Quantification of dopaminergic neurotransmission SPECT studies with 123I-labelled radioligands. A comparison between different imaging systems and data acquisition protocols using Monte Carlo simulation

No impact of attenuation and scatter correction on the interpretation of dopamine transporter SPECT in patients with clinically uncertain parkinsonian syndrome

PURPOSE

The benefit from attenuation and scatter correction (ASC) of dopamine transporter (DAT)-SPECT for the detection of nigrostriatal degeneration in clinical routine is still a matter of debate. The current study evaluated the impact of ASC on visual interpretation and semi-quantitative analysis of DAT-SPECT in a large patient sample.

METHODS

One thousand seven hundred forty consecutive DAT-SPECT with 123I-FP-CIT from clinical routine were included retrospectively. SPECT images were reconstructed iteratively without and with ASC. Attenuation correction was based on uniform attenuation maps, scatter correction on simulation. All SPECT images were categorized with respect to the presence versus the absence of Parkinson-typical reduction of striatal 123I-FP-CIT uptake by three independent readers. Image reading was performed twice to assess intra-reader variability. The specific 123I-FP-CIT binding ratio (SBR) was used for automatic categorization, separately with and without ASC.

RESULTS

The mean proportion of cases with discrepant categorization by the same reader between the two reading sessions was practically the same without and with ASC, about 2.2%. The proportion of DAT-SPECT with discrepant categorization without versus with ASC by the same reader was 1.66% ± 0.50% (1.09-1.95%), not exceeding the benchmark of 2.2% from intra-reader variability. This also applied to automatic categorization of the DAT-SPECT images based on the putamen SBR (1.78% discrepant cases between without versus with ASC).

CONCLUSION

Given the large sample size, the current findings provide strong evidence against a relevant impact of ASC with uniform attenuation and simulation-based scatter correction on the clinical utility of DAT-SPECT to detect nigrostriatal degeneration in patients with clinically uncertain parkinsonian syndrome.

Dopamine transporter single-photon emission computed tomography-derived radiomics signature for detecting Parkinson's disease

BACKGROUND

We hypothesised that the radiomics signature, which includes texture information of dopamine transporter single-photon emission computed tomography (DAT-SPECT) images for Parkinson's disease (PD), may assist semi-quantitative indices. Herein, we constructed a radiomics signature using DAT-SPECT-derived radiomics features that effectively discriminated PD from healthy individuals and evaluated its classification performance.

RESULTS

We analysed 413 cases of both normal control (NC, n = 101) and PD (n = 312) groups from the Parkinson's Progression Markers Initiative database. Data were divided into the training and two test datasets with different SPECT manufacturers. DAT-SPECT images were spatially normalised to the Montreal Neurologic Institute space. We calculated 930 radiomics features, including intensity- and texture-based features in the caudate, putamen, and pallidum volumes of interest. The striatum uptake ratios (SURs) of the caudate, putamen, and pallidum were also calculated as conventional semi-quantification indices. The least absolute shrinkage and selection operator was used for feature selection and construction of the radiomics signature. The four classification models were constructed using a radiomics signature and/or semi-quantitative indicator. Furthermore, we compared the classification performance of the semi-quantitative indicator alone and the combination with the radiomics signature for the classification models. The receiver operating characteristics (ROC) analysis was used to evaluate the classification performance. The classification performance of SUR_putamen was higher than that of other semi-quantitative indicators. The radiomics signature resulted in a slightly increased area under the ROC curve (AUC) compared to SUR_putamen in each test dataset. When combined with SUR_putamen and radiomics signature, all classification models showed slightly higher AUCs than that of SUR_putamen alone.

CONCLUSION

We constructed a DAT-SPECT image-derived radiomics signature. Performance analysis showed that the current radiomics signature would be helpful for the diagnosis of PD and has the potential to provide robust diagnostic performance.

Convolutional neural network based attenuation correction for123I-FP-CIT SPECT with focused striatum imaging

SPECT imaging with¹²³I-FP-CIT is used for diagnosis of neurodegenerative disorders like Parkinson's disease. Attenuation correction (AC) can be useful for quantitative analysis of¹²³I-FP-CIT SPECT. Ideally, AC would be performed based on attenuation maps (μ-maps) derived from perfectly registered CT scans. Suchμ-maps, however, are most times not available and possible errors in image registration can induce quantitative inaccuracies in AC corrected SPECT images. Earlier, we showed that a convolutional neural network (CNN) based approach allows to estimate SPECT-alignedμ-maps for full brain perfusion imaging using only emission data. Here we investigate the feasibility of similar CNN methods for axially focused¹²³I-FP-CIT scans. We tested our approach on a high-resolution multi-pinhole prototype clinical SPECT system in a Monte Carlo simulation study. Three CNNs that estimateμ-maps in a voxel-wise, patch-wise and image-wise manner were investigated. As the added value of AC on clinical¹²³I-FP-CIT scans is still debatable, the impact of AC was also reported to check in which cases CNN based AC could be beneficial. AC using the ground truthμ-maps (GT-AC) and CNN estimatedμ-maps (CNN-AC) were compared with the case when no AC was done (No-AC). Results show that the effect of using GT-AC versus CNN-AC or No-AC on striatal shape and symmetry is minimal. Specific binding ratios (SBRs) from localized regions show a deviation from GT-AC≤2.5% for all three CNN-ACs while No-AC systematically underestimates SBRs by 13.1%. A strong correlation (r≥0.99) was obtained between GT-AC based SBRs and SBRs from CNN-ACs and No-AC. Absolute quantification (in kBq ml^-1) shows a deviation from GT-AC within 2.2% for all three CNN-ACs and of 71.7% for No-AC. To conclude, all three CNNs show comparable performance in accurateμ-map estimation and¹²³I-FP-CIT quantification. CNN-estimatedμ-map can be a promising substitute for CT-basedμ-map.

Acquisition count dependence of the specific binding ratio in 123I-FP-CIT SPECT

OBJECTIVE

In the [¹²³I]FP-CIT single-photon emission computed tomography (SPECT) examination, the specific binding ratio (SBR), calculated from the ratio of the striatal specific to extra-striatal background non-specific binding in the brain, is now commonly used as a quantitative index of parkinsonian syndrome. The purpose of this study was to examine the influence of count reduction on the SBR and to clarify the reliability of SBR values in patients with shorter scan times.

METHODS

A striatum phantom was used in a phantom study, with the radioactivity concentration adjusted so that the right striatum:left striatum:brain parenchyma ratio was 8:4:1. Changes in SBR values and image quality, expressed as the % coefficient of variation (%CV) and normalized mean squared error (NMSE), with decreasing acquisition counts were evaluated. In the clinical study, 106 patients (73.1 ± 9.6 years) with suspected parkinsonian syndrome underwent [¹²³I]FP-CIT SPECT, and SBR values from normal 30 min acquisitions (_fullSBR) and half-count acquisitions (_halfSBR) were compared. SBR values were calculated using the Tossici-Bolt (SBR_TB) and a fully automatic count-based (SBR_cb) methods.

RESULTS

In the phantom study, image quality decreased with a reduction of acquisition counts. The %CV and NMSE decreased by up to 52.5% and 81.5%, respectively. SBR values decreased slightly as acquisition counts decreased. In the clinical study, the mean values of _halfSBR were lower than those of _fullSBR, and they were significantly different except for SBR_TB without attenuation correction. _halfSBR and _fullSBR values correlated well, with _halfSBR values 1-8% lower than _fullSBR. The accuracy of diagnosis did not decrease even after acquisition counts were reduced by half.

CONCLUSION

This study demonstrated that SBR values decrease as a function of reduced acquisition counts. Since _halfSBR and _fullSBR showed excellent correlation, it is suggested that _fullSBR can be estimated from _halfSBR using a calibration formula when scan times are reduced.

Quantitative Monte Carlo-based brain dopamine transporter SPECT imaging

OBJECTIVE

Brain dopamine transporter imaging with I-123-labeled radioligands is technically demanding due to the small size of the imaging target relative to the spatial resolution of most SPECT systems. In addition, I-123 has high-energy peaks which can penetrate or scatter in the collimator and be detected in the imaging energy window. The aim of this study was to implement Monte Carlo (MC)-based full collimator-detector response (CDR) compensation algorithm for I-123 into a third-party commercial SPECT reconstruction software package and to evaluate its effect on the quantitative accuracy of dopaminergic-image analysis compared to a method where only the geometric component of the CDR is compensated.

METHODS

In this work, we utilized a full Monte Carlo collimator-detector model and incorporated it into an iterative SPECT reconstruction algorithm. The full Monte Carlo model reconstruction was compared to standard reconstruction using an anthropomorphic striatal phantom filled with different I-123 striatal/cortex uptake ratios and with clinical I-123 Ioflupane DaTScan studies.

RESULTS

Reconstruction with the full model yielded higher (13-25%) striatal uptake ratios than the conventional reconstruction, but the uptake ratios were still much lower than the true ratios due to partial volume effect. Visually, images reconstructed with the full Monte Carlo model had better contrast and resolution than the conventional images, with both phantom and patient studies.

CONCLUSIONS

Reconstruction with full Monte Carlo collimator-detector model yields higher quantitative accuracy than conventional reconstruction. Additional work to reduce the partial volume effect related errors would improve the accuracy further.

[Verification of Image Reconstruction Method and Collimator Suitable for Quantitative Analysis of Striatum in Dopamine Transporter Scintigraphy]

Specific binding ratio (SBR) is mainly used as a quantitative index of dopamine transporter scintigraphy, although it was reported that standardized uptake value (SUV) is useful for clinical diagnosis in recent years. The aim of this study is to evaluate whether xSPECT is useful for SUV in dopamine transporter scintigraphy. xSPECT is a recently developed, high-resolution image reconstruction technique that transforms single photon emission computed tomography (SPECT) to a computed tomography (CT) coordinate system. Furthermore, low-penetration high-resolution (LPHR), which there has been no previous physical evaluation report was also evaluated. The radioactive concentration of the image with xSPECT is automatically calculated by the periodic sensitivity calibration and one volume sensitivity calibration. In the case of images with conventional reconstruction methods as filtered back projection (FBP) and ordered subset expectation maximization (OSEM), the calibration factor related to the photon count and radioactive concentration was calculated from measuring a cylinder phantom filled with Iodine-123. Radioactive concentrations of the SUV factor were measured by SPECT data acquisition with the striatal phantom in various conditions. Radioactive concentrations with conventional reconstruction methods had a lower value (for example, with FBP it was 7.53 kBq/ml, with OSEM it was 7.22 kBq/ml) compared to the actual measurement value, although that with xSPECT (12.45 kBq/ml) got close to the actual measurement value (14.68 kBq/ml). LPHR showed an approximation to low-energy high-resolution (LEHR) in terms of spatial resolution and scatter fraction estimated from energy windows. The quantitative accuracy of radioactive concentration was the highest under xSPECT.

Diagnostic performance of the specific uptake size index for semi-quantitative analysis of I-123-FP-CIT SPECT: harmonized multi-center research setting versus typical clinical single-camera setting

Introduction

The specific uptake size index (SUSI) of striatal FP-CIT uptake is independent of spatial resolution in the SPECT image, in contrast to the specific binding ratio (SBR). This suggests that the SUSI is particularly appropriate for multi-site/multi-camera settings in which camera-specific effects increase inter-subject variability of spatial resolution. However, the SUSI is sensitive to inter-subject variability of striatum size. Furthermore, it might be more sensitive to errors of the estimate of non-displaceable FP-CIT binding. This study compared SUSI and SBR in the multi-site/multi-camera (MULTI) setting of a prospective multi-center study and in a mono-site/mono-camera (MONO) setting representative of clinical routine.

Methods

The MULTI setting included patients with Parkinson’s disease (PD, n = 438) and healthy controls (n = 207) from the Parkinson Progression Marker Initiative. The MONO setting included 122 patients from routine clinical patient care in whom FP-CIT SPECT had been performed with the same double-head SPECT system according to the same acquisition and reconstruction protocol. Patients were categorized as “neurodegenerative” (n = 84) or “non-neurodegenerative” (n = 38) based on follow-up data. FP-CIT SPECTs were stereotactically normalized to MNI space. SUSI and SBR were computed for caudate, putamen, and whole striatum using unilateral ROIs predefined in MNI space. SUSI analysis was repeated in native patient space in the MONO setting. The area (AUC) under the ROC curve for identification of PD/“neurodegenerative” cases was used as performance measure.

Results

In both settings, the highest AUC was achieved by the putamen (minimum over both hemispheres), independent of the semi-quantitative method (SUSI or SBR). The putaminal SUSI provided slightly better performance with ROI analysis in MNI space compared to patient space (AUC = 0.969 vs. 0.961, p = 0.129). The SUSI (computed in MNI space) performed slightly better than the SBR in the MULTI setting (AUC = 0.993 vs. 0.991, p = 0.207) and slightly worse in the MONO setting (AUC = 0.969 vs. AUC = 0.976, p = 0.259). There was a trend toward larger AUC difference between SUSI and SBR in the MULTI setting compared to the MONO setting (p = 0.073). Variability of voxel intensity in the reference region was larger in misclassified cases compared to correctly classified cases for both SUSI and SBR (MULTI setting: p = 0.007 and p = 0.012, respectively).

Conclusions

The SUSI is particularly useful in MULTI settings. SPECT images should be stereotactically normalized prior to SUSI analysis. The putaminal SUSI provides better diagnostic performance than the SUSI of the whole striatum. Errors of the estimate of non-displaceable count density in the reference region can cause misclassification by both SUSI and SBR, particularly in borderline cases. These cases might be identified by visual checking FP-CIT uptake in the reference region for particularly high variability.

Evaluation of quantitative 123I and 131I SPECT with Monte Carlo-based down-scatter compensation

OBJECTIVE

Quantitative I and I single-photon emission computed tomography (SPECT) is hampered by down-scatter from the high-energy peaks. This paper presents a down-scatter compensation method, where down-scatter generated in the patient and gamma camera collimator and detector is modelled using Monte Carlo simulation in the ordered subsets expectation maximization SPECT reconstruction algorithm.

MATERIALS AND METHODS

The new down-scatter compensation method was compared with conventional triple energy window (TEW) scatter compensation and Gaussian convolution-based forced detection Monte Carlo methods. The comparison was made with the NEMA-IEC phantom using six spherical inserts (diameters from 10 to 37 mm) and a lung compartment. The phantom was filled with I and I solutions to known sphere-to-background concentration ratios. Spherical volumes of interest with the same diameter as the inserts were drawn on the images, and recovery coefficients for the spheres were calculated in addition to lung-to-background ratio.

RESULTS

The new down-scatter compensation method provided higher recovery coefficients than the TEW scatter compensation or Gaussian convolution-based forced detection Monte Carlo algorithm for both isotopes. Background activity concentration could be accurately estimated with the new down-scatter compensation method and with the TEW scatter compensation, whereas activity concentration of the spheres was severely underestimated even with the new method.

CONCLUSION

Down-scatter compensation with Monte Carlo-simulation effectively reduces down-scatter effects in I and I SPECT imaging.

Count-based method for specific binding ratio calculation in [I-123]FP-CIT SPECT analysis

OBJECTIVE

To calculate the specific binding ratio (SBR) appropriately in dopamine transporter (DAT) imaging, a method for extracting the striatal volume of interest (VOI) was developed.

METHODS

This study included 200 patients (72 ± 10 years) who were suspected of parkinsonian syndromes (PS) or dementia with Lewy body (DLB). The patients were divided into three groups of PS with dopaminergic degeneration, DLB and non-PS after [¹²³I]ioflupane (FP-CIT) SPECT and clinical follow-up. The image data were reconstructed with CT attenuation correction and scatter correction, and with only CT attenuation correction (CTAC). The new method extracted striatal VOI according to the high-level counts and the average striatum volume, and calculated SBR using the reference occipital counts. The SBR values for each patient were obtained using the Tossici-Bolt method (SBR_Bolt) and our method. Reproducibility of SBR calculation using our method was compared by two operators.

RESULTS

The mean SBR values for the PS and DLB groups were significantly different from that of the non-PS group with both methods. The coefficients of variation of the SBR were significantly smaller with the proposed method compared with those of SBR_Bolt (p < 0.001), except for the CTAC images. There were no differences in SBR between the two operators using our method. The diagnostic accuracies with our method for the PS and DLB groups were 98.4 and 96.0%, respectively.

CONCLUSION

Our new method for SBR calculation in the FP-CIT SPECT showed less coefficients of variation with high reproducibility, which would be useful for clinical diagnosis and in assessing the severity of diseases in follow-up studies.

Quantitation of specific binding ratio in 123I-FP-CIT SPECT: accurate processing strategy for cerebral ventricular enlargement with use of 3D-striatal digital brain phantom

This study aimed to evaluate the effect of ventricular enlargement on the specific binding ratio (SBR) and to validate the cerebrospinal fluid (CSF)-Mask algorithm for quantitative SBR assessment of ¹²³I-FP-CIT single-photon emission computed tomography (SPECT) images with the use of a 3D-striatum digital brain (SDB) phantom. Ventricular enlargement was simulated by three-dimensional extensions in a 3D-SDB phantom comprising segments representing the striatum, ventricle, brain parenchyma, and skull bone. The Evans Index (EI) was measured in 3D-SDB phantom images of an enlarged ventricle. Projection data sets were generated from the 3D-SDB phantoms with blurring, scatter, and attenuation. Images were reconstructed using the ordered subset expectation maximization (OSEM) algorithm and corrected for attenuation, scatter, and resolution recovery. We bundled DaTView (Southampton method) with the CSF-Mask processing software for SBR. We assessed SBR with the use of various coefficients (f factor) of the CSF-Mask. Specific binding ratios of 1, 2, 3, 4, and 5 corresponded to SDB phantom simulations with true values. Measured SBRs > 50% that were underestimated with EI increased compared with the true SBR and this trend was outstanding at low SBR. The CSF-Mask improved 20% underestimates and brought the measured SBR closer to the true values at an f factor of 1.0 despite an increase in EI. We connected the linear regression function (y = - 3.53x + 1.95; r = 0.95) with the EI and f factor using root-mean-square error. Processing with CSF-Mask generates accurate quantitative SBR from dopamine transporter SPECT images of patients with ventricular enlargement.

Optimization of the reconstruction parameters in [123I]FP-CIT SPECT

The aim of this work was to obtain a set of parameters to be applied in [¹²³I]FP-CIT SPECT reconstruction in order to minimize the error between standardized and true values of the specific uptake ratio (SUR) in dopaminergic neurotransmission SPECT studies. To this end, Monte Carlo simulation was used to generate a database of 1380 projection data-sets from 23 subjects, including normal cases and a variety of pathologies. Studies were reconstructed using filtered back projection (FBP) with attenuation correction and ordered subset expectation maximization (OSEM) with correction for different degradations (attenuation, scatter and PSF). Reconstruction parameters to be optimized were the cut-off frequency of a 2D Butterworth pre-filter in FBP, and the number of iterations and the full width at Half maximum of a 3D Gaussian post-filter in OSEM. Reconstructed images were quantified using regions of interest (ROIs) derived from Magnetic Resonance scans and from the Automated Anatomical Labeling map. Results were standardized by applying a simple linear regression line obtained from the entire patient dataset. Our findings show that we can obtain a set of optimal parameters for each reconstruction strategy. The accuracy of the standardized SUR increases when the reconstruction method includes more corrections. The use of generic ROIs instead of subject-specific ROIs adds significant inaccuracies. Thus, after reconstruction with OSEM and correction for all degradations, subject-specific ROIs led to errors between standardized and true SUR values in the range [-0.5, +0.5] in 87% and 92% of the cases for caudate and putamen, respectively. These percentages dropped to 75% and 88% when the generic ROIs were used.

[Comparison of Quantitative Value of Dopamine Transporter Scintigraphy Calculated from Different Analytical Software]

In the dopamine transporter scintigraphy there are two quantitative analysis softwares, DaTView and DaTQUANT. The quantitative value of both software has to be treated independently because there is a difference between them in the point of how to set the region of interest on the striatum and the background, calculation formula of quantitation. And also DaTQUANT has a capability of performing anatomical standardization which DaTView does not have. The aim of this study was to evaluate the accuracy of registration on DaTQUANT using a phantom, and to evaluate the correlation between the quantitative values between DaTView and DaTQUANT using clinical data. As a result, the accuracy of registration was acceptable. Regardless of the degree of accumulation in the striatum, there was a high correlation to each analysis software (r>0.85).

Validation of semi-quantitative methods for DAT SPECT: influence of anatomical variability and partial volume effect

The aim of this work was to evaluate the influence of anatomical variability between subjects and of the partial volume effect (PVE) on the standardized Specific Uptake Ratio (SUR) in [(123)I]FP-bib SPECT studies. To this end, magnetic resonance (MR) images of 23 subjects with differences in the striatal volume of up to 44% were segmented and used to generate a database of 138 Monte Carlo simulated SPECT studies. Data included normal uptakes and pathological cases. Studies were reconstructed by filtered back projection (FBP) and the ordered-subset expectation-maximization algorithm. Quantification was carried out by applying a reference method based on regions of interest (ROIs) derived from the MR images and ROIs derived from the Automated Anatomical Labelling map. Our results showed that, regardless of anatomical variability, the relationship between calculated and true SUR values for caudate and putamen could be described by a multiple linear model which took into account the spill-over phenomenon caused by PVE (R² ≥ 0.963 for caudate and ≥0.980 for putamen) and also by a simple linear model (R(2) ≥ 0.952 for caudate and ≥0.973 for putamen). Calculated values were standardized by inverting both linear systems. Differences between standardized and true values showed that, although the multiple linear model was the best approach in terms of variability (X² ≥ 11.79 for caudate and ≤7.36 for putamen), standardization based on a simple linear model was also suitable (X² ≥ 12.44 for caudate and ≤12.57 for putamen).

Computer-aided diagnosis of Parkinson's disease based on [(123)I]FP-CIT SPECT binding potential images, using the voxels-as-features approach and support vector machines

OBJECTIVE

The aim of the present study was to develop a fully-automated computational solution for computer-aided diagnosis in Parkinson syndrome based on [(123)I]FP-CIT single photon emission computed tomography (SPECT) images.

APPROACH

A dataset of 654 [(123)I]FP-CIT SPECT brain images from the Parkinson's Progression Markers Initiative were used. Of these, 445 images were of patients with Parkinson's disease at an early stage and the remainder formed a control group. The images were pre-processed using automated template-based registration followed by the computation of the binding potential at a voxel level. Then, the binding potential images were used for classification, based on the voxel-as-feature approach and using the support vector machines paradigm.

MAIN RESULTS

The obtained estimated classification accuracy was 97.86%, the sensitivity was 97.75% and the specificity 98.09%.

SIGNIFICANCE

The achieved classification accuracy was very high and, in fact, higher than accuracies found in previous studies reported in the literature. In addition, results were obtained on a large dataset of early Parkinson's disease subjects. In summation, the information provided by the developed computational solution potentially supports clinical decision-making in nuclear medicine, using important additional information beyond the commonly used uptake ratios and respective statistical comparisons. (ClinicalTrials.gov Identifier: NCT01141023).

Including anatomical and functional information in MC simulation of PET and SPECT brain studies. Brain-VISET: a voxel-based iterative method

Monte Carlo (MC) simulation provides a flexible and robust framework to efficiently evaluate and optimize image processing methods in emission tomography. In this work we present Brain-VISET (Voxel-based Iterative Simulation for Emission Tomography), a method that aims to simulate realistic [ (99m) Tc]-SPECT and [ (18) F]-PET brain databases by including anatomical and functional information. To this end, activity and attenuation maps generated using high-resolution anatomical images from patients were used as input maps in a MC projector to simulate SPECT or PET sinograms. The reconstructed images were compared with the corresponding real SPECT or PET studies in an iterative process where the activity inputs maps were being modified at each iteration. Datasets of 30 refractory epileptic patients were used to assess the new method. Each set consisted of structural images (MRI and CT) and functional studies (SPECT and PET), thereby allowing the inclusion of anatomical and functional variability in the simulation input models. SPECT and PET sinograms were obtained using the SimSET package and were reconstructed with the same protocols as those employed for the clinical studies. The convergence of Brain-VISET was evaluated by studying the behavior throughout iterations of the correlation coefficient, the quotient image histogram and a ROI analysis comparing simulated with real studies. The realism of generated maps was also evaluated. Our findings show that Brain-VISET is able to generate realistic SPECT and PET studies and that four iterations is a suitable number of iterations to guarantee a good agreement between simulated and real studies.

Quantification of rat brain SPECT with (123)I-ioflupane: evaluation of different reconstruction methods and image degradation compensations using Monte Carlo simulation

SPECT studies with (123)I-ioflupane facilitate the diagnosis of Parkinson's disease (PD). The effect on quantification of image degradations has been extensively evaluated in human studies but their impact on studies of experimental PD models is still unclear. The aim of this work was to assess the effect of compensating for the degrading phenomena on the quantification of small animal SPECT studies using (123)I-ioflupane. This assessment enabled us to evaluate the feasibility of quantitatively detecting small pathological changes using different reconstruction methods and levels of compensation for the image degrading phenomena. Monte Carlo simulated studies of a rat phantom were reconstructed and quantified. Compensations for point spread function (PSF), scattering, attenuation and partial volume effect were progressively included in the quantification protocol. A linear relationship was found between calculated and simulated specific uptake ratio (SUR) in all cases. In order to significantly distinguish disease stages, noise-reduction during the reconstruction process was the most relevant factor, followed by PSF compensation. The smallest detectable SUR interval was determined by biological variability rather than by image degradations or coregistration errors. The quantification methods that gave the best results allowed us to distinguish PD stages with SUR values that are as close as 0.5 using groups of six rats to represent each stage.

Cross-camera comparison of ROI-based semi-quantitative ¹²³I-IBZM SPECT data in healthy volunteers using an anthropomorphic phantom for calibration

BACKGROUND

In (123)I-Iolopride (IBZM) SPECT reference values may diverge between camera systems. If multicenter pooling of normal material databases is needed, differences in measured semi-quantitative data due to equipment performance and reconstruction parameters have to be investigated in each instance to determine the comparability.

PURPOSE

To explore the differences in (123)I-IBZM measured uptake ratios between two different gamma cameras in healthy controls, the intra-rater reproducibility of the image evaluation method and the possibility to equalize uptake ratios by calibration through an anthropomorphic phantom.

MATERIAL AND METHODS

Differences in ROI-based semi-quantitative data from two different gamma camera systems, the three-headed brain dedicated Neurocam and the two-headed multipurpose hybrid system Infinia Hawkeye, were studied using image data from a group of healthy volunteers and an anthropomorphic brain-phantom scanned with both cameras. Several reconstruction methods and corrections were applied. To test the reliability of the ROI method, the intra-observer reproducibility was determined for the ROI method in this study.

RESULTS

The ROI method had a high reliability. Differences in mean measured uptake (123)I-IBZM ratios in healthy controls varied between 2.9% and 6.5% depending on reconstruction and correction for attenuation and scatter. After calibration, the differences decreased. There were no statistically significant differences between corrected ratios from the two camera systems in the study when images were reconstructed with attenuation correction.

CONCLUSION

The conformity of uptake ratios in attenuation corrected (123)I-IBZM images derived from the two different cameras was improved by using an anthropomorphic phantom for calibration.

Development of a variable-radius pinhole SPECT system with a portable gamma camera

OBJECTIVE

To develop a small-animal SPECT system using a low cost commercial portable gamma camera equipped with a pinhole collimator, a continuous scintillation crystal and a position-sensitive photomultiplier tube.

MATERIAL AND METHODS

The gamma camera was attached to a variable radius system, which enabled us to optimize sensitivity and resolution by adjusting the radius of rotation to the size of the object. To investigate the capability of the SPECT system for small animal imaging, the dependence of resolution and calibration parameters on radius was assessed and acquisitions of small phantoms and mice were carried out.

RESULTS

Resolution values, ranging from 1.0mm for a radius of 21.4mm and 1.4mm for a radius of 37.2mm were obtained, thereby justifying the interest of a variable radius SPECT system.

CONCLUSIONS

The image quality of phantoms and animals were satisfactory, thus confirming the usefulness of the system for small animal SPECT imaging.

The Role of CT-Based Attenuation Correction and Collimator Blurring Correction in Striatal Spect Quantification

Purpose. Striatal single photon emission computed tomography (SPECT) imaging of the dopaminergic system is becoming increasingly used for clinical and research studies. The question about the value of nonuniform attenuation correction has become more relevant with the increasing availability of hybrid SPECT-CT scanners. In this study, the value of nonuniform attenuation correction and correction for collimator blurring were determined using both phantom data and patient data. Methods. SPECT imaging was performed using 7 anthropomorphic phantom measurements, and 14 patient studies using [I-123]-FP-CIT (DATSCAN). SPECT reconstruction was performed using uniform and nonuniform attenuation correction and collimator blurring corrections. Recovery values (phantom data) or average-specific uptake ratios (patient data) for the different reconstructions were compared at similar noise levels. Results. For the phantom data, improved recovery was found with nonuniform attenuation correction and collimator blurring corrections, with further improvement when performed together. However, for patient data the highest average specific uptake ratio was obtained using collimator blurring correction without nonuniform attenuation correction, probably due to subtle SPECT-CT misregistration. Conclusions. This study suggests that an optimal brain SPECT reconstruction (in terms of the lowest bias) in patients would include a correction for collimator blurring and uniform attenuation correction.

Dopamine D2 receptor SPECT with (123)I-IBZM: evaluation of collimator and post-filtering when using model-based compensation-a Monte Carlo study

In (123)I-IBZM brain SPECT, the main interest is the activity uptake in the striatum relative to the background, and semi-quantitative techniques using regions of interest are typically used for this purpose. Uncertainties in the measured uptakes can however be a problem due to low contrasts and high noise levels. Like SPECT in general, IBZM SPECT should benefit from reconstruction methods that include model-based compensation, but it is important that image acquisition is optimized for this technique. An important factor is the choice of collimator. In this study we compare four different parallel-hole collimators for IBZM SPECT regarding overall quantitative accuracy and measured uptake ratio as a function of image noise and uncertainty. The collimators are low-energy high-resolution (LEHR), low-energy general-purpose (LEGP), extended LEGP (ELEGP) and medium-energy general-purpose (MEGP). The effect of three Butterworth post-filters with cut-off frequencies of 0.3, 0.45 and 0.6 cm(-1) (power factor 8) is also studied. All raw-data projections are produced using Monte Carlo simulations. Of the investigated collimators, the one that is most sensitive to the primary photons, ELEGP, proved to be the most optimal for realistic noise levels. Butterworth post-filtering is advantageous, and the cut-off frequency 0.45 cm(-1) was the best compromise in this study.

The impact of reconstruction method on the quantification of DaTSCAN images

PURPOSE

Reconstruction of DaTSCAN brain studies using OS-EM iterative reconstruction offers better image quality and more accurate quantification than filtered back-projection. However, reconstruction must proceed for a sufficient number of iterations to achieve stable and accurate data. This study assessed the impact of the number of iterations on the image quantification, comparing the results of the iterative reconstruction with filtered back-projection data.

METHODS

A striatal phantom filled with (123)I using striatal to background ratios between 2:1 and 10:1 was imaged on five different gamma camera systems. Data from each system were reconstructed using OS-EM (which included depth-independent resolution recovery) with various combinations of iterations and subsets to achieve up to 200 EM-equivalent iterations and with filtered back-projection. Using volume of interest analysis, the relationships between image reconstruction strategy and quantification of striatal uptake were assessed.

RESULTS

For phantom filling ratios of 5:1 or less, significant convergence of measured ratios occurred close to 100 EM-equivalent iterations, whereas for higher filling ratios, measured uptake ratios did not display a convergence pattern. Assessment of the count concentrations used to derive the measured uptake ratio showed that nonconvergence of low background count concentrations caused peaking in higher measured uptake ratios. Compared to filtered back-projection, OS-EM displayed larger uptake ratios because of the resolution recovery applied in the iterative algorithm.

CONCLUSION

The number of EM-equivalent iterations used in OS-EM reconstruction influences the quantification of DaTSCAN studies because of incomplete convergence and possible bias in areas of low activity due to the nonnegativity constraint in OS-EM reconstruction. Nevertheless, OS-EM using 100 EM-equivalent iterations provides the best linear discriminatory measure to quantify the uptake in DaTSCAN studies.