Computing the Integral of a Gaussian with a Cosine Factor in DTI Analysis

In summary, the homework statement is that the integral of the gaussian is difficult to manipulate and requires knowledge of complex exponential and Cauchy's theorem.
  • #1
iamalexalright
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Homework Statement


Looking through this proof in my DTI(diffusion tensor imaging) book and they don't show the work on how to compute this integral (and I'll need to know it for a presentation):


[tex]\int cos(\gamma G\delta x) * e^{-x^{2}/4D\Delta}dx[/tex]


I haven't really gotten anywhere yet. I've looked at the proof of the integral of the gaussian but I don't know where/how to manipulate it with the cosine there.

I know the answer should be:


[tex]e^{-\gamma^{2}G^{2}\delta^{2}D\Delta}[/tex]
 
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  • #2
You can write the cosine as a complex exponential. You then have to complete the square in the exponential, the integral now is a Gaussian, but is along a path that is off the real axis. You can then use Cauchy's theorem to move the integration path back on the real axis.
 
  • #3
I'll write out (most of) my work because I think I might have done something wrong:

Integral = [tex]\int (1/2)(e^{i\gamma G\delta x} + e^{-i\gamma G\delta x})e^{-x^{2}/4D\Delta}dx =[/tex]

[tex](1/2) \int (e^{-x^{2}/4D\Delta + i\gamma G\delta x} + (e^{-x^{2}/4D\Delta - i\gamma G\delta x}) dx [/tex]

first term:
[tex]e^{(-1/4D\Delta)(x^{2} - 2iD\Delta \gamma G\delta x + 4i^{2}D^{2}\Delta^{2}\gamma^{2}G^{2}\delta^{2} - 4i^{2}D^{2}\Delta^{2}\gamma^{2}G^{2}\delta^{2})} = [/tex]

[tex]e^{(-1/4D\Delta)((x - 2\Delta Di\gamma G\delta)^{2} + 4i^{2}D^{2}\Delta^{2}\gamma^{2}G^{2}\delta^{2})} = [/tex]

[tex]e^{(-1/4D\Delta)(x - 2\Delta Di\gamma G\delta)^{2}}e^{D\Delta \gamma^{2}G^{2}\delta^{2}}[/tex]

Okay, that's the first, second is similar and we should end up with:

[tex](1/2)e^{D\Delta \gamma^{2}G^{2}\delta^{2}} \int e^{-(x - 2\Delta Di\gamma G\delta)^{2}} + e^{-(x + 2\Delta Di\gamma G\delta)^{2}} [/tex]

Now, you mention Cauchy's theorem but I don't exactly know how to use it.
 
  • #4
Nevermind, I got it.
 

1. What is the integral of a Gaussian function?

The integral of a Gaussian function is known as the error function, which is denoted by erf(x).
It is defined as the area under the curve of a Gaussian distribution from negative infinity to x.

2. How do you solve the integral of a Gaussian function?

To solve the integral of a Gaussian function, you can use techniques such as integration by parts or substitution.
Alternatively, you can look up the value of the error function in a table or use a calculator.

3. What is the significance of the integral of a Gaussian function in statistics?

The integral of a Gaussian function is important in statistics because it represents the cumulative probability of a normal distribution.
This means that the area under the curve of a Gaussian distribution from negative infinity to a certain value x represents the probability of a random variable taking on a value less than or equal to x.

4. Can the integral of a Gaussian function be evaluated numerically?

Yes, the integral of a Gaussian function can be evaluated numerically using numerical integration methods such as the trapezoidal rule or Simpson's rule.
These methods approximate the value of the integral by dividing the area under the curve into smaller trapezoids or curved sections, respectively.

5. What happens to the value of the integral of a Gaussian function as the standard deviation increases?

As the standard deviation increases, the value of the integral of a Gaussian function also increases.
This is because a larger standard deviation results in a wider and flatter curve, which means that more area is included under the curve and the integral value becomes larger.

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