Regularized Statistical Material Decomposition in Medical Imaging.

[摘要] In viewing underlying pathology with medical imaging, often specific materialcomponents contain most of the diagnostic information. Therefore, material componentseparation is desirable in many medical applications. Recent generations of MRIand X-ray CT systems can collect multiple measured data sets by changing data acquisitionparameters, e.g., pulse sequence timing parameters in MRI and X-ray tubevoltage in CT. These systems allow one to separate images of material components.In this thesis, we present novel image decomposition methods for MRI and X-rayCT applications. These methods use regularization and multiple data sets. We alsopropose iterative algorithms to minimize appropriate regularized least-squares costfunctions. In MR imaging, we investigated penalized-likelihood approaches that canjointly estimate water components, fat components, and field map. The methodslead to improved chemical components estimates by using regularization of the filedmap. In dual-energy CT reconstruction, we proposed a penalized weighted leastsquare method that separates two material density maps from fast kVp-switchedsinograms without any interpolation. We also developed a novel iterative algorithm that estimates material sinograms from raw DE CT data directly without using a logarithm that is sensitive to noise. Experiments on synthetic data and phantom data suggest that our methods improve the quality and accuracy of the estimated images compared to conventional methods for material separation.

[发布机构] University of Michigan

[效力级别] Regularized Method

[关键词] Material Decomposition;Regularized Method;Electrical Engineering;Engineering;Electrical Engineering: Systems