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dc.contributor.advisor Liu, Qing Huo en_US
dc.contributor.author Jian, Yuchuan en_US
dc.date.accessioned 2012-05-25T20:05:31Z
dc.date.available 2012-05-25T20:05:31Z
dc.date.issued 2012 en_US
dc.identifier.uri http://hdl.handle.net/10161/5397
dc.description Dissertation en_US
dc.description.abstract <p>Based on the compressed sensing theorem, we present the integrated software and hardware platform for developing a total-variation based image restoration algorithm by applying prior image information and free-form deformation fields for image guided therapy. The core algorithm we developed solves the image restoration problem for handling missing structures in one image set with prior information, and it enhances the quality of the image and the anatomical information of the volume of the on-board computed tomographic (CT) with limited-angle projections. Through the use of the algorithm, prior anatomical CT scans were used to provide additional information to help reduce radiation doses associated with the improved quality of the image volume produced by on-board Cone-Beam CT, thus reducing the total radiation doses that patients receive and removing distortion artifacts in 3D Digital Tomosynthesis (DTS) and 4D-DTS. The proposed restoration algorithm enables the enhanced resolution of temporal image and provides more anatomical information than conventional reconstructed images.</p><p>The performance of the algorithm was determined and evaluated by two built-in parameters in the algorithm, i.e., B-spline resolution and the regularization factor. These parameters can be adjusted to meet different requirements in different imaging applications. Adjustments also can determine the flexibility and accuracy during the restoration of images. Preliminary results have been generated to evaluate the image similarity and deformation effect for phantoms and real patient's case using shifting deformation window. We incorporated a graphics processing unit (GPU) and visualization interface into the calculation platform, as the acceleration tools for medical image processing and analysis. By combining the imaging algorithm with a GPU implementation, we can make the restoration calculation within a reasonable time to enable real-time on-board visualization, and the platform potentially can be applied to solve complicated, clinical-imaging algorithms.</p> en_US
dc.subject Electrical engineering en_US
dc.subject Compressive Sensing en_US
dc.subject Cone Beam Computed Tomography en_US
dc.subject Image Guided Therapy en_US
dc.subject Image Registration en_US
dc.subject Image Restoration en_US
dc.subject Parallel Computation en_US
dc.title Compressed Sensing Based Image Restoration Algorithm with Prior Information: Software and Hardware Implementations for Image Guided Therapy en_US
dc.type Dissertation en_US
dc.department Electrical and Computer Engineering en_US

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