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Projection-slice theorem

Projection-slice theorem is a mathematics topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Projection-slice theorem rather than just read about it. In short: In mathematics, the projection-slice theorem, central slice theorem or Fourier slice theorem in two dimensions states that the results of the following two calculations are equal: Take a two-dimensional function f(r), project (e.g. using the Radon transform) it onto a (one-dimensional) line, and do a Fourier transform of that projection. Take that same function, but do a two-dimensional Fourier transform first, and…

Projection-slice theorem — main illustration
Projection-slice theorem — illustration

Key takeaways

  • Projection-slice theorem belongs to mathematics; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Projection-slice theorem to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Projection-slice theorem from memory before moving on to harder problems.

Reference excerpt

In mathematics, the projection-slice theorem, central slice theorem or Fourier slice theorem in two dimensions states that the results of the following two calculations are equal:

Take a two-dimensional function f(r), project (e.g. using the Radon transform) it onto a (one-dimensional) line, and do a Fourier transform of that projection. Take that same function, but do a two-dimensional Fourier transform first, and then slice the function through its origin, parallel to the projection line. In operator terms, if

F1 and F2 are the 1- and 2-dimensional Fourier transform operators mentioned above, P1 is the projection operator (which projects a 2-D function onto a 1-D line), S1 is a slice operator (which extracts a 1-D central slice from a function), then

F 1 P 1 = S 1 F 2 . {\displaystyle F_{1}P_{1}=S_{1}F_{2}.}

This idea can be extended to higher dimensions. This theorem is used, for example, in the analysis of medical CT scans where a "projection" is an x-ray image of an internal organ. The Fourier transforms of these images are seen to be slices through the Fourier transform of the 3-dimensional density of the internal organ, and these slices can be interpolated to build up a complete Fourier transform of that density. The inverse Fourier transform is then used to recover the 3-dimensional density of the object. This technique was first derived by Ronald N. Bracewell in 1956 for a radio-astronomy problem.

The projection-slice theorem in N dimensions In N dimensions, the projection-slice theorem states that the Fourier transform of the projection of an N-dimensional function f(r) onto an m-dimensional linear submanifold is equal to an m-dimensional slice of the N-dimensional Fourier transform of that function consisting of an m-dimensional linear submanifold through the origin in the Fourier space which is parallel to the projection submanifold. In operator terms:

F m P m = S m F N . {\displaystyle F_{m}P_{m}=S_{m}F_{N}.\,}

The generalized Fourier-slice theorem In addition to generalizing to N dimensions, the projection-slice theorem can be further generalized with an arbitrary change of basis. For convenience of notation, we consider the change of basis to be represented as B, an N-by-N invertible matrix operating on N-dimensional column vectors. Then the generalized Fourier-slice theorem can be stated as

F m P m B = S m B − T | B − T | F N {\displaystyle F_{m}P_{m}B=S_{m}{\frac {B^{-T}}{|B^{-T}|}}F_{N}}

where B − T = ( B − 1 ) T {\displaystyle B^{-T}=(B^{-1})^{T}} is the transpose of the inverse of the change of basis transform.

Proof in two dimensions

The projection-slice theorem is easily proven for the case of two dimensions. Without loss of generality, we can take the projection line to be the x-axis. There is no loss of generality because if we use a shifted and rotated line, the law still applies. Using a shifted line (in y) gives the same projection and therefore the same 1D Fourier transform results. The rotated function is the Fourier pair of the rotated Fourier transform, for which the theorem again holds. If f(x, y) is a two-dimensional function, then the projection of f(x, y) onto the x axis is p(x) where

p ( x ) = ∫ − ∞ ∞ f ( x , y ) d y . {\displaystyle p(x)=\int _{-\infty }^{\infty }f(x,y)\,dy.}

The Fourier transform of f ( x , y ) {\displaystyle f(x,y)} is

F ( k x , k y ) = ∫ − ∞ ∞ ∫ − ∞ ∞ f ( x , y ) e − 2 π i ( x k x + y k y ) d x d y . {\displaystyle F(k_{x},k_{y})=\int _{-\infty }^{\infty }\int _{-\infty }^{\infty }f(x,y)\,e^{-2\pi i(xk_{x}+yk_{y})}\,dxdy.}

The slice is then s ( k x ) {\displaystyle s(k_{x})}

… excerpt ends here. Continue reading the full article.

Illustrations

Projection-slice theorem: Fourier slice theorem
Fourier slice theorem
Projection-slice theorem: A graphical illustration of the projection slice theorem in two dimensions. f(r) and F(k) are 2-dimensional Fourier transform pairs. The projection of f(r) onto the x-axis is the integral of f(r) along lines of sight parallel to the y-axis and is labelled p(x). The slice through F(k) is on the kx axis, which is parallel to the x axis and labelled s(kx). The projection-slice theorem states that p(x) and s(kx) are 1-dimensional Fourier transform pairs.
A graphical illustration of the projection slice theorem in two dimensions. f(r) and F(k) are 2-dimensional Fourier transform pairs. The projection of f(r) onto the x-axis is the integral of f(r) along lines of sight parallel to the y-axis and is labelled p(x). The slice through F(k) is on the kx axis, which is parallel to the x axis and labelled s(kx). The projection-slice theorem states that p(x) and s(kx) are 1-dimensional Fourier transform pairs.

Worked examples

Example 1 — a first encounter with Projection-slice theorem

Start with the simplest possible case. Write down what Projection-slice theorem claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In mathematics, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Projection-slice theorem before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Projection-slice theorem ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Projection-slice theorem

In research
Projection-slice theorem appears in mathematics research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Projection-slice theorem in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Projection-slice theorem is common in secondary-school and first-year university syllabi. It links to neighbouring topics Image processing, Integral transforms, Theorems in Fourier analysis, so understanding it makes those chapters shorter.
In everyday life
Look for Projection-slice theorem outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Projection-slice theorem in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Projection-slice theorem means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Projection-slice theorem out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Projection-slice theorem in simple terms?

In mathematics, the projection-slice theorem, central slice theorem or Fourier slice theorem in two dimensions states that the results of the following two calculations are equal: Take a two-dimensional function f(r), project (e.g. using the Radon transform) it onto a (one-dimensional) line, and do…

Why does Projection-slice theorem matter?

Because it connects several mathematics ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Projection-slice theorem?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Projection-slice theorem.

Tags

  • Image processing
  • Integral transforms
  • Theorems in Fourier analysis

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