Comparison of Reconstructive Methods Using Different Filters to Study Cardiac Wall Motions in Gated Single Photon Emission Computerized Tomography
The aim of study is to comparison of two reconstructive
methods using different filters to study the five cardiac wall motions
via Gated single photon emission computerized tomography imaging was done
through Gated SPECT (with a two-day protocol) and quantitative coronary
angiography (QCA) on 25 patients (16 males, 9 females, mean ages, 54.08
year). Angiography was performed on patients about 1 to 5 days before
scanning. Regional wall motion was determined through two methods: using
Gated SPECT, FBP and OSEM reconstructive methods and changes in frequency
and spectrum slope in Metz, Butterworth and Ramp, it creates 42 sets.
Motion disorders were classified in four groups. This data was compared
and evaluated to data which was gained from QCA method in which motion
disorders were classified in to four groups, too. The result reveals that
in order to study function of each WM, the accurate and precise method
is as follows (r = 0.7): For antero-basal wall OSEM reconstructive method
with Ramp 2-8 filter and FBP reconstructive method with Metz 5-9 and Butterworth
0.35-9 filters is an accurate method. Applying OSEM with Ramp 4-8 filter
and FBP with Metz 4.5-9 and Butterworth 0.35-9 filters for postero-basal
wall is a suit method. OSEM with Ramp 2-8 filter and FBP with Metz 4-9
and Butterworth 0.30-9 filters for antero-lateral is a sufficient method.
For apex wall, OSEM with Ramp 4-8 filter and FBP with Metz 4.5-9 and Butterworth
0.35-3 filters is a reliable method. Finally, applying OSEM with Ramp
2-8 filter and FBP with Metz 4.5-9 and Butterworth 0.35-9 filters for
diaphragmatic wall is an accurate method. Electrocardiographic Gated single
photon emission computed tomography (EGS) supplies worthwhile functional
data to cardiologists. Exercising two physical factors of reconstructive
methods and filtration in Gated SPECT, significant information can be
obtained about cardiac wall motions. It suggests using an appropriate
reconstructive method and filtration for studying cardiac wall motions
by non-invasive and economical Gated SPECT method supplies maximum results.
Although regional wall contractility can be quantified, it is more commonly
assessed qualitatively. A normal contraction of the left ventricle should show
uniform thickening and inward motion of the myocardium. A hypo-kinetic region
demonstrates decreased thickening or inward motion compared with other regions.
A kinetic region shows an absence of thickening and motion. A dyskinetic region
shows an absence of thickening and paradoxical motion during systole and diastole.
Some cases such as motionlessness or severe decrease of cardiac wall motion
in infarction regions are observed in which no or little bloodstream exists
in cardiac muscles. Therefore, identifying the severity and location of lesion
plays a significant role in future treatments. Gated SPECT MPI (myocardial perfusion
imaging) is a method, which helps a physician to dynamically study the wall
motion of a patients heart (Sciagra and Leoncini, 2005).
Gated SPECT MPI is a diagnostic method (Suratkal et al.,
2003), helping a physician in her/his decision-making on choosing her/his
treatment process. This method has some advantages, such as diagnose coronary
thrombosis, reveal coronary vessels diseases, estimate severity of ischemia,
categorize a patients risk of getting sick and also categorize illness symptoms.
Furthermore, using a special algorithm, plenty of information can be obtained
about cardiac functional parameters. Depending on the filtration type, images
can be sharper. This will change the image and its data. It is manifested that
there is a correlation between regional wall motion abnormalities and severe
perfusion defects (Johnson et al., 1997). In this
study, the assessment of the five cardiac wall motions: postero-basal, antero-basal,
apex, diaphragmatic and antero-lateral including the local wall motion of each
wall have been considered. With the assistance of a special algorithm, in a
short processing time, EGS method can produce reliable data of global and regional
parameters of cardiac function (Sharir et al., 2001;
Maruyama et al., 2002; Lima
et al., 2003; Hida et al., 2003; Murashita
et al., 2003; Giubbini et al., 2004).
EGS can assess and measure the thickening of wall in systole and diastole. The
ECG-Gated myocardial perfusion SPECT using special softwares can produce reliable
and significant data and also can measure ejection fraction, regional wall motion
and cardiac volumes. Non-invasive EGS imaging is a quick and economical method
(Levine et al., 1999). QCA is another method for
observing dynamic wall motion in which cardiac factors by using advance software
can be shown quantitatively (Sharir et al., 2000a).
This method is known as a precise method for qualitative study of WM, for estimating
coronary structure/stricture and measuring cardiac WM (Sharir
et al., 2000b; Wahba et al., 2001; Candell-Riera
et al., 2004; Sockalingam et al., 2005;
Sciagra and Leoncini, 2005; Gur et
al., 2006; Alfeeli et al., 2007; Berman
et al., 2007). The WM data gained by QCA, is used as a reference
to validate EGS method. As a result, the accuracy of functional parameters acquired
by EGS will be assessed.
The objective of this study is examining functions of FBP (Filter Back
Projection) and OSEM (Ordered-Subsets Expectation Maximization) reconstructive
methods using Metz, Ramp and Butterworth filters to study the five cardiac
wall motions via Gated SPECT method.
MATERIALS AND METHODS
This is a cross-sectional study and a predictive research with continuous
sampling done in Department of Nuclear Medicine, Shahid Rajaee
Heart Hospital, Tehran, Iran, 2007.
Study group: EGS was performed on twenty-five patients (16 males,
9 females with the age range of 40-68 and mean age, 54.08 year) according
to two-day protocol and gating in stress phase. They all had an angiography
five days in advance and none of them had a history of infarction. In
addition, they were not under revascularization at interval between angiography
Exercise protocol: Patients did the exercise test with treadmill
and Bruce protocol while the use of Beta-blockers and Nitrates was cut
off. For 85% of patients the end of the time were target heart rate, the
exercise ECG>2 mm ST segment depression on, or typical ischemic chest
pain. In 15% of the rest, positive exercise ECG and typical exercise-induced
angina and the rate of maximum target determined the end of the exercise
Radionuclide protocol: Two-day Gated SPECT protocol was performed
with 99mTc-MIBI. With respect to the weight of patients 15-22
mCi dose of 99mTc-MIBI was injected intravenously. This amount
of radioactive with increasing the excess numeration would increase the
quality of images. All imaging was done 5 to 30 min after stopping exercise.
The SPECT imaging protocol: A dual-head camera SPECT equipped
with SMV detectors and DST-XLi made by France located in Shahid Rajaee
heart hospital, was used in this study. The size of the detector is 540x400
mm (used for 45 to 560 KeV) with 3.8 inch thickness and having 84 hexagonal
photomultiplier tube (PMT) and eight circular PMT. The inherent power
of resolution = 6.5 mm and the power of energy resolution = less than
10% and has AXL (4.2.1 version) software. Collimator is of high resolution
and low energy. For image acquisition, a 20% acceptance window around
the 140 KeV image peak was used, a 64x64x16 matrix was utilized for all
studies. Stress acquisitions were gated at eight frames/cycle, with 100%
beat acceptance. Detectors were supplied with a cardiac image (in 40 sec)
in quadrant circular orbit; for each six grades, one image was captured.
During this time, each detector provided 16 frames of heart. Hence, two detectors
provided 32 frames from diverse angles of heart. Imaging began in semicircle,
moving from oblique anterior 45 degrees to oblique posterior 135 degrees. The
high rate of counting in 99mTc-MIBI allows to uses Gated SPECT technique,
which is used for estimating cardiac perfusion and function coincidently (Fakhri
et al., 2000; Go et al., 2004;
Berman et al., 2007). In general, using this method the pattern of
bloodstream in heart at high activity or rest, wall motion and ejection fraction
of ventricular, can be generally and locally delineated. The projection data
sets were pre-filtered using studied filters.
OSEM method: Applying Ramp filter in the method gained fifteen
different sets. For using Ramp filter twelve sets were assessed. These
sets were obtained by changing two subsets and iteration parameters. Iteration
changes included: 2, 3 and 4 and subsets changes included: 4, 8, 12 and
16. In this method, the multiple of two parameters affected the images.
FBP method: Metz and Butterworth filters were used in FBP method
from which 30 sets were acquired (15 sets for Butterworth and 15 sets
for Metz). For using Butterworth, 15 different sets were used which were
gained from changes in cutoff frequency and slope spectrum. The changes
of cutoff frequency included: 0.25, 0.30, 0.35, 0.40 and 0.45. Changes
of order included: 3, 6 and 9. In Butterworth filter the radical changes
is caused by cutoff frequency. As the cutoff frequency is in lower amount,
the images are uniform. On the contrary, the higher the number of orders,
more uniform the images. Consequently, an optimal parameter is chosen
to reconstruct the images by this filter.
Fifteen different sets were applied in Metz filter which changes were
caused by changing on FWHM and order parameters. The changes of FWHM are
as follows: 4, 4.5, 5, 5.5 and 6. The changes of order included 3, 6 and
9. The effect of FWHM on images is like the higher the number of parameters,
the more uniform are the images. Similarly, order has the same effect.
Consequently, an optimal parameter is obtained by combining these two
parameters in reconstructing the images using this filter.
Angio-imaging: AI 1000 (General Electric Medical System, USA)
was used for angiography under windows 2000. In QCA, WM can be acquired
by Revision B, No. 2002377-031 software which acts similar to SPECT software
for edge detection action. Information on WM recorded by terminal menu
in card-wall program and the WM of five regions: antero-basal, postero-basal,
apex, diaphragmatic and antero-lateral were analyzed by Compare program.
The motion of each region was illustrated in this program in percentage.
In this program, the information of patients and WM mentioned in 42 sets
was scored and recorded quantitatively. Moreover, WM in five mentioned
regions were scored and evaluated by QCA software.
Scan interpretation: The patients images were converted to three-dimensional
images and reconstructed by OSEM and FBP methods (Yanagisawa
and Maru, 2001). Metz and Butterworth filters were used in FBP method and
Ramp filter was used in OSEM method which 42 sets were gained (15 for Butterworth,
15 for Metz and 12 for Ramp) (Hambye et al., 2004;
Berman et al., 2007). In this process, Region
of Interest (ROI) was delineated around the heart in different views (frames)
and wedges in such a way that the center of heart could be observed (Liu
et al., 2005). Therefore, thoracic cross-sectional slices reoriented
to short axis slices by modern reconstructive method and special software (Germano
et al., 2000; Sharir et al., 2000b). A
reliable algorithm which used by others was used to analyze the data (Sharir
et al., 2000a; Candell-Riera et al., 2004).
Through using this program the border of basal and apical regions in left ventricular
can be calculated and the distance between endocardial and epicardial was also
identified. Distinguishing endocardial borders in the end-systole and end-diastole
can assess the local motion. Then the possibility of studying dynamic WM was
provided. The end levels of systole and diastole were estimated according to
Simpson. After comparing this data with basic data of a healthy heart, they
enable user to define lesion precisely. Regional disorders and cardiac volumes
can also be obtained from the mentioned data.
By changing the physical parameters of data process, such as filtration, by
Butterworth and Metz and Ramp by cutoff frequency and different degree with
OSEM and FBP reconstructive methods, different results can be acquired (Haddad
and Porenta, 1998; Adachi et al., 2000; Yanagisawa
and Maru, 2001). Motion disorders were classified in four scales (0 = normal,
1 = mild hypokinesia, 2 = moderate to severe hypokinesia and 3 = akinesis or
dyskinesia). Such data was obtained and compared by the data gained via QCA
method. Hence, the best choice was gained by selecting the optimal method.
Statistical analysis: In Butterworth filter (Adachi
et al., 2000) fifteen sets were used for each wall (order: 3, 6 and
9 and cutoff: 0.25, 0.30, 0.35.0.40 and 0.45). In Metz filter (King
et al., 1988) also used fifteen sets for each wall (Order: 3, 6 and
9 and FWHM: 4, 4.5, 5, 5.5 and 6) and in Ramp filter (Haddad
and Porenta, 1998) twelve different sets for each wall were obtained. Subsets:
4, 8, 12 and 16 and iteration: 2, 3 and 4). Finally data of WM gained by SPECT
in 42 filter sets and FBP and OSEM reconstructive methods were compared to the
achieved semi-quantitative results of WM by QCA. After evaluating the coincidence
of the acquired figures, excellent correlation was observed among the WM figures
by Gated SPECT and QCA methods for each wall motion.
In this study, 25 patients were referred to 99mTc-MIBI gated
SPECT. There was no documented data suggesting any change in clinical
status of the patients during the time interval between EGS and quantitative
coronary angiography. Clinical characteristics of patients were summarized
in Table 1.
The total wall motion score based on these two projections: RAO
projection the anterior wall, apex and the inferior wall are visualized.
The regional wall motions are thus clearly assessable.
|| Patients clinical characteristics and risk factors
in this study
|| Physical parameters for the suggested filters considering
each of the reconstructive and filtration methods
||The coincidence percentage for the three filters, Ramp,
Metz and Butterworth, with various parameters in the five cardiac
(four-chamber view): septum, apex and the lateral wall are visualized.
All segments show normal WM as indicated by the motion of the endocardial
edge between end-systole and end-diastole. WM is utilized as a measurement
parameter for cardiac functions aimed at diagnosing cardiac illnesses,
trends of a patients recovery or improvement and diagnosing various myocardial
diseases. By assessing the five cardiac WMs with function of organs, the
viability of cardiac muscle after infarction is determined and after surgery,
pursuing how the treatment is responded and evaluating the function of
improved tissues will be available.
The motion of five regions: Antero-basal, antero-basal, apex,
diaphragmatic and antero-lateral was evaluated under 42 sets. Thus, the
coincidence between degrees of wall motion gained in cardiac perfusion
scan by gated methods in different parameters was compared to degrees
gained by angiography. So, as to estimate the coincidence of the acquired
figures of the two methods, the Kendalls TAU-B was used and classified
based on the amount of coincidence. The results revealed that a logical
and powerful correlation exists among groups. The results in Table
2 and 3 show that for studying antero-basal wall,
OSEM reconstructive method with Ramp 2-8 (matching percentage = 92% and
correlation = 0.96) and FBP reconstructive method with Metz 5-9 (matching
percentage = 92% and correlation = 0.96) and with Butterworth filters
0.35-9 (mp = 96% and r = 0.90). For studying postero-basal wall, OSEM
with Ramp 4-8 (mp = 96% and r = 0.84) and FBP with Metz 4.5-9 (mp = 92%
and r = 0.98) and with Butterworth filters 0.35-9 (mp = 92% and r = 0.84).
For studying antero-lateral wall, OSEM with Ramp 2-8 (mp = 92% and r =
0.89) and FBP with Metz 4-9 (mp = 96% and r = 0.94) and with Butterworth
0.30-9 filters (mp = 96% and r = 0.94). For studying apex wall, OSEM with
Ramp 4-8 (mp = 96% and r =0.94) and FBP with Metz 4.5-9 (mp = 96% and
r = 0.94) and with Butterworth 0.35-3 filters (mp = 92% and r = 0.91).
For studying diaphragmatic wall, OSEM with Ramp 2-8 (mp = 96% and r =
0.92) and FBP with Metz 4.5-9 (mp = 96% and r = 0.97) and with Butterworth
0.35-9 (mp = 96% and r = 0.97). The best physical parameters for the suggested
filters considering each of the reconstructive and filtration methods
are shown in Table 2. The coincidence percentage of
the three filters, Ramp, Metz and Butterworth, with various parameters
in five cardiac WMs is compared and results are shown in Table
This study demonstrated the usefulness of EGS in the assessment of wall
motion by using different reconstruction methods and filters in comparison
with QCA. There was a good agreement between EGS and QCA with 99mTc-MIBI
with using Kendalls TAU-B test. The motion of five regions: antero-basal,
antero-basal, apex, diaphragmatic and antero-lateral was evaluated under
two physical factors (42 filter sets and two reconstruction methods).
Thus, the coincidence between degrees of wall motion gained in cardiac
perfusion scan by gated methods in different parameters was compared to
degrees gained by QCA. Among the achieved results of QCA and Gated SPECT,
the best correlation and percent match for each cardiac wall motion was
computed as shown in Fig. 1-3.
The Metz filter is a combination of deconvolution and smoothing filters (King
et al., 1988). Most SPECT filter functions allow the user to control
the degree of high frequency suppression by choosing a cut-off frequency, or
similar filter parameter, which determines where the filter rolls off to zero
gain. The location of this cut-off frequency determines how the filter will
affect both image noise and resolution. Low cut-off frequencies provide good
noise suppression, but they can blur the image. Higher cut-off frequencies can
preserve resolution, but often suppress noise insufficiently. An optimum cut-off
frequency should exist for a particular filter function, which compromises the
trade-off between noise suppression and spatial resolution degradation.
The highest coincidence percentage of acquired figures
via EGS method with FBP reconstructive method and Metz filter for
the five selected cardiac WMs vs. WM obtained figures using QCA method
The highest coincidence percentage of acquired figures
via EGS method with FBP reconstructive method and Butterworth filter
for the five selected cardiac WMs vs. WM obtained figures using QCA
The highest coincidence percentage of acquired figures
via EGS method with OSEM method and Ramp filter for the five selected
cardiac WMs vs. WM obtained figures using QCA method
optimum will depend on factors such as the detector response function, the spatial
frequencies of the object and the count density of the image (Gilland et al., 1988).
The advantage of post reconstruction processing for recovery of resolution
is that spatial resolution (MTF) varies much less across a given tomographic
slice than it does with distance away from the face of a collimator in planar
images (King et al., 1988). This study demonstrates
that the reorientation algorithm and the interpolation method significantly
affect the accuracy of quantitative image analysis in myocardial SPECT perfusion
The proposed cardiac wall motion analysis method uses epicardial and
endocardial boundaries that were obtained from long axis slices for each
time gate. An alternative approach is to use the 3-D reconstructed SPECT
images from all the time gates simultaneously in the determination of
epicardial and endocardial boundaries. Spatial and temporal smoothing
could be performed as part of such an analysis in order to diminish noise-related
The motions of systole and diastole were examined under the name of WM (Sciagra
and Leoncini, 2005). It is clear that, wall deficiencies such as Ischemia
or Infarction create disorder in normal cardiac wall motion. Therefore, with
respect to the significance of correct diagnosis of motion degrees, a highly
accurate method is desired (Sharir et al., 2001;
Maruyama et al., 2002; Lima
et al., 2003; Hida et al., 2003; Murashita
et al., 2003; Giubbini et al., 2004).
This study also shows that the reconstructive method and the kind of filtration
alongside the decrease in noise and also enhancement of image resolution, increasing
the numeration aimed at decreasing the casual errors, omitting of yelp in crude
images, rational increase in the power of smoothing images, increasing the signal
to noise ratio, improving the resolution power and increasing sharpness, all
can significantly play a positive role in the accuracy and correctness of the
The results showed that the lower the product of SxI the more uniform
the images. However, the uniformity of images gained by OSEM were identical
to the images obtained by the saved filtered/filtration method. This caused
the accuracy of the system in distinguishing the borders of cardiac images
taken from diverse views to decrease; consequently, WM lacks the necessary
accuracy. In the OSEM, the higher the subset numbers the longer the process
and the more noise of reconstructed images. The numbers of views of each
subset will be essential in determining the required number of iterations
aimed at estimating the regional quantities such as WMs.
Limitations of the study: There are some limitations in this study,
which need to be addressed as follows:
Firstly, detect the abnormal size of the heart when compared with a normal
heart, or the existence of an irritating activity in the digestive system,
will cause a defect in boundary of cardiac image.
Secondly, the Vision 6 software has been used in the process of preparation
of three-dimensional images. Therefore, the software will not be able to compute
the proper figure of ejection fraction and cardiac wall motions. Generally,
the reduction in image quality is caused by collimator response, scatter and
photon attenuation having a negative effect on contrast and resolution of the
images. Therefore, since in nuclear medicine data is received counts into detectors
any sorts of omission in photons leads to omission of diagnostic data (Gilland
et al., 1988; Fakhri et al., 2000).
Thirdly, comparison of wall motion analysis and ejection fraction calculations
of gated data with echocardiography was not addressed in this study. It
will be of interest to search for a correlation between these modalities.
Fourthly, patients with previous myocardial infarction and multi-vessel
disease were not evaluated in this study.
Fifthly, due to not having a fixed R-R interval, in patients with cardiac
arrhythmia; Gated SPECT method is not possible.
Finally, the use of Gated SPECT can neutralize the negative effects of artifacts.
The degree of WM, gained by EGS, is independent to the ventricular position.
Since the images were captured in 180 degrees around the heart, they are highly
accurate. Whatever seems to be problematic is the changes in filtration for
processing data related to each of the five walls, that with regard to advanced
technology in medical instruments, the changes of filtration parameters and
selection of reconstructive method would be possible. With a little care, the
operator can easily use the appropriate filter for evaluating the motion, extent
and severity of lesion in particular wall worthwhile supplementary method with
regard to cardiac wall under observation. It suggested that for evaluating the
five WMs, the special filter related to that wall should be used and for reconstructing
images in order to study the blood perfusion of cardiac muscules, another appropriate
filter ought to be used. This method causes the accuracy in both methods to
rise, due to the fact that the appropriate filter for such aim has been distinguished.
The only drawback of this method is its being time-consuming due to the need
for a repetition of image reconstruction.
By choosing the appropriate filtration parameter and reconstructive method,
the exact assessment of postero-basal, antero-basal, apex, diaphragmatic
and antero-lateral walls can be calculated in a patients heart. For studying
antero-basal wall, OSEM reconstructive method with Ramp filter 2-8, for
studying postero-basal wall, FBP and Metz filter 4.5-9, for studying antero-lateral
wall, FBP reconstructive method with Metz filter 4-9, for studying apex
wall, FBP reconstructive method with Metz filter 4.5-9, for studying diaphragmatic
wall, FBP reconstructive method with Metz filter 4.5-9, may create the
best accuracy and exactness for functional WM parameter; hence they are
the best choices (p<0.001). Using of optimal method and filter for
specific wall will certainly increase the accuracy of the study. By applying
two physical factors related to reconstructive and filtration methods
in EGS, which is a non-invasive, economical and quick method for estimating
WM, this method may be used as a supplementary method to gain more data,
besides other methods such as QCA and echocardiography.
The researchers thank the partners and staff at Rajee Hospital Nuclear
Medicine for the use of their time, expertise and resources.
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