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Abstract 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.

Abstract 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.

Abstract 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.

Abstract 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.