Abstract:We recommend the 75th percentile in the whole brain as reference for semi-quantitative analysis in FP-CIT SPECT. This combination provided the best agreement of the semi-quantitative analysis with visual evaluation of the SPECT images by an expert and, therefore, is appropriate to support less experienced physicians.
“…The results of the present study suggest that the largest possible reference region for FP-CIT SPECT, the whole brain without striata, indeed stabilizes quantitative analysis. This is in agreement with recent findings by Kupitz et al who compared the whole brain (without striata and the predominantly SERT-binding regions thalamus and brainstem) as the reference region in FP-CIT SPECT to the frontal and occipital lobes [36]. The use of the whole brain resulted in the highest area under the ROC curve of the SBR for differentiation between neurodegenerative and nonneurodegenerative parkinsonian syndrome.…”
Section: Discussionsupporting
confidence: 92%
“…This might limit the use of the whole brain as reference region in clinical patient care, since the CSF space can be strongly dilated in patients. However, the contribution of non-grey matter voxels might be reduced by using the 75th percentile of the voxel intensities in the whole-brain ROI rather than the mean to characterize nonspecific tracer binding [36]. The 75th percentile excludes CSF voxels, and, assuming tracer uptake to be higher in cortical grey matter than in white matter, it is also more representative of grey than of white matter.…”
QSPECT reconstruction appears to be useful for reduction of camera-specific intersubject variability of [(123)I]FP-CIT SPECT in multisite and single-site multicamera settings. Whole brain excluding striatal binding as the reference provides more stable quantitative estimates than occipital or cerebellar binding.
“…The results of the present study suggest that the largest possible reference region for FP-CIT SPECT, the whole brain without striata, indeed stabilizes quantitative analysis. This is in agreement with recent findings by Kupitz et al who compared the whole brain (without striata and the predominantly SERT-binding regions thalamus and brainstem) as the reference region in FP-CIT SPECT to the frontal and occipital lobes [36]. The use of the whole brain resulted in the highest area under the ROC curve of the SBR for differentiation between neurodegenerative and nonneurodegenerative parkinsonian syndrome.…”
Section: Discussionsupporting
confidence: 92%
“…This might limit the use of the whole brain as reference region in clinical patient care, since the CSF space can be strongly dilated in patients. However, the contribution of non-grey matter voxels might be reduced by using the 75th percentile of the voxel intensities in the whole-brain ROI rather than the mean to characterize nonspecific tracer binding [36]. The 75th percentile excludes CSF voxels, and, assuming tracer uptake to be higher in cortical grey matter than in white matter, it is also more representative of grey than of white matter.…”
QSPECT reconstruction appears to be useful for reduction of camera-specific intersubject variability of [(123)I]FP-CIT SPECT in multisite and single-site multicamera settings. Whole brain excluding striatal binding as the reference provides more stable quantitative estimates than occipital or cerebellar binding.
“…The 75th percentile of the count density in a reference region comprising the whole brain without striata, thalamus, and brain stem was used as estimate of non-displaceable count density (Fig. 1d) [28]. Fig.…”
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 whol...
“…Individual SPECT images were normalized (affine) to a custom-made FP-CIT template in the anatomical space of the Montreal Neurological Institute (MNI) using the Statistical Parametric Mapping software package (version SPM12) [26]. Voxel intensities were scaled voxel-wise to the 75th percentile of the voxel intensity in a reference region comprising the whole brain except the striata, thalamus, brain stem, and ventricles [27,28]. The conventional unilateral putamen SBR was computed by applying anatomical ROIs predefined in MNI space by the Automatic Anatomical Labeling atlas (AAL) [29].…”
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 [ 123 I]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.
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