Contribution to a book FZJ-2018-07766

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CHAPTER 11. MR-based Corrections for Quantitative PET Image

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2018
Royal Society of Chemistry Cambridge

Hybrid MR-PET Imaging / Shah, N Jon (Editor) Cambridge : Royal Society of Chemistry, New Developments in NMR 229 - 258 () [10.1039/9781788013062-00229]

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Abstract: In this chapter, we address the correction of positron emission tomography (PET) images using magnetic resonance (MR) information, namely attenuation correction (AC), partial volume correction (PVC) and the arterial input function (AIF). The use of quantitative brain PET imaging is a basic prerequisite in research as well as in the clinical PET environment. Consequently, a reliable attenuation correction method for the PET data is imperative. Here, we describe the attenuation process and provide an outline of several attenuation correction methods, all based on simultaneously acquired MR sequences. When absolute quantification of, e.g., blood flow and metabolism in small anatomical structures is required, the spatial resolution of PET scanners can be a limiting factor. The partial volume effect resulting from the limited spatial resolution is explained and correction methods are presented in this chapter. When the visualisation of kinetic processes, such as cerebral glucose consumption or cerebral blood flow, is required, fully quantitative data can only be acquired with knowledge of the arterial input function. This information can be obtained using MR images, which make it possible to extract exactly the information required. The ways to obtain such a function, the methods to use it and the related uncertainties are also discussed in this chapter.


Contributing Institute(s):
  1. Physik der Medizinischen Bildgebung (INM-4)
  2. Jara-Institut Quantum Information (INM-11)
  3. JARA-BRAIN (JARA-BRAIN)
Research Program(s):
  1. 573 - Neuroimaging (POF3-573) (POF3-573)

Appears in the scientific report 2019
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Hybrid MR-PET Imaging: Systems, Methods and Applications
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 Record created 2018-12-20, last modified 2021-01-30


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