Ann Nucl Med (2014) 28:33–41 DOI 10.1007/s12149-013-0778-5
ORIGINAL ARTICLE
A simple algorithm for subregional striatal uptake analysis with partial volume correction in dopaminergic PET imaging Kun-Han Lue • Hsin-Hon Lin • Chih-Hao K. Kao • Hung-Jen Hsieh • Shu-Hsin Liu • Keh-Shih Chuang
Received: 7 April 2013 / Accepted: 7 October 2013 / Published online: 18 October 2013 Ó The Japanese Society of Nuclear Medicine 2013
Abstract Objective In positron emission tomography (PET) of the dopaminergic system, quantitative measurements of nigrostriatal dopamine function are useful for differential diagnosis. A subregional analysis of striatal uptake enables the diagnostic performance to be more powerful. However, the partial volume effect (PVE) induces an underestimation of the true radioactivity concentration in small structures. This work proposes a simple algorithm for subregional analysis of striatal uptake with partial volume correction (PVC) in dopaminergic PET imaging. Methods The PVC algorithm analyzes the separate striatal subregions and takes into account the PVE based on the recovery coefficient (RC). The RC is defined as the ratio of the PVE-uncorrected to PVE-corrected radioactivity concentration, and is derived from a combination of the traditional volume of interest (VOI) analysis and the large VOI technique. The clinical studies, comprising 11 patients with Parkinson’s disease (PD) and 6 healthy
K.-H. Lue H.-H. Lin K.-S. Chuang (&) Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, 101, Section 2 KuangFu Road, Hsinchu 30013, Taiwan e-mail:
[email protected] C.-H. K. Kao Department of Radiopharmaceutical Production, Buddhist Tzu Chi General Hospital, Hualien 97002, Taiwan
subjects, were used to assess the impact of PVC on the quantitative measurements. Simulations on a numerical phantom that mimicked realistic healthy and neurodegenerative situations were used to evaluate the performance of the proposed PVC algorithm. In both the clinical and the simulation studies, the striatal-to-occipital ratio (SOR) values for the entire striatum and its subregions were calculated with and without PVC. Results In the clinical studies, the SOR values in each structure (caudate, anterior putamen, posterior putamen, putamen, and striatum) were significantly higher by using PVC in contrast to those without. Among the PD patients, the SOR values in each structure and quantitative disease severity ratings were shown to be significantly related only when PVC was used. For the simulation studies, the average absolute percentage error of the SOR estimates before and after PVC were 22.74 % and 1.54 % in the healthy situation, respectively; those in the neurodegenerative situation were 20.69 % and 2.51 %, respectively. Conclusions We successfully implemented a simple algorithm for subregional analysis of striatal uptake with PVC in dopaminergic PET imaging. The PVC algorithm provides an accurate measure of the SOR in the entire striatum and its subregions, and improves the correlation between the SOR values and the clinical disease severity of PD patients. Keywords PET Dopaminergic system Partial volume correction Striatum Subregion
C.-H. K. Kao H.-J. Hsieh S.-H. Liu Department of Radiological Technology, Tzu Chi College of Technology, Hualien 97005, Taiwan
Introduction
H.-J. Hsieh S.-H. Liu Department of Nuclear Medicine, Buddhist Tzu Chi General Hospital, Hualien 97002, Taiwan
Parkinsonism, the syndrome, is a common movement disorder, and Parkinson’s disease (PD), the major cause of
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parkinsonism, is the second most prevalent neurodegenerative disease [1]. PD is characterised by progressive loss of dopaminergic neurons in the substantia nigra and manifests as resting tremors, rigidity, bradykinesia, and postural instability. A number of other neurodegenerative diseases are associated with parkinsonism, including multiple system atrophy, progressive supranuclear palsy, corticobasal ganglionic degeneration, and dementia with Lewy bodies. These parkinsonian syndromes are difficult to differentially diagnose based solely on clinical features, particularly in the early or mild stages of the disease [2–4]. Various causes of parkinsonism can be differentiated partially on the basis of the involvement of separate components of the dopaminergic system [5]. Positron emission tomography (PET) with appropriate radiotracers allows for imaging various components of dopaminergic neurotransmitter system in brains [6]. Several imaging biomarkers have been proposed for this purpose, including 6-[18F]Fluoro-L-DOPA (FDOPA) for assessing presynaptic dopamine synthesis; 11C-PE2I for binding to the dopamine transporter (DAT); 18F-AV-133 for binding to the vesicular monoamine transporter type 2 (VMAT2); and 11C-raclopride, for targeting to the postsynaptic dopamine receptor [7–14]. PET is hence a noninvasive molecular imaging technique that plays an important role both in the evaluation of PD and for differential diagnosis of other parkinsonian syndromes [15]. In general, visual inspection is sufficient to assess PET brain images in numerous clinical situations. In contrast, a quantitative measure of the nigrostriatal dopaminergic function provides more diagnostic information, which can help differentiate parkinsonian syndromes [16], diagnose early stages of the disease [17], monitor progression of the disease [18], and evaluate neuroprotective treatment responses [19] in patients with PD. Various analytical methods have been developed to quantify dopaminergic PET images for these purposes. A simple target-to-background (e.g. striatal-to-occipital) calculation has been commonly used as the quantitative parameter because it can be determined by static data acquisition and does not require any plasma measurements [17, 20, 21]. Moreover, subregional analysis of striatal uptake enables the diagnostic performance to become more powerful [17, 22–25]. Considering the limited spatial resolution of PET scanners and the sizes of the striatal subregions, the partial volume effect (PVE) cannot be neglected [23]. The PVE generally induces an underestimation of the true radioactivity concentration, and thus, hinders the quantitative accuracy of PET measurements [26]. This underestimation depends on the size of the structure and can be considerable for small structures like striatal subregions which are key targets studied in parkinsonism [27].
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The purpose of this study was to establish a simple method to correct the PVE that accurately quantifies the striatal-to-occipital ratio (SOR) in each subregion in dopaminergic PET imaging. The proposed method and initial clinical validation data, as well as the Monte Carlo simulation data, are presented in this work. Materials and methods Partial volume correction algorithm Conventional method In conventional quantitative method, the magnetic resonance imaging (MRI) scans were registered to the PET images using the normalised mutual information [28, 29], and were re-sliced in terms of the PET images. The volumes of interest (VOIs) were delineated on the MR images, and then transferred to the PET images [30]. Subsequently, the SOR values without partial volume correction (PVC) in the caudate (CaSORnpc), anterior putamen (aPuSORnpc), and posterior putamen (pPuSORnpc) were defined as CaSORnpc ¼
CCa Cref
ð1Þ
aPuSORnpc ¼
CaPu Cref
ð2Þ
pPuSORnpc ¼
CpPu Cref
ð3Þ
where CCa, CaPu, CpPu, and Cref are the mean counts per voxel in the caudate nuclei, anterior putamen, posterior putamen, and the occipital reference area, respectively. The uptake values of the caudate and the putamen (i.e. the anterior and posterior putamen) were then used to calculate the whole striatal binding. Thus, the SOR in the striatum before PVC can be calculated according to the formula: SORnpc ¼
CSt Cref
ð4Þ
where CSt is the mean activity concentration (counts per voxel) in the striatum. Fleming method An approach taking into account the PVE in dopaminergic single-photon emission computed tomography (SPECT) imaging was proposed by Fleming and colleagues [31, 32]. This methodology had been applied to generate a European multicentre database of 123I-FP-CIT SPECT scans of healthy controls [33]. It is entirely based on the measurement of the total activity rather than the activity concen-
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tration in the VOI. This technique used geometrical VOIs large enough to ensure the inclusion of partial volume counts detected outside the physical volume of the striatum. According to the Fleming method, the SOR in the striatum after PVC can be calculated as SORpc ¼
ðCtVOI Cref VVOI Þ = VSt þ Cref Cref
ð5Þ
where CtVOI is the total counts in striatal VOI, VVOI is the volume of the striatal VOI, and VSt is the anatomic volume of the striatum from MR images. The border of VVOI is set at a sufficient distance from the striatum to avoid PVE. The uptakes in the reference area are assumed to be similar throughout the cerebral cortex. Further details of this technique can be found in Tossici-Bolt et al. [31] and Fleming et al. [32]. The potential drawback of this method is that a large VOI of the striatum cannot be divided into the caudate nuclei, anterior putamen, and posterior putamen to derive the SOR information in greater detail [34]. To overcome this shortcoming, this study proposes a solution using a correction factor called recovery coefficients (RC), which combine the conventional method with the Fleming method. Combination method To compensate for the PVE in each striatal subregion, the RC was defined as RC ¼
SORnpc SORpc
ð6Þ
where SORnpc and SORpc were the SOR derived from the conventional method and the Fleming method, respectively. The RC approach is commonly used to compensate for PVE in PET studies. It is an extremely simple method for PVC as RC can be readily calculated [26]. Hoffman et al. [35] and Kessler et al. [36] described the RC as the apparent activity concentration of an object divided by its true activity concentration. In the current study, the apparent activity was measured from the conventional VOI analysis, and the true activity was obtained from the Fleming method, which corrected PVE using a large enough VOI. To apply this method, the measured activity concentration in the VOI is divided by a RC factor. Thus, the SOR values without PVC in the caudate nuclei, anterior putamen, and posterior putamen (i.e. CaSORnpc, aPuSORnpc, and pPuSORnpc) were divided by the RC to obtain the PVE-corrected value. Consequently, the SOR values after PVC in the caudate (CaSORpc), anterior putamen (aPuSORpc), and posterior putamen (pPuSORpc) can be calculated as:
CaSORnpc RC aPuSORnpc aPuSORpc ¼ RC pPuSORnpc : pPuSORpc ¼ RC
CaSORpc ¼
ð7Þ ð8Þ ð9Þ
Based on the RC, this combination method could analyze the separate striatal subregions and take into account the PVE. Clinical studies To investigate the effect of PVC on the SOR, 11 patients (4 women, 7 men) diagnosed clinically with PD and 6 healthy control subjects (3 women, 3 men) free of neurologic diseases were included in this retrospective study analysis. All PD patients were scored with the Hoehn and Yahr (H&Y) scale. One patient was at H&Y Stage 1, one at Stage 2, four at Stage 2.5, three at Stage 3, and one at Stage 4. The mean H&Y scale was 2.6 ± 0.7. Each subject underwent a FDOPA PET and an MRI brain scan. Informed consent was obtained from both healthy control subjects and patients prior to the examination. The present study was approved by the local ethical committee. All subjects fasted for at least 6 h prior to FDOPA administration. All antiparkinsonian medications were stopped at least 12 h before the PET studies and subjects were administered 100 mg of carbidopa 1 h before FDOPA administration. FDOPA was produced in a GMP facility, as described elsewhere [37]. Static 3D acquisition was performed 2 h after the intravenous injection of 185 MBq FDOPA with a GE Discovery ST PET/CT scanner (GE Healthcare, Milwaukee, Wisconsin, USA) for 30 min. Computed tomography (CT) scan was performed immediately prior to the PET scan with a multi-detector (16 slices) spiral CT scanner for attenuation correction. All acquisitions were corrected for random and scatter coincidences using manufacturer-specified default correction methods [38, 39]. PET image datasets were reconstructed using an ordered-subset expectation maximisation (OSEM) algorithm [40] with two iterations, 21 subsets, and a 2.14 mm full width at half maximum (FWHM) Gaussian post-filter. The reconstructed image matrix size was 128 9 128, and pixel size was 1.95 mm 9 1.95 mm with a slice thickness of 3.27 mm. Reconstructed spatial resolution was approximately 6.11 mm FWHM. MRI brain scan was performed on a GE Signa Excite 1.5-T MRI scanner using a 3D-FSPGR T1 sequence (repetition time = 12.14 ms; echo time = 5.31 ms) with a matrix size of 512 9 512, a pixel size of 0.39 mm 9 0.39 mm, and a slice thickness of 2 mm.
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In this clinical study, the SOR values obtained before and after PVC in all subjects were calculated using the proposed algorithm to investigate the effect of PVC on the SOR estimates. Simulation studies In clinical data, it is difficult to accurately evaluate our method because the ground truth SOR values are not known. The Monte Carlo simulation was performed to assess the accuracy of the proposed PVC algorithm. The voxel-based Zubal MRI head phantom [41] was used to model the normal and pathological radiotracer distributions. This numerical phantom was made from segmented MRI slices of the head such that the neuroanatomical structures are well indexed and defined. In total, 128 slices were in the head phantom. The matrix size of each slice was 256 9 256 pixels. The pixel size was 1.1 mm, and the slice thickness was 1.4 mm. A single slice in the middle of the Zubal head phantom was used for this simulation, and its thickness was zoomed to 3.27 mm. To derive the SOR information in more detail, the putamen in the Zubal phantom was further modified into the anterior and posterior parts. The non-uniform attenuation map was obtained by setting the appropriate attenuation coefficients to different head materials depending on the energy of the simulated photons. Based on the measurements of the FDOPA images in the clinical studies, the radioactivity distribution within the Zubal phantom was specified to reflect a realistic situation found in healthy subjects and PD patients. The brain cortical activity equaled 1 kBq/mL, which approximately corresponded to the real situation in the clinical studies. The ratio of 3.39:4.04:4.20:1 for the striatum (caudate nuclei, anterior putamen, and posterior putamen) to reference area (occipital cortex) uptake was established for a simulation of the normal state. To simulate the
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neurodegenerative state, respective ratios of 3.02:1, 2.93:1, and 2.24:1 for the caudate nuclei, anterior putamen, and posterior putamen, respectively, to the reference area uptake were established. The SimSET Monte Carlo code [42] was employed to simulate the PET photon acquisition. The 3D PET system that referred to the clinical study was modelled using SimSET with a ring diameter of 88.6 cm, an axial length of 15.7 cm, and a transaxial field of view of 70 cm. Bismuth germanate (BGO) crystals were used. Simplified geometries of the lead shields, which were located at each end of the detector ring, were applied in the simulated system. The Gaussian energy blurring was applied with an FWHM of 20 % at 511 keV. A total of 37 billion photons were generated, and approximately 8 million events were detected in the projections corresponding to the 375–650 keV energy window, mimicking the real situations in the clinical studies. The projections of simulated data included only primary events, which mimicked the ideal random and scatter corrections. Attenuation correction of the projections was performed based on the non-uniform attenuation map. The random, scatter, and attenuation-corrected projections were iteratively reconstructed using an OSEM algorithm with the same parameters as the clinical study. Figure 1 shows the modified Zubal MRI head phantom and the reconstructed images of healthy and neurodegenerative radioactivity concentrations from the Monte Carlo simulated data. In this simulation study, the SOR values with and without PVC were calculated in the normal and pathological states to assess the performance of the proposed PVC algorithm. Quantitative analysis All image analyses were performed using the open-source software OsiriX (Pixmeo, Geneva, Switzerland). Figure 2
Fig. 1 The modified Zubal MRI head phantom (a), and the reconstructed images of healthy (b) and neurodegenerative (c) radioactivity concentrations from the Monte Carlo simulation
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Fig. 2 An example of VOIs delineation of conventional and Fleming method in the proposed PVC algorithm for a real healthy subject (top row) and a PD patient (bottom row). In conventional method, the fusion of PET and MR image was used to verify the correctness of the
coregistration (a, e). Subsequently, the VOIs were drawn on the coregistered MR image (b, f) and transferred to the corresponding PET slice (c, g). Fleming method used large striatal VOIs to take into account the partial volume effect (d, h)
shows an example of the conventional and Fleming method of VOIs delineation for a healthy subject and a PD patient in the proposed PVC algorithm. For both the simulation and clinical data, the SOR values with and without PVC were calculated using the proposed PVC algorithm. The uptake values of the anterior and posterior putamen were used to calculate the SOR in the putamen. Similarly, the uptake values of the caudate and putamen were then used to calculate the SOR in the striatum. The SOR mentioned in this study referred to the average of both sides of the brain. To assess the effect of PVC on the SOR estimates, the SOR values with and without PVC for the clinical studies were statistically analyzed. In contrast, the SOR values obtained before and after PVC for the simulation studies were compared with the ground truth values to estimate the accuracy of the proposed PVC algorithm.
using the independent t test. If the datasets were not normally distributed, the Mann–Whitney U test was used. The paired t test was used to characterise the difference between the SOR values obtained before and after the proposed PVC. Similarly, if the datasets were not normally distributed, the Wilcoxon signed-rank test was used. Furthermore, the relationship between the SOR values and H&Y scales was determined using the Pearson product-moment correlation analysis for normally distributed datasets, and the Spearman rank-order correlation test was used for datasets with no normal distribution. A p value \ 0.05 was considered to be statistically significant for each analysis.
Results Clinical studies
Statistical analysis In the clinical studies, the data were expressed as the mean ± standard deviation (SD). The Shapiro–Wilk test was used to verify the normality of the clinical datasets in the studied group. Comparison of the SOR values (with or without PVC) for the PD and control groups was made
Table 1 summarises the SOR values with and without PVC for the different subject groups. Figure 3 shows the correlation between H&Y scales and the SOR values with and without PVC. Irrespective of whether the PVC was considered, the SOR values for each structure (caudate nuclei, anterior
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putamen, posterior putamen, putamen, and striatum) in the PD group were significantly smaller than those in the control group (p \ 0.05). When the effect of the PVC on the SOR was investigated, the PVC resulted in significantly higher SOR values in each structure (p \ 0.05). Before considering PVC among the PD patients, the SOR values
Table 1 The SOR values obtained before and after PVC for clinical studies Subject group Controls (n = 6)
PD patients (n = 11)
Region
Without PVC (mean ± SD)
With PVC (mean ± SD)
Caudate nuclei
2.50 ± 0.15
3.39 ± 0.17
Anterior putamen
2.98 ± 0.21
4.04 ± 0.26
Posterior putamen
3.09 ± 0.12
4.20 ± 0.15
Putamen
3.02 ± 0.13
4.11 ± 0.16
Striatum
2.86 ± 0.13
3.89 ± 0.15
Caudate nuclei
2.10 ± 0.24
3.02 ± 0.46
Anterior putamen
2.04 ± 0.20
2.93 ± 0.36
Posterior putamen
1.57 ± 0.10
2.24 ± 0.23
Putamen
1.82 ± 0.15
2.60 ± 0.29
Striatum
1.90 ± 0.17
2.72 ± 0.34
and H&Y scales were shown to not be significantly related (r = -0.490, p = 0.63 for the caudate; r = -0.279, p = 0.203 for the anterior putamen; r = -0.391, p = 0.118 for the posterior putamen; r = -0.358, p = 0.140 for the putamen; and r = -0.429, p = 0.094 for the striatum). After using the developed PVC, correlation analysis revealed a significant negative relationship between the H&Y scales and the SOR values (r = -0.683, p = 0.01 for the caudate; r = -0.594, p = 0.027 for the anterior putamen; r = -0.691, p = 0.009 for the posterior putamen; r = -0.675, p = 0.011 for the putamen; and r = -0.691, p = 0.009 for the striatum). These results suggest that the proposed PVC algorithm can improve the correlation between the SOR values and disease severity of PD patients. Simulation studies Table 2 summarises the SOR values (with and without PVC) and their percentage errors in SOR estimates for healthy and neurodegenerative conditions in the simulations. In healthy condition, before PVC, the SOR were underestimated by 25.37 % for the caudate nuclei, 21.04 % for the anterior putamen, 23.10 % for the posterior putamen, 21.90 % for the putamen, and 22.28 % for the
Fig. 3 Correlation between H&Y scales and the SOR values with (closed diamonds) and without PVC (open diamonds) for the caudate (a), anterior putamen (b), posterior putamen (c), putamen (d), and striatum (e) in PD patients
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Ann Nucl Med (2014) 28:33–41 Table 2 The SOR values and percentage errors (in parentheses) for simulation studies
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Simulated state Healthy
Neurodegeneration
Region
Ground truth
Before PVC
After PVC
Caudate nuclei
3.39
2.53 (-25.37)
3.28 (-3.24)
Anterior putamen
4.04
3.19 (-21.04)
4.14 (?2.48)
Posterior putamen
4.20
3.23 (-23.10)
4.19 (-0.24)
Putamen
4.11
3.21 (-21.90)
4.16 (?1.22)
Striatum
3.86
3.00 (-22.28)
3.88 (?0.52)
Caudate nuclei
3.02
2.28 (-24.50)
2.90 (-3.97)
Anterior putamen
2.93
2.33 (-20.48)
2.96 (?1.02)
Posterior putamen
2.24
1.85 (-17.41)
2.35 (?4.91)
Putamen
2.63
2.12 (-19.39)
2.69 (?2.28)
Striatum
2.77
2.17 (-21.38)
2.76 (-0.36)
striatum. The average absolute percentage error before PVC was 22.74 %. When considering PVC, the SOR biases were -3.24 % for the caudate nuclei, ?2.48 % for the anterior putamen, -0.24 % for the posterior putamen, ?1.22 % for the putamen, and ?0.52 % for the striatum. The average absolute percentage error between the measured and ground truth values was 1.54 %. In the neurodegenerative condition, before PVC, the SOR were underestimated by 24.50 % for the caudate nuclei, 20.48 % for the anterior putamen, 17.41 % for the posterior putamen, 19.39 % for the putamen, and 21.38 % for the striatum. The average absolute percentage error before PVC was 20.69 %. When using the developed PVC, the SOR biases were -3.97 % for the caudate nuclei, ?1.02 % for the anterior putamen, ?4.91 % for the posterior putamen, ?2.28 % for the putamen, and -0.36 % for the striatum. The absolute average percentage error between the measured and ground truth values was 2.51 %. These results suggest that the proposed PVC algorithm is able to improve the accuracy of the SOR measurements.
Discussion In this study, a simple algorithm has been described for PVC in dopaminergic PET imaging. This algorithm is able to analyze the separate striatal subregions and take into account the PVE. The results clearly suggest that the proposed method can provide an accurate measure of the SOR in the entire striatum and its subregion, and is helpful for improving the correlation between the SOR values and the disease severity of PD patients. In the clinical data, the correlation between the SOR values and H&Y scales was analyzed. The results revealed that no significant correlation was found before PVC. This is in agreement with the results by Takikawa et al. [21]. However, Eshuis et al. [43] found that striatal uptake was significantly related to the H&Y stage. One possible reason for the lack of statistical significance might lie in the small
size of the PD group. Nevertheless, after using the developed PVC, a correlation analysis revealed a significant relationship between H&Y scales and the SOR values in each structure, even in a small group of PD patients. Clinical data were mainly used to estimate the impact of using PVC on the SOR estimates. The true SOR values in the clinical data were unknown. Thus, the Monte Carlo simulation was performed to assess the accuracy of the proposed PVC method. In the simulation results, few percentage errors remained in the SOR measurements in all structures. These results might be explained by considering the manual delineation error. Nevertheless, the mean absolute difference between the measured SOR and the ground truth values was less than 0.12 for all structures (Table 2). The technique proposed in this paper addresses the PVE by combining large geometrical VOIs and the RC method. Despite a large variety of methods for PVC having been proposed in the past, the PVE is not routinely addressed in clinical practice. It is likely that no single approach exists that would be optimal for all imaging modalities. A comprehensive comparison of different techniques for PVC has been presented by Erlandsson et al. [44]. The advantages of using large geometrical VOIs for PVC have been described by Tossici-Bolt et al. [31]. However, a potential drawback is that large VOIs method cannot help with analyses of separate striatum compartments [34]. The RC method was therefore introduced to address this problem. The results showed that the proposed algorithm has a good performance for subregional analysis of the striatum in FDOPA PET images. This algorithm is extremely simple, and therefore, easy to apply in clinical settings. Although the proposed PVC algorithm was applied only to dopaminergic imaging, the proposed method might also be applicable to cardiology and oncology studies. In brief, the proposed algorithm for the analysis of the striatal subregions in the dopaminergic PET images is efficient and widely applicable. Some cautions are deserved to be mentioned regarding the use of the simple target-to-background calculation in
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measuring the dopaminergic functions. The simple targetto-background ratio is a semiquantitative measure of the radioactivity concentration within the structure of interest. Some factors might affect the accuracy and precision of this simple ratio, such as the patient size, uptake time of the radiotracer, scanner variability, dose of radiopharmaceuticals, image acquisition, and reconstruction parameters [30]. This simple ratio might not stay constant in changing biological conditions or under different technical factors. These issues should be considered when comparing the quantitative parameters between different imaging centres. Several potential limitations should be mentioned in this study. First, in the proposed PVC method, the VOIs were manually drawn over the MR and PET images. Despite being labour-intensive, using this manual operation in the proposed PVC technique was believed to have provided highly accurate results. However, a disadvantage of manual VOI delineation is that it is tedious and time-consuming. To overcome the shortcoming of manual VOI drawing, the proposed PVC technique with automated positioning of the VOIs requires further study. Second, the anatomic volume of the striatum was derived from MR images, and conducting MRI for all patients is impractical. Fortunately, this will no longer be a problem when PET/MR scanners are widely implemented in clinical settings. Third, in the RC calculation step, the RC was derived from the entire striatum and directly applied to its subregions. A small difference exists between the striatum and its subregions as to the degree of influence by the PVE. Nonetheless, when looking into the results from the Monte Carlo simulation, this difference could be omitted. The influence of the correction for this small difference on the diagnostic performance might require further exploration. The potential use of the proposed PVC algorithm is in improving the correlation between the quantitative measurements and the clinical severity of the disease. Even so, studying its diagnosis effectiveness is necessary in a large patient population in the future.
Conclusion This study proposed a simple algorithm for subregional analysis of striatal uptake with PVC in dopaminergic PET imaging. The PVC algorithm is able to provide an accurate measure of the SOR in the entire striatum and its subregions, and is helpful for improving the correlation between the SOR values and the clinical disease severity of PD patients. In addition, the algorithm is simple, and is therefore easy to apply in clinical practice. This simple algorithm should be routinely, widely used in clinical studies and help nuclear medicine physicians in the
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evaluation of PD as well as for differential diagnoses of other parkinsonian syndromes. Acknowledgments This study was supported in part by grants TCRD 99-10 and TCRD 99-27 from the Buddhist Tzu Chi General Hospital. We thank all the members at Medical Imaging and Physics Lab for their contributions to this study. A special thanks to Mrs. Julia Chen for her guidance and suggestions through the revising process. Conflict of interest of interest.
The authors declare that they have no conflict
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