Driven by technology development, the oncologist/radiologist is currently under assault by increasing quantities of higher resolution anatomical and functional imaging data. The new LightSpeed CT scanner by GE is but one typical example of such new acquisition devices. It is imperative that new image processing tools be developed to help specialists filter through the massive amounts of data in order to maintain or even improve their sensitivity and specificity in detecting neoplastic changes. We believe that this grant is a significant step in this important direction and represents the best efforts of a truly multidisciplinary team. Automatic 3D Registration for Enhanced Cancer Management is a proposal to support limited, preclinical testing of the ability of advanced image processing techniques to improve the management of cancer by routinely culling more and earlier information for the clinician from existing diagnostic modalities. This program project grant includes a)the use of fMRI for pre (neuro)surgical planning focused on patients with movement disorders associated with lesions near the motor strip who could never remain sufficiently motionless for current fMRI techniques, b) support for lesion change detection and quantification from repeated, interval CT and ultrasound exams in order to monitor and potentially modify therapy plans based on individual patient response, c) automation in defining organs in radiation therapy treatment planning using noncontrast CT data sets, and d) the development of a method using registered CT and PET for reducing false positive nodes called on CT-only chest exams.
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| Nyati, Shyam; Young, Grant; Ross, Brian Dale et al. (2017) Quantitative and Dynamic Imaging of ATM Kinase Activity. Methods Mol Biol 1596:131-145 |
| Nyati, Shyam; Young, Grant; Ross, Brian Dale et al. (2017) Quantitative and Dynamic Imaging of ATM Kinase Activity by Bioluminescence Imaging. Methods Mol Biol 1599:97-111 |
| Nataraj, Gopal; Nielsen, Jon-Fredrik; Fessler, Jeffrey A (2017) Optimizing MR Scan Design for Model-Based ${T}_{1}$ , ${T}_{2}$ Estimation From Steady-State Sequences. IEEE Trans Med Imaging 36:467-477 |
| Jintamethasawat, Rungroj; Zhang, Xiaohui; Carson, Paul L et al. (2017) Acoustic beam anomalies in automated breast imaging. J Med Imaging (Bellingham) 4:045001 |
| Keith, Lauren; Ross, Brian D; Galbán, Craig J et al. (2016) Semiautomated Workflow for Clinically Streamlined Glioma Parametric Response Mapping. Tomography 2:267-275 |
| Berisha, Visar; Wisler, Alan; Hero, Alfred O et al. (2016) Empirically Estimable Classification Bounds Based on a Nonparametric Divergence Measure. IEEE Trans Signal Process 64:580-591 |
| Hero, Alfred O; Rajaratnam, Bala (2016) Foundational Principles for Large-Scale Inference: Illustrations Through Correlation Mining. Proc IEEE Inst Electr Electron Eng 104:93-110 |
| Nataraj, Gopal; Nielsen, Jon-Fredrick; Fessler, Jeffrey (2016) Optimizing MR Scan Design for Model-Based T1, T2 Estimation from Steady-State Sequences. IEEE Trans Med Imaging : |
| Larson, Eric D; Lee, Won-Mean; Roubidoux, Marilyn A et al. (2016) Automated Breast Ultrasound: Dual-Sided Compared with Single-Sided Imaging. Ultrasound Med Biol 42:2072-82 |
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