- •We have developed a new method to track tumor position using fluoroscopic images.
- •Tracking accuracy averaged over seven liver cases was 1.30 ± 0.54 mm.
- •Computation time was < 33 ms for a pair of fluoroscopic images.
We have developed a new method to track tumor position using fluoroscopic images, and evaluated it using hepatocellular carcinoma case data.
Our method consists of a training stage and a tracking stage. In the training stage, the model data for the positional relationship between the diaphragm and the tumor are calculated using four-dimensional computed tomography (4DCT) data. The diaphragm is detected along a straight line, which was chosen to avoid 4DCT artifact. In the tracking stage, the tumor position on the fluoroscopic images is calculated by applying the model to the diaphragm. Using data from seven liver cases, we evaluated four metrics: diaphragm edge detection error, modeling error, patient setup error, and tumor tracking error. We measured tumor tracking error for the 15 fluoroscopic sequences from the cases and recorded the computation time.
The mean positional error in diaphragm tracking was 0.57 ± 0.62 mm. The mean positional error in tumor tracking in three-dimensional (3D) space was 0.63 ± 0.30 mm by modeling error, and 0.81–2.37 mm with 1–2 mm setup error. The mean positional error in tumor tracking in the fluoroscopy sequences was 1.30 ± 0.54 mm and the mean computation time was 69.0 ± 4.6 ms and 23.2 ± 1.3 ms per frame for the training and tracking stages, respectively.
Our markerless tracking method successfully estimated tumor positions. We believe our results will be useful in increasing treatment accuracy for liver cases.
To read this article in full you will need to make a payment
Purchase one-time access:Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
One-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:Subscribe to Physica Medica: European Journal of Medical Physics
Already a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
- The future of heavy ion radiotherapy.Med Phys. 2008; 35: 5653-5663
- Real-time tumour tracking in particle therapy: technological developments and future perspectives.Lancet Oncol. 2012; 13: e383-e391
- Respiration gated radiotherapy treatment: a technical study.Phys Med Biol. 1996; 41: 83-91
- Evaluation of residual abdominal tumour motion in carbon ion gated treatments through respiratory motion modelling.Phys Med Biol. 2017; 34: 28-37
- Feasibility of insertion/implantation of 2.0-mm-diameter gold internal fiducial markers for precise setup and real-time tumor tracking in radiotherapy.Int J Radiat Oncol Biol Phys. 2003; 56: 240-247
- Fluoroscopic tracking of multiple implanted fiducial markers using multiple object tracking.Phys Med Biol. 2007; 52: 4081-4098
- A feasibility study of markerless fluoroscopic gating for lung cancer radiotherapy using 4DCT templates.Phys Med Biol. 2009; 54: N489-N500
- Markerless gating for lung cancer radiotherapy based on machine learning techniques.Phys Med. Biol. 2009; 54: 1555-1563
- Carbon-ion pencil beam scanning treatment with gated markerless tumor tracking: an analysis of positional accuracy.Int J Radiat Oncol Biol Phys. 2016; 95: 258-266
- Multiple template-based fluoroscopic tracking of lung tumor mass without implanted fiducial markers.Phys Med Biol. 2007; 52: 6229-6242
- A kernel-based method for markerless tumor tracking in kV fluoroscopic images.Phys Med Biol. 2014; 59: 4897-4911
- A Bayesian approach for three-dimensional markerless tumor tracking using kV imaging during lung radiotherapy.Phys Med Biol. 2017; 62: 3065-3080
- Marker-less tumor tracking for lung cancer by tumor image pattern learning.Int J Radiat Oncol. 2016; 96 (E651–E651)
- The diaphragm as an anatomic surrogate for lung tumor motion.Phys Med Biol. 2009; 54: 3529-3541
- Tumor motion prediction with the diaphragm as a surrogate: a feasibility study.Phys Med Biol. 2010; 55: N221-N229
Petkov S, Romero A, Suarez XC, Radeva P, Gatta C. Robust and accurate diaphragm border detection in cardiac X-ray angiographies. Proceedings of the 3rd STACOM, part of MICCAI 2012 pp. 225–234.
- Diaphragm border detection in coronary X-ray angiographies: new method and applications.Comput Med Imaging Graph. 2014; 38: 296-305
- Respiratory motion compensation using diaphragm tracking for CBCT a simulation and a phantom study.Int J Biomed Imaging. 2013; (Article #6, 10 pages)
- Real-time tumor tracking using fluoroscopic imaging with deep neural network analysis.Physica Med. 2019; 59: 22-29
- Dynamic Programming solution for detecting dim moving target.IEEE Trans Aerosp Electron Syst. 1985; : 144-156
- Keysers D and Ney H Tracking using dynamic programming for appearance-based sign language recognition.Proc FGR. 2006; : 293-298
- GPU-based streaming architectures for fast cone-beam CT image reconstruction and demons deformable registration.Phys Med Biol. 2007; 52: 5771-5783
- Insertion and fixation of fiducial markers for setup and tracking of lung tumors in radiotherapy.Int J Radiat Oncol Biol Phys. 2005; 63: 1442-1447
- Dosimetric impact of tantalum markers used in the treatment of uveal melanoma with proton beam therapy.Phys Med Biol. 2007; 52: 3979-3990
Published online: February 08, 2020
Accepted: February 1, 2020
Received in revised form: December 27, 2019
Received: May 17, 2019
© 2020 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.