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[Image reconstruction and quantification in emission tomography].

Michael Mix1, Wolfgang Eschner

  • 1Abteilung Nuklearmedizin, Radiologische Universitätsklinik Freiburg, Albert-Ludwigs-Universität Freiburg im Breisgau. Michael.Mix@uniklinik-freiburg.de

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Summary
This summary is machine-generated.

Statistical image reconstruction has improved radionuclide tomographic imaging. Faster computers now enable widespread clinical use of iterative algorithms like maximum likelihood and maximum a posteriori for enhanced emission tomography.

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Area of Science:

  • Medical Imaging
  • Nuclear Medicine
  • Computational Science

Context:

  • Iterative algorithms for statistical image reconstruction have existed for decades.
  • Recent advancements in computing power have enabled their clinical application.

Purpose:

  • To provide an overview of data acquisition and iterative reconstruction in emission tomography.
  • To discuss maximum likelihood and maximum a posteriori algorithms.
  • To cover quantification prerequisites and corrections in positron emission tomography.

Summary:

  • This article reviews statistical image reconstruction techniques in radionuclide tomography.
  • It details iterative algorithms, including maximum likelihood and maximum a posteriori.
  • Quantification in positron emission tomography is also addressed.

Impact:

  • Facilitates understanding of advanced image reconstruction methods in nuclear medicine.
  • Highlights the role of computational power in clinical imaging advancements.
  • Sets the stage for future research driven by improved technical equipment.