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Adapting medical imaging protocols to the individual patient has the potential to improve the information content and enhance the detection of subtle pathologies. We propose a framework for automated patient adaptive scanning in magnetic resonance imaging (MRI) based on a pseudo-real-time imaging pipeline. Using adaptive scanning, improved tissue contrast is demonstrated for an inversion recovery...
With recent advances in the field, magnetic resonance imaging (MRI) has become a powerful quantitative imaging modality for the study of neurological disorders. The quantitative power of MRI is significantly enhanced with multi-contrast and high-resolution techniques. However, those techniques generate large volumes of data which, combined with the sophisticated state-of-the-art image analysis methods,...
Scan-to-scan intensity variation, even with the same imaging modality, affects a number of intensity-based image processing methods such as feature map based segmentation and non-rigid registration techniques that minimize sum of squared differences (SSD). Current intensity standardization techniques based on either percentile alignment or polynomial mapping suffer from a number of limitations. We...
A fully symmetric nonlinear viscoelastic image registration method, under the demons paradigm is developed. The symmetric cost function includes mutual information (MI) as a similarity measure, regularization of the transformation, and inverse consistent constraint (ICC). Alternative strategy is used to minimize the divergent terms with different properties in the cost function to avoid the difficulties...
An algorithm for correcting the geometric distortions in the echo planar images and diffusion weighted images is described. In this method the geometric distortion in the echo planar imaging (EPI) data is corrected by non-rigid registration to conventional fast-spin echo (FSE) images. The registration is based on a variant of the Demon's algorithm [10] with consistency enforced by an improved bijectivity...
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