Dataset Information

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

ABSTRACT:

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.

SUBMITTER: Schiebler T

PROVIDER: S-EPMC10541531 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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Publications

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

Schiebler Tassilo T Apostolova Ivayla I Mathies Franziska Lara FL Lange Catharina C Klutmann Susanne S Buchert Ralph R

European journal of nuclear medicine and molecular imaging 20230617 11

<h4>Purpose</h4>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.<h4>Methods</h4>One thousand seven hundred forty consecutive DAT-SPECT with <sup>123</sup>I-FP-CIT from clinical routine were included retrospectively. ...[more]

PMID: 37328621

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Project description:PurposeThe 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.MethodsResearch 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.ResultsThe 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).ConclusionThese 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.

Project description:ObjectiveTo investigate dopaminergic function in a large cohort of patients with corticobasal syndrome (CBS) and describe its relationship with clinical features in comparison to Parkinson's disease and healthy control subjects. In addition, we assessed prevalence and features of individuals with CBS and in vivo evidence of preserved nigral neuronal density.BackgroundSubstantia nigra pars compacta (SNc) neuronal degeneration is a mandatory pathological criterion for definite corticobasal degeneration, though sporadic autopsy-proven cases with ante-mortem imaging evidence of preserved nigral terminals have been recently described.MethodsIn this multicenter study, we investigated presynaptic nigrostriatal function in 36 outpatients fulfilling clinical criteria for "probable corticobasal degeneration" (age 71±7.3 years; disease duration 3.9±1.6 years), 37 PD and 24 healthy control subjects using FP-CIT single photon emission computed tomography. Clinical, neuropsychological, and magnetic resonance imaging assessment was performed to characterize CBS patients. Linear discriminant analysis was used to categorize normal vs. pathological scans.ResultsFP-CIT binding reduction in patients with CBS was characterized by larger variability, more uniform reduction throughout the striatum and greater hemispheric asymmetry compared to PD. Moreover, there was no significant correlation between tracer uptake values and clinical features such as disease duration and severity. Despite all CBS subjects showed obvious bilateral extrapyramidal signs, FP-CIT uptake was found to be normal bilaterally in four CBS patients and only unilaterally in other four cases. Extensive clinical, neuropsychological and imaging assessment did not reveal remarkable differences between CBS subjects with normal vs. pathological FP-CIT uptake.ConclusionsOur findings support the hypothesis that extrapyramidal motor symptoms in CBS are not invariably associated with SNc neuronal degeneration and that supranigral factors may play a major role in several cases. CBS individuals with normal FP-CIT uptake do not show any clinical or cognitive feature suggesting a different pathology than CBD.

Project description:PurposeDeep 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.MethodsA 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).ResultsIn 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.ConclusionThe 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.

Project description:PurposeAttenuation correction and scatter compensation (AC/SC) are two main steps toward quantitative PET imaging, which remain challenging in PET-only and PET/MRI systems. These can be effectively tackled via deep learning (DL) methods. However, trustworthy, and generalizable DL models commonly require well-curated, heterogeneous, and large datasets from multiple clinical centers. At the same time, owing to legal/ethical issues and privacy concerns, forming a large collective, centralized dataset poses significant challenges. In this work, we aimed to develop a DL-based model in a multicenter setting without direct sharing of data using federated learning (FL) for AC/SC of PET images.MethodsNon-attenuation/scatter corrected and CT-based attenuation/scatter corrected (CT-ASC) 18F-FDG PET images of 300 patients were enrolled in this study. The dataset consisted of 6 different centers, each with 50 patients, with scanner, image acquisition, and reconstruction protocols varying across the centers. CT-based ASC PET images served as the standard reference. All images were reviewed to include high-quality and artifact-free PET images. Both corrected and uncorrected PET images were converted to standardized uptake values (SUVs). We used a modified nested U-Net utilizing residual U-block in a U-shape architecture. We evaluated two FL models, namely sequential (FL-SQ) and parallel (FL-PL) and compared their performance with the baseline centralized (CZ) learning model wherein the data were pooled to one server, as well as center-based (CB) models where for each center the model was built and evaluated separately. Data from each center were divided to contribute to training (30 patients), validation (10 patients), and test sets (10 patients). Final evaluations and reports were performed on 60 patients (10 patients from each center).ResultsIn terms of percent SUV absolute relative error (ARE%), both FL-SQ (CI:12.21-14.81%) and FL-PL (CI:11.82-13.84%) models demonstrated excellent agreement with the centralized framework (CI:10.32-12.00%), while FL-based algorithms improved model performance by over 11% compared to CB training strategy (CI: 22.34-26.10%). Furthermore, the Mann-Whitney test between different strategies revealed no significant differences between CZ and FL-based algorithms (p-value > 0.05) in center-categorized mode. At the same time, a significant difference was observed between the different training approaches on the overall dataset (p-value < 0.05). In addition, voxel-wise comparison, with respect to reference CT-ASC, exhibited similar performance for images predicted by CZ (R2 = 0.94), FL-SQ (R2 = 0.93), and FL-PL (R2 = 0.92), while CB model achieved a far lower coefficient of determination (R2 = 0.74). Despite the strong correlations between CZ and FL-based methods compared to reference CT-ASC, a slight underestimation of predicted voxel values was observed.ConclusionDeep learning-based models provide promising results toward quantitative PET image reconstruction. Specifically, we developed two FL models and compared their performance with center-based and centralized models. The proposed FL-based models achieved higher performance compared to center-based models, comparable with centralized models. Our work provided strong empirical evidence that the FL framework can fully benefit from the generalizability and robustness of DL models used for AC/SC in PET, while obviating the need for the direct sharing of datasets between clinical imaging centers.

Project description:BackgroundMultiple-pinhole (MPH) collimators improve the resolution-sensitivity trade-off compared to parallel-hole collimators. This study evaluated the impact of MPH collimators on intra- and between-rater agreement, and on the certainty of visual interpretation in dopamine transporter (DAT)-SPECT.MethodsThe study included 71 patients (62.1 ± 12.7 y). Two SPECT acquisitions were performed in randomized order after a single injection of 182 ± 9 MBq 123I-FP-CIT, one with MPH and one with low-energy-high-resolution-high-sensitivity (LEHRHS) collimators. MPH projections were reconstructed with an iterative 3d Monte Carlo algorithm. LEHRHS projections were reconstructed with filtered backprojection (FBP) or with ordered-subsets expectation-maximization and resolution recovery (OSEM). Images were visually evaluated twice by three independent raters with respect to presence/absence of Parkinson-typical reduction of striatal 123I-FP-CIT uptake using a Likert 6-score (- 3 = clearly normal, …, 3 = clearly reduced). In case of intra-rater discrepancy, an intra-rater consensus was obtained. Intra- and between-rater agreement with respect to the Likert score (6-score and dichotomized score) was characterized by Cohen's kappa.ResultsIntra-rater kappa of visual scoring of MPH/LEHRHS-OSEM/LEHRHS-FBP images was 0.84 ± 0.12/0.73 ± 0.06/0.73 ± 0.08 (6-score, mean of three raters) and 1.00 ± 0.00/0.96 ± 0.04/0.97 ± 0.03 (dichotomized score). Between-rater kappa of visual scoring (intra-rater consensus) of MPH/LEHRHS-OSEM/LEHRHS-FBP images was 0.70 ± 0.06/0.63 ± 0.08/0.48 ± 0.05 (6-score, mean of three pairs of raters) and 1.00 ± 0.00/0.92 ± 0.04/0.90 ± 0.06 (dichotomized score). There was a decrease of (negative) Likert scores in normal DAT-SPECT by 0.87 ± 0.18 points from the LEHRHS-OSEM to the MPH setting. The (positive) Likert scores of reduced DAT-SPECT did not change on average.ConclusionsMPH collimators improve intra- and between-rater agreement as well as the certainty of the visual interpretation of DAT-SPECT.

Dataset Information

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

Purpose

Methods

Results

Conclusion

Publications

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

Similar Datasets

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