# A Novel Markerless Lung Tumor-Tracking Method Using Treatment MV Beam Imaging
## 1 Introduction  
The fundamental goal of radiotherapy is to deliver targeted tumoricidal radiation doses while minimizing doses to critical structures and healthy tissues. Large planning margins may be required to expand the target volume since respiratory motion can lead to lung tumor displacements of up to 2-3 cm [1; 2]. The margins must be large enough to encompass the entire tumor motion range; if the margin is incorrectly determined, the lung tumor may move out of the planning target volume (PTV) during treatment. However, large margins produce larger PTVs, limiting the ability to deliver high doses to targets and subsequently compromising dose sparing to critical structures and normal tissues. Although motion ranges are normally quantified by four-dimensional (4D) computed tomography (CT) scans, 4D CT results represent the regular motion patterns and lung tumor placements only at the time of CT scan planning. Actual tumor motions and locations may change significantly duringdaily treatment [3,4]. Monitoring lung tumor motion during radiation therapy treatment will verify if tumors have received the prescribed radiation doses and if critical structures have been spared. It will also help to reduce the planning margins for incoming radiation therapy to spare healthy tissues.  
Tumor motion management techniques, such as respiratory-gating and breath hold, along with lung tumor-tracking techniques, such as internal fiducial markers, external markers, and surrogates, have been used to monitor tumors [5]. However, several difficulties exist when using motion management techniques, including patient discomfort, patient-physician learning curve, and reproducibility. These ultimately determine the effectiveness and feasibility of their usage [5]. Fiducial markers may be implanted in and around the tumor site in order to track tumor motion. However, the nature of this technique poses several issues [3,4]. First, the patient faces a significant risk of pneumothorax and infection due to the invasive nature of the procedure. Second, over the course of the treatment, the markers may migrate so that the accuracy of the correlation between the tumor and the marker could be significantly reduced. Third, the implanted markers provide motion information of the center of mass, with no indication of the extent of tumor deformation and migration in response to radiation therapy over time. For this purpose, a non-invasive real-time lung tumor motion tracking algorithm is crucial for overcoming these issues. Attempts have been made to localize lung tumors on megavoltage (MV) projection images. Multiple research groups have conducted mask or template-based tumor tracking in the last decade [6, 7, 8]. [PERSON] and his colleagues reported a series of studies on tumor tracking from a landmark-based algorithm that matches simulated images with MV projections [9, 10, 11, 12, 13, 14, 15, 16]. [PERSON] et al. reported a kernel-based markerless approach for detecting lung tumor boundaries [17]. However, these methods may not be reliable if the tumor signal is week in comparison to the background. Poor soft tissue resolution may be due to the global discrepancies that exist between different imaging modalities, radiation scatter, and strong signals superimposed on the tumor by neighboring anatomical structures, among others. It is essential to accurately detect tumor motions when tumor projections and background projections overlap with each other. We report a novel but simple algorithm to generate tumor and background projections separately and determine their independent motions by comparing the estimated final projections with the acquired MV images. In order to reduce the effects of global discrepancies, a tiling technique is applied that accurately estimates image similarities [18].