The goal of this application is to apply software tools developed under previous funding to aid in the solution of significant clinical problems in liver cancer and radiation therapy treatment planning. Specifically, we propose to provide computed decision support for liver cancer therapy treatment by enhancing the ability to detect and quantify small intensity and morphological changes in serial follow-up exams, referred to as interval exams. The utility of the ability to register the 3D data set obtained from a current exam with the 3D data set from an earlier exam, and then subtract the coregistered data sets to enhance differences has already been demonstrated for interval brain examinations[7-11]. Because the liver is a soft, malleable organ, the ability to rountinely apply such a process to liver cancer requires the use of automatic nonlinear registration techniques, i.e. warping. Because the conformation of the liver is severely dependent on patient position on the imaging exam table and degree of inspiration during breath holding, simple linear registration methods fail. We hypothesize that the method will be capable of detecting important early changes that would typically be unnoticed by experienced radiologists/oncologists. Such changes include regrowth of previously treated lesions, or the appearance of new metastatic lesions. Such techniques are useful for the spectrum of lesions encountered including single or multifocal lesions, as well as diffuse lesions. Phantom studies will be performed to help quantify differences and variances for the technique and readers. Studies on clinical data will assess the performance of the technique using real patient data. We additionally propose to develop a robust method of accurately outlining organs in patient data sets to assist organ definition for radiation therapy treatment planning requiring dose-volume histograms to objectively quantify and optimize the plan. Currently manual outlining of normal organs is the rate limiting process associated with patient throughput in many institutions. Such definition would not only speed patient throughput, but also make the quantitative planning technique more widely accessible.
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