Image quality.html
Image quality dependence on image processing software in computed radiography
Lourens J Strauss, BMedSc (Hons) (Medical Physics)
William I D Rae, MB BCh, PhD (Medical Physics)
Department of Medical Physics, University of the Free State, Bloemfontein
Corresponding author: L Strauss (lourens.strauss@gmail.com)
Background.
Image post-processing gives computed radiography (CR) a considerable
advantage over film-screen systems. After digitisation of information
from CR plates, data are routinely processed using
manufacturer-specific software. Agfa CR readers use MUSICA software,
and an upgrade with significantly different image appearance was
recently released: MUSICA2 .
Aim.
This
study quantitatively compares the image quality of images acquired
without post-processing (flatfield) with images processed using these
two software packages.
Methods.
Four aspects of image quality were evaluated. An aluminium step-wedge
was imaged using constant mA at tube voltages varying from 40 to 117
kV. Signal-to-noise ratios (SNRs) and contrast-to-noise ratios (CNRs)
were calculated from all steps. Contrast variation with object size was
evaluated with visual assessment of images of a Perspex contrast-detail
phantom, and an image quality figure (IQF) was calculated. Resolution
was assessed using modulation transfer functions (MTFs).
Results. SNRs for MUSICA2 were generally higher than the other two methods. The CNRs were comparable between the two software versions, although MUSICA2
had slightly higher values at lower kV. The flatfield CNR values were
better than those for the processed images. All images showed a
decrease in CNRs with tube voltage. The contrast-detail measurements
showed that both MUSICA programmes improved the contrast of smaller
objects. MUSICA2 was found to give the lowest (best) IQF; MTF measurements confirmed this, with values at 3.5 lp/mm of 10% for MUSICA2 , 8% for MUSICA and 5% for flatfield.
Conclusion. Both MUSICA software packages produced images with better contrast resolution than unprocessed images. MUSICA2 has slightly improved image quality than MUSICA.
S Afr J Rad 2012;16(2):44-48.
Introduction
In radiology, image quality is a measure of the effectiveness with
which clinical diagnoses may be made. It is usually evaluated on the
basis of three measures of imaging performance, namely: spatial
resolution, contrast, and noise.1
Other factors considered are the effect of patient dose on the image
quality, as well as the occurrence of artefacts. Since the introduction
of digital technology to radiology, many additional benefits have been
realised. Probably the most important of these has been the ability to
post-process and manipulate images. To readily obtain the most
information from an image, manufacturers have developed optimised
software algorithms to prepare images for soft copy display and
reporting. The software has specific settings and filters which are
used to achieve optimal images in an attempt to make the most accurate
diagnosis. These settings are applied as appropriate for the specific
views and anatomy being imaged, and include edge enhancement, contrast
enhancement, excess contrast reduction, noise reduction, etc. Making
accurate and efficient diagnoses from radiological images requires good
image contrast and sufficient spatial resolution at acceptable noise
levels, which is what manufacturers are striving to achieve.2
The software package developed by Agfa and used in Agfa CR readers is called MUSICA; an upgrade was recently released: MUSICA2 .3
,
4
The acronym stands for Multi Scale Image Contrast Amplification, and
the algorithm is essentially a multi-scale transform of the image data
into a stack of detail layers. This is done in MUSICA using a Laplacian
pyramid decomposition.5
Contrast can then be improved in each sub-band of spatial frequencies.
Another important function of the processing software is to extract the
appropriate signal sub-range through a process of signal normalisation.
The unprocessed data captured by the CR plates, which are typically in
the range of 211 gray levels, are reduced to only 28 gray levels for
the observer to see most of the relevant image features at the same
time. A detailed discussion of the image processing involved is
presented by Vuylsteke et al.
5
Image quality evaluation studies of CR systems typically use metrics
such as contrast-to-noise ratios (CNRs), limiting spatial resolution,
the modulation transfer function (MTF), noise power spectrum (NPS) and
detective quantum efficiency (DQE). However, when evaluating only a
part of the imaging chain (e.g. assessing the processing software),
these metrics are not all relevant. Aspects which can be readily
evaluated are signal-to-noise ratios (SNRs), contrast-to-noise ratios
(CNRs), object visibility with decreasing size and contrast, and
spatial resolution response using MTFs.
The signal-to-noise ratio within a specified region of interest (ROI) is defined by the following equation:1
(1)
where N is the average pixel value and σ the standard deviation (noise) over the pixel values within the ROI.
Likewise, for the specified ROI, the contrast-to-noise ratio is given by equation 2:
(2)
where A and B are the average pixel values within the ROIs on adjacent steps on the image of the aluminium step wedge.
Object visibility with decreasing size and contrast can be evaluated
qualitatively and/or using an image quality figure (IQF) score
(visually assessed) specific for the in-house manufactured phantom.
This is given by equation 3:6
(3)
where D
i,min the threshold diameter in the i’th contrast column for columns, and C
i is the depth of the hole generating the contrast in that column. Therefore, a low IQF represents better image quality.
The modulation transfer function of a system gives a very thorough
description of its resolving capabilities, showing the fraction of an
object’s contrast recorded by the system as a function of the
object’s size (spatial frequency).1
It is calculated mathematically from the Fourier transform of the line
spread function (LSF), which is the receptor’s response to a
thin, high contrast line. The Fourier transform of this LSF shows the
system’s effectiveness in transferring the different frequency
components in an object into the image of that object.
The aim of this study was to quantitatively compare the image
quality of images acquired without post-processing (flatfield) with
images processed with Agfa’s MUSICA and MUSICA2 software packages.
Materials and method
Data processing was done on images in DICOM format, using Matlab.7
All imaging was done on a standard X-ray machine at a fixed SSD of 100
cm. Owing to the number of images acquired using low kV settings, the
CR plates were placed on the bed to prevent the appearance of the ion
chambers on the images. No scatter grid was present during imaging.
Each image was repeated on three separate CR-plates of 24 x 30 cm and
read out on an Agfa CR 85-X digitiser using the three different
post-processing software algorithms.
An aluminium step-wedge placed on top of a 2.5 cm Perspex sheet was
imaged at tube voltages of 40, 55, 70, 85, 102 and 117 kV. The mA for
each kV setting was determined using the automatic exposure control
(AEC) of the X-ray unit. These exposure parameters were recorded and
used as the standard while acquiring images for all three processing
algorithms.
The SNRs were calculated for all of the steps using equation 1. The
value for N and σ were obtained from a region-of-interest (ROI)
of 100x40 pixels on each step. The ROI covered 50% of the area of a
step, and was drawn in appropriate areas of approximately uniform
signal strength in which artefacts were not noted.
The CNRs were calculated
for all contrast steps, in the same ROIs as for the SNR calculation,
from equation 2. The value of σ in this case was obtained from
the thicker step, which is the step with lowest signal strength (B in
equation 2).
Object visibility with decreasing size and decreasing contrast
was evaluated with visual assessment of images of a Perspex
contrast-detail phantom placed on a 2.5 cm thick Perspex sheet. The
phantom was made in-house by drilling a square array of small holes
into the surface of a Perspex block. The holes varied in depth
(creating variable low contrast spots) in the one direction, and in
diameter in the other. Each contrast row had a centre and randomly
positioned off-centre hole of each size. The contrast steps are
produced by varying hole depths from 0.70 to 2.5 mm (±0.05 mm),
and the hole diameters ranged from 0.40 to 2.30 mm (±0.05 mm)
(verified with a micro meter gauge).
The phantom was imaged at 40 kV using the small focal spot size. The
images were scored on the 3 megapixel screen used for clinical
evaluation. The number of centre circles of which the off-centre circle
was also visible in each contrast row (i.e. the smallest object still
visible for a certain contrast) was determined by three different
observers. Observers were allowed to adjust the window level, window
width and zoom settings for best object visibility. The average score
from the three observers were used to calculate IQFs for each
processing algorithm using equation 3.
Resolution was assessed using MTFs, calculated from images acquired
at 40 kV using the small focal spot size. An APG phantom was used8
that had small metal plates, rotated 1°, specifically for this
purpose (Fig. 1). Several profiles were drawn across the edge of an MTF
plate, each 1 pixel wide. The number of profiles needed for a 1 pixel
shift in the edge owing to the angulation was determined from pixel
values.
A method to generate additional data points across the plate edge was used by the designers of the APG phantom,9
and is explained in the exaggerated illustration in Fig. 4. Each
profile drawn provides a point on the edge with a slightly different
pixel value. The displacement needed to align the pixel values with the
real edge location was calculated using basic trigonometry, and the
data sets for each profile were then shifted by this displacement. The
equation is as follows:
(4)
where α is the tilt angle of the plates (1°). This provides multiple data points across the edge profile.
The resulting curve is called the edge spread function (ESF),
which is then smoothed and differentiated to obtain the line spread
function (LSF), and the Fourier transform of the LSF was taken to
obtain the MTF.
Results
The SNRs on every step for all three processing methods at all
energies were calculated, and are fairly similar. The results for
energy of 70 kV are shown in Fig. 2.
The average CNRs between steps 1 and 4 were calculated and are shown
in Fig. 3. Average CNRs were used to reduce errors introduced by
artefacts (dust, CR defects). The specific steps used are the only
steps clearly separable at all kVs.
The contrast-detail images were scored by the three observers, and
the results show that both MUSICAs allow better visualisation of small
objects for all contrast conditions. The scoring of the observers was
very similar. To quantify overall contrast-detail capabilities, a
hyperbolic fit of the form as shown in equation 5 was used. A fit
parameter (h, x’, y’) fit was done on the data using the Solver Add-In in Excel:
(5)
The fitted curves are shown in Fig. 4. The IQFs calculated for the three processing algorithms are given in Table 1.
Profiles were drawn across the edge of the centre
bottom MTF plate, and the positional displacements calculated using
equation 4. A smoothing function in Matlab (curve fitting tool; Loess,
span=0.2) was applied to obtain the ESF, which was differentiated to
get the LSF and the Fourier transform applied to calculate the MTF. The
MTFs were calculated for the same MTF plate in all three images in the
x- and y-axes. The x-axis is perpendicular to the cathode-anode
direction of the X-ray tube. The MTFs calculated for the three
algorithms are shown in Fig. 5.
Discussion
A number of observations were made from the SNR graphs. Firstly, the
flattening off of the curve over the thick part of the wedge (step 20)
is where the photons cannot penetrate the wedge, and therefore this
effect diminishes at higher kV. Secondly, some outliers are seen (e.g.
Fig. 2, step 1 MI), which are probably caused by artefacts (scratches
on CR plate) in the image causing the average signal to be lower than
it really is, and the standard deviation to be higher. Consequently,
the ratio is reduced. The overall assessment over all energies shows
MUSICA2 to be somewhat better than the other two methods regarding the SNR.
The CNRs are lower for the processed images than the
flatfield, mainly owing to increased noise during the processing. There
is also a downward trend as the energy increases, which is thought to
be mainly due to the decrease in contrast caused by increasing the kV,
as photons become more penetrating. The noise also increases slightly,
but the contrast has a bigger influence. For the higher energies, the
CNRs are fairly constant, and the unprocessed images again show a
higher CNR than the processed images, possibly owing to the noise
increase in the processed images. MUSICA2 shows slightly higher CNR at lower kV, while at higher kV no significant difference is seen between the methods.
The benefit of post-processing in visualising small objects
over all contrast levels can be seen clearly from the contrast-detail
graph (Fig. 4). The IQFs as shown in Table 1 indicate that MUSICA2
has slightly better image quality. Both processing methods show an
improved ability to see low-contrast objects compared with the
unprocessed image. Quantitatively this translates to objects of a size
10% smaller than with flatfield imaging being visible at all contrast
levels using either of the MUSICAs. This is not an improvement in the
spatial resolution capabilities of the system, but simply better
visualisation of small objects owing to manipulation of the pixel
values to enhance the contrast between the edges of small objects and
their surrounding pixels. In other words, the transfer of frequencies
is changed, as shown in the MTFs section. This is effectively selective
signal amplification.
Both processing software packages show an increased transfer at
almost all frequencies, the images therefore appearing visually sharper
and overall resolution is improved (at sufficient contrast). The
ability to see small objects better can be expected since the transfer
of higher frequency information is better than for the flatfield image.
The deviation from the trend in the y-axis of MUSICA2
at high frequencies is thought to be due to some high-frequency noise
still present in the LSF, which gives a false impression of having
higher MTF values.
The image shown in Fig. 6 shows a problem faced in automated
software. This uniform black artefact area was created by the
post-processing software after encountering a perfectly straight edge
throughout the length of the image.
An artefact commonly seen with post-processing is edge
enhancement. This can be seen clearly in Fig. 7 (over-enhanced by
window level/width adjustment), the square on the right illustrating
how the area away from any edge is smoothed out while the edge is
sharpened and so becomes noisier.
Conclusion
Both MUSICA software packages produced images with better
contrast resolution than unprocessed images. The quantitative
measurements indicate that MUSICA2
delivers marginally improved image quality over MUSICA. Both MUSICA
packages show improved spatial resolution, which is the result of
better visualisation of small objects owing to software manipulation of
the pixel values. The MTF calculation might also include some noise,
which does not represent real increased transfer of frequencies, and
illustrates one of the complications faced in calculating MTFs. MUSICA2 has also shown slightly improved SNRs and CNRs at lower kV; however, the images still appear more noisy.
It is also important to keep in mind that this study aimed at
quantitative analysis of image quality differences between the software
packages to provide an unambiguous comparison. To evaluate the system
clinically, further investigation is necessary into all factors,
including clinical image assessment by radiology experts, practical
application aspects for the radiographers, cost, etc.
1. Bushberg J, Seibert J, Leidholt E, Boone J. The Essential Physics of
Medical Imaging, 2nd ed. Philadelphia: Lippincott Williams &
Wilkins, 2002;255-287.
1. Bushberg J, Seibert J, Leidholt E, Boone J. The Essential Physics of
Medical Imaging, 2nd ed. Philadelphia: Lippincott Williams &
Wilkins, 2002;255-287.
2. International Atomic Energy Agency (IAEA). Radiation Protection in
Diagnostic and Interventional Radiology. http://www.iaea.org (accessed
3 December 2010).
2. International Atomic Energy Agency (IAEA). Radiation Protection in
Diagnostic and Interventional Radiology. http://www.iaea.org (accessed
3 December 2010).
3. Vuylsteke P, Schoeters E. Multiscale image contrast amplification (MUSICA). Proc SPIE 1994;167:551-560.
3. Vuylsteke P, Schoeters E. Multiscale image contrast amplification (MUSICA). Proc SPIE 1994;167:551-560.
4. Schaetzing R. Agfa’s MUSICA2, Taking Image Processing to the Next Level. Mortsel, Belgium: Agfa Healthcare, 2007.
4. Schaetzing R. Agfa’s MUSICA2, Taking Image Processing to the Next Level. Mortsel, Belgium: Agfa Healthcare, 2007.
5. Vuylsteke P, Schoeters E. Image Processing in Computed Radiography. 1999. Paper 16, DGZfP proceedings, BB 67-CD, Germany.
5. Vuylsteke P, Schoeters E. Image Processing in Computed Radiography. 1999. Paper 16, DGZfP proceedings, BB 67-CD, Germany.
6. Aichinger H, Dierker J, Joite-Barfuss S, Sabel M. Radiation Exposure
and Image Quality in X-ray Diagnostic Radiology. Heidelberg, Germany:
Springer Verlag, 2004:78-79.
6. Aichinger H, Dierker J, Joite-Barfuss S, Sabel M. Radiation Exposure
and Image Quality in X-ray Diagnostic Radiology. Heidelberg, Germany:
Springer Verlag, 2004:78-79.
7. Matlab – The Language of Technical Computing. V7. Natick MA, USA: Mathworks, 2004.
7. Matlab – The Language of Technical Computing. V7. Natick MA, USA: Mathworks, 2004.
8. Hamza A, Alport MJ, Rae WID. The design and fabrication of a full
field quantitative mammographic phantom. Journal of Science and
Technology 2009;10(3):141-158.
8. Hamza A, Alport MJ, Rae WID. The design and fabrication of a full
field quantitative mammographic phantom. Journal of Science and
Technology 2009;10(3):141-158.
9. Hamza A, Alport MJ, Rae WID. Qualitative and quantitative evaluation
of the APG phantom. Journal of Science and Technology
2009;10(3):168-187.
9. Hamza A, Alport MJ, Rae WID. Qualitative and quantitative evaluation
of the APG phantom. Journal of Science and Technology
2009;10(3):168-187.
Fig. 1. The APG phantom used for MTF evaluation. The enlarged area shows an MTF plate.
Fig. 2. Signal-to-noise ratios for all steps imaged at 70 kV. Readout artefacts can cause outliers as seen at step 1 (MUSICA).
Fig. 3. Average contrast-to-noise ratios calculated from steps 1 to 4 of the step wedge at all energies.
Fig. 4. Graph showing contrast with decreasing object size for the different algorithms.
Fig. 5. Modulation transfer
functions in both x- and y-axes as calculated on the same MTF plate for
all three algorithms. The post-processed images have higher MTF values
over all useful frequencies.
Fig. 6. Image of artefact encountered due to post-processing.
Fig. 7. The result of edge enhancement during software processing.
Table 1. Image quality figures for the three different algorithms
IQF
Flatfield
30.25
MUSICA
24.75
MUSICA2
23.75