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Development of Efficient Dynamic Magnetic Resonance Imaging Methods with Application to Breast Cancer Detection and Diagnosis

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Final rept 1 Sep 94-31 Aug 97

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The goal of this predoctoral fellowship research project is to improve the temporal and spatial resolutions in dynamic contrast-enhanced magnetic resonance imaging of the breast. Specifically, we investigated the use of non-Fourier encoding for collecting the reduced encoding dynamic data sets. The conclusion from our study was that the current wavelet and SVD encoding methods do not achieve the desired goal of exploiting the desirable truncation properties of these basis functions and, at times, can introduce dangerous artifacts. For Fourier-encoded data, the Reduced-encoding Imaging by Generalized-series Reconstruction RIGR method was shown to be a better way to extrapolate the unmeasured dynamic data using a priori constraints than Fourier-keyhole. Next, we further optimized the basis functions of the generalized-series GS model used by the RIGR algorithm. The Two-reference RIGR TRIGR method resulted from suppressing background information in the GS basis functions, enabling them to better represent the regions of dynamic change. Explicit edge constraints derived from the reference image were then used with contrast information from the dynamic data to inject dynamic information into the GS basis functions. Finally, the problem of motion in dynamic imaging was addressed using a similarity norm to accurately detect the motion in spite of contrast changes and the low-resolution nature of the dynamic data.

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  • Medicine and Medical Research
  • Numerical Mathematics
  • Theoretical Mathematics
  • Nuclear Physics and Elementary Particle Physics

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