[123I]FP-CIT ENC-DAT normal database: the impact of the reconstruction and quantification methods

Automated identification of uncertain cases in deep learning-based classification of dopamine transporter SPECT to improve clinical utility and acceptance

PURPOSE

Deep convolutional neural networks (CNN) are promising for automatic classification of dopamine transporter (DAT)-SPECT images. Reporting the certainty of CNN-based decisions is highly desired to flag cases that might be misclassified and, therefore, require particularly careful inspection by the user. The aim of the current study was to design and validate a CNN-based system for the identification of uncertain cases.

METHODS

A network ensemble (NE) combining five CNNs was trained for binary classification of [123I]FP-CIT DAT-SPECT images as "normal" or "neurodegeneration-typical reduction" with high accuracy (NE for classification, NEfC). An uncertainty detection module (UDM) was obtained by combining two additional NE, one trained for detection of "reduced" DAT-SPECT with high sensitivity, the other with high specificity. A case was considered "uncertain" if the "high sensitivity" NE and the "high specificity" NE disagreed. An internal "development" dataset of 1740 clinical DAT-SPECT images was used for training (n = 1250) and testing (n = 490). Two independent datasets with different image characteristics were used for testing only (n = 640, 645). Three established approaches for uncertainty detection were used for comparison (sigmoid, dropout, model averaging).

RESULTS

In the test data from the development dataset, the NEfC achieved 98.0% accuracy. 4.3% of all test cases were flagged as "uncertain" by the UDM: 2.5% of the correctly classified cases and 90% of the misclassified cases. NEfC accuracy among "certain" cases was 99.8%. The three comparison methods were less effective in labelling misclassified cases as "uncertain" (40-80%). These findings were confirmed in both additional test datasets.

CONCLUSION

The UDM allows reliable identification of uncertain [123I]FP-CIT SPECT with high risk of misclassification. We recommend that automatic classification of [123I]FP-CIT SPECT images is combined with an UDM to improve clinical utility and acceptance. The proposed UDM method ("high sensitivity versus high specificity") might be useful also for DAT imaging with other ligands and for other binary classification tasks.

Harmonizing multisite data with the ComBat method for enhanced Parkinson's disease diagnosis via DAT-SPECT

Background

Dopamine transporter single-photon emission computed tomography (DAT-SPECT) is a crucial tool for evaluating patients with Parkinson's disease (PD). However, its implication is limited by inter-site variability in large multisite clinical trials. To overcome the limitation, a conventional prospective correction method employs linear regression with phantom scanning, which is effective yet available only in a prospective manner. An alternative, although relatively underexplored, involves retrospective modeling using a statistical method known as "combatting batch effects when combining batches of gene expression microarray data" (ComBat).

Methods

We analyzed DAT-SPECT-specific binding ratios (SBRs) derived from 72 healthy older adults and 81 patients with PD registered in four clinical sites. We applied both the prospective correction and the retrospective ComBat correction to the original SBRs. Next, we compared the performance of the original and two corrected SBRs to differentiate the PD patients from the healthy controls. Diagnostic accuracy was assessed using the area under the receiver operating characteristic curve (AUC-ROC).

Results

The original SBRs were 6.13 ± 1.54 (mean ± standard deviation) and 2.03 ± 1.41 in the control and PD groups, respectively. After the prospective correction, the mean SBRs were 6.52 ± 1.06 and 2.40 ± 0.99 in the control and PD groups, respectively. After the retrospective ComBat correction, the SBRs were 5.25 ± 0.89 and 2.01 ± 0.73 in the control and PD groups, respectively, resulting in substantial changes in mean values with fewer variances. The original SBRs demonstrated fair performance in differentiating PD from controls (Hedges's g = 2.76; AUC-ROC = 0.936). Both correction methods improved discrimination performance. The ComBat-corrected SBR demonstrated comparable performance (g = 3.99 and AUC-ROC = 0.987) to the prospectively corrected SBR (g = 4.32 and AUC-ROC = 0.992) for discrimination.

Conclusion

Although we confirmed that SBRs fairly discriminated PD from healthy older adults without any correction, the correction methods improved their discrimination performance in a multisite setting. Our results support the utility of harmonization methods with ComBat for consolidating SBR-based diagnosis or stratification of PD in multisite studies. Nonetheless, given the substantial changes in the mean values of ComBat-corrected SBRs, caution is advised when interpreting them.

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.

Stereotactical normalization with multiple templates representative of normal and Parkinson-typical reduction of striatal uptake improves the discriminative power of automatic semi-quantitative analysis in dopamine transporter SPECT

BACKGROUND

The specific binding ratio (SBR) of ¹²³I-FP-CIT in the putamen is widely used to support the interpretation of dopamine transporter (DAT) SPECT. Automatic methods for computation of the putamen SBR often include stereotactical normalization of the individual DAT-SPECT image to an anatomical standard space. This study compared using a single ¹²³I-FP-CIT template image as target for stereotactical normalization versus multiple templates representative of normal and different levels of Parkinson-typical reduction of striatal ¹²³I-FP-CIT uptake.

METHODS

1702 clinical ¹²³I-FP-CIT SPECT images were stereotactically normalized (affine) to the anatomical space of the Montreal Neurological Institute (MNI) with SPM12 either using a single custom-made ¹²³I-FP-CIT template representative of normal striatal uptake or using eight different templates representative of normal and different levels of Parkinson-typical reduction of striatal FP-CIT uptake with and without attenuation and scatter correction. In the latter case, SPM finds the linear combination of the multiple templates that best matches the patient's image. The putamen SBR was obtained using hottest voxels analysis in large unilateral regions-of-interest predefined in MNI space. The histogram of the putamen SBR in the whole sample was fitted by the sum of two Gaussians. The power to differentiate between reduced and normal SBR was estimated by the effect size of the distance between the two Gaussians computed as the differences between their mean values scaled to their pooled standard deviation.

RESULTS

The effect size of the distance between the two Gaussians was 3.83 with the single template versus 3.96 with multiple templates for stereotactical normalization.

CONCLUSIONS

Multiple templates representative of normal and different levels of Parkinson-typical reduction for stereotactical normalization of DAT-SPECT might provide improved separation between normal and reduced putamen SBR that could result in slightly improved power for the detection of nigrostriatal degeneration.

Same same but different: dopamine transporter SPECT on scanners with CZT vs. NaI detectors

BACKGROUND

The aims of this study were to establish a normal database (NDB) for semiquantification of dopamine transporter (DAT) single-photon emission computed tomography (SPECT) with [¹²³I]FP-CIT on a cadmium zinc telluride (CZT) camera, test the preexisting NaI-derived NDB for use in CZT scans, and compare the diagnostic findings in subjects imaged with a CZT scanner with either the preexisting NaI-based NDB or our newly defined CZT NDB.

METHODS

The sample comprised 73 subjects with clinically uncertain parkinsonian syndrome (PS) who prospectively underwent [¹²³I]FP-CIT SPECT on a CZT camera according to standard guidelines with identical acquisition and reconstruction protocols (DaTQUANT). Two experienced readers visually assessed the images and binarized the subjects into "non-neurodegenerative PS" and "neurodegenerative PS". Twenty-five subjects from the "non-neurodegenerative PS" subgroup were randomly selected to establish a CZT NDB. The remaining 48 subjects were defined as "test group". DaTQUANT was used to determine the specific binding ratio (SBR). For the test group, SBR values were transformed to z-scores for the putamen utilizing both the CZT NDB and the manufacturer-provided NaI-based NDB (GE NDB). A predefined fixed cut-off of -2 was used for dichotomization of z-scores to classify neurodegenerative and non-neurodegenerative PS. Performance of semiquantification using the two NDB to identify subjects with neurodegenerative PS was assessed in comparison with the visual rating. Furthermore, a randomized head-to-head comparison of both detector systems was performed semiquantitatively in a subset of 32 out of all 73 subjects.

RESULTS

Compared to the visual rating as reference, semiquantification based on the dedicated CZT NDB led to fewer discordant ratings than the GE NDB in CZT scans (3 vs. 8 out of 48 subjects). This can be attributed to the putaminal z-scores being consistently higher with the GE NDB on a CZT camera (median absolute difference of 1.68), suggesting an optimal cut-off of -0.5 for the GE NDB instead of -2.0. Average binding ratios and z-scores were significantly lower in CZT compared to NaI data.

CONCLUSIONS

Use of a dedicated, CZT-derived NDB is recommended in [¹²³I]FP-CIT SPECT with a CZT camera since it improves agreement between semiquantification and visual assessment.

Relevance of 18F-DOPA visual and semi-quantitative PET metrics for the diagnostic of Parkinson disease in clinical practice: a machine learning-based inference study

PURPOSE

To decipher the relevance of visual and semi-quantitative 6-fluoro-(18F)-L-DOPA (¹⁸F-DOPA) interpretation methods for the diagnostic of idiopathic Parkinson disease (IPD) in hybrid positron emission tomography (PET) and magnetic resonance imaging.

MATERIAL AND METHODS

A total of 110 consecutive patients (48 IPD and 62 controls) with 11 months of median clinical follow-up (reference standard) were included. A composite visual assessment from five independent nuclear imaging readers, together with striatal standard uptake value (SUV) to occipital SUV ratio, striatal gradients and putamen asymmetry-based semi-quantitative PET metrics automatically extracted used to train machine learning models to classify IPD versus controls. Using a ratio of 70/30 for training and testing sets, respectively, five classification models-k-NN, LogRegression, support vector machine, random forest and gradient boosting-were trained by using 100 times repeated nested cross-validation procedures. From the best model on average, the contribution of PET parameters was deciphered using the Shapley additive explanations method (SHAP). Cross-validated receiver operating characteristic curves (cv-ROC) of the most contributive PET parameters were finally estimated and compared.

RESULTS

The best machine learning model (k-NN) provided final cv-ROC of 0.81. According to SHAP analyses, visual PET metric was the most important contributor to the model overall performance, followed by the minimum between left and right striatal to occipital SUV ratio. The 10-time cv-ROC curves of visual, min SUVr or both showed quite similar performance (mean area under the ROC of 0.81, 0.81 and 0.79, respectively, for visual, min SUVr or both).

CONCLUSION

Visual expert analysis remains the most relevant parameter to predict IPD diagnosis at 11 months of median clinical follow-up in ¹⁸F-FDOPA. The min SUV ratio appears interesting in the perspective of simple semi-automated diagnostic workflows.

EANM practice guideline for quantitative SPECT-CT

PURPOSE

Quantitative SPECT-CT is a modality of growing importance with initial developments in post radionuclide therapy dosimetry, and more recent expansion into bone, cardiac and brain imaging together with the concept of theranostics more generally. The aim of this document is to provide guidelines for nuclear medicine departments setting up and developing their quantitative SPECT-CT service with guidance on protocols, harmonisation and clinical use cases.

METHODS

These practice guidelines were written by members of the European Association of Nuclear Medicine Physics, Dosimetry, Oncology and Bone committees representing the current major stakeholders in Quantitative SPECT-CT. The guidelines have also been reviewed and approved by all EANM committees and have been endorsed by the European Association of Nuclear Medicine.

CONCLUSION

The present practice guidelines will help practitioners, scientists and researchers perform high-quality quantitative SPECT-CT and will provide a framework for the continuing development of quantitative SPECT-CT as an established modality.

Assessment of different regions of interest-based methods for [99mTc]Tc DAT-SPECT quantification using an anthropomorphic striatal phantom

BACKGROUND AND AIMS

Molecular imaging of the dopamine transporters (DAT) provides valuable information about neurodegenerative diseases, such as Parkinson's. This study assessed the accuracy and precision of DAT-SPECT quantification methods.

METHODS

Twenty-three DAT-SPECT images of a striatal phantom were acquired. The specific (caudate and putamen) and the non-specific (background activity) chambers were filled with [^99mTc]Tc. Different specific-to-non-specific activity ratios (10, 9, 8, 7, 6, 5, 4, 3 and 2 to 1) and the specific binding ratio (SBR) were calculated. Five methods using ROIs were assessed: (a) Manual ROIs on SPECT images; (b) TwoBox and (c) ThreeBox methods and Volume of Interest (VOI) using structural images; (d) MRI and (e) CT. Accuracy was evaluated by the concordance correlation coefficient (CCC) and precision by Pearson's coefficient and linear regression.

RESULTS

The SBR quantified in the specific and striatal chambers resulted in a CCC increase with a decrease in the nominal values. For lower SBR, MRI and CT showed higher CCCs when caudate ([Formula: see text] = 0.89 e [Formula: see text] = 0.84) and putamen ([Formula: see text] = 0.86 e [Formula: see text] = 0.82) were evaluated. For striatal assessments, the TwoBox method was the most accurate ([Formula: see text] = 0.95). High Pearson's coefficients were found in the correlations between all methods.

CONCLUSIONS

All five methods showed high precision even when applied to images with different activities. MRI and CT were the most accurate for assessing the caudate or putamen. To assess the striatal chamber and in the absence of structural information, the TwoBox method is advisable.

Novel approach to semi-quantification of tracer accumulation in dopamine transporter scan

Purpose

Accurate tracer accumulation evaluation is difficult owing to the partial volume effect (PVE). We proposed a novel semi‐quantitative approach for measuring the accumulation amount by examining the approximate image. Using a striatal phantom, we verified the validity of a newly proposed method to accurately evaluate the tracer accumulations in the caudate and putamen separately. Moreover, we compared the proposed method with the conventional methods.

Methods

The left and right caudate/putamen regions and the whole brain region as background were identified in computed tomography (CT) images obtained by single‐photon emission computed tomography (SPECT)/CT and acquired the positional information of each region. SPECT‐like images were generated by assigning assumed accumulation amounts to each region. The SPECT‐like image, approximated to the actual measured SPECT image, was examined by changing the assumed accumulation amounts assigned to each region. When the generated SPECT‐like image most approximated the actual measured SPECT image, the accumulation amounts assumed were determined as the accumulation amounts in each region. We evaluated the correlation between the count density calculated by the proposed method and the actual count density of the ¹²³I solution filled in the phantom. Conventional methods (CT‐guide method, geometric transfer matrix [GTM] method, region‐based voxel‐wise [RBV] method, and Southampton method) were also evaluated. The significance of differences between the correlation coefficients of various methods (except the Southampton method) was evaluated.

Results

The correlation coefficients between the actual count density and the SPECT count densities were 0.997, 0.973, 0.951, 0.950, and 0.996 for the proposed method, CT‐guide method, GTM method, RBV method, and Southampton method, respectively. The correlation of the proposed method was significantly higher than those of the other methods.

Conclusions

The proposed method could calculate accurate accumulation amounts in the caudate and putamen separately, considering the PVE.

Is Generalized and Segmental Dystonia Accompanied by Impairments in the Dopaminergic System?

Background: The pathogenesis of dystonia is remarkably diverse. Some types of dystonia, such as DYT5 (DYT-GCH1) and tardive dystonia, are related to dysfunction of the dopaminergic system. Furthermore, on pathological examination, cell loss in the substantia nigra (SN) of patients with dystonia has been reported, suggesting that impaired dopamine production may be involved in DYT5 and in other types of dystonia.

Objectives: To investigate functional dopaminergic impairments, we compared patients with dystonia and those with Parkinson's disease (PD) with normal controls using neuromelanin-sensitive magnetic resonance imaging (NM-MRI) and dopamine transporter single photon emission computed tomography (DAT SPECT).

Methods: A total of 18, 18, and 27 patients with generalized or segmental dystonia, patients with PD, and healthy controls, respectively, were examined using NM-MRI. The mean area corresponding to NM in the SN (NM-SN) was blindly quantified. DAT SPECT was performed on 17 and eight patients with dystonia and PD, respectively. The imaging data of DAT SPECT were harmonized with the Japanese database using striatum phantom calibration. These imaging data were compared between patients with dystonia or PD and controls from the Japanese database in 256 healthy volunteers using the calibrated specific binding ratio (cSBR). The symptoms of dystonia were evaluated using the Fahn–Marsden Dystonia Rating Scale (FMDRS), and the correlation between the results of imaging data and FMDRS was examined.

Results: The mean areas corresponding to NM in the SN (NM-SN) were 31 ± 4.2, 28 ± 3.8, and 43 ± 3.8 pixels in patients with dystonia, PD, and in healthy controls, respectively. The mean cSBRs were 5 ± 0.2, 2.8 ± 0.2, 9.2 (predictive) in patients with dystonia, PD, and in healthy controls, respectively. The NM-SN area (r = −0.49, p < 0.05) and the cSBR (r = −0.54, p < 0.05) were inversely correlated with the FMDRS. There was no significant difference between the dystonia and PD groups regarding NM-SN (p = 0.28). In contrast, the cSBR was lower in patients with PD than in those with dystonia (p < 0.5 × 10⁻⁶).

Conclusions: Impairments of the dopaminergic system may be involved in developing generalized and segmental dystonia. SN abnormalities in patients with dystonia were supposed to be different from degeneration in PD.

Data-driven identification of diagnostically useful extrastriatal signal in dopamine transporter SPECT using explainable AI

This study used explainable artificial intelligence for data-driven identification of extrastriatal brain regions that can contribute to the interpretation of dopamine transporter SPECT with ¹²³I-FP-CIT in parkinsonian syndromes. A total of 1306 ¹²³I-FP-CIT-SPECT were included retrospectively. Binary classification as ‘reduced’ or ‘normal’ striatal ¹²³I-FP-CIT uptake by an experienced reader served as standard-of-truth. A custom-made 3-dimensional convolutional neural network (CNN) was trained for classification of the SPECT images with 1006 randomly selected images in three different settings: “full image”, “striatum only” (3-dimensional region covering the striata cropped from the full image), “without striatum” (full image with striatal region removed). The remaining 300 SPECT images were used to test the CNN classification performance. Layer-wise relevance propagation (LRP) was used for voxelwise quantification of the relevance for the CNN-based classification in this test set. Overall accuracy of CNN-based classification was 97.0%, 95.7%, and 69.3% in the “full image”, “striatum only”, and “without striatum” setting. Prominent contributions in the LRP-based relevance maps beyond the striatal signal were detected in insula, amygdala, ventromedial prefrontal cortex, thalamus, anterior temporal cortex, superior frontal lobe, and pons, suggesting that ¹²³I-FP-CIT uptake in these brain regions provides clinically useful information for the differentiation of neurodegenerative and non-neurodegenerative parkinsonian syndromes.

Explainable AI to improve acceptance of convolutional neural networks for automatic classification of dopamine transporter SPECT in the diagnosis of clinically uncertain parkinsonian syndromes

Purpose

Deep convolutional neural networks (CNN) provide high accuracy for automatic classification of dopamine transporter (DAT) SPECT images. However, CNN are inherently black-box in nature lacking any kind of explanation for their decisions. This limits their acceptance for clinical use. This study tested layer-wise relevance propagation (LRP) to explain CNN-based classification of DAT-SPECT in patients with clinically uncertain parkinsonian syndromes.

Methods

The study retrospectively included 1296 clinical DAT-SPECT with visual binary interpretation as “normal” or “reduced” by two experienced readers as standard-of-truth. A custom-made CNN was trained with 1008 randomly selected DAT-SPECT. The remaining 288 DAT-SPECT were used to assess classification performance of the CNN and to test LRP for explanation of the CNN-based classification.

Results

Overall accuracy, sensitivity, and specificity of the CNN were 95.8%, 92.8%, and 98.7%, respectively. LRP provided relevance maps that were easy to interpret in each individual DAT-SPECT. In particular, the putamen in the hemisphere most affected by nigrostriatal degeneration was the most relevant brain region for CNN-based classification in all reduced DAT-SPECT. Some misclassified DAT-SPECT showed an “inconsistent” relevance map more typical for the true class label.

Conclusion

LRP is useful to provide explanation of CNN-based decisions in individual DAT-SPECT and, therefore, can be recommended to support CNN-based classification of DAT-SPECT in clinical routine. Total computation time of 3 s is compatible with busy clinical workflow. The utility of “inconsistent” relevance maps to identify misclassified cases requires further investigation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00259-021-05569-9.

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.

Multi-Atlas MRI-Based Striatum Segmentation for 123I-FP-CIT SPECT (DAT-SPECT) Compared With the Bolt Method and SPECT-Atlas-Based Segmentation Method Toward the Accurate Diagnosis of Parkinson's Disease/Syndrome

Aims: This study aimed to analyze the performance of multi-atlas MRI-based parcellation for ¹²³I-FP-CIT SPECT (DAT-SPECT) in healthy volunteers. The proposed method was compared with the SPECT-atlas-based and Bolt methods. ¹⁸F-FE-PE2I-PET (DAT-PET) was used as a reference.

Methods: Thirty healthy subjects underwent DAT-SPECT, DAT-PET, and 3D-T1WI-MRI. We calculated the striatum uptake ratio (SUR/SBR), caudate uptake ratio (CUR), and putamen uptake ratio (PUR) for DAT-SPECT using the multi-atlas MRI-based method, SPECT-atlas-based method, and Bolt method. In the multi-atlas MRI-based method, the cerebellum, occipital cortex, and whole-brain were used as reference regions. The correlation of age with DAT-SPECT activity and the correlations of SUR/SBR, CUR, and PUR between DAT-SPECT and DAT-PET were calculated by each of the three methods.

Results: The correlation between age and SUR/SBR for DAT-SPECT based on the multi-atlas MRI-based method was comparable to that based on the SPECT-atlas-based method (r = −0.441 to −0.496 vs. −0.488). The highest correlation between DAT-SPECT and DAT-PET was observed using the multi-atlas MRI-based method with the occipital lobe defined as the reference region compared with the SPECT-atlas-based and Bolt methods (SUR, CUR, and PUR: 0.687, 0.723, and 0.676 vs. 0.698, 0.660, and 0.616 vs. 0.655).

Conclusion: Multi-atlas MRI-based parcellation with the occipital lobe defined as the reference region was at least comparable to the clinical methods.

Dopamine transporter SPECT imaging in Parkinson’s disease and parkinsonian disorders

The dopamine transporter (DAT) imaging provides an objective tool for the assessment of dopaminergic function of presynaptic terminals which is valuable for the differential diagnosis of parkinsonian disorders related to a striatal dopaminergic deficiency from movement disorders not related a striatal dopaminergic deficiency. DAT imaging with single-photon emission computed tomography (SPECT) can be used to confirm or exclude a diagnosis of dopamine deficient parkinsonism in cases where the diagnosis is unclear. It can also detect the dopaminergic dysfunction in presymptomatic subjects at risk for Parkinson’s disease (PD) since the reduced radiotracer binding to DATs in striatum is already present in the prodromal stage of PD. This review covers the rationale of using DAT SPECT imaging in the diagnosis of PD and other parkinsonian disorders, specifically focusing on the practical aspects of imaging and routine clinical indications.

A new quantitative index in the diagnosis of Parkinson syndrome by dopamine transporter single-photon emission computed tomography

OBJECTIVE

Dopamine transporter single-photon emission computed tomography (DAT SPECT) has been widely used to diagnose Parkinson syndrome. Using the standardized uptake value (SUV) of DAT SPECT, we propose "functional dopamine transporter volume (f-DTV)" as a new quantitative index to evaluate the three-dimensional volume of functional dopamine transporters and assess its diagnostic ability in differentiating dopaminergic neurodegenerative diseases (dNDD) from non-dNDD.

METHODS

Seventy-nine patients were enrolled (42 dNDD, 37 non-dNDD; 38 men; age 24-88 years). We analyzed seven quantitative indices. The specific binding ratio (SBR) was calculated using a program specialized for DAT SPECT (SBR_Bolt). The SUVmax, SUVpeak, and SUVmean were calculated using a quantification program for bone SPECT. SBR_SUV was calculated by dividing striatal SUVmean by the average of background SUVmean. The cutoff value of the active dopamine transporter level was examined using three methods (threshold of 40% of SUVmax, SUV 2, and SUV 3) to calculate the active dopamine transporter volume (ADV). The f-DTV was calculated by multiplying ADV and SUV_mean. We assessed the correlations between SBR_Bolt and SBR_SUV, and compared the mean value of each index between the dNDD and non-dNDD groups. The abilities of SBR_Bolt, SBR_SUV, SUVmax, SUVpeak, SUVmean, ADV, and f-DTV in differentiating dNDD from non-dNDD were determined by the area under the receiver operating curve (AUC) generated by the receiver operating characteristics analysis.

RESULTS

The SBR_Bolt and SBR_SUV highly correlated with each other (r = 0.71). The cutoff value of the active dopamine transporter level was determined as SUV 3. All seven quantitative indices showed lower values in the dNDD group than in the non-dNDD group, and the difference between the two groups was statistically significant (p < 0.05). Sensitivity, specificity, and AUC of f-DTV were slightly lower than those of SBR_Bolt (71%, 79%, and 0.81, respectively, for f-DTV, and 81%, 84%, 0.88, respectively, for SBR_Bolt). The difference in AUC between f-DTV and SBR_Bolt was not statistically significant.

CONCLUSIONS

This study demonstrates the utility of f-DTV as a novel quantitative index for evaluating the three-dimensional volume of functional dopamine transporters, and that f-DTV has almost the same diagnostic ability to differentiate dNDD from non-dNDD using DAT SPECT.

Clinical value of machine learning-based interpretation of I-123 FP-CIT scans to detect Parkinson's disease: a two-center study

PURPOSE

Our aim was to develop and validate a machine learning (ML)-based approach for interpretation of I-123 FP-CIT SPECT scans to discriminate Parkinson's disease (PD) from non-PD and to determine its generalizability and clinical value in two centers.

METHODS

We retrospectively included 210 consecutive patients who underwent I-123 FP-CIT SPECT imaging and had a clinically confirmed diagnosis. Linear support vector machine (SVM) was used to build a classification model to discriminate PD from non-PD based on I-123-FP-CIT striatal uptake ratios, age and gender of 90 patients. The model was validated on unseen data from the same center where the model was developed (n = 40) and consecutively on data from a different center (n = 80). Prediction performance was assessed and compared to the scan interpretation by expert physicians.

RESULTS

Testing the derived SVM model on the unseen dataset (n = 40) from the same center resulted in an accuracy of 95.0%, sensitivity of 96.0% and specificity of 93.3%. This was identical to the classification accuracy of nuclear medicine physicians. The model was generalizable towards the other center as prediction performance did not differ thereby obtaining an accuracy of 82.5%, sensitivity of 88.5% and specificity of 71.4% (p = NS). This was comparable to that of nuclear medicine physicians (p = NS).

CONCLUSION

ML-based interpretation of I-123-FP-CIT scans results in accurate discrimination of PD from non-PD similar to visual assessment in both centers. The derived SVM model is therefore generalizable towards centers using comparable acquisition and image processing methods and implementation as diagnostic aid in clinical practice is encouraged.

Striatal dopamine transporter SPECT quantification: head-to-head comparison between two three-dimensional automatic tools

Purpose

Our aim was to compare a widely distributed commercial tool with an older free software (i) one another, (ii) with a clinical motor score, (iii) versus reading by experts.

Procedures

We analyzed consecutive scans from one-hundred and fifty-one outpatients submitted to brain DAT SPECT for a suspected parkinsonism. Images were post-processed using a commercial (Datquant®) and a free (BasGanV2) software. Reading by expert was the gold standard. A subset of patients with pathological or borderline scan was evaluated with the clinical Unified Parkinson’s Disease Rating Scale, motor part (MDS-UPDRS-III).

Results

SBR, putamen-to-caudate (P/C) ratio, and both P and C asymmetries were highly correlated between the two software with Pearson’s ‘r’ correlation coefficients ranging from .706 to .887. Correlation coefficients with the MDS-UPDRS III score were higher with caudate than with putamen SBR values with both software, and in general higher with BasGanV2 than with Datquant®. Datquant® correspondence with expert reading was 84.1% (94.0% by additionally considering the P/C ratio as a further index). BasGanV2 correspondence with expert reading was 80.8% (86.1% by additionally considering the P/C ratio).

Conclusions

Both Datquant® and BasGanV2 work reasonably well and similarly one another in semi-quantification of DAT SPECT. Both tools have their own strength and pitfalls that must be known in detail by users in order to obtain the best help in visual reading and reporting of DAT SPECT.

Striatal Dopamine Deficit and Motor Impairment in Idiopathic Normal Pressure Hydrocephalus

BACKGROUND

Idiopathic normal pressure hydrocephalus can present with parkinsonism. However, abnormalities of the striatal dopamine reuptake transporter are unclear.

OBJECTIVES

To explore presence and features of striatal dopaminergic deficit in subjects with idiopathic normal pressure hydrocephalus as compared to Parkinson's disease (PD) patients and healthy controls.

METHODS

We investigated 50 subjects with idiopathic normal pressure hydrocephalus, 25 with PD, and 40 healthy controls. All participants underwent [¹²³ I]-N-ω-fluoropropyl-2β-carbomethoxy-3β-(4-iodophenyl)nortropane and single-photon emission computed tomography to quantify the striatal dopamine reuptake transporter binding. All subjects with idiopathic normal pressure hydrocephalus underwent a levodopa (l-dopa) challenge test and magnetic resonance imaging to evaluate ventriculomegaly and white matter changes. Gait, cognition, balance, and continence were assessed with the Idiopathic Normal Pressure Hydrocephalus Rating Scale, and parkinsonism with the motor section of the Movement Disorder Society-Unified Parkinson's Disease Rating Scale. All patients completed a 2-year follow-up.

RESULTS

A total of 62% of patients with idiopathic normal pressure hydrocephalus featured a reduced striatal dopamine reuptake transporter binding, which correlated with the severity of parkinsonism but not with features of ventriculomegaly or white matter changes. Unlike PD, this dopaminergic deficit in idiopathic normal pressure hydrocephalus was more symmetric and prominent in the caudate nucleus.

CONCLUSIONS

Subjects with idiopathic normal pressure hydrocephalus can present a reduction of striatal dopamine reuptake transporter binding, which is consistent with the severity of parkinsonism and qualitatively differs from that found in PD patients. Longitudinal interventional studies are needed to prove a role for striatal dopamine reuptake transporter deficit in the pathophysiology of idiopathic normal pressure hydrocephalus. © 2020 International Parkinson and Movement Disorder Society.

Impact of age and sex correction on the diagnostic performance of dopamine transporter SPECT

Purpose

The specific binding ratio (SBR) of ¹²³I-FP-CIT (FP-CIT) in the putamen decreases with age by about 5% per decade and most likely is about 10% higher in females. However, the clinical utility of age and sex correction of the SBR is still a matter of debate. This study tested the impact of age and sex correction on the diagnostic performance of the putamen SBR in three independent patient samples.

Methods

Research sample: 207 healthy controls (HC) and 438 Parkinson’s disease (PD) patients. Clinical sample A: 183 patients with neurodegenerative parkinsonian syndrome (PS) and 183 patients with non-neurodegenerative PS from one site. Clinical sample B: 84 patients with neurodegenerative PS and 38 patients with non-neurodegenerative PS from another site. Correction for age and sex of the putamen SBR was based on linear regression in the HC or non-neurodegenerative PS, separately in each sample. The area under the ROC curve (AUC) was used as performance measure.

Results

The putamen SBR was higher in females compared to males (PPMI: 14%, p < 0.0005; clinical sample A: 7%, p < 0.0005; clinical sample B: 6%, p = 0.361). Age-related decline of the putamen SBR ranged between 3.3 and 10.4% (p ≤ 0.019). In subjects ≥ 50 years, age and sex explained < 10% of SBR between-subjects variance. Correction of the putamen SBR for age and sex resulted in slightly decreased AUC in the PPMI sample (0.9955 versus 0.9969, p = 0.025) and in clinical sample A (0.9448 versus 0.9519, p = 0.057). There was a small, non-significant AUC increase in clinical sample B (0.9828 versus 0.9743, p = 0.232).

Conclusion

These findings do not support age and sex correction of the putaminal FP-CIT SBR in the diagnostic workup of parkinsonian syndromes. This most likely is explained by the fact that the proportion of between-subjects variance caused by age and sex is considerably below the symptom threshold of about 50% reduction in neurodegenerative PS.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00259-020-05085-2.

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.

Impact of the size of the normal database on the performance of the specific binding ratio in dopamine transporter SPECT

BACKGROUND

This study investigated the impact of the size of the normal database on the classification performance of the specific binding ratio (SBR) in dopamine transporter (DAT) SPECT with [123I]FP-CIT in different settings.

METHODS

The first subject sample comprised 645 subjects from the Parkinson's Progression Marker Initiative (PPMI), 207 healthy controls (HC), and 438 Parkinson's disease (PD) patients. The second sample comprised 372 patients from clinical routine patient care, 186 with non-neurodegenerative parkinsonian syndrome (PS) and 186 with neurodegenerative PS. Single-photon emission computed tomography (SPECT) images of the clinical sample were reconstructed with two different reconstruction algorithms (filtered backprojection, iterative ordered subsets expectation maximization (OSEM) reconstruction with resolution recovery). The putaminal specific binding ratio (SBR) was computed using an anatomical region of interest (ROI) predefined in standard (MNI) space in the Automated Anatomic Labeling (AAL) atlas or using hottest voxels (HV) analysis in large predefined ROIs. SBR values were transformed to z-scores using mean and standard deviation of the SBR in a normal database of varying sizes (n = 5, 10, 15,…, 50) randomly selected from the HC subjects (PPMI sample) or the patients with non-neurodegenerative PS (clinical sample). Accuracy, sensitivity, and specificity for identifying patients with PD or neurodegenerative PS were determined as performance measures using a predefined fixed cutoff on the z-score. This was repeated for 10,000 randomly selected normal databases, separately for each size of the normal database. Mean and 5th percentile of the performance measures over the 10,000 realizations were computed. Accuracy, sensitivity, and specificity when using the whole set of HC or non-neurodegenerative PS subjects as normal database were used as benchmark.

RESULTS

Mean loss of accuracy of the putamen SBR z-score was below 1% when the normal database included at least 15 subjects, independent of subject sample (PPMI or clinical), reconstruction method (filtered backprojection or OSEM), and ROI method (AAL or HV). However, the variability of the accuracy of the putamen SBR z-score decreased monotonically with increasing size of normal database and was still considerable at size 15. In order to achieve less than 5% "maximum" loss of accuracy (defined by the 5th percentile) in all settings required at least 25 to 30 subjects in the normal database. Reduction of mean and "maximum" loss of accuracy of the putamen SBR z-score by further increasing the size of the normal database was very small beyond size 40.

CONCLUSIONS

The results of this study suggest that 25 to 30 is the minimum size of the normal database to reliably achieve good performance of semi-quantitative analysis in dopamine transporter (DAT) SPECT, independent of the algorithm used for image reconstruction and the ROI method used to estimate the putaminal SBR.

EANM practice guideline/SNMMI procedure standard for dopaminergic imaging in Parkinsonian syndromes 1.0

Purpose

This joint practice guideline or procedure standard was developed collaboratively by the European Association of Nuclear Medicine (EANM) and the Society of Nuclear Medicine and Molecular Imaging (SNMMI). The goal of this guideline is to assist nuclear medicine practitioners in recommending, performing, interpreting, and reporting the results of dopaminergic imaging in parkinsonian syndromes.

Methods

Currently nuclear medicine investigations can assess both presynaptic and postsynaptic function of dopaminergic synapses. To date both EANM and SNMMI have published procedural guidelines for dopamine transporter imaging with single photon emission computed tomography (SPECT) (in 2009 and 2011, respectively). An EANM guideline for D2 SPECT imaging is also available (2009). Since the publication of these previous guidelines, new lines of evidence have been made available on semiquantification, harmonization, comparison with normal datasets, and longitudinal analyses of dopamine transporter imaging with SPECT. Similarly, details on acquisition protocols and simplified quantification methods are now available for dopamine transporter imaging with PET, including recently developed fluorinated tracers. Finally, [¹⁸F]fluorodopa PET is now used in some centers for the differential diagnosis of parkinsonism, although procedural guidelines aiming to define standard procedures for [¹⁸F]fluorodopa imaging in this setting are still lacking.

Conclusion

All these emerging issues are addressed in the present procedural guidelines for dopaminergic imaging in parkinsonian syndromes.

[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.

Improvement of diagnostic accuracy of Parkinson's disease on I-123-ioflupane single photon emission computed tomography (123I FP-CIT SPECT) using new Japanese normal database

OBJECTIVES

I-123-ioflupane single photon emission computed tomography (FP-CIT-SPECT) has been used to assess dopamine transporter (DAT) loss in Parkinson's disease. The specific binding ratio (SBR), a quantitative parameter of DAT density in the striatum, may be affected by differences in age, sex, and SPECT system. The purpose of this study was to evaluate the utility of FP-CIT-SPECT using the Japanese normal database (NDB) in the diagnosis of Parkinson's disease.

METHODS

To standardize the quantitative outcome measures of DAT density obtained with different SPECT systems, striatal phantoms filled with striatal to background materials at ratios between 8:1 and 1:1 were measured using a gamma camera (ECAM) in our institute. Consecutive fifty patients (23 men and 27 women; age range, 40-86 years) with suspected PD undergoing FP-CIT SPECT brain imaging during the period from April to October 2016 were enrolled in this retrospective study. Their final diagnoses were PD in 28 patients and PD in 22 patients. SBRs of the patients were calculated using either new (Japanese database with different age and sex; NEWver) or old (non-Japanese database not specifying age and sex; OLDver) version software (AZE Virtual Place Hayabusa [DaTView], AZE, Ltd. Tokyo, Japan). The McNemar test was used to compare the diagnostic accuracy between old and new versions.

RESULTS

Based on the phantom study, the calibrated SBR could be calculated by Y=1.25×Measured SBR+0.78. The sensitivities for OLDver and NEWver were 100% and 93%, respectively (p=0.5), and the specificities were 55% and 100% (p=0.002). The diagnostic accuracy of NEWver (96%) was better than that of OLDver (80%, p<0.001).

CONCLUSION

FP-CIT-SPECT using the Japanese NDB improved the diagnostic accuracy of PD by improving specificity.

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.

Impact of a combination of quantitative indices representing uptake intensity, shape, and asymmetry in DAT SPECT using machine learning: comparison of different volume of interest settings

BACKGROUND

We sought to assess the machine learning-based combined diagnostic accuracy of three types of quantitative indices obtained using dopamine transporter single-photon emission computed tomography (DAT SPECT)-specific binding ratio (SBR), putamen-to-caudate ratio (PCR)/fractal dimension (FD), and asymmetry index (AI)-for parkinsonian syndrome (PS). We also aimed to compare the effect of two different types of volume of interest (VOI) settings from commercially available software packages DaTQUANT (Q) and DaTView (V) on diagnostic accuracy.

METHODS

Seventy-one patients with PS and 40 without PS (NPS) were enrolled. Using SPECT images obtained from these patients, three quantitative indices were calculated at two different VOI settings each. SBR-Q, PCR-Q, and AI-Q were derived using the VOI settings from DaTQUANT, whereas SBR-V, FD-V, and AI-V were derived using those from DaTView. We compared the diagnostic value of these six indices for PS. We incorporated a support vector machine (SVM) classifier for assessing the combined accuracy of the three indices (SVM-Q: combination of SBR-Q, PCR-Q, and AI-Q; SVM-V: combination of SBR-V, FD-V, and AI-V). A Mann-Whitney U test and receiver-operating characteristics (ROC) analysis were used for statistical analyses.

RESULTS

ROC analyses demonstrated that the areas under the curve (AUC) for SBR-Q, PCR-Q, AI-Q, SBR-V, FD-V, and AI-V were 0.978, 0.837, 0.802, 0.906, 0.972, and 0.829, respectively. On comparing the corresponding quantitative indices between the two types of VOI settings, SBR-Q performed better than SBR-V (p = 0.006), whereas FD-V performed better than PCR-Q (p = 0.0003). No significant difference was observed between AI-Q and AI-V (p = 0.56). The AUCs for SVM-Q and SVM-V were 0.988 and 0.994, respectively; the two different VOI settings displayed no significant differences in terms of diagnostic accuracy (p = 0.48).

CONCLUSION

The combination of the three indices obtained using the SVM classifier improved the diagnostic performance for PS; this performance did not differ based on the VOI settings and software used.

Presynaptic dopaminergic neuroimaging in REM sleep behavior disorder: A systematic review and meta-analysis

The presence of polysomnography-confirmed REM sleep behavior disorder (RBD) is the stronger risk factor for having prodromal Parkinson disease (PD), followed by abnormal presynaptic dopaminergic radionuclide neuroimaging. Aim of the review is to conduct a meta-analysis of literature data regarding presynaptic dopaminergic neuroimaging in RBD. A literature search was conducted, resulting in 16 papers that met the inclusion criteria. Clinical and neuroimaging data were extracted. The studies are heterogeneous, especially for neuroimaging methodology. Two mathematical transformations were used to allow imaging data to be compared among studies. Tracer uptake progressively decreased from controls to idiopathic RBD and eventually PD patients with RBD at putamen level. Tracer uptake at caudate level overlapped between patients with idiopathic RBD and those with PD without RBD. These results support the hypothesis that idiopathic RBD patients are on the path to developing a synucleinopathy. The receiver operation characteristic analysis found good to excellent discrimination capability between all groups. Presynaptic dopaminergic neuroimaging may be a key feature in the stratification of subjects to be included in neuroprotective trials. However, literature data are heterogeneous. Multicentric, harmonized studies are needed to define the usefulness of presynaptic dopaminergic neuroimaging with the aim of testing neuroprotective trials for idiopathic RBD.

A new visual rating scale for Ioflupane imaging in Lewy body disease

BACKGROUND

Dopaminergic loss on ¹²³I-Ioflupane brain imaging is a recognised biomarker for dementia with Lewy bodies. It is usually assessed using a visual rating scale developed for Parkinson's disease, which may not be optimal for dementia with Lewy bodies, as patterns of dopaminergic loss can be different.

OBJECTIVES

We aimed to develop a new visual rating scale for ¹²³I-Ioflupane brain images in Lewy body disease that encompasses appearances seen in dementia with Lewy bodies, and validate this against autopsy diagnosis.

METHODS

Four experienced observers developed and tested a new scale consisting of two metrics, reflecting overall loss and heterogeneity of loss. 66 subjects were used during development including clinical diagnoses of Alzheimer's disease (n = 14), Parkinson's disease (n = 9), Parkinson's disease dementia (n = 9), dementia with Lewy bodies (n = 15) and normal controls (n = 19). The scale was then tested on an independent group of 46 subjects with autopsy confirmed diagnosis: Alzheimer's disease (n = 11), Parkinson's disease (n = 3), Parkinson's disease dementia (n = 15), dementia with Lewy bodies (n = 12), normal controls (n = 4) and Frontotemporal dementia (n = 1).

RESULTS

In the autopsy validation the sensitivity and specificity of the new scale for Lewy body disease was 97% and 100% respectively, compared with the standard scale which had the same sensitivity (97%), but lower specificity (80%). The new scale had excellent inter rater reliability (intra-class correlation coefficient 0.93).

CONCLUSION

A new robust and reliable rating scale is described that straightforwardly captures the visual appearance of ¹²³I-Ioflupane brain images. It demonstrated high accuracy in autopsy confirmed cases and offers advantages over the existing visual rating scale.

What a neurologist should know about PET and SPECT functional imaging for parkinsonism: A practical perspective

The diagnosis of a parkinsonian syndrome based on clinical criteria remains sometimes difficult, especially at disease onset. Brain or heart molecular imaging techniques (SPECT or PET) can provide a major help to improve and speed up diagnosis, influencing treatment strategies. Presynaptic dopaminergic imaging using either [¹⁸F]-Dopa PET or ¹²³I -2β-Carbomethoxy-3β-(4-Iodophenyl)- N-(3-Fluoropropyl) Nortropane ([¹²³I]-Ioflupane)SPECT demonstrates or rules out the presence of a dopaminergic degenerative process. This allows to distinguish Parkinson's disease, Parkinson "plus" syndromes and dementia with Lewy bodies (reduced radiotracers binding) from essential tremor, psychogenic, post-neuroleptic or vascular parkinsonisms, dopa-responsive dystonia and Alzheimer's disease (normal radiotracers binding). For differential diagnosis between Parkinson's disease and Parkinson "plus" syndromes, brain molecular imaging with [¹⁸F]-Fluorodeoxyglucose ([¹⁸F]-FDG) PET or ^99mTc-HMPAO SPECT can provide useful information, whereas [¹⁸F]-Dopa PET or [¹²³I]-Ioflupane does not separate these entities. Finally, sympathetic cardiac [¹²³I]-Metaiodobenzylguanidine ([¹²³I]-MIBG) scintigraphy or SPECT can help distinguishing Parkinson's disease and dementia with Lew bodies (decreased binding) from multiple system atrophy and progressive supranuclear palsy (normal binding). New radiotracers notably those targeting the pathological process itself such as Tau aggregates are under development and may provide interesting informations to delineate the different Parkinson "plus" syndromes.

Japanese multicenter database of healthy controls for [123I]FP-CIT SPECT

PURPOSE

The aim of this multicenter trial was to generate a [¹²³I]FP-CIT SPECT database of healthy controls from the common SPECT systems available in Japan.

METHODS

This study included 510 sets of SPECT data from 256 healthy controls (116 men and 140 women; age range, 30-83 years) acquired from eight different centers. Images were reconstructed without attenuation or scatter correction (NOACNOSC), with only attenuation correction using the Chang method (ChangACNOSC) or X-ray CT (CTACNOSC), and with both scatter and attenuation correction using the Chang method (ChangACSC) or X-ray CT (CTACSC). These SPECT images were analyzed using the Southampton method. The outcome measure was the specific binding ratio (SBR) in the striatum. These striatal SBRs were calibrated from prior experiments using a striatal phantom.

RESULTS

The original SBRs gradually decreased in the order of ChangACSC, CTACSC, ChangACNOSC, CTACNOSC, and NOACNOSC. The SBRs for NOACNOSC were 46% lower than those for ChangACSC. In contrast, the calibrated SBRs were almost equal under no scatter correction (NOSC) conditions. A significant effect of age was found, with an SBR decline rate of 6.3% per decade. In the 30-39 age group, SBRs were 12.2% higher in women than in men, but this increase declined with age and was absent in the 70-79 age group.

CONCLUSIONS

This study provided a large-scale quantitative database of [¹²³I]FP-CIT SPECT scans from different scanners in healthy controls across a wide age range and with balanced sex representation. The phantom calibration effectively harmonizes SPECT data from different SPECT systems under NOSC conditions. The data collected in this study may serve as a reference database.

Validation of Optimum ROI Size for 123I-FP-CIT SPECT Imaging Using a 3D Mathematical Cylinder Phantom

OBJECTIVES

The partial volume effect (PVE) of single-photon emission computed tomography (SPECT) on corpus striatum imaging is caused by the underestimation of specific binding ratio (SBR). A large ROI (region of interest) set using the Southampton method is independent of PVE for SBR. The present study aimed to determine the optimal ROI size with contrast and SBR for striatum images and validate the Southampton method using a three-dimensional mathematical cylinder (3D-MAC) phantom.

METHODS

We used ROIs sizes of 27, 36, 44, 51, 61, 68, and 76 mm for targets with diameters 40, 20, and 10 mm on reference and processed images reconstructed using the 3D-MAC phantom. Contrast values and SBR were compared with the theoretical values to obtain the optimal ROI size.

RESULTS

The contrast values in the ROI with diameters of 51 (target: 40 mm in diameter) and 44 (target: 20 mm in diameter) mm matched the theoretical values. However, this value did not correspond with the 10-mm-diameter target. The SBR matched the theoretical value with an ROI of > 44 mm in the 20-mm-diameter target; but, it was under- and overestimated under any other conditions.

CONCLUSION

These results suggested that an ROI should be 2-4 folds larger than the target size without PVE, and that the Southampton method was remarkably accurate.

Occipital lobe and posterior cingulate perfusion in the prediction of dementia with Lewy body pathology in a clinical sample

OBJECTIVE

The aim of this study was to investigate the diagnostic value of occipital lobe and posterior cingulate perfusion in predicting dopamine transporter imaging outcome using a quantitative measure of analysis.

PATIENTS AND METHODS

In total, 99 patients with cognitive complaints who had undergone both technetium-99m-hexamethylpropyleneamine oxime single-photon emission computed tomography (Tc-HMPAO SPECT) and I ioflupane (I-FP-CIT also called DaTSCAN) imaging in a dementia diagnostic center were analyzed. Measures of perfusion were calculated from HMPAO SPECT images for the medial and lateral occipital lobe, the posterior cingulate cortex, precuneus and cuneus regions of interest using statistical parametric mapping 8. DaTSCAN images were quantified and specific binding ratios were calculated independent from HMPAO SPECT results. Statistical parametric mapping and tests of associations between perfusion and I-FP-CIT imaging were completed.

RESULTS

Regions of interest on HMPAO yielded poor predictive values when used independently to predict I-FP-CIT status; however, the combination of normal posterior cingulate perfusion with medial and lateral occipital hypoperfusion was associated significantly with I-FP-CIT status, χ (1, N=99)=9.72, P=0.002. This combination also yielded a high positive likelihood ratio and specificity (11.1, 98%). Sensitivity was, however, low (22%). No significant perfusion differences were found when abnormal and normal I-FP-CIT groups were compared directly using voxel-based morphometry (P<0.05, family-wise error).

CONCLUSION

The combination of medial and lateral occipital hypoperfusion with preserved posterior cingulate gyrus perfusion is highly specific for individuals with a positive I-FP-CIT scan in a clinical sample where diagnostic doubt exists. This regional combination, however, lacks sensitivity; therefore, absence of the sign cannot be used to rule out dementia with Lewy bodies. A positive finding provides strong evidence to rule in dementia with Lewy bodies.