Journal of Biomedical Engineering Research (대한의용생체공학회:의공학회지)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
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Penalized-Likelihood Image Reconstruction for Transmission Tomography Using Spline Regularizers

스플라인 정칙자를 사용한 투과 단층촬영을 위한 벌점우도 영상재구성

  • Jung, J.E. (Department of Electronic Engineering, Paichai University) ;
  • Lee, S.-J. (Department of Electronic Engineering, Paichai University)
  • Received : 2015.09.07
  • Accepted : 2015.10.15
  • Published : 2015.10.31
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Abstract

Recently, model-based iterative reconstruction (MBIR) has played an important role in transmission tomography by significantly improving the quality of reconstructed images for low-dose scans. MBIR is based on the penalized-likelihood (PL) approach, where the penalty term (also known as the regularizer) stabilizes the unstable likelihood term, thereby suppressing the noise. In this work we further improve MBIR by using a more expressive regularizer which can restore the underlying image more accurately. Here we used a spline regularizer derived from a linear combination of the two-dimensional splines with first- and second-order spatial derivatives and applied it to a non-quadratic convex penalty function. To derive a PL algorithm with the spline regularizer, we used a separable paraboloidal surrogates algorithm for convex optimization. The experimental results demonstrate that our regularization method improves reconstruction accuracy in terms of both regional percentage error and contrast recovery coefficient by restoring smooth edges as well as sharp edges more accurately.

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References

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