Zolpidem improves task-specific dystonia: A randomized clinical trial integrating exploratory transcranial magnetic stimulation and [18F] FDG-PET imaging.

BACKGROUND: Task-specific dystonia (TSFD) is a disabling movement disorder. Effective treatment options are currently limited. Zolpidem was reported to improve primary focal and generalized dystonia in a proportion of patients. The mechanisms underlying its therapeutic effects have not yet been investigated. METHODS: We conducted a randomized, double-blind, placebo-controlled, crossover trial of single-dose zolpidem in 24 patients with TSFD. Patients were clinically assessed using Burke-Fahn-Marsden Dystonia Rating Scale (BFMDRS), Writers' Cramp Rating Scale (WCRS), and Visual Analogue Scale (VAS), before and after receiving placebo and zolpidem. Transcranial magnetic stimulation was conducted on placebo and zolpidem to compare corticospinal excitability - active and resting motor thresholds (AMT and RMT), resting and active input/output curves and intracortical excitability - cortical silent period (CSP), short-interval intracortical inhibition curve (SICI), long-interval intracortical inhibition (LICI) and intracortical facilitation (ICF). Eight patients underwent brain FDG-PET imaging on zolpidem and placebo. RESULTS: Zolpidem treatment improved TSFD. Zolpidem compared to placebo flattened rest and active input/output curves, reduced ICF and was associated with hypometabolism in the right cerebellum and hypermetabolism in the left inferior parietal lobule and left cingulum. Correlations were found between changes in dystonia severity on WCRS and changes in active input/output curve and in brain metabolism, respectively. Patients with lower RMT, and higher rest and active input/output curves exhibited better response to zolpidem compared to placebo. CONCLUSIONS: Zolpidem improved TSFD by reducing corticomotor output and influencing crucial nodes in higher-order sensory and motor networks.

Effect of the identification group size and image resolution on the diagnostic performance of metabolic Alzheimer's disease-related pattern.

BACKGROUND: Alzheimer's disease-related pattern (ADRP) is a metabolic brain biomarker of Alzheimer's disease (AD). While ADRP is being introduced into research, the effect of the size of the identification cohort and the effect of the resolution of identification and validation images on ADRP's performance need to be clarified. METHODS: 240 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography images [120 AD/120 cognitive normals (CN)] were selected from the Alzheimer's disease neuroimaging initiative database. A total of 200 images (100 AD/100 CN) were used to identify different versions of ADRP using a scaled subprofile model/principal component analysis. For this purpose, five identification groups were randomly selected 25 times. The identification groups differed in the number of images (20 AD/20 CN, 30 AD/30 CN, 40 AD/40 CN, 60 AD/60 CN, and 80 AD/80 CN) and image resolutions (6, 8, 10, 12, 15 and 20 mm). A total of 750 ADRPs were identified and validated through the area under the curve (AUC) values on the remaining 20 AD/20 CN with six different image resolutions. RESULTS: ADRP's performance for the differentiation between AD patients and CN demonstrated only a marginal average AUC increase, when the number of subjects in the identification group increases (AUC increase for about 0.03 from 20 AD/20 CN to 80 AD/80 CN). However, the average of the lowest five AUC values increased with the increasing number of participants (AUC increase for about 0.07 from 20 AD/20 CN to 30 AD/30 CN and for an additional 0.02 from 30 AD/30 CN to 40 AD/40 CN). The resolution of the identification images affects ADRP's diagnostic performance only marginally in the range from 8 to 15 mm. ADRP's performance stayed optimal even when applied to validation images of resolution differing from the identification images. CONCLUSIONS: While small (20 AD/20 CN images) identification cohorts may be adequate in a favorable selection of cases, larger cohorts (at least 30 AD/30 CN images) shall be preferred to overcome possible/random biological differences and improve ADRP's diagnostic performance. ADRP's performance stays stable even when applied to the validation images with a resolution different than the resolution of the identification ones.

Cortical Brain Perfusion and Cognitive Event Related Potentials in Patients with Psychomotor Retardation in Late Onset Depression.

BACKGROUND: Late onset depression is characterised by pronounced cognitive impairment, more somatic complaints and psychomotor retardation. Psychomotor slowing may be due to impairment in either motor or cognitive domain. Electrophysiology may be particularly convenient as a tool in studies of psychomotor retardation, as it can separate central cognitive processing from the motor processing. SUBJECTS AND METHODS: In this study we compared event related potentials (ERP) in the two groups of patients with late onset depression and psychomotor slowing as measured by reaction time (RT): a group of patients with lower RT was compared to a group with a higher RT. Twenty patients with late onset depression were included in the study after they had reached remission. Four weeks after reaching remission patients were reevaluated clinically using Hamilton Depression Rating Scale, Mini Mental State Examination, and with a computer version of the Stroop task. ERP, accuracy and RTs were simultaneously recorded. Both groups of patients aditionaly underwent a perfusion SPECT imaging. RESULTS: There were no differences between the short and long RT groups of patients in amplitudes of the late positive Stroop related potentials. The group of patients with longer RTs showed significant hyperperfusion in precentral gyrus, parietal regions, cuneus and hypoperfusion within insular, frontal, temporal and limbic (parahyppocampal gyrus and anterior cingulate) cortices, as well as cerebellum. CONCLUSION: We found no ERP differences between the two groups suggesting that although patients may differ on psychomotor retardation measured as RT, their cognitive abilities may be quite similar. Perfusion SPECT imaging however revealed a significant difference between them. This may be due to a process of compensation and applying different strategies to cope with cognitive impairment in the two groups.

Identification and validation of Alzheimer's disease-related metabolic brain pattern in biomarker confirmed Alzheimer's dementia patients.

Metabolic brain biomarkers have been incorporated in various diagnostic guidelines of neurodegenerative diseases, recently. To improve their diagnostic accuracy a biologically and clinically homogeneous sample is needed for their identification. Alzheimer's disease-related pattern (ADRP) has been identified previously in cohorts of clinically diagnosed patients with dementia due to Alzheimer's disease (AD), meaning that its diagnostic accuracy might have been reduced due to common clinical misdiagnosis. In our study, we aimed to identify ADRP in a cohort of AD patients with CSF confirmed diagnosis, validate it in large out-of-sample cohorts and explore its relationship with patients' clinical status. For identification we analyzed 2-[18F]FDG PET brain scans of 20 AD patients and 20 normal controls (NCs). For validation, 2-[18F]FDG PET scans from 261 individuals with AD, behavioral variant of frontotemporal dementia, mild cognitive impairment and NC were analyzed. We identified an ADRP that is characterized by relatively reduced metabolic activity in temporoparietal cortices, posterior cingulate and precuneus which co-varied with relatively increased metabolic activity in the cerebellum. ADRP expression significantly differentiated AD from NC (AUC = 0.95) and other dementia types (AUC = 0.76-0.85) and its expression correlated with clinical measures of global cognition and neuropsychological indices in all cohorts.

Metabolic brain pattern in dementia with Lewy bodies: Relationship to Alzheimer's disease topography.

PURPOSE: Dementia with Lewy bodies (DLB) is the second most common neurodegenerative dementia, that shares clinical and metabolic similarities with both Alzheimer's and Parkinson's disease. In this study we aimed to identify a DLB-related pattern (DLBRP), study its relationship with other metabolic brain patterns and explore its diagnostic and prognostic value. METHODS: A cohort of 79 participants with DLB, 63 with dementia due to Alzheimer's disease (AD) and 41 normal controls (NCs) and their 2-[18F]FDG PET scans were analysed for identification and validation of DLBRP. Voxel-wise correlation and multiple linear regression were used to study the relation between DLBRP and Alzheimer's disease-related pattern (ADRP), Parkinson's disease-related pattern (PDRP) and PD-related cognitive pattern (PDCP). Diagnostic and prognostic value of DLBRP and of modified DLBRP after accounting for ADRP overlap (DLBRP ⊥ ADRP), were explored. RESULTS: The newly identified DLBRP shared topographic similarities with ADRP (R2 = 24%) and PDRP (R2 = 37%), but not with PDCP. We could accurately discriminate between DLB and NC (AUC = 0.99) based on DLBRP expression, and between DLB and AD (AUC = 0.87) based on DLBRP ⊥ ADRP expression. DLBRP expression correlated with cognitive impairment, but the correlation was lost after accounting for ADRP overlap. DLBRP and DLBRP ⊥ ADRP correlated with patients' survival time. CONCLUSION: DLBRP has proven to be a specific metabolic brain biomarker of DLB, sharing similarities with ADRP and PDRP, but not PDCP. We observed a similar metabolic mechanism underlying cognitive impairment in DLB and AD. DLB-specific metabolic changes were more detrimental for overall survival.

Abnormal metabolic covariance patterns associated with multiple system atrophy and progressive supranuclear palsy.

PURPOSE: Differentiation between neurodegenerative parkinsonisms, whose early clinical presentation is similar, may be improved with metabolic brain imaging. In this study we applied a specific network analysis to 2-[18F]FDG PET brain scans to identify the characteristic metabolic patterns for multiple system atrophy (MSA) and progressive supranuclear palsy (PSP) in a new European cohort. We also developed a new tool to recognize and estimate patients' metabolic brain heterogeneity. METHODS: 20 MSA-P patients, 20 PSP patients and 20 healthy controls (HC) underwent 2-[18F]FDG PET brain imaging. The scaled subprofile model/principal component analysis was applied to identify MSA/PSP-related patterns; MSARP and PSPRP. Additional, 56 MSA, 45 PSP, 116 PD and 61 HC subjects were analyzed for validation. We innovatively applied heat-map analysis to extract and graphically display the pattern's regional sub-scores in individual subjects. RESULTS: MSARP was characterized by hypometabolism in cerebellum and putamen, and PSPRP by hypometabolism in medial prefrontal cortices, nucleus caudatus, frontal cortices and mesencephalon. Patterns' expression discriminated between MSA/PSP patients and HCs as well as between different parkinsonian cohorts (p < 0.001). Both patterns were sensitive and specific (AUC for MSARP/PSPRP: 0.96/0.99). Heat-map analysis showed differences within MSA/PSP subjects and HCs consistent with clinical presentation. CONCLUSIONS: Replication and validation of MSARP and PSPRP confirms robustness of these metabolic biomarkers and supports its application in clinical and research practice. Heat-map analysis gives us an insight into the contribution of various pattern's regions to patterns' expression in individual subjects, which improves our insight into the heterogeneity of studied syndromes.

Image reconstruction using small-voxel size improves small lesion detection for positron emission tomography.

BACKGROUND: PET/CT imaging is widely used in oncology and provides both metabolic and anatomic information. Because of the relatively poor spatial resolution of PET, the detection of small lesions is limited. The low spatial resolution introduces the partial-volume effect (PVE) which negatively affects images both qualitatively and quantitatively. The aim of the study was to investigate the effect of small-voxel (2 mm in-line pixel size) vs. standard-voxel (4 mm in-line pixel size) reconstruction on lesion detection and image quality in a range of activity ratios. MATERIALS AND METHODS: The National Electrical Manufacturers Association (NEMA) body phantom and the Micro Hollow-Sphere phantom spheres were filled with a solution of [18F]fluorodeoxyglucose ([18F]FDG) in sphere-to-background ratios of 2:1, 3:1, 4:1 and 8:1. In all images reconstructed with 2 mm and 4 mm in-line pixel size the visual lesion delineation, contrast recovery coefficient (CRC) and contrast-to-noise ratio (CNR) were evaluated. RESULTS: For smaller (≤ 13 mm) phantom spheres, significantly higher CRC and CNR using small-voxel reconstructions were found, also improving visual lesion delineation. CRC did not differ significantly for larger (≥ 17 mm) spheres using 2 mm and 4 mm in-line pixel size, but CNR was significantly lower; however, lower CNR did not affect visual lesion delineation. CONCLUSIONS: Small-voxel reconstruction consistently improves precise small lesion delineation, lesion contrast and image quality.

Atypical clinical presentation of pathologically proven Parkinson's disease: The role of Parkinson's disease related metabolic pattern.

Regional changes in brain metabolism upgraded with measurements of specific metabolic brain patterns and automated diagnostic algorithms can help to differentiate among neurodegenerative parkinsonisms, but with few reports on pathological confirmation. Here we describe a parkinsonian patient with atypical presentation and 18F-FDG-PET imaging consistent with idiopathic Parkinson's disease. The latter was confirmed at the pathohistological examination.

Differential diagnosis of parkinsonian syndromes: a comparison of clinical and automated - metabolic brain patterns' based approach.

PURPOSE: Differentiation among parkinsonian syndromes may be clinically challenging, especially at early disease stages. In this study, we used 18F-FDG-PET brain imaging combined with an automated image classification algorithm to classify parkinsonian patients as Parkinson's disease (PD) or as an atypical parkinsonian syndrome (APS) at the time when the clinical diagnosis was still uncertain. In addition to validating the algorithm, we assessed its utility in a "real-life" clinical setting. METHODS: One hundred thirty-seven parkinsonian patients with uncertain clinical diagnosis underwent 18F-FDG-PET and were classified using an automated image-based algorithm. For 66 patients in cohort A, the algorithm-based diagnoses were compared with their final clinical diagnoses, which were the gold standard for cohort A and were made 2.2 ± 1.1 years (mean ± SD) later by a movement disorder specialist. Seventy-one patients in cohort B were diagnosed by general neurologists, not strictly following diagnostic criteria, 2.5 ± 1.6 years after imaging. The clinical diagnoses were compared with the algorithm-based ones, which were considered the gold standard for cohort B. RESULTS: Image-based automated classification of cohort A resulted in 86.0% sensitivity, 92.3% specificity, 97.4% positive predictive value (PPV), and 66.7% negative predictive value (NPV) for PD, and 84.6% sensitivity, 97.7% specificity, 91.7% PPV, and 95.5% NPV for APS. In cohort B, general neurologists achieved 94.7% sensitivity, 83.3% specificity, 81.8% PPV, and 95.2% NPV for PD, while 88.2%, 76.9%, 71.4%, and 90.9% for APS. CONCLUSION: The image-based algorithm had a high specificity and the predictive values in classifying patients before a final clinical diagnosis was reached by a specialist. Our data suggest that it may improve the diagnostic accuracy by 10-15% in PD and 20% in APS when a movement disorder specialist is not easily available.

The effects of image reconstruction algorithms on topographic characteristics, diagnostic performance and clinical correlation of metabolic brain networks in Parkinson's disease.

PURPOSE: The purpose of this study was to evaluate the effects of different image reconstruction algorithms on topographic characteristics and diagnostic performance of the Parkinson's disease related pattern (PDRP). METHODS: FDG-PET brain scans of 20 Parkinson's disease (PD) patients and 20 normal controls (NC) were reconstructed with six different algorithms in order to derive six versions of PDRP. Additional scans of 20 PD, 25 atypical parkinsonism (AP) patients and 20 NC subjects were used for validation. PDRP versions were compared by assessing differences in topographies, individual subject scores and correlations with patient's clinical ratings. Discrimination of PD from NC and AP subjects was evaluated across cohorts. RESULTS: The region weights of the six PDRPs highly correlated (R ≥ 0.991; p < 0.0001). All PDRPs' expressions were significantly elevated in PD relative to NC and AP subjects (p < 0.0001) and correlated with clinical ratings (R ≥ 0.47; p < 0.05). Subject scores of the six PDRPs highly correlated within each of individual healthy and parkinsonian groups (R ≥ 0.972, p < 0.0001) and were consistent across the algorithms when using the same reconstruction methods in PDRP derivation and validation. However, when derivation and validation reconstruction algorithms differed, subject scores were notably lower compared to the reference PDRP, in all subject groups. CONCLUSION: PDRP proves to be highly reproducible across FDG-PET image reconstruction algorithms in topography, ability to differentiate PD from NC and AP subjects and clinical correlation. When calculating PDRP scores in scans that have different reconstruction algorithms and imaging systems from those used for PDRP derivation, a calibration with NC subjects is advisable.

Abnormal metabolic brain network associated with Parkinson's disease: replication on a new European sample.

PURPOSE: The purpose of this study was to identify the specific metabolic brain pattern characteristic for Parkinson's disease (PD): Parkinson's disease-related pattern (PDRP), using network analysis of [18F]-fluorodeoxyglucose positron emission tomography (FDG-PET) brain images in a cohort of Slovenian PD patients. METHODS: Twenty PD patients (age 70.1 ± 7.8 years, Movement Disorder Society Unified Parkinson's Disease Motor Rating Scale (MDS-UPDRS-III) 38.3 ± 12.2; disease duration 4.3 ± 4.1 years) and 20 age-matched normal controls (NCs) underwent FDG-PET brain imaging. An automatic voxel-based scaled subprofile model/principal component analysis (SSM/PCA) was applied to these scans for PDRP-Slovenia identification. RESULTS: The pattern was characterized by relative hypermetabolism in pallidum, putamen, thalamus, brain stem, and cerebellum associated with hypometabolism in sensorimotor cortex, posterior parietal, occipital, and frontal cortices. The expression of PDRP-Slovenia discriminated PD patients from NCs (p < 0.0001) and correlated positively with patients' clinical score (MDS-UPDRS-III, p = 0.03). Additionally, its topography agrees well with the original PDRP (p < 0.001) identified in American cohort of PD patients. We validated the PDRP-Slovenia expression on additional FDG-PET scans of 20 PD patients, 20 NCs, and 25 patients with atypical parkinsonism (AP). We confirmed that the expression of PDRP-Slovenia manifests good diagnostic accuracy with specificity and sensitivity of 85-90% at optimal pattern expression cutoff for discrimination of PD patients and NCs and is not expressed in AP. CONCLUSION: PDRP-Slovenia proves to be a robust and reproducible functional imaging biomarker independent of patient population. It accurately differentiates PD patients from NCs and AP and correlates well with the clinical measure of PD progression.

The effect of 18F-FDG-PET image reconstruction algorithms on the expression of characteristic metabolic brain network in Parkinson's disease.

PURPOSE: To evaluate the reproducibility of the expression of Parkinson's Disease Related Pattern (PDRP) across multiple sets of 18F-FDG-PET brain images reconstructed with different reconstruction algorithms. METHODS: 18F-FDG-PET brain imaging was performed in two independent cohorts of Parkinson's disease (PD) patients and normal controls (NC). Slovenian cohort (20 PD patients, 20 NC) was scanned with Siemens Biograph mCT camera and reconstructed using FBP, FBP+TOF, OSEM, OSEM+TOF, OSEM+PSF and OSEM+PSF+TOF. American Cohort (20 PD patients, 7 NC) was scanned with GE Advance camera and reconstructed using 3DRP, FORE-FBP and FORE-Iterative. Expressions of two previously-validated PDRP patterns (PDRP-Slovenia and PDRP-USA) were calculated. We compared the ability of PDRP to discriminate PD patients from NC, differences and correlation between the corresponding subject scores and ROC analysis results across the different reconstruction algorithms. RESULTS: The expression of PDRP-Slovenia and PDRP-USA networks was significantly elevated in PD patients compared to NC (p<0.0001), regardless of reconstruction algorithms. PDRP expression strongly correlated between all studied algorithms and the reference algorithm (r⩾0.993, p<0.0001). Average differences in the PDRP expression among different algorithms varied within 0.73 and 0.08 of the reference value for PDRP-Slovenia and PDRP-USA, respectively. ROC analysis confirmed high similarity in sensitivity, specificity and AUC among all studied reconstruction algorithms. CONCLUSIONS: These results show that the expression of PDRP is reproducible across a variety of reconstruction algorithms of 18F-FDG-PET brain images. PDRP is capable of providing a robust metabolic biomarker of PD for multicenter 18F-FDG-PET images acquired in the context of differential diagnosis or clinical trials.

The impact of reconstruction algorithms and time of flight information on PET/CT image quality.

BACKGROUND: The aim of the study was to explore the influence of various time-of-flight (TOF) and non-TOF reconstruction algorithms on positron emission tomography/computer tomography (PET/CT) image quality. MATERIALS AND METHODS: Measurements were performed with a triple line source phantom, consisting of capillaries with internal diameter of ∼ 1 mm and standard Jaszczak phantom. Each of the data sets was reconstructed using analytical filtered back projection (FBP) algorithm, iterative ordered subsets expectation maximization (OSEM) algorithm (4 iterations, 24 subsets) and iterative True-X algorithm incorporating a specific point spread function (PSF) correction (4 iterations, 21 subsets). Baseline OSEM (2 iterations, 8 subsets) was included for comparison. Procedures were undertaken following the National Electrical Manufacturers Association (NEMA) NU-2-2001 protocol. RESULTS: Measurement of spatial resolution in full width at half maximum (FWHM) was 5.2 mm, 4.5 mm and 2.9 mm for FBP, OSEM and True-X; and 5.1 mm, 4.5 mm and 2.9 mm for FBP+TOF, OSEM+TOF and True-X+TOF respectively. Assessment of reconstructed Jaszczak images at different concentration ratios showed that incorporation of TOF information improves cold contrast, while hot contrast only slightly, however the most prominent improvement could be seen in background variability - noise reduction. CONCLUSIONS: On the basis of the results of investigation we concluded, that incorporation of TOF information in reconstruction algorithm mostly affects reduction of the background variability (levels of noise in the image), while the improvement of spatial resolution due to incorporation of TOF information is negligible. Comparison of traditional and modern reconstruction algorithms showed that analytical FBP yields comparable results in some parameter measurements, such as cold contrast and relative count error. Iterative methods show highest levels of hot contrast, when TOF and PSF corrections were applied simultaneously.

Optimal scan time for evaluation of parathyroid adenoma with [(18)F]-fluorocholine PET/CT.

BACKGROUND: Parathyroid adenomas, the most common cause of primary hyperparathyroidism, are benign tumours which autonomously produce and secrete parathyroid hormone. [(18)F]-fluorocholine (FCH), PET marker of cellular proliferation, was recently demonstrated to accumulate in lesions representing enlarged parathyroid tissue; however, the optimal time to perform FCH PET/CT after FCH administration is not known. The aim of this study was to determine the optimal scan time of FCH PET/CT in patients with primary hyperparathyroidism. PATIENTS AND METHODS: 43 patients with primary hyperparathyroidism were enrolled in this study. A triple-phase PET/CT imaging was performed five minutes, one and two hours after the administration of FCH. Regions of interest (ROI) were placed in lesions representing enlarged parathyroid tissue and thyroid tissue. Standardized uptake value (SUVmean), retention index and lesion contrast for parathyroid and thyroid tissue were calculated. RESULTS: Accumulation of FCH was higher in lesions representing enlarged parathyroid tissue in comparison to the thyroid tissue with significantly higher SUVmean in the second and in the third phase (p < 0.0001). Average retention index decreased significantly between the first and the second phase and increased significantly between the second and the third phase in lesions representing enlarged parathyroid tissue and decreased significantly over all three phases in thyroid tissue (p< 0.0001). The lesion contrast of lesions representing enlarged parathyroid tissue and thyroid tissue was significantly better in the second and the third phase compared to the first phase (p < 0.05). CONCLUSIONS: According to the results the optimal scan time of FCH PET/CT for localization of lesions representing enlarged parathyroid tissue is one hour after administration of the FCH.

Parkinson's disease dementia: clinical correlates of brain spect perfusion and treatment.

BACKGROUND: The main clinical feature of dementia in Parkinson's disease is a dysexecutive syndrome. The neuropathology of PD dementia (PDD) is likely multifactorial and affects several neuronal populations. There is evidence that Parkinson's disease dementia is associated with a cholinergic deficit, supporting the therapeutic role of cholinesterase inhibitors, which are already first-line agents in the treatment of Alzheimer's disease. The paper includes short report on a pilot study with description of cognitive and imaging profiles in patients with mild to moderate stage of Parkinson disease dementia (PDD). SUBJECTS AND METHODS: A random sample of 16 patients with clinical diagnostic criteria for probable PDD was included in the study. Patients were characterized with mild to moderate cognitive decline slightly depressive mood and moderate motor performance. Brain perfusion [(99m)Tc]ECD / SPECT and structural MRI with emphasis on evaluation of the degree of cortical atrophy and the medial temporal atrophy index was performed. All patients had detailed neuropsychological evaluation using a "cognitive process approach". Neuropsychological data were correlated voxel-wise with normalized brain perfusion images, creating whole-brain correlation maps. CONCLUSIONS: Previously reported generalized cognitive impairment in PDD with predominant executive, visouspatial and attentional deficits was confirmed. Performance on specific cognitive measures was correlated with perfusion brain SPECT findings. It could be speculated that different pathological mechanisms underlie widespread significant brain perfusion decrements in temporal, parietal and frontal regions.

Influence of radiation source geometry on determination of (111)In and (90)Y activity of radiopharmaceuticals.

OBJECTIVE: Yttrium-90 (Y)-labelled peptides such as DOTATOC and antibodies such as Zevalin are widely used in radionuclide therapy. Indium-111 (In) is used as a Y surrogate for imaging and dosimetry purposes. We aimed to investigate accuracy, geometry (vials and syringes) and volume dependencies for both radionuclides in several different radionuclide calibrators. METHODS: YCl3 and InCl3 solutions were gravimetrically dispensed into the most frequently used containers. In each container several dilutions of the parent solutions were performed. Mass, activity and time were recorded for each calibrator and measurement. Aliquots of both parent solutions were calibrated at the National Metrology Laboratory, Vienna, Austria (BEV). From our measurements and results from BEV, correction factors were determined and further partitioned into calibration, geometry and volume correction factors. RESULTS: Using the nominal calibration factors provided by the manufacturer, measured activity in P6 vials was overestimated by up to 25% for In, depending on the calibrator. Y activity was either underestimated (by up to 20%) or overestimated (by up to 25%) using different radionuclide calibrators. This is the result of the difference in containers used to set the manufacturer's calibration factor values and the containers used in nuclear medicine departments and in this study. There was little geometry dependence for glass vials but strong geometry dependence for syringes for both radionuclides in all calibrators. CONCLUSION: The results should constitute a warning for all personnel responsible for preparation of radiopharmaceuticals. Every nuclear medicine department should incorporate a proper quality-control regimen for radionuclide calibrators and a quality-assurance system.