title: Tracking tumor boundary in MV-EPID images without implanted markers: A feasibility study.
abstract: OBJECTIVE
  To develop a markerless tracking algorithm to track the tumor boundary in megavoltage (MV)-electronic portal imaging device (EPID) images for image-guided radiation therapy.
  
  
  METHODS
  A level set method (LSM)-based algorithm is developed to track tumor boundary in EPID image sequences. Given an EPID image sequence, an initial curve is manually specified in the first frame. Driven by a region-scalable energy fitting function, the initial curve automatically evolves toward the tumor boundary and stops on the desired boundary while the energy function reaches its minimum. For the subsequent frames, the tracking algorithm updates the initial curve by using the tracking result in the previous frame and reuses the LSM to detect the tumor boundary in the subsequent frame so that the tracking processing can be continued without user intervention. The tracking algorithm is tested on three image datasets, including a 4-D phantom EPID image sequence, four digitally deformable phantom image sequences with different noise levels, and four clinical EPID image sequences acquired in lung cancer treatment. The tracking accuracy is evaluated based on two metrics: centroid localization error (CLE) and volume overlap index (VOI) between the tracking result and the ground truth.
  
  
  RESULTS
  For the 4-D phantom image sequence, the CLE is 0.23 ± 0.20 mm, and VOI is 95.6% ± 0.2%. For the digital phantom image sequences, the total CLE and VOI are 0.11 ± 0.08 mm and 96.7% ± 0.7%, respectively. In addition, for the clinical EPID image sequences, the proposed algorithm achieves 0.32 ± 0.77 mm in the CLE and 72.1% ± 5.5% in the VOI. These results demonstrate the effectiveness of the authors' proposed method both in tumor localization and boundary tracking in EPID images. In addition, compared with two existing tracking algorithms, the proposed method achieves a higher accuracy in tumor localization.
  
  
  CONCLUSIONS
  In this paper, the authors presented a feasibility study of tracking tumor boundary in EPID images by using a LSM-based algorithm. Experimental results conducted on phantom and clinical EPID images demonstrated the effectiveness of the tracking algorithm for visible tumor target. Compared with previous tracking methods, the authors' algorithm has the potential to improve the tracking accuracy in radiation therapy. In addition, real-time tumor boundary information within the irradiation field will be potentially useful for further applications, such as adaptive beam delivery, dose evaluation.
authors.affiliation: Department of Radiological Imaging and Informatics, Tohoku University Graduate School of Medicine, Sendai 980-8579, Japan.
authors.forename: Xiaoyong
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Department of Radiological Imaging and Informatics, Tohoku University Graduate School of Medicine, Sendai 980-8579, Japan.
authors.forename: Noriyasu
authors.initials: N
authors.lastname: Homma
authors.affiliation: Research Institute of Electrical Communication, Tohoku University, Sendai 980-8579, Japan.
authors.forename: Kei
authors.initials: K
authors.lastname: Ichiji
authors.affiliation: Department of Radiology and Radiation Oncology, Hirosaki University Graduate School of Medicine, Hirosaki 036-8562, Japan.
authors.forename: Yoshihiro
authors.initials: Y
authors.lastname: Takai
authors.affiliation: Research Division on Advanced Information Technology, Cyberscience Center, Tohoku University, Sendai 980-8579, Japan.
authors.forename: Makoto
authors.initials: M
authors.lastname: Yoshizawa
mesh_terms: D000465:Algorithms; D066264:Datasets as Topic; D005240:Feasibility Studies; D006801:Humans; D008175:Lung Neoplasms / Q000473:pathology / Q000532:radiotherapy*; D010315:Particle Accelerators; D010363:Pattern Recognition, Automated; D019047:Phantoms, Imaging; D061089:Radiotherapy, Image-Guided / Q000295:instrumentation* / Q000379:methods*; D014584:User-Computer Interface