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Rationales: Restless leg syndrome (RLS) is a common complication in patients with end-stage renal disease (ESRD). However, there is a lack of biomarkers linking uremic RLS to dopaminergic neurons. Previous studies demonstrated that Tc-99m TRODAT-1 SPECT was a biomarker for RLS but the correlation between the physiologic parameter was lacking. Overall, 32 patients were enrolled in the study and divided into the following 3 groups: (1) control ( = 13), (2) ESRD without RLS ( = 8) and (3) ESRD with RLS ( = 11). All patients had a clinical diagnosis of RLS and received Tc-99m TRODAT-1 SPECT. A subgroup analysis was performed to compare differences between the control and ESRD with RLS groups. Tc-99m TRODAT-1 SPECT was performed and activities in the striatum and occipital areas were measured using manually delineated regions of interest (ROIs) by an experienced nuclear medicine radiologist who was blinded to clinical data. The total ratio of Tc-99m TRODAT SPECT was lower in the ESRD with RLS group ( = 0.046). The uptake ratio of TRODAT negatively correlated with serum parathyroid hormone ( = -0.577, = 0.015) and ferritin ( = -0.464, = 0.039) concentrations. However, the uptake positively correlated with the hemoglobin concentration ( = 0.531, = 0.011). The sensitivity and specificity of the total TRODAT ratio for predicting RLS in the overall population were 95.0% and 67.7%, respectively, at a cutoff value of 0.980 (area under the curve of receiver operating characteristic curve was 0.767, = 0.024). In patients with ESRD and RLS, Tc-99m TRODAT might be a potential biomarker. Dysregulated hemoglobin, serum parathyroid hormone and serum ferritin concentrations might influence the uptake of the TRODAT ratio.

We present a case of Anti-IgLON 5 encephalitis with Lewy body dementia. F fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) scan and 99 mTc-TRODAT-1 SPECT/CT scan were done. 99 mTc-TRODAT-1 scan findings revealed severely reduced concentration of dopamine transporter in bilateral basal ganglia, suggestive of a degenerative parkinsonian disorder. 18F-FDG PET scan findings were suggestive of moderate-to-severe hypometabolism in the bilateral parieto-temporal and bilateral occipital cortices including the primary visual cortices, supporting Lewy body spectrum disease with associated hypermetabolism in the bilateral sensorimotor cortices, bilateral basal ganglia, thalami, brain stem, and bilateral cerebellar hemispheres suggestive of inflammatory pathology.

The neuroimaging techniques such as dopaminergic imaging using Single Photon Emission Computed Tomography (SPECT) with 99mTc-TRODAT-1 have been employed to detect the stages of Parkinson’s disease (PD). In this retrospective study, a total of 202 99mTc-TRODAT-1 SPECT imaging were collected. All of the PD patient cases were separated into mild (HYS Stage 1 to Stage 3) and severe (HYS Stage 4 and Stage 5) PD, according to the Hoehn and Yahr Scale (HYS) standard. A three-dimensional method was used to estimate six features of activity distribution and striatal activity volume in the images. These features were skewness, kurtosis, Cyhelsky’s skewness coefficient, Pearson’s median skewness, dopamine transporter activity volume, and dopamine transporter activity maximum. Finally, the data were modeled using logistic regression (LR) and support vector machine (SVM) for PD classification. The results showed that SVM classifier method produced a higher accuracy than LR. The sensitivity, specificity, PPV, NPV, accuracy, and AUC with SVM method were 0.82, 1.00, 0.84, 0.67, 0.83, and 0.85, respectively. Additionally, the Kappa value was shown to reach 0.68. This claimed that the SVM-based model could provide further reference for PD stage classification in medical diagnosis. In the future, more healthy cases will be expected to clarify the false positive rate in this classification model.

[99mTc]Tc TRODAT-1 is a widely used single photon emission tomography (SPECT) radiopharmaceutical in Asian practice for early detection of central dopaminergic disorders. However, its imaging quality remains sub-optimal. To overcome this problem, mannitol, an osmotic agent was used to observe its effect on improving striatal [99mTc]Tc TRODAT-1 uptake in rat brain by titrated human dosages to investigate a clinically feasible way to improve human imaging quality. [99mTc]Tc TRODAT-1 synthesis and quality control were performed as described. Sprague–Dawley rats were used for this study. The animal in vivo nanoSPECT/CT and ex vivo autoradiography were employed to observe and verify the striatal [99mTc]Tc TRODAT-1 uptake in rat brains using clinically equivalent doses (i.e., 0, 1 and 2 mL groups, each n = 5) of mannitol (20% w/v, equivalent to 200 mg/mL) by an intravenous administration. Specific binding ratios (SBRs) were calculated to express the central striatal uptake in different experimental groups. In the NanoSPECT/CT imaging, the highest SBRs of striatal [99mTc]Tc TRODAT-1 were reached at 75–90 min post-injection. The averaged striatal SBRs were 0.85 ± 0.13 (2 mL normal saline, the control group), 0.94 ± 0.26 (1 mL mannitol group) and 1.36 ± 0.12 (2 mL mannitol group, p < 0.01 which were significantly different than the control as well as 1 mL mannitol groups (p < 0.05). The SBRs from ex vivo autoradiography also showed a comparable trend of the striatal [99mTc]Tc TRODAT-1 uptake in the 2 mL, 1 mL mannitol and the control groups (1.76 ± 0.52, 0.91 ± 0.29, and 0.21 ± 0.03, respectively, p < 0.05). No remarkable changes of vital signs were found in the mannitol groups and the controls. Pre-treated mannitol revealed a significant increase of the central striatal [99mTc]Tc TRODAT-1 uptake in a rat model which not only enabled us to perform pre-clinical studies of dopaminergic related disorders but also provided a potential way to further optimize image quality in clinical practice.

Introduction: Dopamine transporter (DAT) receptors are reduced in the striatum in dementia of Lewy body (DLB) but normal in Alzheimer’s disease (AD). We assessed the diagnostic accuracy of TRODAT-1 imaging to differentiate patients with DLB from AD. Methods: Patients with DLB or AD underwent SPECT TRODAT imaging by [99mTc]Tc-TRODAT-1. Visual interpretation and quantification analyses were done. The activity of the right and left caudate nucleus (CN), putamen (P), striatum (S) as a whole, background (BG), and occipital area (OC) were calculated in addition to the ratio of right and left CN/OC, P/OC, and S/BG. Absolute right and left value difference of the striatum (∆S), putamen (∆P), and caudate (∆CN) to OC or BG were also calculated. The diagnostic accuracy of the visual and quantitative method were compared between patients with AD and LBD. The area under the ROC curve (AUC) was analyzed. Results: Twenty-five patients (15 DLB and 10 AD) were included. Scans were visually interpreted as DLB, AD, and non-diagnostic in 11, 13, and one patients, respectively. Sensitivity, specificity, and accuracy of the scan were 57.1% (28.9-82.3), 70% (34.8-93.3), and 62.5% (40.6-81.2), respectively. CN/OC, P/OC, and S/BG in the left, right, and bilaterally were statistically same between two groups. The AUC of ∆CN/OC was 70.7%. The optimal cut-off value for ∆CN/BG to diagnose DLB was 6.6% with a sensitivity, specificity, and accuracy, of 86.7%, 50%, and 72.0%, respectively. Conclusion: The [99mTc]Tc-TRODAT-1 imaging has limited diagnostic accuracy for discrimination of DLB and AD patients.

BackgroundAttenuation correction (AC) is an important correction method to improve the quantification accuracy of dopamine transporter (DAT) single photon emission computed tomography (SPECT). Chang's method was developed for AC (Chang-AC) when CT-based AC was not available, assuming uniform attenuation coefficients inside the body contour. This study aims to evaluate Chang-AC and different deep learning (DL)-based AC approaches on 99mTc-TRODAT-1 brain SPECT using clinical patient data on two different scanners.MethodsTwo hundred and sixty patients who underwent 99mTc-TRODAT-1 SPECT/CT scans from two different scanners (scanner A and scanner B) were retrospectively recruited. The ordered-subset expectation-maximization (OS-EM) method reconstructed 120 projections with dual-energy scatter correction, with or without CT-AC. We implemented a 3D conditional generative adversarial network (cGAN) for the indirect deep learning-based attenuation correction (DL-ACμ) and direct deep learning-based attenuation correction (DL-AC) methods, estimating attenuation maps (μ-maps) and attenuation-corrected SPECT images from non-attenuation-corrected (NAC) SPECT, respectively. We further applied cross-scanner training (cross-scanner indirect deep learning-based attenuation correction [cull-ACμ] and cross-scanner direct deep learning-based attenuation correction [call-AC]) and merged the datasets from two scanners for ensemble training (ensemble indirect deep learning-based attenuation correction [eDL-ACμ] and ensemble direct deep learning-based attenuation correction [eDL-AC]). The estimated μ-maps from (c/e)DL-ACμ were then used in reconstruction for AC purposes. Chang's method was also implemented for comparison. Normalized mean square error (NMSE), structural similarity index (SSIM), specific uptake ratio (SUR), and asymmetry index (%ASI) of the striatum were calculated for different AC methods.ResultsThe NMSE for Chang's method, DL-ACμ, DL-AC, cDL-ACμ, cDL-AC, eDL-ACμ, and eDL-AC is 0.0406 ± 0.0445, 0.0059 ± 0.0035, 0.0099 ± 0.0066, 0.0253 ± 0.0102, 0.0369 ± 0.0124, 0.0098 ± 0.0035, and 0.0162 ± 0.0118 for scanner A and 0.0579 ± 0.0146, 0.0055 ± 0.0034, 0.0063 ± 0.0028, 0.0235 ± 0.0085, 0.0349 ± 0.0086, 0.0115 ± 0.0062, and 0.0117 ± 0.0038 for scanner B, respectively. The SUR and %ASI results for DL-ACμ are closer to CT-AC, Followed by DL-AC, eDL-ACμ, cDL-ACμ, cDL-AC, eDL-AC, Chang's method, and NAC.ConclusionAll DL-based AC methods are superior to Chang-AC. DL-ACμ is superior to DL-AC. Scanner-specific training is superior to cross-scanner and ensemble training. DL-based AC methods are feasible and robust for 99mTc-TRODAT-1 brain SPECT.

Purpose Dopamine transporter (DAT) SPECT is an effective tool for early Parkinson’s disease (PD) detection and heavily hampered by attenuation. Attenuation correction (AC) is the most important correction among other corrections. Transfer learning (TL) with fine-tuning (FT) a pre-trained model has shown potential in enhancing deep learning (DL)-based AC methods. In this study, we investigate leveraging realistic Monte Carlo (MC) simulation data to create a pre-trained model for TL-based AC (TLAC) to improve AC performance for DAT SPECT. Methods A total number of 200 digital brain phantoms with realistic 99m Tc-TRODAT-1 distribution was used to generate realistic noisy SPECT projections using MC SIMIND program and an analytical projector. One hundred real clinical 99m Tc-TRODAT-1 brain SPECT data were also retrospectively analyzed. All projections were reconstructed with and without CT-based attenuation correction (CTAC/NAC). A 3D conditional generative adversarial network (cGAN) was pre-trained using 200 pairs of simulated NAC and CTAC SPECT data. Subsequently, 8, 24, and 80 pairs of clinical NAC and CTAC DAT SPECT data were employed to fine-tune the pre-trained U-Net generator of cGAN (TLAC-MC). Comparisons were made against without FT (DLAC-MC), training on purely limited clinical data (DLAC-CLI), clinical data with data augmentation (DLAC-AUG), mixed MC and clinical data (DLAC-MIX), TL using analytical simulation data (TLAC-ANA), and Chang’s AC (ChangAC). All datasets used for DL-based methods were split to 7/8 for training and 1/8 for validation, and a 1-/2-/5-fold cross-validation were applied to test all 100 clinical datasets, depending on the numbers of clinical data used in the training model. Results With 8 available clinical datasets, TLAC-MC achieved the best result in Normalized Mean Squared Error (NMSE) and Structural Similarity Index Measure (SSIM) (TLAC-MC; NMSE = 0.0143 ± 0.0082/SSIM = 0.9355 ± 0.0203), followed by DLAC-AUG, DLAC-MIX, TLAC-ANA, DLAC-CLI, DLAC-MC, ChangAC and NAC. Similar trends exist when increasing the number of clinical datasets. For TL-based AC methods, the fewer clinical datasets available for FT, the greater the improvement as compared to DLAC-CLI using the same number of clinical datasets for training. Joint histograms analysis and Bland-Altman plots of SBR results also demonstrate consistent findings. Conclusion TLAC is feasible for DAT SPECT with a pre-trained model generated purely based on simulation data. TLAC-MC demonstrates superior performance over other DL-based AC methods, particularly when limited clinical datasets are available. The closer the pre-training data is to the target domain, the better the performance of the TLAC model.

Objective(s): To evaluate the role of Tc-99m TRODAT-1 Single Photon Emission Computed Tomography (SPECT) in Parkinson’s Disease (PD) by assessing the correlation of clinical disease severity, disease duration and age at onset of disease with specific uptake ratio of Tc-99m TRODAT-1 in striatum.Methods: The study included 63 patients in age range of 40-72 years with clinical diagnosis of PD and nine controls. Clinical history of patients was obtained regarding age at onset of disease and disease duration. Disease severity in each patient was assessed using H and Y stage and UPDRS. Tc-99m TRODAT-1 SPECT was performed and specific uptake ratios were calculated for six regions in bilateral striata, caudate nuclei and putamina. Difference in specific uptake ratios between different stages of disease was analyzed for statistical significance. Specific uptake ratios were correlated with UPDRS, motor score of UPDRS, duration of disease and age at onset of disease using Pearson’s correlation co-efficient.Results: Median specific uptake ratio was found to be least in contralateral putamen for all H and Y stages. There was a statistically significant difference between specific uptake ratios of controls vs stage 1, stage 1 vs 2, 1 vs 3, 1 vs 4, and 2 vs 4 for all 6 regions. The difference in uptake ratio between 3 and 4 H and Y stages was significant only for contralateralregions. There was no significant difference in uptake ratio between 2 and 3 H and Y stages. The uptake ratios showed a strong negative correlation with UPDRS and motor score, a weak negative correlation with duration of disease and no significant correlation with age at onset of disease.Conclusion: We conclude that Tc-99m TRODAT-1 SPECT can be used to assess the disease severity in PD patients.

We studied the presynaptic nigrostriatal dopaminergic function using single photon emission computed tomography (SPECT) imaging of a 99mTc-TRODAT-1 (TRODAT) scan in a dopa-responsive dystonia (DRD) family with the guanosine triphosphate cyclohydrolase 1 (GCH-1) gene mutation. Clinically, there was presentation of intrafamilial variability in the DRD family. The index patient was a 10-year-old girl with classic DRD and normal presynaptic nigrostriatal dopaminergic function. However, her grandmother, a 79-year-old woman, presented with slowly progressive Parkinson’s disease (PD) without dystonic symptoms and excellent response to dopaminergic therapy for 21 years. Her brain TRODAT SPECT imaging revealed a markedly and asymmetrically reduced uptake of dopamine transporter at the bilateral striatum. Her father, a 54-year-old man, was an asymptomatic gene carrier and his brain TRODAT SPECT imaging revealed asymmetrically reduced nigrostriatal dopaminergic transmission in the bilateral striatum. We conclude variability of presynaptic nigrostriatal dopaminergic function in patients with DRD is related to their clinical heterogeneity. Significantly, impairment of presynaptic dopamine function actually occurs in the asymptomatic gene carrier.

Neuroimaging (NI) in Parkinson's disease (PD) includes functional techniques like positron emission tomography (PET) and single photon emission computed tomography (SPECT), and morphological imaging using magnetic resonance imaging (MRI) and transcranial sonography to probe different aspects of the neurobiology of PD. Changes in neurotransmitters in various regions of the brain and their influence on brain networks is the basis for the motor symptoms of PD which are interrogated by NI. The recent Movement Disorders Society Clinical Diagnostic Criteria for PD (MDS-PD) have included the results of a few of these neuroimaging techniques to serve as single supportive criteria or absolute exclusion criteria for the diagnosis of PD. While dopaminergic imaging is useful in the early stages of disease to differentiate the neurodegenerative versus non-degenerative causes of parkinsonism like essential tremors, it has also been used for the differential diagnosis of dementia with Lewy bodies (DLB) from Alzheimer's disease (AD), for inclusion of PD patients into clinical trials and for evaluating response to cell-replacement therapies in PD. Metabolic patterns on F-18 fluorodeoxyglucose positron emission tomography have been used effectively for the classification and differential diagnosis of the parkinsonian syndromes using visual and quantitative approaches. Disease related network-patterns have been used for a completely automated approach to differential diagnosis of parkinsonian syndromes on a single case basis. Structural MRI and advanced MR techniques have been used for the classification of PD and the atypical parkinsonian syndromes. Thus, multimodal imaging in PD may aid in an early, accurate and objective diagnostic classification by highlighting the underlying neurochemical and neuroanatomical changes that underlie this spectrum of disorders. The present challenge in PD is to develop radioligands which could bind selectively to alphasynuclein in-vivo.