * E-mail: (JMP); (JK)
| | - Jong Min Park
- Department of Radiation Oncology, Seoul National University Hospital, Seoul, Korea
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea
- Biomedical Research Institute, Seoul National University Hospital, Seoul, Korea
- Robotics Research Laboratory for Extreme Environments, Advanced Institute of Convergence Technology, Suwon, Korea
- * E-mail: (JMP); (JK)
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Zhao W, Han B, Yang Y, Buyyounouski M, Hancock SL, Bagshaw H, Xing L. Incorporating imaging information from deep neural network layers into image guided radiation therapy (IGRT). Radiother Oncol 2019; 140:167-174. [PMID: 31302347 DOI: 10.1016/j.radonc.2019.06.027] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Revised: 05/06/2019] [Accepted: 06/17/2019] [Indexed: 02/07/2023]
Abstract
BACKGROUND AND PURPOSE To investigate a novel markerless prostate localization strategy using a pre-trained deep learning model to interpret routine projection kilovoltage (kV) X-ray images in image-guided radiation therapy (IGRT). MATERIALS AND METHODS We developed a personalized region-based convolutional neural network to localize the prostate treatment target without implanted fiducials. To train the deep neural network (DNN), we used the patient's planning computed tomography (pCT) images with pre-delineated prostate target to generate a large amount of synthetic kV projection X-ray images in the geometry of onboard imager (OBI) system. The DNN model was evaluated by retrospectively studying 10 patients who underwent prostate IGRT. Three out of the ten patients who had implanted fiducials and the fiducials' positions in the OBI images acquired for treatment setup were examined to show the potential of the proposed method for prostate IGRT. Statistical analysis using Lin's concordance correlation coefficient was calculated to assess the results along with the difference between the digitally reconstructed radiographs (DRR) derived and DNN predicted locations of the prostate. RESULTS Differences between the predicted target positions using DNN and their actual positions are (mean ± standard deviation) 1.58 ± 0.43 mm, 1.64 ± 0.43 mm, and 1.67 ± 0.36 mm in anterior-posterior, lateral, and oblique directions, respectively. Prostate position identified on the OBI kV images is also found to be consistent with that derived from the implanted fiducials. CONCLUSIONS Highly accurate, markerless prostate localization based on deep learning is achievable. The proposed method is useful for daily patient positioning and real-time target tracking during prostate radiotherapy.
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Affiliation(s)
- Wei Zhao
- Stanford University, Department of Radiation Oncology, Stanford, USA.
| | - Bin Han
- Stanford University, Department of Radiation Oncology, Stanford, USA.
| | - Yong Yang
- Stanford University, Department of Radiation Oncology, Stanford, USA.
| | - Mark Buyyounouski
- Stanford University, Department of Radiation Oncology, Stanford, USA.
| | - Steven L Hancock
- Stanford University, Department of Radiation Oncology, Stanford, USA.
| | - Hilary Bagshaw
- Stanford University, Department of Radiation Oncology, Stanford, USA.
| | - Lei Xing
- Stanford University, Department of Radiation Oncology, Stanford, USA.
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Lafrenière M, Mahadeo N, Lewis J, Rottmann J, Williams CL. Continuous generation of volumetric images during stereotactic body radiation therapy using periodic kV imaging and an external respiratory surrogate. Phys Med 2019; 63:25-34. [PMID: 31221405 DOI: 10.1016/j.ejmp.2019.05.012] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Revised: 04/26/2019] [Accepted: 05/18/2019] [Indexed: 12/25/2022] Open
Abstract
We present a technique for continuous generation of volumetric images during SBRT using periodic kV imaging and an external respiratory surrogate signal to drive a patient-specific PCA motion model. Using the on-board imager, kV radiographs are acquired every 3 s and used to fit the parameters of a motion model so that it matches observed changes in internal patient anatomy. A multi-dimensional correlation model is established between the motion model parameters and the external surrogate position and velocity, enabling volumetric image reconstruction between kV imaging time points. Performance of the algorithm was evaluated using 10 realistic eXtended CArdiac-Torso (XCAT) digital phantoms including 3D anatomical respiratory deformation programmed with 3D tumor positions measured with orthogonal kV imaging of implanted fiducial gold markers. The clinically measured ground truth 3D tumor positions provided a dataset with realistic breathing irregularities, and the combination of periodic on-board kV imaging with recorded external respiratory surrogate signal was used for correlation modeling to account for any changes in internal-external correlation. The three-dimensional tumor positions are reconstructed with an average root mean square error (RMSE) of 1.47 mm, and an average 95th percentile 3D positional error of 2.80 mm compared with the clinically measured ground truth 3D tumor positions. This technique enables continuous 3D anatomical image generation based on periodic kV imaging of internal anatomy without the additional dose of continuous kV imaging. The 3D anatomical images produced using this method can be used for treatment verification and delivered dose computation in the presence of irregular respiratory motion.
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Affiliation(s)
- M Lafrenière
- Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, 75 Francis St, Boston, MA 02215, USA.
| | - N Mahadeo
- Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, 75 Francis St, Boston, MA 02215, USA
| | - J Lewis
- University of California, Los Angeles, CA 90095, USA
| | - J Rottmann
- Paul Scherrer Institute, Forschungsstrasse 111, 5232 Villigen, Switzerland
| | - C L Williams
- Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, 75 Francis St, Boston, MA 02215, USA.
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Happersett L, Wang P, Zhang P, Mechalakos J, Li G, Eley E, Zelefsky M, Mageras G, Damato AL, Hunt M. Developing a MLC modifier program to improve fiducial detection for MV/kV imaging during hypofractionated prostate volumetric modulated arc therapy. J Appl Clin Med Phys 2019; 20:120-124. [PMID: 31116478 PMCID: PMC6560246 DOI: 10.1002/acm2.12614] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Revised: 04/03/2019] [Accepted: 04/22/2019] [Indexed: 12/25/2022] Open
Abstract
Purpose To develop an Eclipse plug‐in (MLC_MODIFIER) that automatically modifies control points to expose fiducials obscured by MLC during VMAT, thereby facilitating tracking using periodic MV/kV imaging. Method Three‐dimensional fiducial tracking was performed during VMAT by pairing short‐arc (3°) MV digital tomosynthesis (DTS) images to triggered kV images. To evaluate MLC_MODIFIER efficacy, two cohorts of patients were considered. For first 12 patients, plans were manually edited to expose one fiducial marker. Next for 15 patients, plans were modified using MLC_MODIFIER script. MLC_MODIFIER evaluated MLC apertures at appropriate angles for marker visibility. Angles subtended by control points were compressed and low‐dose “imaging” control points were inserted and exposed one marker with 1 cm margin. Patient's images were retrospectively reviewed to determine rate of MV registration failures. Failure categories were poor DTS image quality, MLC blockage of fiducials, or unknown reasons. Dosimetric differences in rectum, bladder, and urethra D1 cc, PTV maximum dose, and PTV dose homogeneity (PTV HI) were evaluated. Statistical significance was evaluated using Fisher's exact and Student's t test. Result Overall MV registration failures, failures due to poor image quality, MLC blockage, and unknown reasons were 33% versus 8.9% (P < 0.0001), 8% versus 6.4% (P < 0.05), 13.6% versus 0.1% (P < 0.0001), and 7.6% versus 2.4% (P < 0.0001) for manually edited and MLC_MODIFIER plans, respectively. PTV maximum and HI increased on average from unmodified plans by 2.1% and 0.3% (P < 0.004) and 22.0% and 3.3% (P < 0.004) for manually edited and MLC_MODIFIED plans, respectively. Changes in bladder, rectum, and urethra D1CC were similar for each method and less than 0.7%. Conclusion Increasing fiducial visibility via an automated process comprised of angular compression of control points and insertion of additional “imaging” control points is feasible. Degradation of plan quality is minimal. Fiducial detection and registration success rates are significantly improved compared to manually edited apertures.
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Affiliation(s)
| | - Ping Wang
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Pengpeng Zhang
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Guang Li
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Eleanor Eley
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Gig Mageras
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Margie Hunt
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
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Alnaghy S, Kyme A, Caillet V, Nguyen DT, O’Brien R, Booth JT, Keall PJ. A six-degree-of-freedom robotic motion system for quality assurance of real-time image-guided radiotherapy. ACTA ACUST UNITED AC 2019; 64:105021. [DOI: 10.1088/1361-6560/ab1935] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Abstract
The world is embracing the information age, with real-time data at hand to assist with many decisions. Similarly, in cancer radiotherapy we are inexorably moving toward using information in a smarter and faster fashion, to usher in the age of real-time adaptive radiotherapy. The three critical steps of real-time adaptive radiotherapy, aligned with driverless vehicle technology are a continuous see, think, and act loop. See: use imaging systems to probe the patient anatomy or physiology as it evolves with time. Think: use current and prior information to optimize the treatment using the available adaptive degrees of freedom. Act: deliver the real-time adapted treatment. This paper expands upon these three critical steps for real-time adaptive radiotherapy, provides a historical context, reviews the clinical rationale, and gives a future outlook for real-time adaptive radiotherapy.
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Affiliation(s)
- Paul Keall
- ACRF Image X Institute, Sydney Medical School, University of Sydney, Sydney, NSW, Australia.
| | - Per Poulsen
- Department of Oncology and Danish Center for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
| | - Jeremy T Booth
- Northern Sydney Cancer Centre, Royal North Shore Hospital and Institute of Medical Physics, School of Physics, University of Sydney, Sydney Australia
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Liu PZY, O'Brien R, Heng SM, Newall M, Downes S, Shieh CC, Corde S, Jackson M, Keall P. Development and commissioning of a full-size prototype fixed-beam radiotherapy system with horizontal patient rotation. Med Phys 2019; 46:1331-1340. [DOI: 10.1002/mp.13356] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Revised: 11/27/2018] [Accepted: 12/17/2018] [Indexed: 12/25/2022] Open
Affiliation(s)
- Paul Zhi Yuan Liu
- ACRF Image X Institute; University of Sydney Central Clinical School; Sydney NSW Australia
| | - Ricky O'Brien
- ACRF Image X Institute; University of Sydney Central Clinical School; Sydney NSW Australia
| | - Soo-Min Heng
- Nelune Comprehensive Cancer Centre; Prince of Wales Hospital; Randwick NSW Australia
| | - Matthew Newall
- Nelune Comprehensive Cancer Centre; Prince of Wales Hospital; Randwick NSW Australia
| | - Simon Downes
- Nelune Comprehensive Cancer Centre; Prince of Wales Hospital; Randwick NSW Australia
| | - Chun-Chen Shieh
- ACRF Image X Institute; University of Sydney Central Clinical School; Sydney NSW Australia
| | - Stephanie Corde
- Nelune Comprehensive Cancer Centre; Prince of Wales Hospital; Randwick NSW Australia
| | - Michael Jackson
- Nelune Comprehensive Cancer Centre; Prince of Wales Hospital; Randwick NSW Australia
| | - Paul Keall
- ACRF Image X Institute; University of Sydney Central Clinical School; Sydney NSW Australia
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In Regard to Keall et al. Int J Radiat Oncol Biol Phys 2018; 103:282-283. [PMID: 30563659 DOI: 10.1016/j.ijrobp.2018.08.065] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2018] [Revised: 07/29/2018] [Accepted: 08/30/2018] [Indexed: 11/22/2022]
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Keall PJ, Nguyen DT, O'Brien R, Zhang P, Bertholet J, Poulsen PR. In Reply to Dahele and Verbakel. Int J Radiat Oncol Biol Phys 2018; 103:283-284. [PMID: 30563660 DOI: 10.1016/j.ijrobp.2018.08.063] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Revised: 08/29/2018] [Accepted: 08/30/2018] [Indexed: 12/01/2022]
Affiliation(s)
- Paul J Keall
- ACRF Image X Institute, University of Sydney, Sydney, Australia
| | - D Trang Nguyen
- ACRF Image X Institute, University of Sydney, Sydney, Australia
| | - Ricky O'Brien
- ACRF Image X Institute, University of Sydney, Sydney, Australia
| | - Pengpeng Zhang
- Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York
| | - Jenny Bertholet
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK; Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - Per R Poulsen
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
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