I think the answer is in the DICOM dictionary:
(0018,9064) k-space Filtering KSpaceFiltering CS 1
This corresponds to what I am reading on the metadata transmitted (no identifiers):
44 | 0018 | 9064 | CS | RIESZ
So is it a filter in k-space (i.e. frequency space) to denoise.
More support now that the keywords are at hand. From Philips:
k-space Filtering 0018,9064 CS ANAP AUTO Applied values: COSINE,
COSINE_SQUARED, FERMI,
GAUSSIAN, HAMMING, HANNING,
LORENTZIAN, LRNTZ_GSS_TRNSFM,
NONE, RIESZ, TUKEY,
USER_DEFINED
And finally wrapping it up (from here):
For “in vivo” experiments, the MRI noise is composed of
physiological noise from the sample and electronic noise of thermal
origin from the conductors in the MR equipment (white noise). The
statistics of the white noise are spatially invariant; however, the
Fourier decomposition of the physiological noise typically peaks at
low temporal frequencies (< 1/l0 sec) (Solo et al., 1997). While the
level of white noise can be lowered by improving the hardware, such as
by advancing the transmit-receive coil, the physiological noise, which
rises with increasing signal intensity, can be reduced by increasing
the spatial resolution (Kruger et al., 2001; Triantafyllou et al.,
2005). Because both kinds of noise degrade the quality of the MRI
datasets, affecting the interpretation of the results, low-pass
filters are frequently used to eliminate its high-spatial-frequency
components. Generally, denoising algorithms assume that noise is an
additive Gaussian parameter, because the noise contribution that arise
from the scanner’s electronics are additive for each of the real and
imaginary parts of the k-space data, uncorrelated and characterized by
a zero-mean Gaussian probability-density function (Hoult and
Lauterbur, 1979).
The Fermi filter, normally employed in GE scanners (Friedman et al.,
2006; Lowe and Sorenson, 1997), and the Hamming filter (Hamming, 1982;
Holland et al., 2001; Lowe and Sorenson, 1997) are commonly used for
smoothing of fMRI data. These filters usually ensure that the
low-frequency data remains intact, but attenuate the high frequency
components of the k-space signal. Since the power of the MRI spectrum
is greatest at the center of k-space, low-pass filters predominantly
eliminate noise information.
These filters seem to be options in the Philips DICOM dictionary above, although it is the Riecz filter that they happen to use in their magnets (?).
In conclusion, it is a filter operating in k-space to remove salt-and-pepper noise (SPN). From here:
Salt-and-pepper noise (SPN) is a type of impulse noise, which mainly occurs during the process of acquisition and transmission. In this type of noise, some pixels of a digital image have a maximum or minimum value.
Adaptive Riesz mean filter
In this section, firstly, we present two essential definitions, i.e. of pixel similarity and pixel similarity-based Riesz mean of a k-approximate matrix. Here, the pixel similarity is a similarity function used to calculate the value of the similarity between two pixels. The function produces a value close to 1, if the pixels are close. In the summability theory, the Riesz mean is widely used to converge a non-converging sequence via a sequence of non-negative real numbers which are not all zero. In the present study, Riesz-mean has been exploited to calculate the new value of the centre pixel of a k-approximate matrix by using the values of pixel similarity between the centre pixel and the other pixels therein.
Let A be an IM (Image Matrix). Then, the value
$$\mathrm{ps}\left({a}_{ij},{a}_{st}\right):= {\left(\frac{1}{1+\left|i-s\right|+\left|j-t\right|}\right)}^2$$
is called pixel similarity between ${a}_{ij}$ and ${a}_{st}$.
Let $A$ be an NIM (noise image matrix). Then the value
$$Rm\left({A}_{ij}^k\right):= \frac{\sum \limits_{\left(s,t\right)\in {I}_{ij}^k}\mathrm{ps}\left({a}_{st},{a}_{\left(k+1\right)\left(k+1\right)}\right){a}_{st}}{\sum \limits_{\left(s,t\right)\in {I}_{ij}^k}\mathrm{ps}\left({a}_{st},{a}_{\left(k+1\right)\left(k+1\right)}\right)}$$
is called Riesz mean of ${A}_{ij}^k.$ Here ${I}_{ij}^k:= \left\{\left(s,t\right):\kern0.5em {a}_{st}\ is\ a\ regular\ entry\ of\ {A}_{ij}^k\right\}.$
> Secondly, we discuss Adaptive Riesz Mean Filter (ARmF), a new filter. Moreover, it avails of the concept of the pixel similarity-based Riesz mean different from the classical mean/Cesàro mean or medians to calculate the new value of the centre pixel of a k-approximate matrix. The reason for we prefer the Riesz mean based on the pixel similarity is when an SPN removal comes into question it performs better than other different similarity/distance functions, such as p-norm, Euclid, and Hamming.
