Added value of FDG-PET for detection of progressive supranuclear palsy.

BACKGROUND: Diagnostic criteria for progressive supranuclear palsy (PSP) include midbrain atrophy in MRI and hypometabolism in [18F]fluorodeoxyglucose (FDG)-positron emission tomography (PET) as supportive features. Due to limited data regarding their relative and sequential value, there is no recommendation for an algorithm to combine both modalities to increase diagnostic accuracy. This study evaluated the added value of sequential imaging using state-of-the-art methods to analyse the images regarding PSP features. METHODS: The retrospective study included 41 PSP patients, 21 with Richardson's syndrome (PSP-RS), 20 with variant PSP phenotypes (vPSP) and 46 sex- and age-matched healthy controls. A pretrained support vector machine (SVM) for the classification of atrophy profiles from automatic MRI volumetry was used to analyse T1w-MRI (output: MRI-SVM-PSP score). Covariance pattern analysis was applied to compute the expression of a predefined PSP-related pattern in FDG-PET (output: PET-PSPRP expression score). RESULTS: The area under the receiver operating characteristic curve for the detection of PSP did not differ between MRI-SVM-PSP and PET-PSPRP expression score (p≥0.63): about 0.90, 0.95 and 0.85 for detection of all PSP, PSP-RS and vPSP. The MRI-SVM-PSP score achieved about 13% higher specificity and about 15% lower sensitivity than the PET-PSPRP expression score. Decision tree models selected the MRI-SVM-PSP score for the first branching and the PET-PSPRP expression score for a second split of the subgroup with normal MRI-SVM-PSP score, both in the whole sample and when restricted to PSP-RS or vPSP. CONCLUSIONS: FDG-PET provides added value for PSP-suspected patients with normal/inconclusive T1w-MRI, regardless of PSP phenotype and the methods to analyse the images for PSP-typical features.

The Early Perfusion Image Is Useful to Support the Visual Interpretation of Brain Amyloid-PET With 18F-Flutemetamol in Borderline Cases.

PURPOSE: Visual interpretation of brain amyloid-ß (Aß) PET can be difficult in individuals with borderline Aß burden. Coregistration with individual MRI is recommended in these cases, which, however, is not always available. This study evaluated coregistration with the early perfusion frames acquired immediately after tracer injection to support the visual interpretation of the late Aß-frames in PET with 18F-flutemetamol (FMM). PATIENTS AND METHODS: Fifty dual-time-window FMM-PET scans of cognitively normal subjects with 0 to 60 Centiloids were included retrospectively (70.1 ± 6.9 years, 56% female, MMSE score 28.9 ± 1.3, 42% APOE ɛ4 carrier). Regional Aß load was scored with respect to a 6-point Likert scale by 3 independent raters in the 10 regions of interest recommended for FMM reading using 3 different settings: Aß image only, Aß image coregistered with MRI, and Aß image coregistered with the perfusion image. The impact of setting, within- and between-readers variability, region of interest, and Aß-status was tested by repeated-measure analysis of variance of the Likert score. RESULTS: The Centiloid scale ranged between 2 and 52 (interquartile range, 7-19). Support of visual scoring by the perfusion image resulted in the best discrimination between Aß-positive and Aß-negative cases, mainly by improved certainty of excluding Aß plaques in Aß-negative cases (P = 0.030). It also resulted in significantly higher between-rater agreement. The setting effect was most pronounced in the frontal lobe and in the posterior cingulate cortex/precuneus area (P = 0.005). CONCLUSIONS: The early perfusion image is a suitable alternative to T1-weighted MRI to support the visual interpretation of the late Aß image in FMM-PET.

Organ and tumor dosimetry including method simplification for [177Lu]Lu-PSMA-I&T for treatment of metastatic castration resistant prostate cancer.

BACKGROUND: Internal dosimetry in individual patients is essential for safe and effective radioligand therapy. Multiple time point imaging for accurate dosimetry is time consuming and hence can be demanding for nuclear medicine departments as well as patients. The objectives of this study were (1) to assess absorbed doses to organs at risk and tumor lesions for [177Lu]Lu-PSMA-I&T using whole body SPECT imaging and (2) to investigate possible simplified dosimetry protocols. METHODS: This study included 16 patients each treated with 4 cycles of [177Lu]Lu-PSMA-I&T. They underwent quantitative whole body SPECT/CT imaging (3 bed positions) at four time points (TP) comprising 2 h, 24 h, 48 h and 72-168 h post-injection (p.i.). Full 3D dosimetry (reference method) was performed for all patients and dose cycles for organs at risk (kidneys, parotid glands and submandibular glands) and up to ten tumor lesions per patient (resulting in 90 lesions overall). The simplified dosimetry methods (SM) included (1) generating time activity curves for subsequent cycles using a single TP of imaging applying the kinetics of dose cycle 1, and for organs at risk also (2) simple extrapolation from dose cycle 1 and (3) from both, dose cycle 1 and 2. RESULTS: Normalized absorbed doses were 0.71 ± 0.32 mGy/MBq, 0.28 ± 0.12 mGy/MBq and 0.22 ± 0.08 mGy/MBq for kidneys, parotid glands and submandibular glands, respectively. Tumor doses decreased from 3.86 ± 3.38 mGy/MBq in dose cycle 1 to 2.01 ± 2.65 mGy/MBq in dose cycle 4. Compared to the full dosimetry approach the SM 1 using single TP imaging at 48 h p.i. resulted in the most accurate and precise results for the organs at risk in terms of absorbed doses per cycle and total cumulated dose. For tumor lesions better results were achieved using the fourth TP (≥ 72 h p.i.). CONCLUSION: Simplification of safety dosimetry protocols is possible for [177Lu]Lu-PSMA-I&T therapy. If tumor dosimetry is of interest a later imaging TP (≥ 72 h p.i.) should be used/added to account for the slower kinetics of tumors compared to organs at risk.

Amyloid and SCD jointly predict cognitive decline across Chinese and German cohorts.

INTRODUCTION: Subjective cognitive decline (SCD) in amyloid-positive (Aß+) individuals was proposed as a clinical indicator of Stage 2 in the Alzheimer's disease (AD) continuum, but this requires further validation across cultures, measures, and recruitment strategies. METHODS: Eight hundred twenty-one participants from SILCODE and DELCODE cohorts, including normal controls (NC) and individuals with SCD recruited from the community or from memory clinics, underwent neuropsychological assessments over up to 6 years. Amyloid positivity was derived from positron emission tomography or plasma biomarkers. Global cognitive change was analyzed using linear mixed-effects models. RESULTS: In the combined and stratified cohorts, Aß+ participants with SCD showed steeper cognitive decline or diminished practice effects compared with NC or Aß- participants with SCD. These findings were confirmed using different operationalizations of SCD and amyloid positivity, and across different SCD recruitment settings. DISCUSSION: Aß+ individuals with SCD in German and Chinese populations showed greater global cognitive decline and could be targeted for interventional trials. HIGHLIGHTS: SCD in amyloid-positive (Aß+) participants predicts a steeper cognitive decline. This finding does not rely on specific SCD or amyloid operationalization. This finding is not specific to SCD patients recruited from memory clinics. This finding is valid in both German and Chinese populations. Aß+ older adults with SCD could be a target population for interventional trials.

Unrealistic Data Augmentation Improves the Robustness of Deep Learning-Based Classification of Dopamine Transporter SPECT Against Variability Between Sites and Between Cameras.

We propose strongly unrealistic data augmentation to improve the robustness of convolutional neural networks (CNNs) for automatic classification of dopamine transporter SPECT against the variability between sites and between cameras. Methods: A CNN was trained on a homogeneous dataset comprising 1,100 123I-labeled 2ß-carbomethoxy-3ß-(4-iodophenyl)-N-(3-fluoropropyl)nortropane SPECT images using strongly unrealistic data augmentation based on gaussian blurring and additive noise. Strongly unrealistic data augmentation was compared with no augmentation and intensity-based nnU-Net augmentation on 2 independent datasets with lower (n = 645) and considerably higher (n = 640) spatial resolution. Results: The CNN trained with strongly unrealistic augmentation achieved an overall accuracy of 0.989 (95% CI, 0.978-0.996) and 0.975 (95% CI, 0.960-0.986) in the independent test datasets, which was better than that without (0.960, 95% CI, 0.942-0.974; 0.953, 95% CI, 0.934-0.968) and with nnU-Net augmentation (0.972, 95% CI, 0.956-0.983; 0.950, 95% CI, 0.930-0.966) (all McNemar P < 0.001). Conclusion: Strongly unrealistic data augmentation results in better generalization of CNN-based classification of 123I-labeled 2ß-carbomethoxy-3ß-(4-iodophenyl)-N-(3-fluoropropyl)nortropane SPECT images to unseen acquisition settings. We hypothesize that this can be transferred to other nuclear imaging applications.

Correlation of striatal dopamine D2/3 receptor availability with GABA level in the anterior cingulate cortex in healthy controls but not in alcohol-dependent subjects and individuals at high risk: A multimodal magnetic resonance spectroscopy and positron emission tomography study.

BACKGROUND: The association of impaired dopaminergic neurotransmission with the development and maintenance of alcohol use disorder is well known. More specifically, reduced dopamine D2/3 receptors in the striatum of subjects with alcohol dependence (AD) compared to healthy controls have been found in previous studies. Furthermore, alterations of gamma-aminobutyric acid (GABA) and glutamate (Glu) levels in the anterior cingulate cortex (ACC) of AD subjects have been documented in several studies. However, the interaction between cortical Glu levels and striatal dopamine D2/3 receptors has not been investigated in AD thus far. METHODS: This study investigated dopamine D2/3 receptor availability via 18F-fallypride positron emission tomography (PET) and GABA as well as Glu levels via magnetic resonance spectroscopy (MRS) in 19 detoxified AD subjects, 18 healthy controls (low risk, LR) controls and 19 individuals at high risk (HR) for developing AD, carefully matched for sex, age and smoking status. RESULTS: We found a significant negative correlation between GABA levels in the ACC and dopamine D2/3 receptor availability in the associative striatum of LR but not in AD or HR individuals. Contrary to our expectations, we did not observe a correlation between Glu concentrations in the ACC and striatal D2/3 receptor availability. CONCLUSIONS: The results may reflect potential regulatory cortical mechanisms on mesolimbic dopamine receptors and their disruption in AD and individuals at high risk, mirroring complex neurotransmitter interactions associated with the pathogenesis of addiction. This is the first study combining 18F-fallypride PET and MRS in AD subjects and individuals at high risk.

BrainLossNet: a fast, accurate and robust method to estimate brain volume loss from longitudinal MRI.

PURPOSE: MRI-derived brain volume loss (BVL) is widely used as neurodegeneration marker. SIENA is state-of-the-art for BVL measurement, but limited by long computation time. Here we propose "BrainLossNet", a convolutional neural network (CNN)-based method for BVL-estimation. METHODS: BrainLossNet uses CNN-based non-linear registration of baseline(BL)/follow-up(FU) 3D-T1w-MRI pairs. BVL is computed by non-linear registration of brain parenchyma masks segmented in the BL/FU scans. The BVL estimate is corrected for image distortions using the apparent volume change of the total intracranial volume. BrainLossNet was trained on 1525 BL/FU pairs from 83 scanners. Agreement between BrainLossNet and SIENA was assessed in 225 BL/FU pairs from 94 MS patients acquired with a single scanner and 268 BL/FU pairs from 52 scanners acquired for various indications. Robustness to short-term variability of 3D-T1w-MRI was compared in 354 BL/FU pairs from a single healthy men acquired in the same session without repositioning with 116 scanners (Frequently-Traveling-Human-Phantom dataset, FTHP). RESULTS: Processing time of BrainLossNet was 2-3 min. The median [interquartile range] of the SIENA-BrainLossNet BVL difference was 0.10% [- 0.18%, 0.35%] in the MS dataset, 0.08% [- 0.14%, 0.28%] in the various indications dataset. The distribution of apparent BVL in the FTHP dataset was narrower with BrainLossNet (p = 0.036; 95th percentile: 0.20% vs 0.32%). CONCLUSION: BrainLossNet on average provides the same BVL estimates as SIENA, but it is significantly more robust, probably due to its built-in distortion correction. Processing time of 2-3 min makes BrainLossNet suitable for clinical routine. This can pave the way for widespread clinical use of BVL estimation from intra-scanner BL/FU pairs.

Short post-injection seizure duration is associated with reduced power of ictal brain perfusion SPECT to lateralize the seizure onset zone.

BACKGROUND: The aim of this study was to assess the impact of the post-injection electrical seizure duration on the identification of the seizure onset zone (SOZ) in ictal brain perfusion SPECT in presurgical evaluation of drug-resistant epilepsy. METHODS: 176 ictal SPECT performed with 99mTc-HMPAO (n = 140) or -ECD (n = 36) were included retrospectively. Visual interpretation of the SPECT images (together with individual MRI and statistical hyperperfusion maps) with respect to lateralization (right, left, none) and localization (temporal, frontal, parietal, occipital) of the SOZ was performed by 3 independent readers. Between-readers agreement was characterized by Fleiss' κ. An ictal SPECT was considered "lateralizing" if all readers agreed on right or left hemisphere. It was considered "localizing" if it was lateralizing and all readers agreed on the same lobe within the same hemisphere. The impact of injection latency and post-injection seizure duration on the proportion of lateralizing/localizing SPECT was tested by ANOVA with dichotomized (by the median) injection latency and post-injection seizure duration as between-subjects factors. RESULTS: Median [interquartile range] (full range) of injection latency and post-injection seizure duration were 30 [24, 40] (3-120) s and 50 [27, 70] (-20-660) s, respectively. Fleiss' κ for lateralization of the SOZ was largest for the combination of early (< 30 s) injection and long (> 50 s) post-injection seizure duration (κ = 0.894, all other combinations κ = 0.659-0.734). Regarding Fleiss' κ for localization of the SOZ in the 141 (80.1%) lateralizing SPECT, it was largest for early injection and short post-injection seizure duration (κ = 0.575, all other combinations κ = 0.329-0.368). The proportion of lateralizing SPECT was lower with short compared to long post-injection seizure duration (estimated marginal means 74.3% versus 86.3%, p = 0.047). The effect was mainly driven by cases with very short post-injection seizure duration ≤ 10 s (53.8% lateralizing). Injection latency in the considered range had no significant impact on the proportion of lateralizing SPECT (p = 0.390). The proportion of localizing SPECT among the lateralizing cases did not depend on injection latency or post-injection seizure duration (p ≥ 0.603). CONCLUSIONS: Short post-injection seizure duration is associated with a lower proportion of lateralizing cases in ictal brain perfusion SPECT.

Removing outliers from the normative database improves regional atrophy detection in single-subject voxel-based morphometry.

PURPOSE: Single-subject voxel-based morphometry (VBM) compares an individual T1-weighted MRI to a sample of normal MRI in a normative database (NDB) to detect regional atrophy. Outliers in the NDB might result in reduced sensitivity of VBM. The primary aim of the current study was to propose a method for outlier removal ("NDB cleaning") and to test its impact on the performance of VBM for detection of Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD). METHODS: T1-weighted MRI of 81 patients with biomarker-confirmed AD (n = 51) or FTLD (n = 30) and 37 healthy subjects with simultaneous FDG-PET/MRI were included as test dataset. Two different NDBs were used: a scanner-specific NDB (37 healthy controls from the test dataset) and a non-scanner-specific NDB comprising 164 normal T1-weighted MRI from 164 different MRI scanners. Three different quality metrics based on leave-one-out testing of the scans in the NDB were implemented. A scan was removed if it was an outlier with respect to one or more quality metrics. VBM maps generated with and without NDB cleaning were assessed visually for the presence of AD or FTLD. RESULTS: Specificity of visual interpretation of the VBM maps for detection of AD or FTLD was 100% in all settings. Sensitivity was increased by NDB cleaning with both NDBs. The effect was statistically significant for the multiple-scanner NDB (from 0.47 [95%-CI 0.36-0.58] to 0.61 [0.49-0.71]). CONCLUSION: NDB cleaning has the potential to improve the sensitivity of VBM for the detection of AD or FTLD without increasing the risk of false positive findings.

Increased off-target binding of [18F]florbetaben in the skull of women with reduced skull density.

AIM: To investigate the relationship between off-target binding of the amyloid tracer [18F]florbetaben (FBB) in the skull and skull density. METHODS: Forty-three consecutive patients were included retrospectively (age 70.2±7.5y, 42% females, 65% amyloid-positive). For each patient, CT skull density (in Hounsfield units) and (late) FBB uptake in the skull were obtained using an individual skull mask generated by warping the skull tissue probability map provided by the statistical parametric mapping software package (version SPM12) to the native patient space. Skull FBB uptake (mean of the 10% hottest voxels) was scaled to the individual median FBB uptake in the pons. The association between skull FBB uptake and skull density was tested by correlation analyses. Univariate analysis of variance (ANOVA) of skull FBB uptake with dichotomized skull density (low: ≤ median, high), sex (female, male) and amyloid-status (positive, negative) as between-subjects factors was used to assess the impact of sex and amyloid status. RESULTS: There was a significant inverse correlation between skull FBB uptake and skull density (Pearson correlation coefficient -0.518, p < 0.001; Spearman rho -0.321, p = 0.036). The ANOVA confirmed the bone density effect on the FBB uptake in the skull (p = 0.019). In addition, sex (p = 0.012) and density*sex interaction (p = 0.016) had a significant impact. Skull FBB uptake was significantly higher in females with low skull density than for all other combinations of sex and skull density. Amyloid status did not reach statistical significance (p = 0.092). CONCLUSION: Off-target binding of FBB in the skull is inversely associated with skull density. The relationship is mainly driven by females. Amyloid status does not have a major impact on skull FBB binding.

Dopamine Transporter SPECT with 12-Minute Scan Duration Using Multiple-Pinhole Collimators.

This study evaluated the potential to reduce the scan duration in dopamine transporter (DAT) SPECT when using a second-generation multiple-pinhole (MPH) collimator designed for brain SPECT with improved count sensitivity and improved spatial resolution compared with parallel-hole and fanbeam collimators. Methods: The retrospective study included 640 consecutive clinical DAT SPECT studies that had been acquired in list mode with a triple-head SPECT system with MPH collimators and a 30-min net scan duration after injection of 181 ± 10 MBq of [123I]FP-CIT. Raw data corresponding to scan durations of 20, 15, 12, 8, 6, and 4 min were obtained by restricting the events to a proportionally reduced time interval of the list-mode data for each projection angle. SPECT images were reconstructed iteratively with the same parameter settings irrespective of scan duration. The resulting 5,120 SPECT images were assessed for a neurodegeneration-typical reduction in striatal signal by visual assessment, conventional specific binding ratio analysis, and a deep convolutional neural network trained on 30-min scans. Results: Regarding visual interpretation, image quality was considered diagnostic for all 640 patients down to a 12-min scan duration. The proportion of discrepant visual interpretations between 30 and 12 min (1.2%) was not larger than the proportion of discrepant visual interpretations between 2 reading sessions of the same reader at a 30-min scan duration (1.5%). Agreement with the putamen specific binding ratio from the 30-min images was better than expected for 5% test-retest variability down to a 10-min scan duration. A relevant change in convolutional neural network-based automatic classification was observed at a 6-min scan duration or less. Conclusion: The triple-head SPECT system with MPH collimators allows reliable DAT SPECT after administration of about 180 MBq of [123I]FP-CIT with a 12-min scan duration.

Automated identification of uncertain cases in deep learning-based classification of dopamine transporter SPECT to improve clinical utility and acceptance.

PURPOSE: Deep 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. METHODS: A 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). RESULTS: In 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. CONCLUSION: The 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.