Research Article  Open Access
Optimisation of Simultaneous Tl201/Tc99m Dual Isotope Reconstruction with MonteCarloBased Scatter Correction
Abstract
Simultaneous Tl201/Tc99m dual isotope myocardial perfusion SPECT is seriously hampered by downscatter from Tc99m into the Tl201 energy window. This paper presents and optimises the orderedsubsetsexpectationmaximisation(OSEM) based reconstruction algorithm, which corrects the downscatter using an efficient Monte Carlo (MC) simulator. The algorithm starts by first reconstructing the Tc99m image with attenuation, collimator response, and MCbased scatter correction. The reconstructed Tc99m image is then used as an input for an efficient MCbased downscatter simulation of Tc99m photons into the Tl201 window. This downscatter estimate is finally used in the Tl201 reconstruction to correct the crosstalk between the two isotopes. The mathematical 4D NCAT phantom and physical cardiac phantoms were used to optimise the number of OSEM iterations where the scatter estimate is updated and the number of MC simulated photons. The results showed that two scatter update iterations and 10^{5} simulated photons are enough for the Tc99m and Tl201 reconstructions, whereas 10^{6} simulated photons are needed to generate good quality downscatter estimates. With these parameters, the entire Tl201/Tc99m dual isotope reconstruction can be accomplished in less than 3 minutes.
1. Introduction
Tc99m and Tl201 are the two most commonly used isotopes in myocardial perfusion SPECT. Both isotopes have different benefits: Tc99m has near ideal physical imaging properties for current gamma cameras, while Tl201 acts more linearly according to the blood flow and allows the evaluation of myocardial viability. In order to take the full advantage of the properties of both isotopes, Tl201/Tc99m dualisotope imaging has been suggested [1, 2]. Acquisition protocols where Tc99m and Tl201 are imaged separately have already been validated for clinical use [3, 4].
Simultaneous Tl201/Tc99m dual isotope acquisition has also gained some interest during the last couple of years. Simultaneous acquisition has several preferable properties compared to the separate protocol, namely perfect image registration between stress and rest images and faster patient throughput. This results in reduction in patient discomfort, and possible motion artefacts and production of identical physiological conditions during stress and rest scans. The main disadvantage of the simultaneous Tl201/Tc99m acquisition is the high downscatter from Tc99m into the Tl201 window, which reduces the image quality of Tl201images and can interfere with the visualization of the possible perfusion defects. It has been shown that in order to use simultaneous Tl201/Tc99m acquisition protocol this crosstalk has to be corrected efficiently. Several correction methods have already been introduced with different results. However, there is currently no clear consensus with regards to how the downscatter should be handled [5–11].
MonteCarlo(MC) simulationbased scatter correction is one of the most general and accurate scatter correction methods available today [12]. MCsimulation is unfortunately very slow and heavy acceleration is needed in order to run reconstructions with MCbased scatter correction in clinically meaningful times. We have previously presented the reconstruction algorithm with MCbased scatter correction, which was accelerated using coarse grid and intermittent scatter modelling, allowing short reconstruction times [13].
The aim of this study was to extend our reconstruction method into dual isotope studies. Scatter correction in dual isotope studies is more demanding than in single isotope studies, due to the aforementioned Tc99m to Tl201 cross talk. Therefore the performance of the previously developed acceleration methods has to be validated for Tl201/Tc99m dual isotope myocardial perfusion SPECT. The goal was to optimise the new reconstruction algorithm in terms of reconstruction speed, without sacrificing image quality.
2. Materials and Methods
2.1. Reconstruction Algorithm Description
Our previously published reconstruction algorithm is ordered subsets expectation maximisation (OSEM) based. It uses rotationbased projectors and performs collimator modelling by incremental Gaussian diffusion and attenuation correction by multiplying raysums incrementally with appropriate attenuation coefficients and scatter correction using an efficient MCsimulator. The algorithm is described in more detail in [14] and it is used as the reconstruction engine of HERMES Medical Solutions’ HybridRecon reconstruction package (Hermes Medical Solutions, Stockholm, Sweden). The algorithm was extended into Tl201/Tc99m dual isotope reconstruction by splitting the reconstruction into three steps: Tc99m reconstruction, MCbased Tc99m downscatter simulation into the Tl201 window, and (3) Tl201 reconstruction incorporating the downscatter estimate. The Tc99m reconstruction is run using the previously published method and it should provide scatterfree Tc99m isotope distribution. The reconstructed Tc99m image is then used as an input for the same MCsimulator, which is used in the reconstruction for scattermodelling and to simulate downscatter from Tc99m into the Tl201 window. This downscatter estimate is then added in the Tl201 reconstruction to the Tl201 selfscatter estimate.
The reconstruction speed is heavily influenced by the MCsimulator performance. We previously accelerated the MCsimulator by downfolding the MCsimulator input image to smaller matrix size (coarse grid modelling) and by reducing the number of OSEM iterations where the scatter estimated is updated (intermittent scatter modelling). Both of these acceleration methods are based on the observation that scatter projections have relatively low resolution. Thus scatter can be calculated with bigger pixels and by updating scatter estimate only during the early OSEM iterations where image resolution is lower.
In addition to reducing the matrix size and lowering the number of scatter update iterations MC simulation can also be accelerated by reducing the number of simulated photons. MC simulation is used in three different occasions in our dual isotope Tl201/Tc99m reconstruction method: during Tc99m reconstruction, in Tc99m downscatter simulation, and during Tl201 reconstruction. The number of simulated photons required has to be optimised for each occasion. The optimisation was performed using Monte Carlo simulated projection data of mathematical cardiac phantom and experimentally acquired projection data of a physical cardiac phantom.
The number of OSEM iterations and subsets, coarse factor, and postfilter parameters also affect the reconstruction. The number of iterations and subsets were chosen so that the contrast values did not increase with higher parameter values. Ten iterations and eight subsets were noticed to produce good quality data and subsequently used in all studies. The effect of coarse factor was not studied in detail, as it is used in an identical manner as in the previous study. Coarse factor was set to 2. All the reconstructed images were postfiltered with a 3D Gaussian postfilter whose fullwidth at halfmaximum was 0.9 cm. This value was chosen by a visual evaluation of a large number of images.
2.2. Phantom Studies
The 4dimensional NURBSbased CardiacTorso (4D NCAT) phantom [15, 16] was used to optimise the reconstruction algorithm. Four female and four male phantoms were generated to simulate different outcomes of stressTl201/restTc99m dual isotope studies, with reversible and irreversible defects and with a hot liver in the Tc99m data. Table 1 shows the summary of the phantom activity distributions [17] and defects. The attenuation map was simulated by using appropriate linear attenuation coefficients for lungs, soft tissue, and bone at Tl201 and Tc99m energies.

Projection data were generated using an MCsimulator developed by the authors [18]. SIEMENS Symbia SPECT/CT scanner (Siemens Healthcare, Erlangen, Germany) with Low Energy High Resolution (LEHR) collimators was simulated. Noisefree projection data of simultaneous dual isotope Tl201/Tc99m data and pure Tl201 projection without Tc99m downscatter data were generated into matrix size with 64 projections, 3.3 mm pixel size, and with energy window width of 15%. After simulation projection data were collapsed into matrix size and Poissonnoise was added the number of total counts in the Tl201 window in the dual isotope study was 80 Mcts, 76 Mcts in the Tc99m window, and 10 Mcts in the pure Tl201 study.
The Jaszczak phantom with a cardiac insert with fillable defects (Data Spectrum Corporation, Hillsborough, NC, USA) was used to verify the simulation study results. Table 2 shows cardiac insert and defect activities. The defects were set in the anterior and inferior walls, with the defect in the anterior wall being reversible and the defect in the inferior wall irreversible.

Phantom data were acquired with SIEMENS Symbia SPECT/CT scanner with LEHR collimators. The dual isotope acquisition was performed with matrix size, 64 projections, two 15% energy windows centered on 72 keV and 140 keV, and 25 s acquisition time per projection. Accompanying CT was acquired with matrix, 0.98 mm pixel size, 130 keV voltage and 17 mAs, and 1.5 pitch. CT was later converted into attenuation map using bilinear scaling. Pure Tl201 data were acquired using the same filled phantom as was used in the dual isotope acquisition, but the acquisition was performed 72 hours after the dual isotope study, when Tc99m activity had nearly completely decayed. The acquisition protocol for the pure Tl201 acquisition was almost equal to the dual isotope protocol. The only difference was the longer 40 s projection time, which compensated the decay in Tl201 activity.
2.3. Reconstruction Algorithm Optimisation
The reconstruction algorithm performance was optimised in three phases using the Monte Carlo simulated projection data described above. Ten scatter update iterations and 10^{6} simulated photons in both the Tc99m and Tl201 reconstructions and 10^{7} downscatter simulated photons were used as a starting point and reference level for the optimisation. The optimisation was initiated by studying the effect of scatter update iterations and number of simulated photons in the Tc99m reconstruction.
2.3.1. Tc99m Reconstruction Optimisation
The Tc99m reconstruction was optimised by comparing 2 and 10 scatter update iterations with 10^{5} and 10^{6} simulated photons while keeping the number of downscatter simulated photons at 10^{6}. The defecttohealthy myocardium and leftventricle(LV) tohealthy myocardium contrasts were used to compare the different parameters. The contrasts were calculated as: where corresponds to the total counts of the region mentioned in the subscript. The counts were obtained by drawing regions of interest (ROI) with same area on the defect, myocardium, and LV on the oblique plane where the defect was best visible. Figure 1 shows an example of the positioning of the ROIs.
In addition to Tc99m image contrast, the downscatter projection images were also analysed by plotting profiles through the anterior and lateral downscatter projections (Figure 2).
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2.3.2. DownScatter Simulation Optimisation
After Tc99m reconstruction optimisation the effect of the number of downscatter simulated photons was studied. Downscatter was simulated with 10^{5}, 10^{6}, and 10^{7} photons and Tl201 reconstruction was run using each downscatter estimate with 10 scatter update iterations and 10^{6} Tl201 scatter photons. The reconstructed Tl201 images were analysed by investigating Tl201 image contrasts and downscatter projection profiles in a similar manner as in the Tc99m reconstruction optimisation.
2.3.3. Tl201 Reconstruction Optimisation
The final step in the optimisation process was to study the effect of scatter update iterations and the number of simulated photons in the Tl201 reconstruction. The previously optimised Tc99m reconstruction and downscatter simulation parameters were used for the two first steps of the reconstruction. Tl201 reconstruction was optimised by comparing 2 and 10 scatter update iterations and with 10^{5} and 10^{6} simulated photons. The reconstructed Tl201 image contrasts were analysed as in the previous steps.
The Jaszczak phantom dual isotope study was reconstructed using the parameters optimised with the simulated data. These images were compared with images obtained using completely unoptimised reconstruction parameters and with reconstructed images obtained using pure Tl201 data. The contrast analysis for the reconstructed Tl201 data was performed as in the previous steps. The Tc99m images were not studied.
3. Results
3.1. Tc99m Reconstruction Optimisation
Table 3 and Figure 3 show the Tc99m reconstruction optimisation results for the contrast and profile analyses, respectively, for one phantom only. These results suggest that in Tc99m reconstruction 2 scatter update iterations and 10^{5} simulated photons produce nearly identical images compared to reconstruction with 10 scatter update iterations and 10^{6} simulated photons.

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3.2. DownScatter Simulation Optimisation
Table 4 and Figure 4 present the downscatter simulation optimisation results for the contrast and profile analyses, respectively, for one phantom only. The results show that 10^{6} simulated downscatter photons provide contrasts and downscatter projections that are nearly identical to 10^{7} simulated downscatter photons. With 10^{5} simulated downscatter photons the profiles are too noisy.

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3.3. Tl201 Reconstruction Optimisation
Table 5 shows the Tl201 reconstruction optimisation results obtained with one phantom only. The table shows that 2 scatter update iterations and 10^{5} simulated photons are enough for the Tl201 reconstruction.

According to the results shown above, the optimised parameters for the Tl201/Tc99m dual isotope reconstruction are 2 scatter update iterations and 10^{5} simulated photons for the Tc99m and Tl201 reconstruction and 10^{6} simulated downscatter photons.
These optimised parameters were further used to compare optimised and unoptimised reconstructions (10 scatter update iterations and 10^{6} simulated photons for the Tc99m and Tl201 reconstruction and 10^{7} simulated downscatter photons) to reconstructions from pure Tl201 data using all the simulated phantoms. Pure Tl201 data were reconstructed using 2 scatter update iterations and 10^{5} simulated photons. Table 6 and Figure 5 present these results. The results show that optimised parameters provide contrast values that are equal with the results obtained with unoptimised parameters, but that the reconstruction times are much shorter. The downscatter compensation cannot, however, fully correct for the downscatter as can be seen by comparing the contrast values and the images of the dualisotope and pure Tl201 reconstructions.

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Table 7 and Figure 6 compare the optimised and unoptimised dual isotope reconstruction with reconstruction obtained with pure Tl201 data of the Jaszczak phantom with the cardiac insert. These physical phantom study results confirm the simulation study findings: optimised parameters give equal contrasts when compared to unoptimised reconstruction, but downscatter correction cannot fully reach the level of pure Tl201 data results.

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4. Discussion
In this study our previously presented reconstruction algorithm was extended and optimised for dual isotope Tl201/Tc99m studies in terms of reconstruction speed. The new dual isotope algorithm performs Tl201/Tc99m reconstructions by splitting the reconstruction into Tc99m reconstruction, Tc99m downscatter simulation, and Tl201 reconstruction incorporating the downscatter estimate. Two scatter update iterations and 10^{5} simulated photons for the Tc99m and Tl201 reconstructions and 10^{6} simulated downscatter photons were found to provide accurate results in clinically acceptable reconstruction times (Table 6).
The 2 scatter update iterations and 10^{5} have been found to suffice also for single isotope Tc99m reconstruction [14, 19]. Simulating more scatter photons does not lead to improvement in either ventricular or lesion contrast. For the downscatter estimation, however, we used more photons in order to make the downscatter estimate less noisy. The contrast values in the downscatter compensated Tl201 images are practically identical for 10^{5}, 10^{6}, and 10^{7} simulated downscatter photons (Table 4) but 10^{5} photons seems to generate much noisier downscatter estimate than 10^{6} photons. This noise might progress to the reconstructed images if the Tl201 activity is very low.
The developed downscatter correction method cannot fully compensate for the Tc99m/Tl201 crosstalk as can be seen by comparing the results obtained with the dual isotope data and pure Tl201 data in Tables 6 and 7. The pure Tl201 data contrasts are better than the downscatter compensation contrasts. This can be explained by the lead Xray emissions, which occur when Tc99m photons hit the collimator. These Xrays are emitted at the Tl201 energy level and they contaminate the Tl201 data. Currently our reconstruction method corrects only for patient scatter, but more accurate correction might be needed. De Jong et al. [11] have included lead Xray correction into their Tl201/Tc99m reconstruction method and have shown that it improves image quality. The only problem in including collimator effects into MCbased scatter correction is the increase in reconstruction time. The common Gaussian collimator model, which was also used in this work, is very efficient. Changing that to something more complicated will definitely have a big effect on the reconstruction times and it will also make reconstruction algorithm implementation more challenging.
Our reconstruction method has similar features to the methods published by Kadrmas et al. [10] and de Jong et al. [11]. Both of these methods also broke the reconstruction into three parts: Tc99m reconstruction, Tc99m downscatter simulation, and Tl201 reconstruction. Kadrmas et al. used effective source scatter estimation for scatter modelling, whereas de Jong et al. used Monte Carlo as we did. We focused also on optimising the reconstruction parameters, which is very important for an algorithm to be clinically acceptable. In our case the reconstruction parameter optimisation reduced the reconstruction times to approximately 1/4 of the unoptimised reconstruction times (Table 6). At the moment our dual isotope reconstruction algorithm is limited to Tl201/Tc99m reconstructions. The same concept could, however, probably be extended to other isotope pairs or to isotopes with several peaks. Reconstruction would always start with the isotope or energy peak, which has the highest energy. This data would be reconstructed and downscattered to lower windows and subsequent reconstructions would run until the isotope or energy peak with the lowest energy is reached.
Dual isotope imaging with simultaneous acquisition is clinically attractive, as it increases the possible patient throughput and reduces patient discomfort. Dual isotope imaging also offers perfect alignment and identical physiological conditions between stress and rest images, which may give additional information to the physician.
This study has limitations. MCsimulated projection data and physical phantoms were used instead of real patient studies. We tried to compensate the lack of real patient data by using realistic phantoms and we also tried to adjust the activity levels to clinically meaningful values. We believe that our optimised reconstruction method works well also with patient data but a large number of patient studies are still required to validate our method and the entire Tl201/Tc99m dual isotope SPECT.
5. Conclusion
The newly developed Tl201/Tc99m reconstruction algorithm was efficiently accelerated using a reduced number of scatter update iterations and simulated photons. Two scatter update iterations and 10^{5} simulated photons for the Tc99m and Tl201 reconstructions and 10^{6} simulated downscatter photons were sufficient for good quality images.
Conflict of Interests
There is no conflict of interests for any of the authors.
Acknowledgment
The authors dedicate this paper to the memory of professor Jyrki T. Kuikka who passed away during the preparation of this study.
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Copyright
Copyright © 2012 Tuija Kangasmaa et al. 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 cited.