A Selection of Distributions and Their Fourier Transforms with Applications in Magnetic Resonance Imaging
Fuente:
arXiv
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908417273102336 |
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| author | Tang, Kaibo |
| author_facet | Tang, Kaibo |
| contents | This note presents a rigorous introduction to a selection of distributions along with their Fourier transforms, which are commonly encountered in signal processing and, in particular, magnetic resonance imaging (MRI). In contrast to many textbooks on the principles of MRI, which place more emphasis on the signal processing aspect, this note will take a more mathematical approach. In particular, we will make explicit the underlying topological space of interest and clarify the exact sense in which these distributions and their Fourier transforms are defined. Key results presented in this note involve the Poisson summation formula and the Fourier transform of a Gaussian function via an ordinary differential equation (ODE) argument, etc. Although the readers are expected to have prior exposure to functional analysis and distribution theory, this note is intended to be self-contained. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_18638 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Selection of Distributions and Their Fourier Transforms with Applications in Magnetic Resonance Imaging Tang, Kaibo Functional Analysis Image and Video Processing Signal Processing Medical Physics This note presents a rigorous introduction to a selection of distributions along with their Fourier transforms, which are commonly encountered in signal processing and, in particular, magnetic resonance imaging (MRI). In contrast to many textbooks on the principles of MRI, which place more emphasis on the signal processing aspect, this note will take a more mathematical approach. In particular, we will make explicit the underlying topological space of interest and clarify the exact sense in which these distributions and their Fourier transforms are defined. Key results presented in this note involve the Poisson summation formula and the Fourier transform of a Gaussian function via an ordinary differential equation (ODE) argument, etc. Although the readers are expected to have prior exposure to functional analysis and distribution theory, this note is intended to be self-contained. |
| title | A Selection of Distributions and Their Fourier Transforms with Applications in Magnetic Resonance Imaging |
| topic | Functional Analysis Image and Video Processing Signal Processing Medical Physics |
| url | https://arxiv.org/abs/2506.18638 |