Noise in MRI (magnitude) data

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Background (Long Answer)

Standard imaging techniques acquire complex k-space data, then an image is calculated as the magnitude of the Fourier transform of the k-space data. The noise in the magnitude images [1] has a Rician distribution, based on the assumption that the noise on the real and imaginary channels is Gaussian. The probability density function for a Rice distribution is defined [2] as:

$P_{\mathrm{Rice}}(x) = \frac{x}{\sigma^2}\exp\left(-\frac{\mu^2+x^2}{2\sigma^2} \right) I_0\left(\frac{\mu x }{\sigma^2} \right)$

\label{eq:rician_pdf}

where $\sigma$ is the “standard deviation” of the function, $\mu$ is the mean, $x$ is the parameter, and $I_0\left(x\right)$ is a modified Bessel function of the first kind. The two interesting implications of the Rice
distribution is what happens as a function of $\mu/\sigma$ .

Rice distribution as a function of $\mu/\sigma$ . When $\mu/\sigma$ is zero, for example in the black area of an MRI image, then the noise is Rayleigh distributed. When $\mu/\sigma$ is large, for example in the object of an MRI image, then the noise approximates a Gaussian distribution.

Limiting case as $\mu -> 0$

As $\mu/\sigma \to 0$ , the Rician distribution becomes a Rayleigh distribution. The Bessel function $I_0(\mu) = 1$ when $\mu = 0$ and $\exp\left(-\frac{\mu^2+ x^2}{2 \sigma^2} \right) = \exp\left(-\frac{x^2}{2 \sigma^2}\right)$ when $\mu = 0$ . Therefore,

$P_{\mathrm{Rice}}(x) &=& \frac{x}{\sigma^2} \exp\left(-\frac{x^2}{2\sigma^2} \right) \\ &=& P_{\mathrm{Rayleigh}}(x)$

\label{eq:rayleigh_pdf}

Limiting case as $\mu$ is large

As $\mu/\sigma \to \infty$ , the Rician distribution becomes similar to a Gaussian distribution. The asymptotic expansion [3] of the Bessel function $I_0(x)$ when $x$ is large is:

$latex I_0(x) \sim \frac{e^x}{\sqrt{2 \pi x}} \left[ 1 + \frac{1}{8x} + \frac{1\cdot 9}{2!(8x)^2} + \frac{1\cdot 9 \cdot 25}{3!(8x)^3} + \cdots \right]$
\label{eq:bessel_expansion}

then, because $x \approx \mu$ the scaling factor in front of the exponential can be simplified leaving:

$P(x) & = & \frac{1}{\sqrt{2\pi \sigma^2}} \exp\left[-\frac{\left(x-\mu\right)^2}{2\sigma^2}\right] \\& = & P_{\mathrm{Gaussian}}(x)$

which is the probability function for Gaussian distribution.

All simulations, in the following chapters, used Rician noise. The Rician noise was created as $y_e(t_i) = \sqrt{ \left[y(t_i) + e_1 \right]^2 + e_2^2 }$ , where $y$ is the true signal, and $e_1$ and $e_2$ are random numbers from a Gaussian distribution with zero mean and standard deviation $\sigma$ . The standard deviation, $\sigma$ , for the Gaussian distribution was defined as $y(t_1) / {\rm SNR}$ .

References

1. R. M. Henkelman. Measurement of signal intensities in the presence of noise in MRI images. Med. Phys., 12(2):232–233, 1985 (↑)
2. E. W. Weisstein. Rice distribution. Eric Weisstein’s World of Mathematics, 2000. http://mathworld.wolfram.com/RiceDistribution.html. (↑)
3. James A. Roberts. http://people.eecs.ku.edu/~jroberts/private/Appendix A.pdf (↑)

Notes:

Noise in MRI (magnitude) data

Background (Long Answer)

Limiting case as $\mu -> 0$

Limiting case as $\mu$ is large

References

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Notes:

Noise in MRI (magnitude) data

Background (Long Answer)

Limiting case as

Limiting case as is large

References

Leave a Reply

What’s here…

Craig K Jones, PhD

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Limiting case as $\mu -> 0$

Limiting case as $\mu$ is large