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Zhang S, Guo C, Xu J, Qian P, Guo J, Liu T, Wu Y, Hong J, Wang Q, He X, Sun L. Quantitative assessment of intertarget position variations based on 4D-CT and 4D-CBCT simulations in single-isocenter multitarget lung stereotactic body radiation therapy. J Cancer Res Clin Oncol 2024; 150:359. [PMID: 39044013 PMCID: PMC11266286 DOI: 10.1007/s00432-024-05836-w] [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: 05/06/2024] [Accepted: 06/04/2024] [Indexed: 07/25/2024]
Abstract
BACKGROUND In single-isocenter multitarget stereotactic body radiotherapy (SBRT), geometric miss risks arise from uncertainties in intertarget position. However, its assessment is inadequate, and may be interfered by the reconstructed tumor position errors (RPEs) during simulated CT and cone beam CT (CBCT) acquisition. This study aimed to quantify intertarget position variations and assess factors influencing it. METHODS We analyzed data from 14 patients with 100 tumor pairs treated with single-isocenter SBRT. Intertarget position variation was measured using 4D-CT simulation to assess the intertarget position variations (ΔD) during routine treatment process. Additionally, a homologous 4D-CBCT simulation provided RPE-free comparison to determine the impact of RPEs, and isolating purely tumor motion induced ΔD to evaluate potential contributing factors. RESULTS The median ΔD was 4.3 mm (4D-CT) and 3.4 mm (4D-CBCT). Variations exceeding 5 mm and 10 mm were observed in 31.1% and 5.5% (4D-CT) and 20.4% and 3.4% (4D-CBCT) of fractions, respectively. RPEs necessitated an additional 1-2 mm safety margin. Intertarget distance and breathing amplitude variability showed weak correlations with variation (Rs = 0.33 and 0.31). The ΔD differed significantly by locations (upper vs. lower lobe and right vs. Left lung). Notably, left lung tumor pairs exhibited the highest risk. CONCLUSIONS This study provide a reliable way to assess intertarget position variation by using both 4D-CT and 4D-CBCT simulation. Consequently, single-isocenter SBRT for multiple lung tumors carries high risk of geometric miss. Tumor motion and RPE constitute a substantial portion of intertarget position variation, requiring correspondent strategies to minimize the intertarget uncertainties.
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Affiliation(s)
- Siyu Zhang
- Department of Radiotherapy, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, the Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, 210009, China
| | - Chang Guo
- Department of Radiotherapy, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, the Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, 210009, China
| | - Jun Xu
- Department of Radiotherapy, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, the Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, 210009, China
| | - Pudong Qian
- Department of Radiotherapy, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, the Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, 210009, China
| | - Jiali Guo
- Department of Radiation Oncology, Dantu People's Hospital, Anhui, Maanshan, 243100, China
| | - Tingting Liu
- Department of Radiotherapy, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, the Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, 210009, China
| | - Yifan Wu
- Department of Radiotherapy, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, the Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, 210009, China
| | - Jun Hong
- Department of Radiation Oncology, the Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian, 223300, Jiangsu, China
| | - Qi Wang
- Department of Radiotherapy, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, the Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, 210009, China
| | - Xia He
- Department of Radiotherapy, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, the Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, 210009, China.
| | - Li Sun
- Department of Radiotherapy, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, the Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, 210009, China.
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Shao HC, Mengke T, Pan T, Zhang Y. Dynamic CBCT imaging using prior model-free spatiotemporal implicit neural representation (PMF-STINR). Phys Med Biol 2024; 69:115030. [PMID: 38697195 PMCID: PMC11133878 DOI: 10.1088/1361-6560/ad46dc] [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: 12/01/2023] [Revised: 04/12/2024] [Accepted: 05/01/2024] [Indexed: 05/04/2024]
Abstract
Objective. Dynamic cone-beam computed tomography (CBCT) can capture high-spatial-resolution, time-varying images for motion monitoring, patient setup, and adaptive planning of radiotherapy. However, dynamic CBCT reconstruction is an extremely ill-posed spatiotemporal inverse problem, as each CBCT volume in the dynamic sequence is only captured by one or a few x-ray projections, due to the slow gantry rotation speed and the fast anatomical motion (e.g. breathing).Approach. We developed a machine learning-based technique, prior-model-free spatiotemporal implicit neural representation (PMF-STINR), to reconstruct dynamic CBCTs from sequentially acquired x-ray projections. PMF-STINR employs a joint image reconstruction and registration approach to address the under-sampling challenge, enabling dynamic CBCT reconstruction from singular x-ray projections. Specifically, PMF-STINR uses spatial implicit neural representations to reconstruct a reference CBCT volume, and it applies temporal INR to represent the intra-scan dynamic motion of the reference CBCT to yield dynamic CBCTs. PMF-STINR couples the temporal INR with a learning-based B-spline motion model to capture time-varying deformable motion during the reconstruction. Compared with the previous methods, the spatial INR, the temporal INR, and the B-spline model of PMF-STINR are all learned on the fly during reconstruction in a one-shot fashion, without using any patient-specific prior knowledge or motion sorting/binning.Main results. PMF-STINR was evaluated via digital phantom simulations, physical phantom measurements, and a multi-institutional patient dataset featuring various imaging protocols (half-fan/full-fan, full sampling/sparse sampling, different energy and mAs settings, etc). The results showed that the one-shot learning-based PMF-STINR can accurately and robustly reconstruct dynamic CBCTs and capture highly irregular motion with high temporal (∼ 0.1 s) resolution and sub-millimeter accuracy.Significance. PMF-STINR can reconstruct dynamic CBCTs and solve the intra-scan motion from conventional 3D CBCT scans without using any prior anatomical/motion model or motion sorting/binning. It can be a promising tool for motion management by offering richer motion information than traditional 4D-CBCTs.
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Affiliation(s)
- Hua-Chieh Shao
- The Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America
| | - Tielige Mengke
- The Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America
| | - Tinsu Pan
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, United States of America
| | - You Zhang
- The Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America
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Shao HC, Mengke T, Pan T, Zhang Y. Dynamic CBCT Imaging using Prior Model-Free Spatiotemporal Implicit Neural Representation (PMF-STINR). ARXIV 2023:arXiv:2311.10036v2. [PMID: 38013886 PMCID: PMC10680908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Objective Dynamic cone-beam computed tomography (CBCT) can capture high-spatial-resolution, time-varying images for motion monitoring, patient setup, and adaptive planning of radiotherapy. However, dynamic CBCT reconstruction is an extremely ill-posed spatiotemporal inverse problem, as each CBCT volume in the dynamic sequence is only captured by one or a few X-ray projections, due to the slow gantry rotation speed and the fast anatomical motion (e.g., breathing). Approach We developed a machine learning-based technique, prior-model-free spatiotemporal implicit neural representation (PMF-STINR), to reconstruct dynamic CBCTs from sequentially acquired X-ray projections. PMF-STINR employs a joint image reconstruction and registration approach to address the under-sampling challenge, enabling dynamic CBCT reconstruction from singular X-ray projections. Specifically, PMF-STINR uses spatial implicit neural representation to reconstruct a reference CBCT volume, and it applies temporal INR to represent the intra-scan dynamic motion with respect to the reference CBCT to yield dynamic CBCTs. PMF-STINR couples the temporal INR with a learning-based B-spline motion model to capture time-varying deformable motion during the reconstruction. Compared with the previous methods, the spatial INR, the temporal INR, and the B-spline model of PMF-STINR are all learned on the fly during reconstruction in a one-shot fashion, without using any patient-specific prior knowledge or motion sorting/binning. Main results PMF-STINR was evaluated via digital phantom simulations, physical phantom measurements, and a multi-institutional patient dataset featuring various imaging protocols (half-fan/full-fan, full sampling/sparse sampling, different energy and mAs settings, etc.). The results showed that the one-shot learning-based PMF-STINR can accurately and robustly reconstruct dynamic CBCTs and capture highly irregular motion with high temporal (~0.1s) resolution and sub-millimeter accuracy. Significance PMF-STINR can reconstruct dynamic CBCTs and solve the intra-scan motion from conventional 3D CBCT scans without using any prior anatomical/motion model or motion sorting/binning. It can be a promising tool for motion management by offering richer motion information than traditional 4D-CBCTs.
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Affiliation(s)
- Hua-Chieh Shao
- The Medical Artificial Intelligence and Automation (MAIA) Laboratory Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Tielige Mengke
- The Medical Artificial Intelligence and Automation (MAIA) Laboratory Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Tinsu Pan
- Department of Imaging Physics University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - You Zhang
- The 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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Zhang Y, Shao HC, Pan T, Mengke T. Dynamic cone-beam CT reconstruction using spatial and temporal implicit neural representation learning (STINR). Phys Med Biol 2023; 68:045005. [PMID: 36638543 PMCID: PMC10087494 DOI: 10.1088/1361-6560/acb30d] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 12/27/2022] [Accepted: 01/13/2023] [Indexed: 01/15/2023]
Abstract
Objective. Dynamic cone-beam CT (CBCT) imaging is highly desired in image-guided radiation therapy to provide volumetric images with high spatial and temporal resolutions to enable applications including tumor motion tracking/prediction and intra-delivery dose calculation/accumulation. However, dynamic CBCT reconstruction is a substantially challenging spatiotemporal inverse problem, due to the extremely limited projection sample available for each CBCT reconstruction (one projection for one CBCT volume).Approach. We developed a simultaneous spatial and temporal implicit neural representation (STINR) method for dynamic CBCT reconstruction. STINR mapped the unknown image and the evolution of its motion into spatial and temporal multi-layer perceptrons (MLPs), and iteratively optimized the neuron weightings of the MLPs via acquired projections to represent the dynamic CBCT series. In addition to the MLPs, we also introduced prior knowledge, in the form of principal component analysis (PCA)-based patient-specific motion models, to reduce the complexity of the temporal mapping to address the ill-conditioned dynamic CBCT reconstruction problem. We used the extended-cardiac-torso (XCAT) phantom and a patient 4D-CBCT dataset to simulate different lung motion scenarios to evaluate STINR. The scenarios contain motion variations including motion baseline shifts, motion amplitude/frequency variations, and motion non-periodicity. The XCAT scenarios also contain inter-scan anatomical variations including tumor shrinkage and tumor position change.Main results. STINR shows consistently higher image reconstruction and motion tracking accuracy than a traditional PCA-based method and a polynomial-fitting-based neural representation method. STINR tracks the lung target to an average center-of-mass error of 1-2 mm, with corresponding relative errors of reconstructed dynamic CBCTs around 10%.Significance. STINR offers a general framework allowing accurate dynamic CBCT reconstruction for image-guided radiotherapy. It is a one-shot learning method that does not rely on pre-training and is not susceptible to generalizability issues. It also allows natural super-resolution. It can be readily applied to other imaging modalities as well.
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Affiliation(s)
- You Zhang
- Advanced Imaging and Informatics in Radiation Therapy (AIRT) Laboratory, Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, 75235, United States of America
| | - Hua-Chieh Shao
- Advanced Imaging and Informatics in Radiation Therapy (AIRT) Laboratory, Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, 75235, United States of America
| | - Tinsu Pan
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX, 77030, United States of America
| | - Tielige Mengke
- Advanced Imaging and Informatics in Radiation Therapy (AIRT) Laboratory, Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, 75235, United States of America
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Stünkel R, Zeller AN, Bohne T, Böhrnsen F, Wedi E, Raschke D, Kauffmann P. Accuracy of intraoral real-time navigation versus static, CAD/CAM-manufactured pilot drilling guides in dental implant surgery: an in vitro study. Int J Implant Dent 2022; 8:41. [PMID: 36198996 PMCID: PMC9535055 DOI: 10.1186/s40729-022-00430-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 07/11/2022] [Indexed: 11/18/2022] Open
Abstract
Background Nowadays, 3D planning and static for dynamic aids play an increasing role in oral rehabilitation of the masticatory apparatus with dental implants. The aim of this study is to compare the accuracy of implant placement using a 3D-printed drilling guide and an intraoral real-time dynamic navigation system. Methods A total of 60 implants were placed on 12 partially edentulous lower jaw models. 30 were placed with pilot drilling guides, the other half with dynamic navigation (DENACAM®). In addition, implant placement in interdental gaps and free-end situations were investigated. Accuracy was assessed by cone-beam computed tomography (CBCT). Results Both systems achieved clinically acceptable results, yet more accurate results regarding the offset of implant base and tip in several spatial dimensions were achieved using drilling guides (each p < 0.05). With regard to angulation, real-time navigation was more precise (p = 0.0016). Its inaccuracy was 3°; the template-guided systems was 4.6°. Median horizontal deviation was 0.52 mm at base and 0.75 mm at tip using DENACAM®. When using the pilot drill guide, horizontal deviation was 0.34 mm in the median and at the tip by 0.59 mm. Regarding angulation, it was found that the closer the drill hole was to the system's marker, the better navigation performed. The template did not show this trend (p = 0.0043; and p = 0.0022). Conclusion Considering the limitations of an in vitro study, dynamic navigation can be used be a tool for reliable and accurate implantation. However, further clinical studies need to follow in order to provide an evidence-based recommendation for use in vivo. Supplementary Information The online version contains supplementary material available at 10.1186/s40729-022-00430-6.
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Affiliation(s)
- Robert Stünkel
- Department of Maxillofacial Surgery, Georg August University, Göttingen, Germany
| | - Alexander-Nicolai Zeller
- Department of Maxillofacial Surgery, Hannover Medical School, Carl-Neuberg-Straße 1, 30625, Hannover, Germany.
| | | | - Florian Böhrnsen
- Department of Maxillofacial Surgery, Georg August University, Göttingen, Germany
| | - Edris Wedi
- Department of Gastroenterology and Gastrointestinal Oncology, Interdisciplinary Endoscopy, University Medical Center, Georg August University, Göttingen, Germany
| | - David Raschke
- Department of Maxillofacial Surgery, Georg August University, Göttingen, Germany
| | - Philipp Kauffmann
- Department of Maxillofacial Surgery, Georg August University, Göttingen, Germany
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Vergalasova I, Cai J. A modern review of the uncertainties in volumetric imaging of respiratory-induced target motion in lung radiotherapy. Med Phys 2020; 47:e988-e1008. [PMID: 32506452 DOI: 10.1002/mp.14312] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Revised: 05/15/2020] [Accepted: 05/26/2020] [Indexed: 12/25/2022] Open
Abstract
Radiotherapy has become a critical component for the treatment of all stages and types of lung cancer, often times being the primary gateway to a cure. However, given that radiation can cause harmful side effects depending on how much surrounding healthy tissue is exposed, treatment of the lung can be particularly challenging due to the presence of moving targets. Careful implementation of every step in the radiotherapy process is absolutely integral for attaining optimal clinical outcomes. With the advent and now widespread use of stereotactic body radiation therapy (SBRT), where extremely large doses are delivered, accurate, and precise dose targeting is especially vital to achieve an optimal risk to benefit ratio. This has largely become possible due to the rapid development of image-guided technology. Although imaging is critical to the success of radiotherapy, it can often be plagued with uncertainties due to respiratory-induced target motion. There has and continues to be an immense research effort aimed at acknowledging and addressing these uncertainties to further our abilities to more precisely target radiation treatment. Thus, the goal of this article is to provide a detailed review of the prevailing uncertainties that remain to be investigated across the different imaging modalities, as well as to highlight the more modern solutions to imaging motion and their role in addressing the current challenges.
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Affiliation(s)
- Irina Vergalasova
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ, USA
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong
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