Hindawi Publishing Corporation
International Journal of Biomedical Imaging
Volume 2011, Article ID 693795, 14 pages
FilteringinSPECT Image Reconstruction
Maria Lyra andAgapi Ploussi
Department of Radiology, Radiation Physics Unit, University of Athens, 76, Vas. Sophias Ave., Athens 11528, Greece
Correspondence should be addressed to Maria Lyra, firstname.lastname@example.org
Received 25 January 2011; Accepted 5 April 2011
Academic Editor: M’hamed Bentourkia
Copyright © 2011 M. Lyra and A. Ploussi. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
Single photon emission computed tomography (SPECT) imaging is widely implemented in nuclear medicine as its clinical role in
the diagnosis and management of several diseases is, many times, very helpful (e.g., myocardium perfusion imaging). The quality
of SPECT images are degraded by several factors such as noise because of the limited number of counts, attenuation, or scatter
of photons. Image filtering is necessary to compensate these effects and, therefore, to improve image quality. The goal of filtering
in tomographic images is to suppress statistical noise and simultaneously to preserve spatial resolution and contrast. The aim of
this work is to describe the most widely used filters in SPECT applications and how these affect the image quality. The choice
of the filter type, the cut-off frequency and the order is a major problem in clinical routine. In many clinical cases, information
for specific parameters is not provided, and findings cannot be extrapolated to other similar SPECT imaging applications. A
literature review for the determination of the mostly used filters in cardiac, brain, bone, liver, kidneys, and thyroid applications
is also presented. As resulting from the overview, no filter is perfect, and the selection of the proper filters, most of the times,
is done empirically. The standardization of image-processing results may limit the filter types for each SPECT examination to
certain few filters and some of their parameters. Standardization, also, helps in reducing image processing time, as the filters
and their parameters must be standardised before being put to clinical use. Commercial reconstruction software selections lead
to comparable results interdepartmentally. The manufacturers normally supply default filters/parameters, but these may not be
relevant in various clinical situations. After proper standardisation, it is possible to use many suitable filters or one optimal filter.
Tomography is a noninvasive imaging technique that is used
to generate cross-sectionals images of a three dimensional
(3D) object without superimposing tissues. Tomography
can be categorized in transmission tomography such as
computed tomography (CT) and emission tomography like
single photon emission computed tomography (SPECT) and
positron emission tomography (PET). Computed tomog-
raphy is a technique based on X-ray transmission through
a patient to create images of sections (slices) of the
body. Photon emission computed tomography and positron
emission tomography provide 3D image information about
the radionuclide injected into the patient that shows the
metabolic and physiological activities within an organ.
In tomographic techniques, projections are acquired
from many different angles around the body by one or more
rotating detectors. These data are then reconstructedand put
together to form 3D images of the body. The reconstruction
of tomographic images is made by two methods: filtered
backprojection and iterative methods .
The quality of the final tomographic image is limited
by several factors. Some of these are the attenuation and
scatter of gamma ray photons, the detection efficiency and
the spatial resolution of the collimator-detector system .
These factors have as a result poor spatial resolution, low
contrast, and high noise levels. Image filtering techniques
are very important in tomography as they strongly affect the
quality of the image.
Image filtering is the term used for any operation that is
applied to pixels in an image. It is a mathematical process
by which images are suppressed in noise and also includes
smoothing, edge enhancement and resolution recovery.
Filters are used during reconstruction and applied to
data in frequency domain. The goal of the filtering is to
compensate for loss of detail in an image while reducing
2 International Journal of Biomedical Imaging
noise. The application of filters is the most common method
to reduce high-frequency noise component in projection
images. In this way, filters can greatly improve the image
resolution and limit the degradation of the image. There are
of the appropriate filter is a headache in clinical practice .
The aim of this article is to describe the most commonly
used filters in SPECT imaging by analytical techniques.
These filters are applied in filtered back projection (FBP)
reconstruction techniques. Filtering can also be considered
as a postprocessing step in iterative reconstruction. Though
many times iteratively reconstructed images need to be
postfiltered, as they tend to be noisy, special dedicated
iterative filters are not established yet to be included in
commercial software. We present also choices of filters for
some SPECT examinations that are common in clinical
routine, as they are suggested in the literature.
Nowadays single-photon emission computed Tomography
(SPECT) is widely used in nuclear medical imaging. SPECT
is a nuclear medical tomographic imaging technique that
represents the distribution of an administered radioactive
camera mounted on a gantry so that the detector can rotate
around the patient. From the acquired one dimensional
projection data from different views around the object, two
dimensional (2D) planar projections images are obtained in
many evenly spaced angles around the patient and provide
an estimate of 3D distribution of the radiotracer using image
reconstruction from multiple projections. Some systems
acquire the images during their rotational movement, while
others stop and record (stop and shoot) an image at selected
angles. In SPECT the projections images are generally
acquired over a full 360-degree or 180-degree arc (in case
of SPECT myocardium perfusion study or kidneys SPECT
acquisition), on a matrix of 64 ∗ 64 or 128 ∗ 128 pixels.
Typically the projections are acquired every 3–6 degrees
and the total scan time is about 15–20 minutes. The 2D
then mathematical algorithms are used to reconstruct 3D
matrices of selected planes from the 2D projection data.
The purpose of reconstruction algorithms is to calculate
an accurate 3D radioactivity distribution from the acquired
projections. There are two methods to reconstruct SPECT
images, either iteratively or by FBP technique.
3.1. Iterative Reconstruction Method. Iterative reconstruction
starts with an initial estimate of the image . Most of
the times the initial estimate is very simple, for example a
uniform activity distribution. Then a set of projection data is
cess called forward projection. The resulting projections are
compared with the recorded projections and the differences
between the two are used to update the estimated image. The
iterative process is repeated until the differences between the
calculated and measured data are smaller than a specified
preselected value. The iterative reconstruction methods
include algebraic methods like the algebraic reconstruction
technique (ART) and statistical algorithms like maximum
likelihood expectation maximization (MLEM) or ordered-
subsets expectation maximization (OSEM) .
3.2. Filtered Backprojection Method (FBP). FBP is an analyti-
It consists of two steps: filtering of data and back projection
of the filtered data .
In 2D acquisition, each row of projections represents
the sum of all counts along a straight line through the
depth of the object being imaged. Back projection technique
redistributes the number of counts at each particular point
back along a line from which they were originally detected.
This process is repeated for all pixels and all angles. The
limited number of projection sets has as a result the creation
of a star artifact and the blurring of the image. To eliminate
this problem the projections are filtered before being back
projected onto the image matrix. It has to be noticed that the
backprojection process is taken place in spatial domain while
data filtration is done in the frequency domain.
3.3. Image SPECT Filtering. The image restoration process
is an example of Fourier spectrum filtering. Once a Fourier
Spectrum has been generated for an image, it can be filtered
so that certain spatial frequencies can be modified, enhanced
or suppressed. This filtered spectrum can then be inverse
transformed to generate a filtered image with, for example,
sharpened or smoothed features. A feature we need to
consider in more detail is the spatial frequency nature of
the image data itself. Images are generally sampled digitally
using a square matrix composed of pixels, the size of which
dictates how well a digital image approximates its analogue
The filters used in FBP are simply mathematical equa-
tions that vary with frequency. The filters used in SPECT
imaging can vary to achieve different purposes such as star
artifact reduction, noise suppression, or signal enhancement
The choice of filter for a given image reconstruction
task is generally a compromise between the extent of noise
reduction and fine detail suppression (and of contrast
enhancement in some cases) as well as the spatial frequency
pattern of the image data of interest.
3.3.1. Filtering to Reduce the Star Artifact
Ramp Filter. The ramp filter is a high pass filter that does
not permit low frequencies that cause blurring to appear in
given by (1).
where kx, kyare the spatial frequencies.
= k =
International Journal of Biomedical Imaging3
Figure 1: A simple representation of filtered back projection. (a) Acquisition of three projections. (b) Backprojected projections. (c) Filtered
The Ramp is a compensatory filter as it eliminates the
star artifact resulting from simple backprojection. Because
the blurring is only appeared in the transaxial plane, the
filter, is only applied in that plane . The filter as shown in
Figure 1(a)), is linearly proportional to the spatial frequency.
High pass filters sharpen the edges of the image (areas in
an image where the signal changes rapidly) and enhance
object edge information. A severe disadvantage of high pass
filtering is the amplification of statistical noise present in the
measured counts. In order to reduce the amplification of
high-frequencies the ramp filter is always combined with a
3.3.2. Filtering to Reduce Noise. The common method to
reduce or remove statistical noise in a SPECT image is
the application of smoothing filters. These filters are low-
pass filters which allow the low frequencies to be retained
unaltered and block the high frequencies. Low-pass filters
are characterized mainly by two parameters—the “cut-off
frequency” and the “Order” (or the “Power”). The cut-off
frequency (or roll-off frequency) defines the frequency above
which the noise is eliminated. The filter function is defined
to be zero for all frequencies above cut-off frequency. The
Nyquist (Nq) frequency—the highest frequency that can be
displayed in an image—is apparently the highest cut-off
frequency for a filter. The cut-off frequency is expressed in
cycles per pixel or as a fraction of the Nq frequency. The
cut-off frequency varies typically from 0.2 to 1.0 times the
Nq frequency. The value of the cut-off frequency determines
how the filter will affect both image noise and resolution. A
therefore much detail can be seen but the image will remain
noisy. A low cut-off frequency will increase smoothing but
will degrade image contrast in the final reconstruction.
The parameter Order controls the slope of the filter
order will result in a sharp fall. Sometimes, the term power
instead of order is used. The power is twice the order.
There is a number of low-pass filters that are available
for SPECT reconstruction. The most commonly used are
Butterworth Filter. Butterworth filter is the more usual
choice in nuclear medicine. The butterworth filter is a low-
pass filter. It is characterized by two parameters: the critical
frequency which is the point at which the filter starts its roll
off to zero and the order or power . As it is mentioned
earlier, the order changes the slope of the filter. Because of
this ability of changing not only the critical frequency but
also the steepness of the roll-off, the butterworth filter can
do both, smoothes noise and preserves the image resolution.
A butterworth filter in spatial domain is described by:
where f is the spatial frequency domain, fc the critical
frequency and n the Order of the filter (Figure 3).
A ramp function and a butterworth function of variable
order and cut-off (critical) frequency, are multiplied to form
the fourier filter used in the FBP process (Figure 4).
Hanning Filter. The Hanning filter is a relatively simple low-
pass filter which is described by one parameter, the cut-off
(critical) frequency (Figure 5) .
The Hanning filter is defined in the frequency domain as
where f are the spatial frequencies of the image and fmthe
cut-off (critical) frequency.
In signal processing, the Hann window is a window
function, called the Hann function, named after Julius
Ferdinand von Hann, an Austrian meteorologist. The use of
the Hann window is called “Hanning”, as a signal to apply
the Hann window to a digital signal processing. http://en
The Hanning (Hann) filter is very effective in reducing
image noise as it reaches zero very quickly; however, it does
not preserve edges (Figure 5).
0.50 +0.50 cos
4International Journal of Biomedical Imaging
00.2 0.40.6 0.81
Figure 2: The Ramp filter: (a) Ramp filter in frequency domain. (b) Ramp filter in spatial domain .
(A) n = 2, fc= 0.1
(B) n = 8
(C) n = 32
(D) n = 2, fc= 0.3
(E) n = 8
(F) n = 32
Spatial frequency (cycle/pixel)
Figure 3: Butterworth smoothing filter six curves by different
fc and n parameters (equation (2)). A, B, C curves created by
critical frequency fc = 0.1c/pixel and order n equal to 2, 8, 32
correspondingly. D, E, F curves created by critical frequency fc =
0.3c/pixel and order n equal to 2, 8, 32 similarly .
Hamming Filter. The Hamming filter is also a low pass
filter, which presents a high degree of smoothing, named
Nyquist frequency (%)
Figure 4: Illustration of the Butterworth filtering process. A Ramp
function and a Butterworth function (of Order 3 and cut-off
frequency 40% of Nq frequency) are multiplied to form the Fourier
filter used in the FBP process. Generated by Kieran Maher, 2006,
accessed in http://en.wikibooks.org/wiki/File:NM16 14.gif.
The mathematical definition is shown as (4) .
0.54 +0.46 cos
where f are the spatial frequencies of the image and fmthe
As it can be observed the only difference with the
Hanning filter is on the amplitude at the cut-off frequency.
International Journal of Biomedical Imaging5
Cut-off frequency 0.8c/cm
Figure 5: Hanning filter and Ramp in FBP reconstruction.
Parzen Filter. The Parzen filter is another example of low
pass filter and it is defined in the frequency domain as ,
where f are the spatial frequencies of the image and fmthe
cut-off frequency.The Parzen filter is the most smoothing
filter; it eliminates high-frequency noise, but it also degrades
the image resolution .
Shepp-Logan Filter. The Shepp-Logan is one more filter that
belongs to the family of low pass filters. Its mathematical
equation is shown as (6) .
the highest resolution.
Numerous types of filters exist, and all filters aim, except
for the restoration filters, at reducing frequency information
through an amplitude-adjusting function between 0 and
1Nq. The interpretation and comparison of SPECT studies
is beclouded by the use of too many different filter types.
Optimal parameters have been calculated  for But-
terworth or Hanning filters to match the shape of various
existing filter types. Butterworth filters cannot approximate
any other kind of filter shape since the amplification
of the high-frequency components always asymptotically
approaches zero, whereas for the Hann filter, high-frequency
components can be set to zero. This is demonstrated for
Filters functions’ curves
Figure 6: Shepp-Logan, Butterworth, Hann, Parzen filter func-
tions’. (from Van Laere et al., (2001), modified ) .
the approximation of a Hann filter by Butterworth matching
(Figure 6). A Shepp-Logan filter can be very accurately
matched to a Butterworth filter with the appropriate param-
eters. A Parzen filter is closely matched by a Hann filter with
cut-off 1 (Figure 6).
From the practical point of view, all filter shapes
can be fairly accurately addressed by a specific cut-
off/order/restoration combination of Butterworth and Hann
3.3.3. Filtering to Enhance the Signal. A low-pass filter may
smooth image to a high degree that does not permit discern-
ing small lesions, leading to contrast loss. For this reason a
is used in SPECT imaging. The restoration filters enhance
the signal with a simultaneous reduction of noise without
resolution lost. Metz and Wiener are two types of resolution
recovery filters that have been used in nuclear medicine
Metz Filter. Metz filter is a function of modulation transfer
function (MTF), and it is based on the measured MTF of the
gamma camera system. The MTF describes how the system
handles or degrades the frequencies. The Metz restoration
filter is defined in the frequency domain as
where f is the spatial domain and x is a parameter that
controls the extent to which the inverse filter is followed
before the low-pass filter rollsoff to zero . Equation (7)
6International Journal of Biomedical Imaging
is the product of the inverse filter (first term) and a low pass
filter (second term).
The Metz filter is count dependent. Figure 7 shows the
Metz filter plotted for six different total image counts .
From Figure 7 results that, as the counts increase, more
resolution recovery occurs (filter rises farther above 1.0),
together with less suppression (filter moves farther to right)
Wiener. The Wiener filter is based on the signal-to-noise
ratio (SNR) of the specific image. The one dimensional
frequency domain form of the Wiener filter is defined as
where MTF is the modulation transfer function of the
imaging system, N is the noise power spectrum, and O is
the object power spectrum . As with the Metz filter, the
Wiener is the product of the inverse filter (which shows the
resolution recovery) and the low pass filter (which shows the
noise suppression). In order to apply the Wiener filter it is
necessary to know a priori the MTF, the power spectrum of
the object and the power spectrum of the noise. It has to be
in any image. As a result, the mathematical models used to
optimize both Metz and Wiener filters are uncertain .
3.4. Parameters Determining the Choice of the SPECT Filter
filters which may be selected depending on the type of
examination. The filter choice depends on [3, 12]:
(i) the energy of the isotope, the number of counts and
the activity administration.
(ii) the statistical noise and the background noise level.
(iii) the type of the organ being imaged.
(iv) the kind of information we want to obtain from the
(v) the collimator that is used.
The choice of the filter must ensure the best compromise
between the noise reduction and the resolution in the image.
4.Typeof FiltersDependingon Typeof Study
The selection of the proper filter and the determination of
filter parameters is a major problem in clinical routine. In
this section, the filters used for widespread applications of
filtering is an important, though mostly subjectively applied,
image-processing parameter. It is shown that ramp, Hann
and Butterworth filters are the most commonly used image
pre- and postprocessing filters. In many clinical evaluations,
literature does not provide useful information for specific
parameters of the imaging filters. In most clinical routine
cases the choice of a filter is done empirically, and the use
Spatial frequency (cycles/pixel)
Metz filter functions
Figure 7: Plot of Metz filter for total counts of 20.000, 50.000,
100.000, 200.000, 500.000, and 1 million counts from lowest to
highest curve .
Figure 8: Transverse slices of kidneys’. Various pre- postfiltered in
Filters used were (a) prefilter Hanning (cut-off 0.8cm−1), postfilter
Ramp. (b) prefilter Butterworth (cut-off 0.5cm−1power value 10),
postfilter Ramp. (c) prefilter Butterworth (cut-off 0.8cm−1, power
value 10), postfilter Ramp. (d) only Ramp prefilter applied—no
other smoothing filter. (e) prefilter Ramp, postfilter Hanning (cut
off 0.8cm−1) (f) prefilter Ramp, postfilter Butterworth (cut-off
0.8cm−1, power value 10). Study has been completed in Radiation
Physics Unit, Department of Radiology, University of Athens.
of limited filter types, in an attempt to standardise image-
processing approaches, may lead to better diagnostic com-
patibility and interpretation of interdepartmental results. In
Butterworth filters is shown, in coronal slices of a SPECT
renal study of a 6-month old boy.
International Journal of Biomedical Imaging7
4.1. Cardiac SPECT. Cardiac SPECT has an important
clinical role in the detection of myocardial perfusion and
the diagnosis of ischemic heart disease. The commonly used
radiotracers for cardiac SPECT are Thallium-201 (201Tl)
and Technetium-99m (99mTc) labeled agents such as99mTc-
Sestamibi and99m-Tetrafosmin. In clinical practice, Hanning
filters were preferred for
for99mTc images . In the literature, there are extensive
studies about the determination of the appropriate filter for
myocardial SPECT imaging.
Takavar et al. (2004)  studied the determination
of the optimum filter in99mTc myocardial SPECT using
a phantom that simulates the heart’s left ventricle. Filters
such as Parzen, Hanning, Hamming, and Butterworth and a
combination of their characteristic parameters were applied
on the phantom images. The cut-off frequency of 0.325Nq
and 0.5Nq gave the best overall result for Hanning and
Hamming filters, respectively. For Butterworth filter order
11 and cutoff 0.45Nq gave the best image quality and size
A determination of the appropriate filter for myocardial
SPECT was conducted by Salihin Yussoff and Zakaria .
The filters’ functions evaluated in this study included Butter-
worth, Hamming, Hanning, and Parzen filters. From these
filters, 272 combinations of filter parameters were selected
and applied to the projection data. The study suggested that
Butterworth filter succeeds the best compromise between
SNR and detail in the image while Parzen filter produced the
best accurate size.
The same group  investigated the relationship
between the optimum cut-off frequency for Butterworth
filter and lung-heart ratio in99mTc myocardial SPECT. A
linear relationship between cut-off frequency and lung-heart
ratio had been found which shows that the lung-heart ratio
must refer in each patient in order to choose the optimum
cut off frequency for Butterworth filter.
Links et al. (1990)  examined the affect of Wiener
filter in myocardial perfusion with201Tl SPECT. The study
wad done in 19 dogs and showed that Wiener filter
improves the quantization of regional myocardial perfusion
In a201Tl gated SPECT study in patients with major
myocardial infraction , a Butterworth filter of order 5
with six cut-off frequencies (0.13, 0.15, 0.20, 0.25, 0.30,
0.35 cycle/pixel) were successively tested. The report showed
that filtering affect end-diastolic volume (EDV), end-systolic
volume (ESV) and left ventricular ejection fraction (LVEF).
Marie et al. (2005)  suggested that the best results for
cardiac gated SPECT image reconstruction with201Tl were
achieved using a Butterworth filter with an order of 5 and
cut-off frequency 0.30 cycles/pixel.
201Tl images and Butterworth
4.2. Brain SPECT. Brain SPECT is a powerful diagnostic
tool for evaluating neurologic and psychiatric diseases. Brain
SPECT provides a measure of cerebral blood flow (CBF)
and it is very useful for functional imaging of subcortical
structures of the brain. There are currently two commercial
radiotracers for brain SPECT imaging: Iodium-123 labeled
amphetamine (IMB) and99mTc hexamethylpropyleneamine
oxime (HMPAO). Due to the low SNR in this type of study
the choice of the optimum filter is difficult enough.
Groch and Erwin (2000)  showed that the most
suitable filter for
the Butterworth filter with order 10 and 0.5Nq cut-off
frequency. This filter gave the best compromise between
noise and spatial resolution with respect to Hamming filter.
In another report , the optimization of Butterworth
filter for brain SPECT imaging was studied. The aim of the
work was to find a relationship between the total counts and
the optimal cut off frequencies of the Butterworth filter. The
study proved that as the number of total counts increased the
optimal cut-off frequency linearly increased within a specific
range of counts.
Raeisi et al. (2007)  examined Ramp, Shepp-Logan,
Hanning, Hamming, Butterworth, Metz, and Wiener filters
in data from brain SPECT. The study suggested that both
Metz and Wiener give the maximum resolution and contrast
while Butterworth generate the best image quality.
99mTc-HMPAO brain SPECT study is
4.3. Other SPECT Studies. Although myocardial and brain
SPECT studies are the most widespread applications in
tomographic nuclear medicine examinations, there are
several other organs’ SPECT studies that were not very
commonlyused in clinicalroutine. Inthis time, SPECTdiag-
assistance in the clinical diagnostic procedures, and accurate
volume estimations by SPECT are feasible when accurate
corrections are performed . Some of them are bone,
liver, lungs, kidneys, and thyroid SPECT examinations. For
these applications, the most popular filters are Butterworth
and Hanning with different critical frequency values for
Hanning and various power and critical frequencies with
Butterworth filter. In many clinical cases, information for
specific parameters is not provided and filters’ parameters
findings cannot be evaluated and categorized per organ
Bone. SPECT is an important diagnostic tool in nuclear
medicine for evaluating a detail image of the bones and
especially for detecting malignant. There are limited reports
in the literature for the appropriate filter in bone SPECT.
anatomic details than other types of filters [20, 21]. Image-
dependent Metz filters have been shown to provide consis-
tently good image quality for bone study .
Liver. disease can be imaged using SPECT to determine
the existence of sarcoma, hepatic tumour, haemangiomas,
metastases, cyst, glycogen storage disease, and so forth, using
99mTc sulfur colloid (SC) . In a SPECT study for the
anatomy of normal liver, Carrasquillo et al. (1983) 
suggested a modified Butterworth-ramp filter for the image
reconstruction. King et al. (1984)  showed that two
dimensional filtering, before and after reconstruction, using
the Metz and Wiener filters can improve significantly the
and the high SNR in liver SPECT images, filters with a high
cut-off frequency are recommended to be used .
8International Journal of Biomedical Imaging
Figure 9: A liver-spleen study SPECT and 3D SPECT. (a) Transverse slices, prefiltered only by Ramp filter could emerge a small piece of the
and 15% gradient, could show the liver and a short fracture in the middle of right lobe of the liver but misses any residual spleen fragment.
(Study has been offered by Radiation Physics Unit, Department of Radiology, University of Athens).
Figure 10: 3D liver surface images. (a) Liver and spleen fragment caused by accident. Images reconstructed by FBP, prefiltered by Hanning
power factor = 10) and ramp filter. (Studies offered by Radiation Physics Unit, Department of Radiology, University of Athens).
Figure 9 shows a young boy’s liver-spleen study following
a car accident, searching for residual spleen pieces.
Two more cases of liver 3D SPECT images, reconstructed
by FBP, and different filters applied in (Figure 10).
Renal. SPECT by99mTc-DMSA is recommended to be used
instead of or complementarily to planar scintigraphy as
the preferable study to help especially in paediatrics with
early diagnosis, followup, and monitoring of the effects
of treatment in acute pyelonephritis and possible scars
In a renal SPECT study with99mTc-DMSA, De Sadeleer
et al. (1996)  suggested the use of a Butterworth filter
with an order of 7 and a cut-off frequency of 0.55Nq,
for the reconstruction of the projection data. According to
Groshar et al. (1997) , a Hanning filter with a cut-off
frequency of 0.5cycle/cm was applied in the data, in a kidney
SPECT imaging with99mTc-DMSA for best results.
In a study, by Yen et al. (1996)  for monitoring pae-
diatric acute pyelonephritis by99mTc-DMSA renal SPECT
imaging, a Metz prefilter was applied and transverse images
were reconstructed with back projection and a ramp filter to
show signs of acute pyelonephritis not indicated in planar
A semiquantitative evaluation of cortical damage to the
kidneys, in children, was performed by tomographic renal
filter (critical frequency 0.8cm−1and attenuation correction
0.12cm−1). The result of this procedure was the calculation
of three integrated over volume (IOV) indices that offer a
quantitative comparison of the planar, tomographic, and 3D
reconstructed images .
study by dual head gamma camera, reconstruction was
performed similarly on both cameras using a Hann pre-
filter (cutoff frequency, 0.9cm−1; order, 0) and a Butter-
worth postfilter (cutoff frequency, 0.5cm−1; order, 10) with
two iterations and 10 subsets for the detection of renal
parenchyma focal defects .
Sheehy et al. (2009) have compared two methods
of reconstructing99mTc-dimercaptosuccinic acid (DMSA)
renal SPECT data—ordered subset expectation maximiza-
tion with OSEM-3D and FBP—in children in terms of
improving image quality and reducing the radiopharmaceu-
tical activity and radiation dose. Authors do not indicate the
International Journal of Biomedical Imaging9
filters and relative parameters that were applied during FBP
. OSEM-3D was described by R¨ omer et al. (2006) as an
iterative SPECT reconstruction algorithm that is performed
by using OSEM with 3-dimensional resolution recovery,
whichisappliedinthex, y,andz directions.Theyhadfound
that this approach, as compared with FBP, substantially
improves SPECT image quality and can be performed with
fewer gamma photon counts .
Lungs. SPECT techniques were, up to few years ago, used in
clinical diagnosis only by a limited number of centers. Given
the improvements in sensitivity and diagnostic accuracy
that has generally accompanied the transition from two-
dimensional planar to three-dimensional (3D) imaging,
SPECT technique in ventilation/perfusion (V/P) scintig-
raphy historically, one of the most commonly performed
resolution and improved anatomical detail compared with
V/P perfusion scintigraphy, in the diagnosis of perfusion
Gutte et al. (2010) concluded that V/Q SPECT should
be preferred in diagnosing of perfusion embolism. In their
study, SPECT datasets were attenuation corrected using the
low-dose CT acquisition with iterative reconstruction using
the software Autospect+ and Astonish with three iterations
and 16 subsets .
An automated linear registration algorithm based on
the maximization of mutual information was applied by
Reinartz et al. (2006) to the V/Q scans using the Hermes
Multimodality software including processing filters. This
automated analysis leads to a significant improvement in the
detection rate of pathologic lesions .
V/Q scintigraphy provides a useful tool to help reduce the
number of nondiagnostic scintigraphy results. In their study
SPECT data were reconstructed to 128 slices using Ordered
Subset Expectation Maximization iterative reconstruction (8
iterations, 4 subsets), and smoothed with a postreconstruc-
tion three-dimensional Butterworth filter .
A comparison of usefulness of SPECT versus planar
lung scintigraphy in suspected pulmonary embolism, in
daily practice was completed by Weinmann et al. (2008).
Reconstruction of coronal, sagittal and transverse slices was
done by FBP followed by two iterations with a 5-order
Butterworth filter and a cut-off frequency at 0.45Nq .
An example of lungs’ perfusion embolism study by
SPECT is following (Figure 11).
A method for lungs’ volume determination by SPECT
and 3D SPECT images has been demonstrated . Recon-
struction was performed by quantitative FBP by Hann filter
(critical frequency 0.9) and Chang attenuation correction
order 0, coefficient 0.11 in the GE Xeleris2 image processing
system. Phantom volume calculations were completed under
conditions similar to those of the patients’ studies. The
method assists to the accurate interpretation of perfu-
sion scans by volume, semi quantitative lung perfusion
index [LPI] and pulmonary improvement factor [PIF]
determination (Figure 12).
embolism in left lung lobe (LL) not indicating in planar images.
Image reconstruction has been completed by FBP and Butterworth
filter (critical frequency 0.5, order 10). Courtesy of M. Gavrilelli
(MSc, “Medical Imaging Center” Athens, Greece).
Thyroid. SPECT volume estimation is an important tool for
dosimetry measurements and radionuclide therapy activity
by SPECT, Zaidi (1996) used the third order Butterworth
tion of transaxial images of one pixel thickness. Images were
obtained without any correction and with two correction
methods. A slightly lower value of the attenuation coefficient
(μ = 0.12cm−1rather than μ = 0.15cm−1for99mTc) was
accepted better in quantifying thyroid volume by SPECT
et al., (2003), in patients with Graves’ disease . The
planar images were subjected to filtering and thresholding,
and a standard surface formula was used to calculate the
thyroid volume. With SPECT, the iteratively reconstructed
thyroid images were filtered, and after applying a threshold
method, an automatic segmentation algorithm was used
for the volume determinations. Transmission scans, by two
gadolinium-153 (153Gd) line sources, were reconstructed
with FBP and were corrected for down scatter of99mTc into
the153Gd window. For the emission scan an iterative maxi-
mum likelihood reconstruction algorithm with attenuation
correction and window-based scatter correction as well as
preserving 3×3×3-point median filter was applied.
Many times, phantoms of known dimensions have been
used in evaluating the accuracy of results in a methodology.
Bahk et al., (1998) used an acrylic thyroid phantom in their
study for pinhole SPECT imaging in normal and morbid
ankles. The phantom was subjected to planar, SPECT and
pinhole SPECT acquisitions. The gamma camera system
was connected to an Icon data processor that enabled
image reconstruction using the FBP algorithm and a Butter-
worth filter. The ankles’ SPECT scintigraphy was performed
immediately after pinhole scan by 360◦detector rotation.
The FBP algorithm and a Butterworth filter were used for
reconstruction as in the phantom study .
10International Journal of Biomedical Imaging
R lobeL lobe
Lungs’ perfusion recovery
R lobeL lobe
Lungs’ perfusion recovery
0.11. (a) 4 hours postevent, Right lobe embolism, R lobe volume 0.66lt, and total lungs’ volume 2.85lt. (b) 11 days posttherapy, R lobe
volume 3.28lt, and total lungs’ volume 5.28lt.  (modified).
Noise reduction is one of the important tasks in clinical
SPECT imaging. One has to be judicious in the selection of
filters and its parameters for reducing noise, as there may
be some common frequencies in the noise and real image
data. Various digital filters (for reducing noise) have been
Butterworth, Gaussian, Hamming, Hanning, and Parzen
are commonly used SPECT filters during FBP reconstruc-
tion, which greatly affect the quality and size accuracy of
image. Salihin Yussoff and Zakaria (2009) , in a study by
a cardiac phantom, had selected 272 combinations of filter
parameters and applied on image. Their measurements were
used to calculate contrast, signal-to-noise ratio (SNR), and
defect size. The different filter types produced myocardial
image with different contrast, SNR, and defect size. For
contrast and SNR, Gaussian filter was the best, while Parzen
Butterworth filter was found the best for trade off between
contrast, SNR, and defect size accuracy. Selection of filter
should consider the qualitative or quantitative type of
analysis. For qualitative analysis, high contrast and SNR,
Gaussian filter was suggested. The Butterworth filter was
suggested for quantitative analysis as it is greatly dependent
on both, image quality and size accuracy.
The manufacturers normally supply default filter param-
eters to the user which may not be relevant in different clini-
cal situations. A phantom study was used in filter parameters
by the vendor. The images were reconstructed using FBP
technique with Chang’s method (attenuation coefficient =
0.125) attenuation correction. A Ramp filter; sixteen differ-
ent Hanning filter, thirteen different Metz filter, and nine
different Butterworth filter parameters were applied during
image reconstruction. Those results did not exactly match
before being put to clinical use is the recommendation .
FBP reconstruction has been, for a long time, the only
reconstruction algorithm used in SPECT (Figure 13) and
Thyroid coronal slices
Figure 13: Sequential coronal slices of Thyroid SPECT study.
Reconstruction made by FBP and Ramp-Butterworth prefiltering.
Right lobe node delineation. Study is presented courtesy of M.
Gavrilelli, MSc, “Medical Imaging Center” Athens, Greece.
is still the reconstruction algorithm recommended for use
in National Electrical Manufacturers Association (NEMA)
performance tests .
The Society of Nuclear Medicine in the Guideline for
General Imaging V6.0 9, 2010 gives some recommenda-
tions on prefiltering and reconstruction in SPECT imaging
. Prefiltering of the projection data in SPECT studies
for smoothing in the axial direction must be included.
Reconstruction by FBP demands a ramp filter that corrects
for the smoothing caused by the back projection process.
Filters must be used to restore some of the resolution
lost in the reconstruction process. The particular filter that
is used depends upon the imaging equipment, the depth
of the organ of interest and the radius of rotation. Care
should be taken with image enhancement since it is possible
to produce artifacts. Though, in iterative reconstruction
of SPECT studies the methodology makes it possible to
incorporate correction for many physical effects such as
nonuniform attenuation correction, scatter reduction or
removal, variation of spatial resolution with distance, and so
forth, many times the filtering support is necessary.
International Journal of Biomedical Imaging 11
TOMO FBP coronal
TOMO IRAC coronal
TOMO IRAC coronal
Figure 14: Coronal slices of renal SPECT study. The effect of filtering in smoothing and contrast of SPECT reconstruction, by FBP and
Chang attenuation correction (coefficient 0.11) or OSEM iterations is reflected on clinical images. (a) FBP reconstruction, Butterworth filter
(critical frequency 0.5cm−1, power 10) (b) FBP reconstruction, Hann filter (critical frequency 0.9cm−1) (c) OSEM 10 subsets/10 iterations
(d) OSEM 10 subsets/10 iterations and postfiltering by Butterworth (critical frequency 0.5cm−1, power 10). Study has been completed in
Radiation Physics Unit, Department of Radiology, University of Athens.
Iterative image reconstruction methods allow the incorpora-
tion of more accurate imaging models rather than the Radon
model assumed in the FBP algorithm. These include scatter
response and more realistic statistical noise models.
Iterative techniques such as OSEM take into account the
Poisson count distribution and the filters are applied mostly
postprocessing in 3D. Postfiltering with a Butterworth filter
has been shown to result in higher contrasts compared to
reconstructions without filtering (Figures 14(c) and 14(d)).
However, postfiltering with 3D Gaussian filter kernels
should be avoided when collimator detector response com-
pensation is included in the reconstruction. Contrast as a
function of noise has been studied for prefiltering of123IDAT
showed that contrast as a function of noise is comparable for
the prefiltered and nonfiltered OSEM reconstructed images
OSEM algorithm convergence properties depend on
the activity distribution in the field of view. Resolution
properties for OSEM have been studied  with different
types of regularization. Although different parts of the image
converge at different rates, pure and post OSEM filtration
achieve reasonably uniform resolution. Inter-iteration filter-
ing (IF OSEM) with smoothing filters, such as a Gaussian,
produces images with varying spatial resolution that is
the resolution nonuniformity is entirely due to the filtering.
A spatially varying filter has been proposed to overcome
this problem and to obtain images with nearly uniform
Seret in his work  in comparison of OSEM and FBP
concludes that one might suggest that the number of subsets
and iterations chosen should be close to the convergence
12International Journal of Biomedical Imaging
for all studied regions before quantitative comparisons are
made between FBP and OSEM. The number of requested
iterations will probably result in images that are too noisy,
and a postprocessing filter should be applied.
In a study for comparison of different types of commer-
reconstructions were performed by use of the Ramp filter
limited at the Nq frequency (0.5 cycle per pixel). Prefiltering
of the projections with either the Hanning filter or the
order 3 or 6 Butterworth filter was also considered. Three
cut-off frequencies (0.20, 0.35, and 0.50 cycles per pixel)
were used with the Hanning filter, and 4 cut-off frequencies
(0.10, 0.20, 0.35, and 0.50 cycles per pixel) were applied to
the Butterworth filter. Most of the types of software were
equivalent for FBP or OSEM reconstruction. However, a few
differences were observed with some types of software and
should be considered when they are used.
Comparing four sets of coronal images reconstructed by
FBP or OSEM and different filters (Figure 14) one evaluates
of the image.
Using 3D OSEM with suitable AC may improve lesion
detectability due to the significant improvement of image
contrast. 3D iterative reconstruction algorithms are likely to
replace the FBP technique for many SPECT clinical appli-
cations. Though, more exact image compensation methods
need to be developed and optimal image reconstruction
on quantitative SPECT imaging is yet to be assessed .
An efficient postprocessing method to compensate for
scattering and blurring effects in inhomogeneous media was
presented by Yan and Zeng (2008) . The major challenge
of the method is to accurately estimate the 2D point spread
function (PSF) in the image domain. From the clinical
aspect, the implementation of the method is faster than
the iterative reconstruction-based compensation method.
This method is developed in two dimensions and does
not consider scattered photons from out-of-plane sources.
Future work will possibly include modelling the scattering
with a 3D-PSF and a comparison between 3D-PSF method
and 3D-OSEM could be done.
SPECT has become an important diagnostic tool in nuclear
medicine. SPECT images show characteristic anatomical and
functional information of the structures and the tissues.
The quality of the image depends on several factors such as
spatial resolution (detail or sharpness), contrast and noise
(statistical and structure).
One of the most important factors that greatly affect the
quality of clinical SPECT images is image filtering. Image
filtering is a smoothness process for noise removal and
resolution recovery. A number of filters have been designed
and are available in the reconstruction of tomographic
images. All of them are characterized by two parameters: the
parameters can affect variously the image quality. The type
of the filter and the application of the filter parameters
cannot be generalized in all types of clinical SPECT
The selection of the optimal filter and the determination
cially the selection of the cut-off frequency is very important
in order to reduce noise and preserve the image details.
Proper filter selection is significant for the improvement of
However, no filter is perfect and there is no specific filter for
all applications. In the literature there are limited reports for
the choice of the appropriate filter parameters in a certain
SPECT examination, as the findings of filter application per
organ reconstruction cannot be generalized.
Nowadays, FBP reconstruction is progressively replaced
with the OSEM-iterative reconstruction algorithm. Unlike
FBP, OSEM is not a linear algorithm, and the reconstructed
contrast depends on the true contrast and on object size.
Moreover, FBP is still faster than OSEM and remains widely
used in clinical practice . Iterative reconstruction meth-
ods have seen a significant growth in tomographic recon-
struction because of the increased computerizing speed.
Iterative reconstruction algorithms produce accurate images
of radioactive distribution and seem to be more sensitive
than FBP technique . Further development in iterative
reconstruction methods will be very promising in improving
image quality. Alzimami et al. (2009) have demonstrated
an improved performance of the new 3-D OSEM method
compared to FBP, particularly for low count statistics. It is
necessary, though, optimal image reconstruction parameters
to be used for the comparison of the full potential of these
methods and evaluation of their impact on quantitative
SPECT imaging .
FBP and OSEM are generally both available on all
SPECT processing software developed by gamma camera
manufacturers and the nuclear medicine processing software
companies. The SPECT filters can greatly affect the quality
of clinical images by their degree of smoothing. Proper filter
selection and adequate smoothing helps the physician in
results’ interpretation and accurate diagnosis.
The authors wish to thank Ms. Maria Gavrilelli, MSc,
Medical Physicist in Medical Imaging Center, Athens, Greece
for the permission to use her images in this work.
in SPECT,” Journal of Nuclear Medicine, vol. 45, no. 10, pp.
 B. M. W. Tsui, “The AAPM/RSNA physics tutorials for
resident,” Radiographics, vol. 16, no. 1, pp. 173–183, 1996.
 K. van Laere, M. Koole, I. Lemahieu, and R. Dierckx, “Image
filtering in single-photon emission computed tomography:
principles and applications,” Computerized Medical Imaging
and Graphics, vol. 25, no. 2, pp. 127–133, 2001.
International Journal of Biomedical Imaging 13
 S. R. Cherry, J. A. Sorenson, and M. E. Phelps, Physics
in Nuclear Medicine, Saunders, Philadelphia, Pa, USA, 3rd
 M. W. Groch and W. D. Erwin, “SPECT in the year 2000: basic
4, pp. 233–244, 2000.
 M. M. Khalil, Ed., Basic Sciences of Nuclear Medicine, Springer,
Berlin, Germany, 2010.
 M. N. Salihin Yusoff and A. Zakaria, “Determination of
the optimum filter for qualitative and quantitative
myocardial SPECT imaging,” Iranian Journal of Radiation
Research, vol. 6, no. 4, pp. 173–181, 2009.
 E. P. Michael, Ed., PET: Molecular Imaging and Its Biological
Applications, Springer, New York, NY,USA.
 M. A. King, S. J. Glick, B. C. Penney, R. B. Schwinger, and
P. W. Doherty, “Interactive visual optimization of SPECT pre
reconstruction filtering,” Journal of Nuclear Medicine, vol. 28,
no. 7, pp. 1192–1198, 1987.
 M. A. King, R. B. Schwinger, P. W. Doherty, and B. C. Penney,
“Two-dimensional filtering of SPECT images using the Metz
and Wiener filters,” Journal of Nuclear Medicine, vol. 25, no.
11, pp. 1234–1240, 1984.
 J. M. Links, R. W. Jeremy, S. M. Dyer, T. L. Frank, and L. C.
Becker, “Wiener filtering improves quantification of regional
myocardial perfusion with thallium-201 SPECT,” Journal of
Nuclear Medicine, vol. 31, no. 7, pp. 1230–1236, 1990.
 G. V. Heller, A. Mann, and R. C. Hendel, Nuclear Cardiology:
Technical Applications, McGraw-Hill, New York, NY, USA,
 A. Takavar, G. Shamsipour, M. Sohrabi, and M. Eftekhari,
“Determination of optimum filter in myocardial SPECT: a
phantom study,” Iranian Journal of Radiation Research, vol. 1,
no. 4, pp. 205–210, 2004.
 M. N. Salihin Yussoff and A. Zakaria, “Relationship between
heart ratio in99mTc myocardial SPECT,” Iranian Journal of
Radiation Research, vol. 8, no. 1, pp. 17–24, 2010.
 P. V´ era, A. Manrique, V. Pontvianne, A. Hitzel, R. Koning,
and A. Cribier, “Thallium-gated SPECT in patients with
major myocardial infarction: effect of filtering and zooming
ventriculography,” Journal of Nuclear Medicine, vol. 40, no. 4,
pp. 513–521, 1999.
 P.-Y. Marie, W. Djaballah, P. R. Franken et al., “OSEM
reconstruction, associated with temporal Fourier and depth-
dependant resolution recovery filtering, enhances results
from Sestamibi and 201Tl 16-Interval gated SPECT,” Journal
Nuclear Medicine, vol. 46, no. 11, pp. 1789–1795, 2005.
 S. Minoshima, H. Maruno, N. Yui et al., “Optimization
of Butterworth filter for brain SPECT imaging,” Annals of
Nuclear Medicine, vol. 7, no. 2, pp. 71–77, 1993.
 E. Raeisi, H. Rajabi, M. R. Aghamiri et al., “Qualitative
evaluation of filter function in brain SPECT,” Iranian Journal
of Nuclear Medicine, vol. 15, no. 27, pp. 1–8, 2007.
 M. Lyra, “Single photon emission tomography (SPECT)
and 3D images evaluation in nuclear medicine,” in Image
Processing, Y.-S. Chen, Ed., InTech, Vienna, Austria, 2009.
 I. Gunes, I. Sarikaya, T. Ozkan, and T. Akbunar, “Detection
efficiency of different bone SPECT processing protocols for
the diagnosis of “spina bifida”,” Journal of Nuclear Biology and
Medicine, vol. 37, no. 2, pp. 49–52, 1993.
 L. E. Holder, J. L. Machin, P. L. Asdourian, J. M. Links,
and C. C. Sexton, “Planar and high resolution SPECT bone
Medicine, vol. 36, no. 1, pp. 37–44, 1995.
 M. A. King, B. C. Penney, and S. J. Glick, “An-imagedependent
Metz filter for nuclear medicine images,” Journal of Nuclear
Medicine, vol. 29, no. 12, pp. 1980–1989, 1988.
 K. Bethge, G. Kraft, P. Kreisler, and P. Walter, Medical
Applications of Nuclear Medicine, Springer, 2004.
 J. A. Carrasquillo, J. V. Rogers, D. L. Williams, W. P. Shuman,
D. O. Olson, and S. M. Larson, “Single-photon emission
computed tomography of the normal liver,” American Journal
of Roentgenology, vol. 143, pp. 937–943, 1983.
 C. de Sadeleer, A. Bossuyt, E. Goes, and A. Piepsz, “Renal
technetium-99m-DMSA SPECT in normal volunteers,” Jour-
nal of Nuclear Medicine, vol. 37, no. 8, pp. 1346–1349, 1996.
 D. Groshar, B. Moskovitz, E. Issaq,and O. Nativ, “Quantitative
SPECT of DMSA uptake by the kidneys: assessment of
reproducibility,” Kidney International, vol. 52, no. 3, pp. 817–
 T.-C. Yen, W.-P. Chen, S.-L. Chang, R.-S. Liu, S.-H. Yeh,
and C.-Y. Lin, “Technetium-99m-DMSA renal SPECT in
diagnosing and monitoring pediatric acute pyelonephritis,”
Journal of Nuclear Medicine, vol. 37, no. 8, pp. 1349–1353,
 M. Lyra, K. Skouroliakou, C. Georgosopoulos, C. Stefanides,
and J. Jordanou, “Single photon emission computed tomogra-
raphy,” in Proceedings of the 4th International Conference on
Medical Image Computing and Computer-Assisted Intervention
(MICCAI ’01), W.J. Niessen and M. A. Viergever, Eds., vol.
2208 of Lecture Notes in Computer Science, pp. 1222–1223,
Springer, London, UK, 2001.
 M. Brenner, D. Bonta, H. Eslamy, and H. A. Ziessman,
“Comparison of99mTc-DMSA dual-head SPECT versus high-
resolution parallel-hole planar imaging for the detection of
renal cortical defects,” American Journal of Roentgenology, vol.
193, pp. 333–337, 2009.
 N. Sheehy, T. A. Tetrault, D. Zurakowski, A. H. Vija, F.
H. Fahey, and S. T. Treves, “Pediatric99mTc-DMSA SPECT
performed by using iterative reconstruction with isotropic
resolution recovery: improved image quality and reduced
radiopharmaceutical activity,” Radiology, vol. 251, no. 2, pp.
 W. R¨ omer, N. Reichel, H. A. Vija et al., “Isotropic reconstruc-
tion of SPECT data using OSEM 3D: correlation with CT,”
Academic Radiology, vol. 13, no. 4, pp. 496–502, 2006.
 P. J. Roach, D. L. Bailey, and G. P. Schembri, “Reinventing
ventilation/perfusion lung scanning with SPECT,” Nuclear
Medicine Communications, vol. 29, no. 12, pp. 1023–1025,
 H. Gutte, J. Mortensen, C. V. Jensen et al., “Comparison of
V/Q SPECT and planar V/Q lung scintigraphy in diagnosing
acute pulmonary embolism,” Nuclear Medicine Communica-
tions, vol. 31, no. 1, pp. 82–86, 2010.
 P. Reinartz, H. J. Kaiser, J. E. Wildberger, C. Gordji, B.
Nowak, and U. Buell, “SPECT imaging in the diagnosis of
pulmonary embolism: automated detection of match and
mismatch defects by means of image-processing techniques,”
Journal of Nuclear Medicine, vol. 47, no. 6, pp. 968–973, 2006.
 B. Harris, D. Bailey, S. Miles et al., “Objective analysis of
embolism,” American Journal of Respiratory and Critical Care
Medicine, vol. 175, no. 11, pp. 1173–1180, 2007.
14 International Journal of Biomedical Imaging Download full-text
 P. Weinmann, J.-L. Morettil I, and M. W. Brauner, “Usefulness
of tomographic versus planar lung scintigraphy in suspected
pulmonary embolism in a daily practice,” The Open Medical
Imaging Journal, vol. 2, pp. 49–55, 2008.
 M. Lyra, M. Gavrilelli, V. Lyra, G. Kokona, and K. Skouro-
liakou, “Lungs SPECT image processing for volume and
perfusion index estimation,” in Proceedings of the 8th IEEE
International Conference on BioInformatics and BioEngineering
(BIBE ’08), pp. 1–5, Athens, Greece, October 2008.
 H. Zaidi, “Comparative methods for quantifying thyroid
volume using planar imaging and SPECT,” Journal of Nuclear
Medicine, vol. 37, no. 8, pp. 1421–1426, 1996.
 J. W. van Isselt, J. M. H. de Klerk, P. P. van Rijk et al.,
“Comparison of methods for thyroid volume estimation in
patients with Graves’ disease,” European Journal of Nuclear
Medicine and Molecular Imaging, vol. 30, no. 4, pp. 525–531,
 Y.-W. Bahk, S.-K. Chung, Y.-H. Park, S.-H. Kim, and H.-K.
Lee, “Pinhole SPECT imaging in normal and morbid ankles,”
Journal of Nuclear Medicine, vol. 39, no. 1, pp. 130–139, 1998.
 A. K. Pandey, G. S. Pant, and A. Malhotra, “Standardization of
SPECT filter parameters,” Indian Journal of Nuclear Medicine,
vol. 19, no. 2, pp. 30–35, 2004.
 Performance Measurement of Scintillation Cameras, National
Electrical Manufacturers Association. NEMA Standards Pub-
lication, Rosslyn, Va, USA, 2001.
 J. A. Parker et al., “Task Force Members (V6.0),” SNM
Procedure Guideline for General Imaging, 2010, http://inter-
active.snm.org/docs/General Imaging Version 6.0.pdf.
 A. Larsson, S. J. Mo, T. Sundstr¨ om, and K. Riklund, “Gaussian
pre filtering of 123I DAT SPECT images when using depth-
independent resolution recovery,” Physics in Medicine and
Biology, vol. 52, no. 18, pp. N393–N399, 2007.
 S. Mustafovic, K. Thielemans, D. Hogg, and P. Bloomfield,
“Object dependency of resolution and convergence rate in
OSEM with filtering,” in Proceedings of the Nuclear Science
Symposium Conference Record, vol. 3, pp. 1786–1790, Novem-
 A. Seret, “Number of iterations when comparing MLEM/
OSEM with FBP,” Journal of Nuclear Medicine, vol. 45, no. 12,
p. 2125, 2004.
 A. Seret and J. Forthomme, “Comparison of different types
of commercial filtered backprojection and ordered-subset
expectation maximization SPECT reconstruction software,”
Journal of Nuclear Medicine Technology, vol. 37, no. 3, pp. 179–
 S. Vandenberghe, Y. D’Asseler, R. van de Walle et al., “Iterative
Medical Imaging and Graphics, vol. 25, no. 2, pp. 105–111,
 Y. Yan and G. L. Zeng, “Scatter and blurring compensation
in inhomogeneous media using a post processing method,”
International Journal of Biomedical Imaging, vol. 2008, Article
ID 806705, 11 pages, 2008.
 K. S. Alzimami, S. A. Sassi, and N. M. Spyrou, “A comparison
between 3D OSEM and FBP image reconstruction algorithms
in SPECT,” in Advances in Electrical Engineering and Computa-
tional Science, vol. 39 of Lecture Notes in Electrical Engineering,
pp. 195–206, Springer, 2009.