Deformable image registration is widely used in various radiation therapy applications including daily treatment planning adaptation to map planned tissue or dose to changing anatomy. In this work, a simple and efficient inverse consistency deformable registration method is proposed with aims of higher registration accuracy and faster convergence speed. Instead of registering image I to a second image J, the two images are symmetrically deformed toward one another in multiple passes, until both deformed images are matched and correct registration is therefore achieved. In each pass, a delta motion field is computed by minimizing a symmetric optical flow system cost function using modified optical flow algorithms. The images are then further deformed with the delta motion field in the positive and negative directions respectively, and then used for the next pass. The magnitude of the delta motion field is forced to be less than 0.4 voxel for every pass in order to guarantee smoothness and invertibility for the two overall motion fields that are accumulating the delta motion fields in both positive and negative directions, respectively. The final motion fields to register the original images I and J, in either direction, are calculated by inverting one overall motion field and combining the inversion result with the other overall motion field. The final motion fields are inversely consistent and this is ensured by the symmetric way that registration is carried out. The proposed method is demonstrated with phantom images, artificially deformed patient images and 4D-CT images. Our results suggest that the proposed method is able to improve the overall accuracy (reducing registration error by 30% or more, compared to the original and inversely inconsistent optical flow algorithms), reduce the inverse consistency error (by 95% or more) and increase the convergence rate (by 100% or more). The overall computation speed may slightly decrease, or increase in most cases because the new method converges faster. Compared to previously reported inverse consistency algorithms, the proposed method is simpler, easier to implement and more efficient.

Purpose: Recent years have witnessed tremendous progress in image guide radiotherapy technology and a growing interest in the possibilities for adapting treatment planning and delivery over the course of treatment. One obstacle faced by the research community has been the lack of a comprehensive open-source software toolkit dedicated for adaptive radiotherapy ͑ART͒. To address this need, the authors have developed a software suite called the Deformable Image Registration and Adaptive Radiotherapy Toolkit ͑DIRART͒. Methods: DIRART is an open-source toolkit developed in MATLAB. It is designed in an objectoriented style with focus on user-friendliness, features, and flexibility. It contains four classes of DIR algorithms, including the newer inverse consistency algorithms to provide consistent displacement vector field in both directions. It also contains common ART functions, an integrated graphical user interface, a variety of visualization and image-processing features, dose metric analysis functions, and interface routines. These interface routines make DIRART a powerful complement to the Computational Environment for Radiotherapy Research ͑CERR͒ and popular image-processing toolkits such as ITK. Results: DIRART provides a set of image processing/registration algorithms and postprocessing functions to facilitate the development and testing of DIR algorithms. It also offers a good amount of options for DIR results visualization, evaluation, and validation. Conclusions: By exchanging data with treatment planning systems via DICOM-RT files and CERR, and by bringing image registration algorithms closer to radiotherapy applications, DIRART is potentially a convenient and flexible platform that may facilitate ART and DIR research.

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Radiation therapy dose distributions to eradicate tumor cells are typically constrained in extent or intensity to minimize the risk of injury to nearby critical normal tissues. With the widespread use of 3D image-based treatment planning systems, the question naturally arises how patient-specific anatomy and treatment differences affect outcome. It has long been known, that for many organs, variations in the fractional volume irradiated to high doses greatly alters the dose to achieve a given complication level (the "isoeffective dose") [1]. Smaller irradiated fractional volumes often lead to a much lower risk of complication; this is often referred to as the "volume-effect" in the literature, but would be more correctly referred to as the "dose-volume" effect. Normal tissue complication probability (NTCP) modeling is simply the ongoing effort to understand the risk of normal tissue injury as a function of the 3D dose distribution. Recently, there has been a steady accumulation of NTCP studies [2], and this is expected to continue or even accelerate in the future. NTCP models are particularly needed when the "volume-effect" becomes important (i.e., injury depends on the detailed dose distribution), such as for skin, lung, or liver. In this chapter we will review the basic principles of NTCP modeling, as well as publications related to selected endpoints (xerostomia, radiation pneumonitis, late rectal toxicity), and several issues related to the use of NTCP models, especially relating to their safe use. Other recent reviews which further discuss data on endpoints of interest in treatment planning include Deasy and Fowler [3], Moiseenko et al. [4], and the slightly older but still invaluable Seminars in Radiation Oncology issue, edited by Randy Ten Haken, devoted to dose-volume effects in normal tissues [2]. A useful review of models and model principles are the chapters by Jackson and Yorke [5], and Yorke [6]. Many technical issues in modeling dose-volume outcomes were also discussed by Deasy et al. [7].This chapter describes standard NTCP models as well as our own approach, which is more data-driven and image-based [8]. This contrasts with the more common approach of assuming the validity of a specific model and then attempting to fit the model parameters to a given data set. The term "image-based" indicates

Data visualization technique was applied to analyze the daily QA results of photon and electron beams. Special attention was paid to any trend the beams might display. A Varian Trilogy Linac equipped with dual photon energies and five electron energies was commissioned in early 2010. Daily Linac QA tests including the output constancy, beam flatness and symmetry (radial and transverse directions) were performed with an ionization chamber array device (QA BeamChecker Plus, Standard Imaging). The data of five years were collected and analyzed. For each energy, the measured data were exported and processed for visual trending using an in-house Matlab program. These daily data were cross-correlated with the monthly QA and annual QA results, as well as the preventive maintenance records. Majority of the output were within 1% of variation, with a consistent positive/upward drift for all seven energies (~+0.25% per month). The baseline of daily device is reset annually right after the TG-51 calibration. This results in a sudden drop of the output. On the other hand, the large amount of data using the same baseline exhibits a sinusoidal behavior (cycle = 12 months; amplitude = 0.8%, 0.5% for photons, electrons, respectively) on symmetry and flatness when normalization of baselines is accounted for. The well known phenomenon of new Linac output drift was clearly displayed. This output drift was a result of the air leakage of the over-pressurized sealed monitor chambers for the specific vendor. Data visualization is a new trend in the era of big data in radiation oncology research. It allows the data to be displayed visually and therefore more intuitive. Based on the visual display from the past, the physicist might predict the trend of the Linac and take actions proactively. It also makes comparisons, alerts failures, and potentially identifies causalities.