By Mohammad Dawood, Xiaoyi Jiang, Klaus Schäfers
Written by means of an interdisciplinary staff of doctors, machine scientists, physicists, engineers, and mathematicians, Correction concepts in Emission Tomography provides a variety of correction equipment utilized in emission tomography to generate and improve pictures. It discusses the innovations from a working laptop or computer technological know-how, arithmetic, and physics viewpoint.
The e-book supplies a finished evaluation of correction thoughts at assorted degrees of the information processing workflow. It covers nuclear medication imaging, hybrid emission tomography (PET-CT, SPECT-CT, PET-MRI, PET-ultrasound), and optical imaging (fluorescence molecular tomography). It illustrates simple ideas in addition to contemporary advances, resembling model-based iterative algorithms and 4D tools. an immense point of the e-book is on new and complex movement correction thoughts in puppy imaging. those recommendations permit high-resolution, fine quality photos, resulting in greater imaging research and image-based diagnostics.
Reflecting cutting-edge study, this quantity explores the variety of difficulties that ensue in emission tomography. It appears to be like at how the ensuing photographs are affected and provides sensible repayment easy methods to conquer the issues and increase the images.
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Extra resources for Correction techniques in emission tomography
Jacobs, and Martin J. van den Bent. Advanced MRI and PET imaging for assessment of treatment response in patients with gliomas. The Lancet Neurology, 9(9):906–20, 2010.  W. C. Eckelman, R. C. Reba, and G. J. Kelloff. Targeted imaging: an important biomarker for understanding disease progression in the era of personalized medicine. Drug Discovery Today, 13(17-18):748–759, 2008.  D. A. Hammoud, J. M. Hoffman, and M. G. Pomper. neuroimaging: from conventional to emerging techniques. 245(1):21–42, 2007.
Consequently, the filter function is set to zero away from a limit frequency Ω. This can be done using a sharp cutoff (ramp) filter (cf. Ram-Lak filter) or in a smooth way, resulting in a selection of filters with more or less comparable output. We thus arrive at the final formulation for the filtered backprojection formula fΩ (x) = (R∗ h)(x) h(θ, σ) = χ(σ)|σ|Rf (θ, σ) χ(σ) = 0 for |σ| > Ω where we choose χ(σ) = 1, |σ| ≤ Ω for the Ram–Lak filter. The implementation is obvious; simply use the same algorithm as before, but set the value of the Fourier transform to zero beyond Ω.
Analytical algorithms . . . . . . . . . . . . . . . . . . . . . . 1 Mathematical basis . . . . . . . . . . . . . . . . . . . 2 Filtered backprojection . . . . . . . . . . . . . . . . . 3 Implementation: Resolution and complexity . . . . . . 4 Implementation and rebinning . . . . . . . . . . . . . 1 2D Rebinning . . . . . . . . . . . . . . . . . 2 3D filtered backprojection . . . . . .