SU-E-J-189: The Kullback-Leiber Divergence for Quantifying Changes in Radiotherapy Treatment Response.

PURPOSE: Repeated imaging is an extremely powerful tool in current radiotherapy practice since it allows advanced tumor detection and personalized treatment assessment by quantify tumor response. Change detection algorithms have been developed for remote sensing images to mathematically quantify relevant modifications occurring between datasets of the same subject acquired at different times. We propose usage of change detectors in radiotherapy for an automated quantification of clinical changes occurring in repeated imaging. METHODS: We explore usage of the Kullback-Leiber divergence as indicator of tumor change and quantification of treatment response. The Kullbach-Leiber divergence uses the likelihood theory to measures the distance between two statistical distributions and thus does not assume consistency in imaging. By it's general nature, it can accommodate the presence of noise and variations in imaging acquisition parameters that usually hinder automated identification of clinically-relevant features. RESULTS: In a comparison of simple difference maps and the Kullbach-Leiber divergence operator, the difference maps were affected by noise and did not consistently detect changes of low intensity. In contrast, the proposed operator discerned noise by considering regional statistics around each voxel, and marked both regions with low and high contrast changes. CONCLUSIONS: Statistical comparison through Kullback-Leiber divergence provides a reliable means to automatically quantify changes in repeated radiotherapy imaging.

SU-E-J-191: A Multivariate Framework for N-Tissue Classification in Treatment Assessment of Glioblastomas.

PURPOSE: Glioblastoma is the most common primary brain tumor in adults and is rapidly fatal. Treatment monitoring of these patients has increased awareness that many patients have new areas of contrast enhancement without progressive clinical signs and symptoms. Although the enhancing areas mimic tumor progression, the lesions result from treatment effects and subsequently stabilize or improve without further treatment and are not correlated with poorer outcomes. This phenomenon has been termed pseudoprogression and is hypothesized to occur secondarily to edema and vessel permeability in the tumor area as a result of the combined effects of radiation and chemotherapy. Since the new enhancing lesions of pseudoprogression are indistinguishable from true disease progression, there is a need for a predictive model to distinguish the two phenomena. METHOD: We developed a classification algorithm that combines perfusion and diffusion MRI imaging to effectively partition the cases as one exhibiting true or pseudo progression based on a vector of features containing T1, rCBV and ADC imaging. The multi-sequence classification algorithm uses an expectation maximization (EM) algorithm that learns from training cases with known clinical outcome to assigns each voxel to a type of tissue. RESULTS: A training set of 20 where the clinical outcome is known from biopsy or from long-term follow-up was used by EM algorithm to model typical imaging values within tissue of pseudo, tumor, edema, necrosis, vessels or brain anatomy to construct a database of expected values for each tissue type. When presented with a new case, the algorithm automatically classifies voxels by their geographical proximities and Mahalanobis distance to the pre-sampled values. CONCLUSION: Usage of advanced classification techniques allows automated labeling of voxels into normal, pseudoprogression or tumoral tissue types. The technique allows for early detection of pseudo progression to spare patients from unnecessary surgery or toxic chemotherapy.

Motion correction for improved target localization with on-board cone-beam computed tomography.

On-board imager (OBI) based cone-beam computed tomography (CBCT) has become available in radiotherapy clinics to accurately identify the target in the treatment position. However, due to the relatively slow gantry rotation (typically about 60 s for a full 360 degrees scan) in acquiring the CBCT projection data, the patient's respiratory motion causes serious problems such as blurring, doubling, streaking and distortion in the reconstructed images, which heavily degrade the image quality and the target localization. In this work, we present a motion compensation method for slow-rotating CBCT scans by incorporating into image reconstruction a patient-specific motion model, which is derived from previously obtained four-dimensional (4D) treatment planning CT images of the same patient via deformable registration. The registration of the 4D CT phases results in transformations representing a temporal sequence of three-dimensional (3D) deformation fields, or in other words, a 4D model of organ motion. The algorithm was developed heuristically in two-dimensional (2D) parallel-beam geometry and extended to 3D cone-beam geometry. By simulations with digital phantoms capable of translational motion and other complex motion, we demonstrated that the algorithm can reduce the motion artefacts locally, and restore the tumour size and shape, which may thereby improve the accuracy of target localization and patient positioning when CBCT is used as the treatment guidance.

Radiation dose reduction in four-dimensional computed tomography.

Four-dimensional (4D) CT is useful in many clinical situations, where detailed abdominal and thoracic imaging is needed over the course of the respiratory cycle. However, it usually delivers a larger radiation dose than the standard three-dimensional (3D) CT, since multiple scans at each couch position are required in order to provide the temporal information. Our purpose in this work is to develop a method to perform 4D CT scans at relatively low current, hence reducing the radiation exposure of the patients. To deal with the increased statistical noise caused by the low current, we proposed a novel 4D penalized weighted least square (4D-PWLS) smoothing method, which can incorporate both spatial and phase information. The 4D images at different phases were registered to the same phase via a deformable model, thereby, a regularization term combining temporal and spatial neighbors can be designed for the 4D-PWLS objective function. The proposed method was tested with phantom experiments and a patient study, and superior noise suppression and resolution preservation were observed. A quantitative evaluation of the benefit of the proposed method to 4D radiotherapy and 4D PET/CT imaging are under investigation.