<> "The repository administrator has not yet configured an RDF license."^^ . <> . . "Accurate Low-Dose Iterative CT Reconstruction from\r\nFew Projections using Sparse and Non-Local Regularization Functions"^^ . "This dissertation aims at reducing the dose and the acquisition time in medical and in industrial\r\nComputed Tomography (CT).\r\nSince X-rays carry enough energy to free electrons from atoms, they are extremely harmful to human cells and therefore the dose in X-ray CT should be as low as possible. For industrial CT, short acquisition and reconstruction times decrease the availability time of the X-ray machine and therefore increase the sales, due to a higher throughput. As a matter of principle, there are three strategies to reduce the dose, defined as the product of the tube current and the pulse length of the CT system: (1) Lowering the X-ray exposure by reducing\r\nthe X-ray tube current; (2) lowering the pulse length of the industrial CT system which allows for\r\na shorter acquisition process as well and (3) acquiring less projection views per full rotation of the\r\nsource around the object, which enables both, a faster acquisition and reconstruction. However,\r\nall of these strategies have a strong negative impact on the resulting image quality, especially when\r\nthey are combined. (1) and (2) introduce additional noise and (3) leads to streaking artifacts in\r\nthe reconstructed images. Therefore, efficient reconstruction algorithms have to be found which\r\ncan compensate the resulting image quality degradation.\r\nThe X-ray model can be solved analytically by Filtered Backprojection (FBP) or iteratively by\r\nsolving (regularized) objective functions. Up to now, commercial scanners still employ analytical\r\nFBP due to its fast execution times. However, the aforementioned strategies of dose reduction\r\nare not suitable for this method: The images are heavily corrupted by noise and artifacts and are\r\ntherefore not suitable for medical inspection or industrial CT quality control. Total Variation\r\n(TV) is the current state of the art method for the regularization term of iterative algorithms in\r\nX-ray CT. It can remove the noise and the streaks in the images at the cost of over-smoothing of\r\nsmall-scaled image features and those of small intensity. Furthermore, the images suffer from a\r\nloss of contrast and spatial resolution and \"stair-casing\" artifacts are introduced in image regions\r\nwhich should be homogeneous.\r\nThis dissertation presents three new regularization functions for low-dose, under-sampled, iterative\r\nCT which successively improve the reconstruction results of the current state of the art techniques:\r\nFBP and Total Variation.\r\nThe first method is called the Anisotropic Total Variation (ATV). We propose a gradient re-definition so as to overcome TV's problem of over-smoothing fine structures. The re-definition is accomplished by multiplying the gradient in the definition of TV by an exponential function.\r\nWe include a parameter in this function and this parameter acts like a threshold of the noise and\r\ncontrols which structures (noise and prominent edges) to penalize during the reconstruction.\r\nThe second method focuses on the main drawbacks of TV: The production of stair-casing artifacts in regions which should be homogeneous and the over-smoothing of fine structures. To reduce the stair-casing effect of TV and at the same time to reconstruct high resolution images, we\r\ncombine first and second order derivatives and we create a new regularization function. The first\r\norder Anisotropic Total Variation can separate noise and prominent edges up to a certain noise\r\nmagnitude and the second order Total Variation better penalizes undesired edges than first order\r\nTV. The resulting method is called ATV+TV².The third method discusses a novel generalization of TV. It is called Generalized Anisotropic Total\r\nVariation (GATV). GATV uses a priori information about the Gradient Magnitude Distribution (GMD) of the underlying object for the reconstruction. By efficient parameterization, this method can separate noise and prominent image features and it can therefore overcome the problems of TV and reconstruct high quality CT images.\r\nWe reconstruct real patient data and digitally simulated phantom data. We evaluate the efficiency of our proposed regularization methods based on a large experiment with 560 measurements where different numbers of projections, noise levels and 10 different realizations of the noise random\r\nvariable were selected. We judge the results from a qualitative point of view by analyzing the\r\nreconstructed images in terms of edge sharpness and accuracy, image homogeneity and image\r\ndetails, like small structures and features. Furthermore, we apply quantitative measures to assess\r\nthe image quality: The Relative Root Mean Squared Error (RRMSE), the Contrast to Noise\r\nRatio (CNR), the Kullback-Leibler distance and a measure to rate the spatial resolution and the\r\nhomogeneity of the image.\r\nThe main findings of this dissertation indicate that all of the three methods successively improve\r\nthe visual impression of the reconstruction results in terms of preservation of small-scaled image\r\nfeatures and features of small intensity. Furthermore, they can improve the edge sharpness and\r\naccuracy, spatial resolution, image contrast and homogeneity and each method, ATV, ATV+TV²\r\nand GATV, thereby improves the reconstruction results of its preceding method.\r\nIn case of noise-free projections, GATV can accurately reconstruct digitally simulated data from\r\n20 projections and it can achieve a RRMSE which is up to 1770 times smaller than the RRMSE\r\nof TV. In case of noisy projections, all of the three methods can achieve an extreme dose reduction\r\nfactor of approximately 16 compared to the results of TV. Furthermore, at this reduction factor,\r\nATV, ATV+TV² and GATV can still lower the RRMSE by approximately 11%, 20% and 33%\r\ncompared to the results of TV, obtained from a high dose and many view setting.\r\nFrom previous publications (Xun Jia et al., Medical physics, 37:1757, 2010) we know that a 72 times dose reduction can be achieved for a TV\r\nregularized iterative reconstruction compared to FBP. Combining this information with the dose\r\nreduction potential of the proposed methods, ATV, ATV+TV² and GATV, reveals the potential\r\nto decrease the dose and acquisition time in CT by a factor of approximately three orders of\r\nmagnitude (1000), compared to conventional FBP."^^ . "2016" . . . . . . . "Maurice"^^ . "Debatin"^^ . "Maurice Debatin"^^ . . . . . . "Accurate Low-Dose Iterative CT Reconstruction from\r\nFew Projections using Sparse and Non-Local Regularization Functions (Compressed Archive)"^^ . . . "2016_Debatin_phd_thesis.zip"^^ . . "HTML Summary of #21527 \n\nAccurate Low-Dose Iterative CT Reconstruction from \nFew Projections using Sparse and Non-Local Regularization Functions\n\n" . "text/html" . . . "004 Informatik"@de . "004 Data processing Computer science"@en . . . "600 Technik, Medizin, angewandte Wissenschaften"@de . "600 Technology (Applied sciences)"@en . . . "670 Industrielle Fertigung"@de . "670 Manufacturing"@en . .