Ann Nucl Med (2014) 28:103–111 DOI 10.1007/s12149-013-0789-2

ORIGINAL ARTICLE

Radius dependence of FP-CIT quantification: a Monte Carlo-based simulation study Walter Koch • Peter Bartenstein • Christian la Fouge`re

Received: 12 June 2013 / Accepted: 3 November 2013 / Published online: 20 November 2013 Ó The Japanese Society of Nuclear Medicine 2013

Abstract Objective Dopamine transporter imaging with SPECT is a valuable tool for both clinical routine and research studies. Semi-quantitative analysis plays a key role in interpreting the scans, but is dependent on numerous factors, rotational radius being one of them. This study systematically evaluates the potential influence of radius of rotation on apparent tracer binding and describes methods for correction. Methods Monte Carlo simulation scans of a digital brain phantom with various disease states and various radii of rotation ranging from 13 to 30 cm were analyzed using 4 different methods of semi-quantification. Different volumes of interest as well as a method with partial volume correction were applied. Results For conventional 3D semi-quantification methods the decrease of measured striatal binding per cm additional radius rotation lied in the range between 2.5 and 3.1 %, whereas effects were negligible when applying recoverycorrected quantification. Effects were independent of disease state. Conclusion Partial volume effects with increasing radius of rotation can lead to considerable decrease of measured binding ratios, particularly when applying dopamine transporter imaging in a research setting. Standardization

W. Koch (&)  P. Bartenstein  C. la Fouge`re Department of Nuclear Medicine, University of Munich, Marchioninistr. 15, 81377 Munich, Germany e-mail: [email protected] W. Koch Department of Radiology, University of Munich, Marchioninistr. 15, 81377 Munich, Germany

of acquisition radius can avoid the effect; correction seems feasible, but the correction factors depend on the quantification approach applied. Keywords Iterative reconstruction  FP-CIT  Radius of rotation  Monte Carlo simulation  Dopamine transporter Abbreviations and acronyms: DAT Dopamine transporter FP-CIT I-123-N-x-Fluoropropyl-2b-carbomethoxy-3b(4-iodophenyl)nortropane FWHM Full width at half maximum OSEM Ordered subset expectation maximization ROI Region of interest SPECT Single photon emission computed tomography VOI Volume of interest

Introduction Imaging of the presynaptic dopamine transporter (DAT) with single-emission computed tomography (SPECT) has evolved to be an important diagnostic tool in patients with Parkinsonian syndromes [1]. DAT-SPECT scans are used to confirm or exclude a neurodegenerative Parkinsonian syndrome [2] and in combination with semi-quantification [3] can detect subtle changes in DAT binding in striatal subregions and allow monitoring disease progression [4, 5]. Recently, a large multicenter project, the ENC-DAT study, aimed to establish camera correction factors for DAT imaging while also providing age and gender-specific normal reference values [6]. These provide an excellent basis for routine patient examinations as long as high

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standardization of acquisition and reconstruction parameters is guaranteed. Apart from gender and age, acquisition and study processing have significant impact on semi-quantitative radiotracer binding observed. It is well known in SPECT that resolution is a function of distance from the detector. The further the distance of the object from the detector, the lower will be the resolution. Low spatial resolution will cause partial volume effects and reduce the quantitative measures from small structures such as the striatum. Little is known about to what extent these effects influence quantification in FP-CIT DAT imaging. Recently, Larsson et al. [7] described effects of rotation radius on DAT images based on Monte Carlo simulation of the healthy state. Here, we systematically evaluate the effects of SPECT rotational radius on quantification results and show methods to cope with this factor using Monte Carlo simulation, OSEM reconstruction and different ways of semi-quantitative analysis. The study further extends the results to various disease states.

Materials and methods Phantom The Zubal digital brain phantom (http://noodle.med.yale. edu/zubal/, G. Zubal, Yale University, New Haven, CT, USA; [8]) was modified to simulate the typical profiles of normal radiotracer binding status and of neurodegeneration in Parkinsonian syndromes (loss of dopamine transporter binding [2]). Based on published measurements with a physical phantom [9] and patient scans [10], the activity distribution within the phantom was chosen to reflect a realistic situation found in healthy controls and in patients. For simulation of normal DAT binding, the activity concentrations of I-123 ratios between the striatal structures of each hemisphere and the remaining brain were six-to-one [10]. In order to simulate neurodegeneration, an exponential loss of dopamine transporter binding was modeled separately for the caudate and the putamen, based on tau values published in a longterm follow-up study of patients with idiopathic Parkinsonian syndromes [11] according to the formula: CS ¼
C0  exp  Ts where Cs is the activity concentration of the respective striatal region, C0 equals the striatal concentration in healthy state, T is years of disease, and the time constant s values (reflecting the rate of disease progression) being derived from [11] (caudate nucleus: s = 10.62, putamen: s = 5.18). Disease simulation was applied symmetrically to both hemispheres.

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Monte Carlo simulation The SIMIND Monte Carlo code [12, 13] was used to calculate projection data based on the digital brain phantom (256 9 256 matrix with 128 slices, 1.1 9 1.1 9 1.4 mm pixel size). A dual-headed MiE ECAM variable SPECT camera (MiE/Medical imaging Electronics, Seth, Germany) equipped with low-energy, highresolution parallel hole collimators (parallel hexagonal holes with cells of 1.11 mm diameter, 2.405 cm height, and 0.16 mm septal thickness) was entirely modeled in the software. The comparability of simulated data of this camera type with real equipment has been confirmed elsewhere [14]. The acquisition parameters were based on recommendations outlined in the procedure guidelines for neurotransmission SPECT with dopamine transporter ligands published by the European Association of Nuclear Medicine [3] and were applied to the Monte Carlo simulation. All acquisitions were optimized to obtain high spatial resolution but also to reflect the clinical use of the SPECT systems. 120 projections were obtained for each simulation with the detector heads following a 360° circular orbit in a 128 9 128 matrix with a main energy window from 143.1 to 174.9 keV. In addition, a lower (131.9–143.0 keV) as well as an upper (175.0–186.1 keV) scatter window adjacent to the main window were acquired. Pixel size was 3.0 9 3.0 mm. Physical effects, such as photon attenuation and scatter in the phantom and the crystal, degradation due to collimator resolution, septal penetration, photon interaction in the collimator as well as backscatter from detector cover material were included in the simulations. The full energy spectrum of I-123 was simulated. Ten million counts in the main energy window were simulated for each acquired projection to obtain low noise simulation data. Study counts of the main window were then scaled to obtain total counts of 2.5 million per acquisition as typically acquired in true patient scans. Scatter windows were consecutively scaled with identical factors. Poisson noise was added to each of the energy windows. Apart from the original main energy window acquisitions, scatter-corrected data were calculated based on the triple energy window correction method [15, 16]. Simulated disease states and radii of rotation A healthy state (T = 0 years) as well as disease states 2, 4, 6, 8, 10, 15, 20 and 30 years after disease onset were simulated. Therefore, simulations cover a wide range from entirely normal to advanced disease states. Each disease state was imaged with 13, 14, 15, 16, 17, 18, 19, 20 and 30 cm radius of SPECT rotation.

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SPECT processing Simulated SPECT acquisition data were transferred to a real MiE ECAM variable camera acquisition workstation and reconstructed with an OSEM algorithm (OSEM implementation based on the algorithm of Richard Larkin from Macquarie University [17]) using the MiE Scintron software. As part of the standard workflow of this commercial reconstruction software, both scatter-corrected and uncorrected projection data were smoothed using a twodimensional Gaussian filter with a FWHM of 5.65 mm. Smoothing was performed by convolution of the projection with a filter mask of [1 2 1]/4 in each direction. 4 iterations with 16 subsets were used to reconstruct data and reconstructions were corrected for attenuation (automated contour finding with separate contours for each slice [3], l = 0.143/cm for scatter-corrected data, l = 0.110/cm for uncorrected data [18], accuracy of the attenuation correction was verified with line profiles in a homogeneously ‘filled’ phantom). Automated VOI evaluation Using shift transformations only, the digital phantom was co-registered to the reconstructed transverse slices of the 13 cm rotational radius acquisition of the healthy state. Afterwards, four sets of different 3-D volumes of interest (VOIs) for striatal regions were defined: (1) based on the digital phantom morphology only (caudate, putamen, referred to as ‘morphology VOI’ method; (2) VOIs manually drawn solely on reconstructed images of the healthy state (caudate, putamen), a method commonly used in routine studies, referred to as ‘manual VOI’ method; (3) isocontour VOIs based on an 70 % isocontour threshold in the healthy state (for the entire striatum only), referred to as ‘isocontour VOI’ method; (4) large box regions covering

the striatum as well the surrounding brain (according to an approach proposed by Fleming et al. [19, 20], referred to as ‘Southampton VOI’ method). A large occipital background region was added (8624 voxels) which served as reference for all semi-quantitative analyses. VOI sizes are given in Table 1. The morphology VOI method and the manual VOI method provide separate VOIs for the putamen and the caudate nucleus, enabling additional calculation of putamen-to-caudate ratios, whereas the isocontour VOI and the Southampton VOI only allow analysis of the entire striatum. Semi-quantitative evaluation Specific radiotracer binding was determined with a common ‘conventional’ technique (for morphology, manual and isocontour VOI methods), as well with a method taking partial volume effects into consideration (Southampton VOI method). For conventional semi-quantification specific binding within striatum, caudate and putamen were calculated from the mean counts per voxel with the occipital cortex serving as reference (specific bindingstriatum = [striatum - occ. reference]/occ. reference). Since the underlying disease in patients with Parkinsonian syndromes often affects caudate nucleus and putamen with different severity, the putamen-to-caudate ratios (ratios between the specific binding in the putamen and the caudate) were also calculated. To compensate for partial volume effects in analogy to a method described by the Southampton group [20], the Southampton VOIs applied cover all counts resulting from radiotracer uptake within the striatum. Based upon these VOIs, specific binding indices (SBI) according to the forTs mula SBI ¼ cr V [19, 20] were determined. Ts is the spe0 cific radiotracer uptake in the striatum, given by the total

Table 1 Volumes of interest used for analysis and resulting percentage binding loss per cm of additional radius of rotation Method of analysis

Region

Size (ml)

Percentage binding loss per cm additional radius of rotation (±SE)—‘normal’ data (%)

Percentage binding loss per cm additional radius of rotation (±SE)— scatter-corrected data (%)

Isocontour VOI method

Striatum

9.95

2.9 ± 0.5 (2.7 ± 0.3)

2.8 ± 0.6

Manual VOI method

Striatum

8.55

2.5 ± 0.5 (2.2 ± 0.3)

2.5 ± 0.5

Morphological VOI method

Caudate

3.55

2.5 ± 0.6 (2.1 ± 0.3)

2.7 ± 0.5

Putamen

5.00

2.5 ± 0.5 (2.3 ± 0.3)

2.3 ± 0.5

Striatum

6.40

3.1 ± 0.6 (2.7 ± 0.3)

2.9 ± 0.7

Caudate

2.39

3.1 ± 0.6 (2.6 ± 0.3)

3.1 ± 0.7

Putamen

4.01

3.0 ± 0.6 (2.7 ± 0.3)

2.8 ± 0.7

Southampton VOI method

Striatum

103.40

0.1 ± 0.2 (0.1 ± 0.1)

0.0 ± 0.3

Occipital reference VOI. (used as reference for all methods of analysis)

Occipital

132.87

SE standard error

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counts measured minus the non-specific uptake, which was estimated from the occipital reference concentration. V0 is the striatal volume derived from the phantom morphology and cr is the activity concentration per unit volume measured in the occipital reference region. Further details about this procedure can be found in [19, 20]. Statistical analysis Linear regression analysis was applied to describe the relationship between specific binding ratios and radii of rotation; additionally, slopes and standard error of slopes were calculated. For the sake of comparison, binding values are given as percentage of the specific binding value determined at minimum rotational radius (13 cm), in order to account for the influence of the four different approaches of VOI analysis. To detect differences in the slopes of the linear regression curves, analysis of co-variance was applied, investigating the significance of the interaction between the classification effect (such as disease duration) and the covariate (specific binding ratio). All statistical analyses have been performed using SPSS Software version 13 (SPSS Inc, Chicago, Illinois, USA). To automate digital phantom ‘filling’, multi-threaded Monte Carlo simulation, file format conversions, calculation of noise with Poisson distribution, DICOM packaging, data exchange with a real MiE SPECT camera and automation of VOI quantification, in-house software written in VB.NET 2010 (Visual Studio 2010, Microsoft Corp., Redmond, USA) was used. VOI quantification was performed with MIPAV software (Center for Information Technology, National Institutes of Health, Bethesda, Maryland, USA). Reconstruction of the raw acquisition datasets on Scintron software was controlled by in-house automation software based on AutoIt scripting technology (AutoIt Consulting Ltd, England, http://www.autoitscript. com).

Results Results and figures presented are based on data without scatter correction, if not mentioned otherwise.

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(Fig. 1d) delivers stable binding values across all radii of rotation. Figure 2 directly compares the relationship between the specific binding and radius as a function of the method of quantification by plotting percentage binding change against radius in the healthy state. Again, a decline of binding with increasing rotational radius can be demonstrated as long as quantification methods without recovery correction are used. For these conventional methods of evaluation, percentage binding decrease with increasing radius follows a linear model and appears to be similar but not identical between the different types of semi-quantification. Co-variance analysis showed that the differences in the slopes were statistically significant (F test p \ 0.05), indicating the influence of radius of rotation depends on the method of quantification. Figure 3 demonstrates the reason for the observed binding loss with increasing SPECT rotational radius. With higher radius, resolution decreases, activity spreads beyond the true borders of the striatum and partial volume effects increase in all 3 dimensions, resulting in a loss of recovery. A horizontal 15-mm-wide line profile through the striatal area in the image acquired at low rotational radius shows a steep count increase from unspecific binding to striatal binding, whereas the increase becomes shallower with increasing radius of rotation. Only the large VOIs used for the Southampton quantification method still cover all counts resulting from striatal binding with the binding remaining stable despite increasing partial volume effects. Are effects of rotational radius dependent on disease extent? Percentage striatal binding losses with increasing SPECT rotational radius for different extents of disease are shown in Fig. 4. For this analysis, all values were normalized to the value observed at minimum rotational radius (13 cm). The analyses were limited to a disease progression up to 10 years after onset. At this time point putaminal-specific binding is less than 15 % of that in the healthy state with smaller quantification errors resulting in large percentage change. Effects of the rotational radius seem to be independent of the extent of disease, no statistical differences between the resulting regression slopes could be found (analysis of co-variance F test p = 0.457).

Effects of rotational radius on specific striatal binding Magnitude of rotation-radius effects Correlation analysis between specific binding ratios and radius of rotation in the healthy state as well as for various stages of disease is shown for each individual VOI-analysis method in Fig. 1. Loss of striatal binding with increasing radius of rotation was observed for all ‘conventional’ methods of analysis, whereas the Southampton approach

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As rotation-radius effects were independent of disease state, we confined further analysis to the simulation of the healthy state. To determine the magnitude of radius effects on quantification, we calculated percentage losses of striatal binding (as well as its subregions putamen and

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Fig. 1 Dependence of striatal binding on radius of rotation (cm): a for isocontour VOI analysis, b morphology VOI analysis, c manual VOI analysis, d Southampton VOI quantification approach with recovery correction

(Southampton VOI quantification approach) did not show rotational radius effects. Putamen-to-caudate ratios

Fig. 2 Percentage binding change with increasing acquisition radius (cm), direct comparison of quantification methods (data normalized to 100 % at 13 cm radius of rotation)

caudate) per cm radius increase (Table 1). Both uncorrected as well as scatter-corrected datasets were analyzed. Effects of rotational radius were similar for the entire striatum as well as for its subregions, independent of whether scatter correction was performed or not. However, again, effects depended on the quantification method used. Only results obtained with partial volume correction

Figure 5 shows the correlation of measured putamen-tocaudate ratios and true putamen-to-caudate ratios in the phantom using data of the morphology-based quantification approach (disease duration limited to a maximum of 10 years). The figure shows a good agreement between measured and true values; however, variation between measurements with different rotational radius is higher in studies with low putamen-to-caudate ratios. Analysis of covariance confirmed effects of the rotational radius on putamen-to-caudate quantification results (F test p \ 0.05). The figure suggests an overestimation of putamen-to-caudate ratios in scans with high rotational radius.

Discussion Imaging of the presynaptic dopamine transporter (DAT) has evolved into an important diagnostic tool for patients with Parkinsonian syndromes [1, 21–23], and thus has become a routine clinical procedure. Visual assessment of

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Fig. 3 Examples for the effects of rotational radius on image quality: below each example a 15-mm horizontal line profile centered on the striatal region is shown. a 13 cm radius of rotation, b 20 cm radius of rotation, c 30 cm radius of rotation

Fig. 4 Rotational radius effects in different stages of disease (isocontour VOI-analysis method). Rotational radii are given in centimeters

DAT-SPECT studies in many cases enables clinicians to decide whether neurodegeneration of presynaptic neurons has occurred, and confirm or exclude a neurodegenerative Parkinsonian syndrome [2]. Especially for early diagnosis, the detection of subtle changes in DAT binding in striatal subregions, and for the monitoring of disease progression [4, 5, 24] or beneficial effects of putative neuroprotective drugs [4, 5, 11, 25, 26], additional semi-quantitative measurements are mandatory [27]. Semi-quantitative analysis of DAT is influenced by various factors such as acquisition and reconstruction parameters, as well as the method of ROI or VOI analysis [3]. During the past years, several efforts have been made to standardize semi-quantitative analysis of DAT scans by generating age- and gender-specific normal reference values, optimization of reconstruction as well as automated

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Fig. 5 True vs. measured putamen-to-caudate ratios in SPECT scans with different radius of rotation (cm)

processing of SPECT images. Stringent quality control, as well as standardization of acquisition and reconstruction parameters is considered mandatory to obtain reproducible DAT-SPECT quantification results. Still, basic aspects like the influence of the rotational radius on quantitative results in sensitive imaging conditions such as for DAT-SPECT remained unclear. Our results show a considerable decrease in specific binding with increasing SPECT rotational radius in a range between 2.3 and 3.1 %/cm using conventional quantifications algorithms with VOIs based on the shape of the striatum (or its subregions putamen and caudate). For comparison, the annual loss of dopamine transporter binding in patients with idiopathic Parkinsonian syndromes is approximately 5.2 % per year [25]. A 2 cm higher acquisition radius in a follow-up-scan of a healthy person could thus mimic the typical progression of Parkinson’s disease—or—in a patient with Parkinson’s disease could

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suggest a rapid disease progression indicating a case of non-idiopathic Parkinsonian Syndrome. This factor could impact the results of critical research studies where followup scans are performed to monitor treatment effects or to determine potential neuroprotective effects of drugs. It will, however, not have significant impact in standard routine scans, since here typically other effects such as observer-dependence of semi-quantification [28, 29] may contribute more to variability of results than rotational radius. Total variability is less critical in confirmation or rejection of a Parkinson’s disease diagnosis, as dopaminergic cell loss at onset of the disease is typically higher than 50 % [30]. Even when utilizing a large rotation radius near the mechanical limit (here simulated with 30 cm rotational radius), a maximum decrease of striatal binding of approximately 30 % could be observed. Under these imaging conditions visual analysis suffers severely from the impact of partial volume effects, as shown in Fig. 3. Rotation-radius dependence of DAT-SPECT semiquantification is attributed to partial volume effects due to a drop of resolution with increasing radius. With decreasing resolution in reconstructed images, activity from within the striatum spreads over an increasing area as shown in Fig. 3, resulting in a loss of counts within the quantification VOIs (spill-out). Semi-quantitative algorithms reducing partial volume effects such as the ‘Southampton’ method proposed by Fleming et al. [19, 20] overcome the effects induced by rotational radius. The large box regions used for this approach cover the entire activity spread (both spill-out and spill-in). As expected, the effect of different rotational radii is independent of whether scatter correction is applied or not. Unfortunately we cannot conclude, to what extent the choice of rotational radius contributed to the variability of repeated scans observed in previous test/retest studies [31–35]. However, based on conclusion of our study this variability could be estimated by re-evaluation of these data using the Fleming semi-quantification approach. Our results are perfect in line with a study by Larsson et al. [7] who described a binding loss of 1.2 %/cm of rotation radius for a 2D quantification approach and loss of 2.1 %/cm for 3D quantification in FP-CIT imaging. The study design was quite similar to ours, but a different SPECT camera system was modeled and noise simulation was not performed, to minimize influence on ROI positioning. Since the evaluation of our data did not require any registration of studies to a template or manual VOI placement, we were able to include Poisson noise as important physical effect in SPECT imaging. Interestingly, our result show, that the percentage influence of rotational radius on striatal binding is independent of total striatal binding and, therefore, independent of the existence or magnitude of dopaminergic

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neurodegeneration. This seems to be contrary to findings of Larsson et al. [7] who additionally simulated scans with lower specific radiotracer binding and found lower influence of the radius of rotation in these scans (0.9 %/cm for 2D ROIs, 1.7 % for 3D VOIs). This might be attributed to the fact, that the scans with lower binding were performed to simulate dopamine D2 receptor imaging with IBZM in the study of Larsson et al. No simulation of disease states was intended. True neurodegeneration in Parkinson’s disease, as simulated in our study, involves putamen and caudate nucleus at different extent. The typically preserved higher activity in the caudate nucleus may be responsible for similar high radius dependence of binding ratios as in the simulation of the healthy state. Faster neurodegeneration in the putamen than in the caudate nucleus will most likely also be responsible for the overestimation of putamen-to-caudate ratios in scans with high radius of rotation (as well as the high variation of measured values in scans with progressed disease): as resolution decreases with radius, spill-in from preserved caudate binding to the putaminal VOI increases, artificially raising putamen-tocaudate ratios. Simple linear correction of radius effects on measured striatal binding seems feasible and could easily be implemented in software for semi-quantification. Unfortunately, the required correction values are dependent on the method of analysis/the set of VOIs used for quantification. As no clear relationship between VOI size and rotation-radius effects could be shown, one might speculate that the shape of the region also matters. This would be in line with the fact of partial volume effects being dependent on spatial resolution, region of interest and lesion size as well as shape [36]. We would also expect a dependence of potential correction factors on both the camera equipment used, particularly when dealing with fanbeam collimator geometry instead of parallel collimators, as well as the reconstruction method used [9]. Furthermore, effects of radius of rotation may also depend on the patient anatomy as the observed changes as function of radius of rotation may be different for each patient due to structural variation. Adequate correction for use in clinical studies therefore would require the establishment of equipment-specific, reconstruction-specific, analysis-specific and maybe even patient-specific factors. This is a complex task, since Monte Carlo simulation models would first need to be validated to match with true camera equipment before deriving correction factors for clinical routine use. As an alternative, correction factors could be established from measurements with physical anthropomorphic phantoms, but due to limitations resulting from their construction geometry, these phantoms cannot resemble true anatomy as close as digital phantoms. Multiple acquisitions of the same patient to calculate correction factors also seem not

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feasible, as the radiotracer equilibrium time window for scanning is limited [37] and confounding factors such as patient movement during acquisition [38] or misalignment/ observer-dependence of quantification [39] need to be additionally considered. In our opinion, the best way to deal with rotation-radius dependence would be to avoid it in first place by using a standardized low-rotation orbit (for example, 13.5 cm) that is achievable in most patient scans. If follow-up scans are required, the rotational radius of the second scan should match that of the first scan. If a standardized rotational radius is not achievable resulting in different rotational radius in follow-up scans, analysis with the algorithm suggested by Fleming et al. [19, 20, 38] should be considered as it was shown to be unaffected by changes in rotational radius as well as more robust to misalignment and movement artifacts. Although correction seems feasible, due to the reasons mentioned above, we would consider this the last choice. Some limitations of our results should be acknowledged. Our data were created under ideal conditions without movement artifacts, with standardized reorientation and with symmetrical activity concentrations in the phantom in both hemispheres. Often patients with idiopathic or nonidiopathic Parkinsonian syndromes show asymmetric radiotracer uptake. Loss of resolution with increasing rotational radius could result in some spillover of striatal activity to the contralateral side, particularly when using large volumes of interested such as for the Fleming approach. Apart from the rotation-radius effects described here, asymmetric binding could theoretically lead to an uncorrectable overestimation of binding in the striatum with lower tracer update and vice versa. We also did not examine, whether the method of reconstruction has significant influence on the results observed. Excellent correlations between reconstructions with filtered backprojection and OSEM in terms of semiquantitative values were shown previously [40]. A promising approach to reduce the influence of the rotation-radius effect might be 3-D modifications of the OSEM algorithm such as depth response-ordered subset expectation maximization which take spatially variant point spread functions into account [41, 42]. These techniques are currently not widely available, but may be of interest for further studies.

Conclusion Rotation-radius dependence of measured striatal binding values in DAT-SPECT is an effect to be considered in critical imaging conditions such as disease monitoring or evaluation of potential neuroprotective agents in research

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studies. The best way to cope with these effects is to avoid them in first place by a standardized acquisition radius. Post-quantification data correction seems feasible, but correction factors need to be defined for each method of quantification and probably also for different imaging equipment and reconstruction algorithms. Acknowledgments We would like to thank Dr. rer. nat. Dipl.-Inf. Hanno Schumacher (MiE Germany) for providing insights in the MiE implementation of OSEM reconstruction and filtering as well as providing information on how to transfer the Monte Carlo simulation data to the gamma camera system. Thanks also go to Michael Ljungberg for providing the SIMIND Monte Carlo program. Conflict of interest of interest.

The authors declare that they have no conflict

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