Balanced correlation method and the Maximum Entropy Method (MEM) were implemented to reconstruct a laboratory X-ray source as imaged by a Uniformly Redundant Array (URA) system. Although the MEM method has advantages over the balanced correlation method, it is computationally time consuming because of the iterative nature of its solution. Massively Parallel Processing, with its parallel array structure is ideally suited for such computations. These preliminary results indicate that it is possible to use the MEM method in future coded-aperture experiments with the help of the MPP

Bielefeld, Michael J.

Yin, Lo I.

English

NASA Technical Reports Server

N87-26544
RECONSTRUCTION OF CODED APERTURE IMAGES
Michael J. Bielefeld
Astronomy Programs, Computer Sciences Corporation
Space Telescope Science Institute
3700 San Martin Dr. Baltimore, MD 21218
NASA
I_o I Yin
Code 682, Solar Physics Branch
Laboratory for' Astronomy and Solar Physics
Goddard Space Flight Center, Greenbelt, MD 20771
ABSTRACT
Balanced correlation method and
the Maximum Entropy Method (MEM)
were implemented to reconstruct a
laboratory x-ray source as imaged
by a Uniformly Redundant Array
(URA) system. Although the MEM
method has important physical
advantages over the balanced
correlation method, it is
computationally time consuming
because of the iterative nature
of its solution. MPP, with its
parallel array structure is
ideal ly sui ted for such
computations. These preliminary
results indicate that it, is
possib]e to use the MEM method in
future coded-aperture experiments
with the help of the MPP.
INTRODUCTION
In the energy range
x-rays, i.e., 30 keV to
there is no focusing
Consequently, in order
the many interesting
which emit radiations
energy range
the sun,
components,
tagged with
must resort, to the use of
collimators or pinholes made from
high density materials. In this
paper, we will focus on the use
of pinholes.
Because a single pinhole is
extremely inefficient, there is a
strong interest in the use of
multiple pinholes to image x-ray
objects [1-12]. However, in most
cases, the images on the detector
formed by the many pinholes
strongly overlap each other
resulting in a detected image
that is not recognizable. A
decoding process must then be
applied to the detected image in
order to recover or reconstruct
the image of the original object.
Such multiple-pinhole masks are
usually referred to as coded
apertures.
In principle, the large
collection efficiency (close to
of hard 50%) of coded apertures offer the
100 keV, possiblility of a greatly
optics, enhanced signal-to-noise ratio
to image while maintaining the high
objects .spatial resolution of a single
in this pinhole. Furthermore, for
such as galaxies, terrestrial applications, coded
nuclear reactor apertures can provide tomographic
and human organs information of the x-ray object.
radioisotopes, one In practice, however, there are
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several difficulties associated
with the use of coded apertures,
especial ly in the imaging of
extended or large x-ray objects.
Because of the strongly
overlapping images on the
detector, the signal in one
location of the reconstructed
image may contain contributions
from all other portions of the
object. This type of signal
cross-talk is object-dependent
and can be present in addition to
s tatistical noise. Such
signal-cross talk can cause
severe contrast degradation in
the reconstructed image of
extended objects. There has been
many studies in recent years on
the performance properties of
various kinds of coded apertures.
One of the most promising type of
coded apertures is the Uniformly
Redundant Array (URA) [I0, Ii] . A
URA is a special kind of
multiple-pinhole mask in which
the number of times a particular
separation occurs between any
pair of pinholes is the same for
all separations. The separations
are the re fore uniformly
redundant. URA has some very
desirable properties ; one of
_hich is that with proper
decoding the signal cross-talk
mentioned above can be eliminated
completely. However, noise
cross-talk still exists. That
is, the statistical noise from
one part of the object can still
contribute to the signal of
another part in the reconstructed
image.
A few years ago, we proposed
another simple alternative: the
Non-Overlapping Redundant Array
(NORA) [121. It consists of a
regular array of pinholes (e.g.,
a hexagonal array) where the
separation between pinholes as
_ell as the separation between
the NORA mask and the detector
can be carefully chosen such that
the images on the detector formed
by the individual pinholes do not
overlap. We have shown that in
NORA, there is neither signal
cross-talk nor noise cross-talk
in the reconstructed image and
the only inherent noise in the
system is that due to counting
statistics. The signal-to-noise
ratio of NORA, assuming Poisson
statistics, is always square-root
of N times that of a
single-pinhole camera, where N is
the total number of pinholes.
This is true even for extended
objects and is the ideal limit
achievable by a multiple-pinhole
system. Another important
feature of NORA is that it is
possible to reconstruct the
extended x-ray object in 3-D by
simple optical correlation. We
have already demonstrated in the
laboratory that the optically
reconstructed image can be viewed
in true 3-D with both horizontal
and vertical parallax. In
addition, NORA should also
provide quantitative tomographic
information through digital
reconstruciton. It is this
latter goal that prompted us to
seek out the capabilities of the
Massively Parallel Processor
(MPP).
DIGITAL DECODING
General
Although it is possible to
reconstruct an image by analog
method when NORA is used as the
coded aperture, digital
reconstruction is mandatory when
the coded aperture, such as URA,
produces overlapping images on
the detector. Furthermore, to
obtain quantitative tomographic
112
information, digital computation
is always necessary.
In digital decoding, the large
number of pinholes, pixels and
mathematical operations demand
large amounts of computing time
even wit}, available fast
algorithms. At present, because
we are still investigating
various coding and decoding
methods _hich involve many
repeated trials and iterative
calculations, long computation
times and turn around delays can
be both costly and frustrating.
The MPP, with its parallel array
structure is ideally suited for
this type of computations. In
fact, as we will show below, the
MPP makes our investigations
feasible, while the conventional
mainframe computer, in normal
use, has proven to be inadequate.
Deeoding Methods for URA
Balanced Correlation Method -
Because of the uniformly
redundant and the cyclic nature
of the lIRA, its point spread
function is a delta function with
constant and flat sidelobes.
That is, if the object is a point
source and is detected by an
ideal URA system, the decoded
image by means of an
autocorrelation operation will be
also a point source (delta
function), but with a constant
and uniform background. This
flat de background can be
eliminated by using the balanced
correlation method. In this
method, although the decoding
array has the same pattern as the
coding lIRA array with l's
representing the holes, the
non-holes are represented by -l's
rather than 0's [i0]. However,
in contrast to well-separated
point sources, noise due to
statistical fluctuations in the
background which is not aperture
related can still contribute to
the reconstructed signal as the
object gets large, even with
balanced correlation decoding.
This kind of noise cross- talk
may give rise to artifacts in the
low-contrast background region of
the reconstructed image.
Maximum Entropy Method (MEM) -
Recently many investigators have
become interested in applying the
maximum entropy method (MEM) to
the field of image restoration
including the reconstruction of
coded-aperture images [13-18].
MEM is an iterative method which
maximizes the configurational
entropy while using prior
knowledge such as ehi-squared
(X 2) statistic and total detected
intensity as constraints.
Through iteration, the solution
with the maximum eonfigurationa]
entropy, i.e., with the least
configurational information, is
selected from a set of solutions
all of which satisfy the
chi-squared fit of the actual
data. This solution is
considered as the most likely
estimate of the original object
that is consistent with the
available data.
Following Willingale [15], the
solution has the form:
^-
(I)
where (_ ) is the estimated
intensity of the ith pixel of the
object as seen by the instrument
which maximizes the
113
configurational e entropy
is strument efficiency,
i.e. (fL /z i ) is tile true
intensity of the ith pixel of the
object; (B_;) is th_ transpose of
the blurring matrix of the coded
aii)eL'ture; (d_) is the actual data
on the detector; (_K ) is an
estimated data, without noise,
which would be produced on the
detector if the object ^were
correctly represented by (fl ) ;
( _ ) is the variance of the data
(d_) ; (k) and (_t) are Lagrange
multipliers. The function Q
which is being maximized to
produce the solution as
represented by Equation (I) is:
(2)
The first two terms of (2)
comprise the configurational
entropy; the third term with the
Lagrange multiplier ()k) is the
(X _ }, and the fourth term with
Lagrange multiplier (_l) is the
total intensity of t_e object.
For large number of data points
N, (N _ ) _ N. To conserve total
counts, ( z = ).
Noise in the data is accounted
for by the variance ( G'K*).
There are several important
advantages in using (1) as the
decoding solution. Because (I)
is in the exponential form, this
solution is never negative. The
first exponential is a constant
scaling factor which gives the
reconstruction a uniformly
distributed intensity without
features. When the noise in the
data is very high, this
featureless solution (k =0) w_ll
be consistent with the data, (f_)
will be simply proportional to
(z,) by maximum configurational
entropy. When the signal in the
data is high, the featureless
background as given by the first
exponential will be modulated by
the features provided by the
second exponential. The
summation in the second
exponential represents a
cross-correlation between the
blurring function of the coded
aperture and the difference
between the estimated data and
the actual data weighted by its
statistical variance. Since this
reconstruction occurs in the
exponential, iterative algorithms
are needed for its solution.
The relative weighting of entropy
and (X _ ) is controlled by (A).
As mentioned above, when (_) = 0,
(X z ) has no weighting, and the
solution is a uniform
distribution as given by maximum
entropy. When( k ) is increased,
the process reduces (X z ). A
final (A) will be selected when
(X z ) becomes close to N, the
expected value. To help
convergence, we also adopted the
search algorithm of Willingale
[15] by taking weightedaverages
of successive iterations.
EXPERIMENTAL DATA
As an initial test toward digital
decoding using the MPP we have
chosen some data which we had
obtained previously with a URA
coded aperture. The experimental
arrangement is sketched in Figure
i. The URA mask consisted of a
two-cycle mosaic of a basic 15 x
17 m-sequence array. The
114
pseudo-random m-sequence pattern
was generated according to the
procedure given by MacWilliams
and Sloane [19]. The pattern was
drilled into a 0.5-mm thick Pb
sheet by a computer-driven lathe.
The holes were 0.3 mm in
diameter; the center-to-center
separation of adjacent holes was
0.6 mm. Thus, the transparency of
this mask was about 10%. For the
imaging detector, we used a
Lixiscope [20] with a digitizing
anode. Briefly, for the present
data, the Lixiscope consisted of
a thin layer of YSiO3(Ge) powder
serving as an x-ray to visible
light converter which was
deposited on the entrance
faceplate of a I:I image
intensifier containing a triple
microchannel-p]ate (MCP) electron
multiplier [21]. The output
electron signals from the triple
MCP are detected by a resistive
anode which can provide both the
position and the amplitude of an
electron pulse. For the simplest
case of the present experiment a
single small 1-125 x-ray source
(28 keV) was used as the x-ray
emitting object. The distance
between the source, the mask and
the Lixiscope were chosen such
that the sensitive area of the
detector recorded at least one
complete basic array of the
magnified shado,_ of the two-cycle
URA mask. The experimental image
of the source, which was
positioned at 31 cm from the
detector, is shown in the upper
left corner of Figure 2. The
display exaggerates the constrast
in the data for this array of 256
x 256 pixels. The average counts
per pixel is about 2. Because
the emitting object is a point
source, the basic URA pattern is
clearly visible witin the
circular active area of the
detector. The digitized version
of this image (Fig. 2) is used
as the data to be decoded by both
the balanced correlation method
and the MEM.
DECODING OF URA IMAGE
The basic implementations of the
balanced correlation method and
the MEM are relatively
straightforward. However, an
important distinction should be
mentioned between this type of
x-ray image processing and that
of the more common visible/IR
image processing. In our case,
one is dealing with extremely low
count rates. Because of this,
the statistical uncertainty of
individual pixels has to be
followed through the decoding
process at the basic level of
computation. The formalism for
the MEM in Equation (1) takes
full account of this
requirement.
The specification of spatial
resolution for the experimental
system displayed in Figure 1
includes not only resolution in
the x-y plane but also in the z
direction. Hence, the digital
decoding of 3-D objects requires
much finer sampling than the
basic pinhole array. This
requirement for high sampling
rates along with the iterative
nature of the MEM are the main
factors which directed us toward
using the MPP.
The decoding process requires at
a minimum sampling rate of 17 x
15 pixels per cycle of the URA.
Because the detected image (Fig.
2", top left) is an array of 256 x
256 pixels, this image is
collapsed to the minimum array of
17 x ]5 through summing as shown
in Figure 2, top right. This
115
coded image i s almost
featureless. Tile bottom images
of Figure 2 show the results of
digitaI decoding by the MEM
(left) and tile balanced
rot'relation method (right). Both
methods clearly reconstructs the
point source to tile same degree.
However, these images illustrate
several advantages of the MEM.
First, the MEM does not permit
the physicalls" impossible
negative counts _een in the
background region of the image
reconstructed by the balanced
correlat ion method (bottom
right,) . Secondly, the MEM
produces a much smoother
background (bottom left). This
smoothness helps to minimize the
erroneous interpretat, i on o f
artifacts arising from noise
cross- talk.
As mentioned ear] ier, the
i terative process should be
terminated when (X _) become close
to N, the number of data points.
Letting (X _) t,o reduce further
_i 11 only add artifacts to the
already smooth background. (_,)
controls the relationship between
the entropy portion and the data
portion in Equation (I).
For extended
shapes, the
of tile HEM
[mpo r rant
sou rt:es. In
capabi 1 i ty
background
justifies
expense of
objects with unknown
smoothing capability
becomes even more
than for points
our opinion, this
of suppressing
artifacts amply
the computationa]
the MEM. Timing
experiments on the MPP indicate
that MEN decoding of our x-ray
URA data is indeed feasible. In
general, the MEM requires
approximately ten times more
c:omputational power than the
balanced correlation method
because of the iterations. The
116
CPU requirements for the basic
filtering kernel calculation
increases as the factor N for the
MPP, but as the square of N for a
typical mainframe computer, where
N is the total number of pixels
in a decoded data array. For a
future experiment which requires
the reconstruction of five
tomographio planes at ten
iterations per plane with three
values of ( _ ), our estimate is
that decoding would take 1.5
minutes of bIPP/CPU time for the
minimum sampling rate of 17 x 15.
At a very high rate of 170 x 150
pixels per URA cycle, it would
take about 2 hours. A
compromising sampling rate of 51
x 45 would take about 12 minutes.
Even for this compromised level
of decoding, we estimate that the
equivalent processing on a large
un-vectorized mainframe would
take in excess of 24 hours of CPU
time.
CONCLUSIONS
The results of our work to date
have been encouraging. The
continuation of this research
would be greatly enhanced with
the computational power of the
MPP. We need trial-and-error
experience to find the optimum
decoding algorithms as
experimental configurations are
refined, and to determine the
practical tomographic depth
resolution for 3-D x-ray objects.
Our near future plans include the
decoding of x-ray data obtained
with NORA aperture using the MPP.
ACKNOWLEDGMENT
We acknowledge the continued
support of our investigations
from within NASA, especially the
MPP Working Group. It is a
pleasure to thank Judy Devaney of
SAR Corp. and of the MPP
supporting staff for her
invaluable insights and
assistance. We are also indebted
to D. Vitagliano and V. Krueger
for their technical assistance
with the manuscript.
REFERENCES
I. J.G. Ables, Proc• Astron.
Soc. Aust. I, No. 4. 172
(1968).
2. R.H. Dicke, Astrophys.
153, LI01, (1968).
J •
3. H.H. Barrett and F.A.
Horrigan, Appl. Opt. 12, 2686,
(1973).
4. T.M.
Space Sci.
277 (1974).
Palmieri, Astrophys.
26, 431, (1974); 28,
5. C. Brown, J.
45, 1806, (1974)•
Appl. Phys.
6. B. MacDonald, L.T. Chang, V.
Perez-Mendez, and L. Shiraishi,
IEEE Trans. Nucl. Sci. NS-21,
672, (1974).
7. R.G. Simpson, H.H. Barrett,
J.A. Suback, and H.D. Fisher,
Opt. Eng. 14, 490, (1975).
8. F. Gunsen and B.
Polychronopulos, Mon. Not. R.
Astron. Soc. 177, 485,
(1976).
9. L.T. Chang, B. MacDonald,
and V. Perez-Mendez, Proc. Soc.
Photo-Opt. Instrum. Eng.
(SPIE) 89, 9 (1976).
i0. E•E. Fenimore and T.M.
Cannon, Appl. Opt. 17, 337,
(1978); 18, 1052, (1979).
II. E.E. Fenimore, Appl.
17, 3562, (1978).
Opt.
12. L. Yin, J. Trombka, S.
Seltzer, and M. Bielefeld, Appl.
Opt. 22, 2155 (1983).
13. S.F. Gull and G.J.
Daniell, Nature 272, 686, (1978).
14. M. Sims, M.J.L. Turner, and
R. Willingale, Space Sci.
Instrum. 5, 109, (1980).
Willingale, Mon. Not.
Soc. 194, 359, (1981).
16. S.F. Burch, S.F. Gull, and
J. Skilling, Computer Vision,
Graphics, and Image Processing
23, 113, (1983).
17. R. Willingale, M.R. Sims,
and M.J.L. Turner, Nucl.
Instrum. & Methods in Phys. Res.
221, 60, (1984).
18. S.F. Gull and T.J. Newton,
Appl. Opt. 25, 156, (1986).
19. F.J. MacWilliams and N.J.A.
Sloane, Proc. IEEE 64, 1715,
(1976).
20. L. Yin, J. Trombka, and S.
Seltzer, Nucl. Instrum. &
Methods 158, 175, (1979).
21. L. Yin, J. Trombka, R.
Schmadebeck, S. Seltzer, and M.
Bielefeld, SPIE 268, 97, (1981).
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Reconstruction of coded aperture images

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