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Huang L, Kurz C, Freislederer P, Manapov F, Corradini S, Niyazi M, Belka C, Landry G, Riboldi M. Simultaneous object detection and segmentation for patient-specific markerless lung tumor tracking in simulated radiographs with deep learning. Med Phys 2024; 51:1957-1973. [PMID: 37683107 DOI: 10.1002/mp.16705] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 04/23/2023] [Accepted: 05/12/2023] [Indexed: 09/10/2023] Open
Abstract
BACKGROUND Real-time tumor tracking is one motion management method to address motion-induced uncertainty. To date, fiducial markers are often required to reliably track lung tumors with X-ray imaging, which carries risks of complications and leads to prolonged treatment time. A markerless tracking approach is thus desirable. Deep learning-based approaches have shown promise for markerless tracking, but systematic evaluation and procedures to investigate applicability in individual cases are missing. Moreover, few efforts have been made to provide bounding box prediction and mask segmentation simultaneously, which could allow either rigid or deformable multi-leaf collimator tracking. PURPOSE The purpose of this study was to implement a deep learning-based markerless lung tumor tracking model exploiting patient-specific training which outputs both a bounding box and a mask segmentation simultaneously. We also aimed to compare the two kinds of predictions and to implement a specific procedure to understand the feasibility of markerless tracking on individual cases. METHODS We first trained a Retina U-Net baseline model on digitally reconstructed radiographs (DRRs) generated from a public dataset containing 875 CT scans and corresponding lung nodule annotations. Afterwards, we used an independent cohort of 97 lung patients to develop a patient-specific refinement procedure. In order to determine the optimal hyperparameters for automatic patient-specific training, we selected 13 patients for validation where the baseline model predicted a bounding box on planning CT (PCT)-DRR with intersection over union (IoU) with the ground-truth higher than 0.7. The final test set contained the remaining 84 patients with varying PCT-DRR IoU. For each testing patient, the baseline model was refined on the PCT-DRR to generate a patient-specific model, which was then tested on a separate 10-phase 4DCT-DRR to mimic the intrafraction motion during treatment. A template matching algorithm served as benchmark model. The testing results were evaluated by four metrics: the center of mass (COM) error and the Dice similarity coefficient (DSC) for segmentation masks, and the center of box (COB) error and the DSC for bounding box detections. Performance was compared to the benchmark model including statistical testing for significance. RESULTS A PCT-DRR IoU value of 0.2 was shown to be the threshold dividing inconsistent (68%) and consistent (100%) success (defined as mean bounding box DSC > 0.6) of PS models on 4DCT-DRRs. Thirty-seven out of the eighty-four testing cases had a PCT-DRR IoU above 0.2. For these 37 cases, the mean COM error was 2.6 mm, the mean segmentation DSC was 0.78, the mean COB error was 2.7 mm, and the mean box DSC was 0.83. Including the validation cases, the model was applicable to 50 out of 97 patients when using the PCT-DRR IoU threshold of 0.2. The inference time per frame was 170 ms. The model outperformed the benchmark model on all metrics, and the comparison was significant (p < 0.001) over the 37 PCT-DRR IoU > 0.2 cases, but not over the undifferentiated 84 testing cases. CONCLUSIONS The implemented patient-specific refinement approach based on a pre-trained baseline model was shown to be applicable to markerless tumor tracking in simulated radiographs for lung cases.
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Affiliation(s)
- Lili Huang
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, München, Germany
| | - Christopher Kurz
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Philipp Freislederer
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Farkhad Manapov
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Stefanie Corradini
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Maximilian Niyazi
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Claus Belka
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
- German Cancer Consortium (DKTK), partner site Munich, a partnership between DKFZ and LMU University Hospital Munich, Germany
- Bavarian Cancer Research Center (BZKF), Munich, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Marco Riboldi
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, München, Germany
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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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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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Tennis Rehabilitation Training-Assisted Paclitaxel Nanoparticles in Treatment of Lung Tumor. J CHEM-NY 2020. [DOI: 10.1155/2020/8823915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Paclitaxel nanoparticles are a compound with unique anticancer effects. Its mechanism of action is to prevent tumor rupture by stabilizing tumor proteins, while preventing cell division, leading to cell death, thereby inhibiting the spread of lung tumors. This article aims to study the treatment of lung tumors with paclitaxel nanoparticles assisted by tennis rehabilitation training. In this paper, paclitaxel nanoparticles were prepared by a solvent displacement method, and their particle size and morphology were measured. The TA2 series of experimental rats were selected to establish animal lung tumor models, and they were randomly divided into 5 groups: local injection of saline, porphyrin, and low-, medium-, and high-dose paclitaxel nanoparticles for treatment. The experimental results in this paper show that the average particle size of the paclitaxel nanoparticles prepared in the experiment is about 153,54 nm. Each treatment group inhibited tumor development to varying degrees. Among them, the inhibitory volume rate of paclitaxel nanoparticles in the middle- and high-dose groups was significantly higher than that in the paclitaxel treatment group, indicating that paclitaxel nanoparticles can release drugs slowly.
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Ferguson D, Harris T, Shi M, Jacobson M, Myronakis M, Lehmann M, Huber P, Morf D, Fueglistaller R, Baturin P, Valencia Lozano I, Williams C, Berbeco R. Automated MV markerless tumor tracking for VMAT. Phys Med Biol 2020; 65:125011. [PMID: 32330918 DOI: 10.1088/1361-6560/ab8cd3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Tumor tracking during radiotherapy treatment can improve dose accuracy, conformity and sparing of healthy tissue. Many methods have been introduced to tackle this challenge utilizing multiple imaging modalities, including a template matching based approach using the megavoltage (MV) on-board portal imager demonstrated on 3D conformal treatments. However, the complexity of treatments is evolving with the introduction of VMAT and IMRT, and successful motion management is becoming more important due to a trend towards hypofractionation. We have developed a markerless lung tumor tracking algorithm, utilizing the electronic portal imager (EPID) of the treatment machine. The algorithm has been specifically adapted to track during complex treatment deliveries with gantry and MLC motion. The core of the algorithm is an adaptive template matching method that relies on template stability metrics and local relative orientations to perform multiple feature tracking simultaneously. Only a single image is required to initialize the algorithm and features are automatically added, modified or removed in response to the input images. This algorithm was evaluated against images collected during VMAT arcs of a dynamic thorax phantom. Dynamic phantom images were collected during radiation delivery for multiple lung SBRT breathing traces and an example patient data set. The tracking error was 1.34 mm for the phantom data and 0.68 mm for the patient data. A multi-region, markerless tracking algorithm has been developed, capable of tracking multiple features simultaneously without requiring any other a priori information. This novel approach delivers robust target localization during complex treatment delivery. The reported tracking error is similar to previous reports for 3D conformal treatments.
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Affiliation(s)
- D Ferguson
- Brigham and Women's Hospital, Dana-Farber Cancer Institute, and Harvard Medical School, Boston, MA, United States of America
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