Cerebrovascular disease in suspected non-Alzheimer's pathophysiology and cognitive decline over time.

BACKGROUND: The underlying cause of cognitive decline in individuals who are positive for biomarkers of neurodegeneration (N) but negative for biomarkers of amyloid-beta (A), designated as Suspected non-Alzheimer's pathophysiology (SNAP), remains unclear. We evaluate whether cerebrovascular disease (CeVD) is more prevalent in those with SNAP compared to A-N- and A+N+ individuals and whether CeVD is associated with cognitive decline over time in SNAP patients. METHODS: A total of 216 individuals from a prospective memory clinic cohort (mean [SD] age, 72.7 [7.3] years, 100 women [56.5%]) were included and were diagnosed as no cognitive impairment (NCI), cognitive impairment no dementia (CIND), Alzheimer's dementia (AD) or vascular dementia (VaD). All individuals underwent clinical evaluation and neuropsychological assessment annually for up to 5 years. Carbon 11-labeled Pittsburgh Compound B ([11 C]-PiB) or [18 F]-flutafuranol-positron emission spectrometry imaging was performed to ascertain amyloid-beta status. Magnetic resonance imaging was performed to assess neurodegeneration as measured by medial temporal atrophy ≥2, as well as significant CeVD (sCeVD) burden, defined by cortical infarct count ≥1, Fazekas score ≥2, lacune count ≥2 or cerebral microbleed count ≥2. RESULTS: Of the 216 individuals, 50 (23.1%) A-N+ were (SNAP), 93 (43.1%) A-N-, 36 (16.7%) A+N- and 37 (17.1%) A+N+. A+N+ individuals were significantly older, while A+N+ and SNAP individuals were more likely to have dementia. The SNAP group had a higher prevalence of sCeVD (90.0%) compared to A-N-. Moreover, SNAP individuals with sCeVD had significantly steeper decline in global cognition compared to A-N- over 5 years (p = 0.042). CONCLUSIONS: These findings suggest that CeVD is a contributing factor to cognitive decline in SNAP. Therefore, SNAP individuals should be carefully assessed and treated for CeVD.

The Associations Between Intracranial Stenosis, Brain Amyloid-beta, and Cognition in a Memory Clinic Sample.

BACKGROUND: Intracranial stenosis (ICS) and brain amyloid-beta (Aß) have been associated with cognition and dementia. We aimed to investigate the association between ICS and brain Aß and their independent and joint associations with cognition. METHODS: We conducted a cross-sectional study of 185 patients recruited from a memory clinic. ICS was measured on 3-dimensional time-of-flight magnetic resonance angiography and defined as stenosis ≥50%. Brain Aß was measured with [ 11 C] Pittsburgh compound B-positron emission tomography imaging. Cognition was assessed with a locally validated neuropsychological battery. RESULTS: A total of 17 (9.2%) patients had ICS, and the mean standardized uptake value ratio was 1.4 (±0.4 SD). ICS was not significantly associated with brain Aß deposition. ICS was significantly associated with worse global cognition (ß: -1.26, 95% CI: -2.25; -0.28, P =0.013), executive function (ß: -1.04, 95% CI: -1.86; -0.22, P =0.015) and visuospatial function (ß: -1.29, 95% CI: -2.30; -0.27, P =0.015). Moreover, in ICS patients without dementia (n=8), the presence of Aß was associated with worse performance on visuomotor speed. CONCLUSIONS: ICS was significantly associated with worse cognition and showed interaction with brain Aß such that patients with both pathologies performed worse on visuomotor speed specifically in those without dementia. Further studies may clarify if ICS and brain Aß deposition indeed have a synergistic association with cognition.

Improved amyloid burden quantification with nonspecific estimates using deep learning.

PURPOSE: Standardized uptake value ratio (SUVr) used to quantify amyloid-ß burden from amyloid-PET scans can be biased by variations in the tracer's nonspecific (NS) binding caused by the presence of cerebrovascular disease (CeVD). In this work, we propose a novel amyloid-PET quantification approach that harnesses the intermodal image translation capability of convolutional networks to remove this undesirable source of variability. METHODS: Paired MR and PET images exhibiting very low specific uptake were selected from a Singaporean amyloid-PET study involving 172 participants with different severities of CeVD. Two convolutional neural networks (CNN), ScaleNet and HighRes3DNet, and one conditional generative adversarial network (cGAN) were trained to map structural MR to NS PET images. NS estimates generated for all subjects using the most promising network were then subtracted from SUVr images to determine specific amyloid load only (SAßL). Associations of SAßL with various cognitive and functional test scores were then computed and compared to results using conventional SUVr. RESULTS: Multimodal ScaleNet outperformed other networks in predicting the NS content in cortical gray matter with a mean relative error below 2%. Compared to SUVr, SAßL showed increased association with cognitive and functional test scores by up to 67%. CONCLUSION: Removing the undesirable NS uptake from the amyloid load measurement is possible using deep learning and substantially improves its accuracy. This novel analysis approach opens a new window of opportunity for improved data modeling in Alzheimer's disease and for other neurodegenerative diseases that utilize PET imaging.

Head-to-head comparison of amplified plasmonic exosome Aß42 platform and single-molecule array immunoassay in a memory clinic cohort.

BACKGROUND AND PURPOSE: Various blood biomarkers reflecting brain amyloid-ß (Aß) load have recently been proposed with promising results. However, to date, no comparative study amongst blood biomarkers has been reported. Our objective was to examine the diagnostic performance and cost effectiveness of three blood biomarkers on the same cohort. METHODS: Using the same cohort (n = 68), the performances of the single-molecule array (Simoa) Aß40, Aß42, Aß42/Aß40 and the amplified plasmonic exosome (APEX) Aß42 blood biomarkers were compared using amyloid positron emission tomography (PET) as the reference standard. The extent to which these blood tests can reduce the recruitment cost of clinical trials was also determined by identifying amyloid positive (Aß+) participants. RESULTS: Compared to Simoa biomarkers, APEX-Aß42 showed significantly higher correlations with amyloid PET retention values and excellent diagnostic performance (sensitivity 100%, specificity 93.3%, area under the curve 0.995). When utilized for clinical trial recruitment, our simulation showed that pre-screening with blood biomarkers followed by a confirmatory amyloid PET imaging would roughly half the cost (56.8% reduction for APEX-Aß42 and 48.6% for Simoa-Aß42/Aß40) compared to the situation where only PET imaging is used. Moreover, with 100% sensitivity, APEX-Aß42 pre-screening does not increase the required number of initial participants. CONCLUSIONS: With its high diagnostic performance, APEX is an ideal candidate for Aß+ subject identification, monitoring and primary care screening, and could efficiently enrich clinical trials with Aß+ participants whilst halving recruitment costs.

Plasma P-tau181 to Aß42 ratio is associated with brain amyloid burden and hippocampal atrophy in an Asian cohort of Alzheimer's disease patients with concomitant cerebrovascular disease.

INTRODUCTION: There is increasing evidence that phosphorylated tau (P-tau181) is a specific biomarker for Alzheimer's disease (AD) pathology, but its potential utility in non-White patient cohorts and patients with concomitant cerebrovascular disease (CeVD) is unknown. METHODS: Single molecule array (Simoa) measurements of plasma P-tau181, total tau, amyloid beta (Aß)40 and Aß42, as well as derived ratios were correlated with neuroimaging modalities indicating brain amyloid (Aß+), hippocampal atrophy, and CeVD in a Singapore-based cohort of non-cognitively impaired (NCI; n = 43), cognitively impaired no dementia (CIND; n = 91), AD (n = 44), and vascular dementia (VaD; n = 22) subjects. RESULTS: P-tau181/Aß42 ratio showed the highest area under the curve (AUC) for Aß+ (AUC = 0.889) and for discriminating between AD Aß+ and VaD Aß- subjects (AUC = 0.903). In addition, P-tau181/Aß42 ratio was associated with hippocampal atrophy. None of the biomarkers was associated with CeVD. DISCUSSION: Plasma P-tau181/Aß42 ratio may be a noninvasive means of identifying AD with elevated brain amyloid in populations with concomitant CeVD.

Longitudinal trajectory of Amyloid-related hippocampal subfield atrophy in nondemented elderly.

Hippocampal atrophy and abnormal ß-Amyloid (Aß) deposition are established markers of Alzheimer's disease (AD). Nonetheless, longitudinal trajectory of Aß-associated hippocampal subfield atrophy prior to dementia remains unclear. We hypothesized that elevated Aß correlated with longitudinal subfield atrophy selectively in no cognitive impairment (NCI), spreading to other subfields in mild cognitive impairment (MCI). We analyzed data from two independent longitudinal cohorts of nondemented elderly, including global PET-Aß in AD-vulnerable cortical regions and longitudinal subfield volumes quantified with a novel auto-segmentation method (FreeSurfer v.6.0). Moreover, we investigated associations of Aß-related progressive subfield atrophy with memory decline. Across both datasets, we found a converging pattern that higher Aß correlated with faster CA1 volume decline in NCI. This pattern spread to other hippocampal subfields in MCI group, correlating with memory decline. Our results for the first time suggest a longitudinal focal-to-widespread trajectory of Aß-associated hippocampal subfield atrophy over disease progression in nondemented elderly.

Improved quantification of amyloid burden and associated biomarker cut-off points: results from the first amyloid Singaporean cohort with overlapping cerebrovascular disease.

PURPOSE: The analysis of the [11C]PiB-PET amyloid images of a unique Asian cohort of 186 participants featuring overlapping vascular diseases raised the question about the validity of current standards for amyloid quantification under abnormal conditions. In this work, we implemented a novel pipeline for improved amyloid PET quantification of this atypical cohort. METHODS: The investigated data correction and amyloid quantification methods included motion correction, standardized uptake value ratio (SUVr) quantification using the parcellated MRI (standard method) and SUVr quantification without MRI. We introduced a novel amyloid analysis method yielding 2 biomarkers: AßL which quantifies the global Aß burden and ns that characterizes the non-specific uptake. Cut-off points were first determined using visual assessment as ground truth and then using unsupervised classification techniques. RESULTS: Subject's motion impacts the accuracy of the measurement outcome but has however a limited effect on the visual rating and cut-off point determination. SUVr computation can be reliably performed for all the subjects without MRI parcellation while, when required, the parcellation failed or was of mediocre quality in 10% of the cases. The novel biomarker AßL showed an association increase of 29.5% with the cognitive tests and increased effect size between positive and negative scans compared with SUVr. ns was found sensitive to cerebral microbleeds, white matter hyperintensity, volume, and age. The cut-off points for SUVr using parcellated MRI, SUVr without parcellation, and AßL were 1.56, 1.39, and 25.5. Finally, k-means produced valid cut-off points without the requirement of visual assessment. CONCLUSION: The optimal processing for the amyloid quantification of this atypical cohort allows the quantification of all the subjects, producing SUVr values and two novel biomarkers: AßL, showing important increased in their association with various cognitive tests, and ns, a parameter sensitive to non-specific retention variations caused by age and cerebrovascular diseases.

In vivo PET imaging with [(18)F]FDG to explain improved glucose uptake in an apolipoprotein A-I treated mouse model of diabetes.

AIMS/HYPOTHESIS: Type 2 diabetes is characterised by decreased HDL levels, as well as the level of apolipoprotein A-I (apoA-I), the main apolipoprotein of HDLs. Pharmacological elevation of HDL and apoA-I levels is associated with improved glycaemic control in patients with type 2 diabetes. This is partly due to improved glucose uptake in skeletal muscle. METHODS: This study used kinetic modelling to investigate the impact of increasing plasma apoA-I levels on the metabolism of glucose in the db/db mouse model. RESULTS: Treatment of db/db mice with apoA-I for 2 h significantly improved both glucose tolerance (AUC 2574 ± 70 mmol/l × min vs 2927 ± 137 mmol/l × min, for apoA-I and PBS, respectively; p < 0.05) and insulin sensitivity (AUC 388.8 ± 23.8 mmol/l × min vs 194.1 ± 19.6 mmol/l × min, for apoA-I and PBS, respectively; p < 0.001). ApoA-I treatment also increased glucose uptake by skeletal muscle in both an insulin-dependent and insulin-independent manner as evidenced by increased uptake of fludeoxyglucose ([(18)F]FDG) from plasma into gastrocnemius muscle in apoA-I treated mice, both in the absence and presence of insulin. Kinetic modelling revealed an enhanced rate of insulin-mediated glucose phosphorylation (k 3) in apoA-I treated mice (3.5 ± 1.1 × 10(-2) min(-1) vs 2.3 ± 0.7 × 10(-2) min(-1), for apoA-I and PBS, respectively; p < 0.05) and an increased influx constant (3.7 ± 0.6 × 10(-3) ml min(-1) g(-1) vs 2.0 ± 0.3 × 10(-3) ml min(-1) g(-1), for apoA-I and PBS, respectively; p < 0.05). Treatment of L6 rat skeletal muscle cells with apoA-I for 2 h indicated that increased hexokinase activity mediated the increased rate of glucose phosphorylation. CONCLUSIONS/INTERPRETATION: These findings indicate that apoA-I improves glucose disposal in db/db mice by improving insulin sensitivity and enhancing glucose phosphorylation.

4D PET iterative deconvolution with spatiotemporal regularization for quantitative dynamic PET imaging.

Quantitative measurements in dynamic PET imaging are usually limited by the poor counting statistics particularly in short dynamic frames and by the low spatial resolution of the detection system, resulting in partial volume effects (PVEs). In this work, we present a fast and easy to implement method for the restoration of dynamic PET images that have suffered from both PVE and noise degradation. It is based on a weighted least squares iterative deconvolution approach of the dynamic PET image with spatial and temporal regularization. Using simulated dynamic [(11)C] Raclopride PET data with controlled biological variations in the striata between scans, we showed that the restoration method provides images which exhibit less noise and better contrast between emitting structures than the original images. In addition, the method is able to recover the true time activity curve in the striata region with an error below 3% while it was underestimated by more than 20% without correction. As a result, the method improves the accuracy and reduces the variability of the kinetic parameter estimates calculated from the corrected images. More importantly it increases the accuracy (from less than 66% to more than 95%) of measured biological variations as well as their statistical detectivity.

A data driven method for estimation of B(avail) and appK(D) using a single injection protocol with [¹¹C]raclopride in the mouse.

PURPOSE: The partial saturation approach (PSA) is a simple, single injection experimental protocol that will estimate both B(avail) and appK(D) without the use of blood sampling. This makes it ideal for use in longitudinal studies of neurodegenerative diseases in the rodent. The aim of this study was to increase the range and applicability of the PSA by developing a data driven strategy for determining reliable regional estimates of receptor density (B(avail)) and in vivo affinity (1/appK(D)), and validate the strategy using a simulation model. METHODS: The data driven method uses a time window guided by the dynamic equilibrium state of the system as opposed to using a static time window. To test the method, simulations of partial saturation experiments were generated and validated against experimental data. The experimental conditions simulated included a range of receptor occupancy levels and three different B(avail) and appK(D) values to mimic diseases states. Also the effect of using a reference region and typical PET noise on the stability and accuracy of the estimates was investigated. RESULTS: The investigations showed that the parameter estimates in a simulated healthy mouse, using the data driven method were within 10±30% of the simulated input for the range of occupancy levels simulated. Throughout all experimental conditions simulated, the accuracy and robustness of the estimates using the data driven method were much improved upon the typical method of using a static time window, especially at low receptor occupancy levels. Introducing a reference region caused a bias of approximately 10% over the range of occupancy levels. CONCLUSIONS: Based on extensive simulated experimental conditions, it was shown the data driven method provides accurate and precise estimates of B(avail) and appK(D) for a broader range of conditions compared to the original method.

Simulation-based optimisation of the PET data processing for partial saturation approach protocols.

Positron emission tomography (PET) with [(11)C]Raclopride is an important tool for studying dopamine D2 receptor expression in vivo. [(11)C]Raclopride PET binding experiments conducted using the Partial Saturation Approach (PSA) allow the estimation of receptor density (B(avail)) and the in vivo affinity appK(D). The PSA is a simple, single injection, single scan experimental protocol that does not require blood sampling, making it ideal for use in longitudinal studies. In this work, we generated a complete Monte Carlo simulated PET study involving two groups of scans, in between which a biological phenomenon was inferred (a 30% decrease of B(avail)), and used it in order to design an optimal data processing chain for the parameter estimation from PSA data. The impact of spatial smoothing, noise removal and image resolution recovery technique on the statistical detection was investigated in depth. We found that image resolution recovery using iterative deconvolution of the image with the system point spread function associated with temporal data denoising greatly improves the accuracy and the statistical reliability of detecting the imposed phenomenon. Before optimisation, the inferred B(avail) variation between the two groups was underestimated by 42% and detected in 66% of cases, while a false decrease of appK(D) by 13% was detected in more than 11% of cases. After optimisation, the calculated B(avail) variation was underestimated by only 3.7% and detected in 89% of cases, while a false slight increase of appK(D) by 3.7% was detected in only 2% of cases. We found during this investigation that it was essential to adjust a factor that accounts for difference in magnitude between the non-displaceable ligand concentrations measured in the target and in the reference regions, for different data processing pathways as this ratio was affected by different image resolutions.

Comorbid amyloid with cerebrovascular disease in domain-specific cognitive and neuropsychiatric disturbances: a cross-sectional memory clinic study.

Dementia is a multifactorial disorder that is likely influenced by both Alzheimer's disease (AD) and vascular pathologies. We evaluated domain-specific cognitive and neuropsychiatric dysfunction using a two-neuroimaging biomarker construct (beta-amyloid [Aß] and cerebrovascular disease [CeVD]). We analyzed data from 216 memory clinic participants (mean age = 75.9 ± 6.9; 56.5% female) with neuropsychological and neuropsychiatric assessments, 3T-MRI, and Aß-PET imaging. Structural equation modeling showed that the largest Aß (A+) effect was on memory (B = -1.50) and apathy (B = 0.26), whereas CeVD effects were largest on language (B = -1.62) and hyperactivity (B = 0.32). Group comparisons showed that the A+C+ group had greater memory impairment (B = -1.55), hyperactivity (B = 0.79), and apathy (B = 0.74) compared to A-C+; and greater language impairment (B = -1.26) compared to A+C-. These potentially additive effects of Aß and CeVD burden underline the importance of early detection and treatment of Aß alongside optimal control of vascular risk factors as a potential strategy in preventing cognitive and neurobehavioral impairment.

Brain atrophy and white matter hyperintensities are independently associated with plasma neurofilament light chain in an Asian cohort of cognitively impaired patients with concomitant cerebral small vessel disease.

Introduction: Plasma neurofilament light chain (NfL) is a potential biomarker for neurodegeneration in Alzheimer's disease (AD), ischemic stroke, and non-dementia cohorts with cerebral small vessel disease (CSVD). However, studies of AD in populations with high prevalence of concomitant CSVD to evaluate associations of brain atrophy, CSVD, and amyloid beta (Aß) burden on plasma NfL are lacking. Methods: Associations were tested between plasma NfL and brain Aß, medial temporal lobe atrophy (MTA) as well as neuroimaging features of CSVD, including white matter hyperintensities (WMH), lacunes, and cerebral microbleeds. Results: We found that participants with either MTA (defined as MTA score ≥2; neurodegeneration [N]+WMH-) or WMH (cut-off for log-transformed WMH volume at 50th percentile; N-WMH+) manifested increased plasma NfL levels. Participants with both pathologies (N+WMH+) showed the highest NfL compared to N+WMH-, N-WMH+, and N-WMH- individuals. Discussion: Plasma NfL has potential utility in stratifying individual and combined contributions of AD pathology and CSVD to cognitive impairment.

Clinical utility of PET/MRI in multiple myeloma.

Introduction: This study aimed to evaluate the clinical utility of positron emission tomography/magnetic resonance imaging (PET/MRI), especially in comparison with PET/computed tomography (CT), which has been widely used in clinical practice in multiple myeloma. Method: F-18 fluorodeoxyglucose PET/MRI and PET/ CT studies were done at baseline and when at least a partial response to treatment was achieved. These were done for newly-diagnosed myeloma patients who have not had more than 1 cycle of anti-myeloma treatment, or for relapsed and/or refractory myeloma patients before the start of next line of therapy. Results: PET/MRI correlated significantly with PET/CT, in terms of number of lesions detected, standardised uptake value (SUVmean and SUVmax, both at baseline and post-treatment. PET/MRI and PET/CT correlated with survival at baseline, but not post-treatment. Conclusion: In this study, PET/MRI was more sensitive in detecting early disease and disease resolution post-treatment, compared with PET/CT. However, PET/MRI was less sensitive in detecting lesions in the ribs, clavicle and skull.

Longitudinal associations between ß-amyloid and cortical thickness in mild cognitive impairment.

How beta-amyloid accumulation influences brain atrophy in Alzheimer's disease remains contentious with conflicting findings. We aimed to elucidate the correlations of regional longitudinal atrophy with cross-sectional regional and global amyloid in individuals with mild cognitive impairment and no cognitive impairment. We hypothesized that greater cortical thinning over time correlated with greater amyloid deposition, particularly within Alzheimer's disease characteristic regions in mild cognitive impairment, and weaker or no correlations in those with no cognitive impairment. 45 patients with mild cognitive impairment and 12 controls underwent a cross-sectional [11C]-Pittsburgh Compound B PET and two retrospective longitudinal structural imaging (follow-up: 23.65 ± 2.04 months) to assess global/regional amyloid and regional cortical thickness, respectively. Separate linear mixed models were constructed to evaluate relationships of either global or regional amyloid with regional cortical thinning longitudinally. In patients with mild cognitive impairment, regional amyloid in the right banks of the superior temporal sulcus was associated with longitudinal cortical thinning in the right medial orbitofrontal cortex (P = 0.04 after False Discovery Rate correction). In the mild cognitive impairment group, greater right banks amyloid burden and less cortical thickness in the right medial orbitofrontal cortex showed greater visual and verbal memory decline over time, which was not observed in controls. Global amyloid was not associated with longitudinal cortical thinning in any locations in either group. Our findings indicate an increasing influence of amyloid on neurodegeneration and memory along the preclinical to prodromal spectrum. Future multimodal studies that include additional biomarkers will be well-suited to delineate the interplay between various pathological processes and amyloid and memory decline, as well as clarify their additive or independent effects along the disease deterioration.

Comparison of quantitative parameters and radiomic features as inputs into machine learning models to predict the Gleason score of prostate cancer lesions.

INTRODUCTION: The classification of prostate cancer (PCa) lesions using Prostate Imaging Reporting and Data System (PI-RADS) suffers from poor inter-reader agreement. This study compared quantitative parameters or radiomic features from multiparametric magnetic resonance imaging (mpMRI) or positron emission tomography (PET), as inputs into machine learning (ML) to predict the Gleason scores (GS) of detected lesions for improved PCa lesion classification. METHODS: 20 biopsy-confirmed PCa subjects underwent imaging before radical prostatectomy. A pathologist assigned GS from tumour tissue. Two radiologists and one nuclear medicine physician delineated the lesions on the mpMR and PET images, yielding 45 lesion inputs. Seven quantitative parameters were extracted from the lesions, namely T2-weighted (T2w) image intensity, apparent diffusion coefficient (ADC), transfer constant (KTRANS), efflux rate constant (Kep), and extracellular volume ratio (Ve) from mpMR images, and SUVmean and SUVmax from PET images. Eight radiomic features were selected out of 109 radiomic features from T2w, ADC and PET images. Quantitative parameters or radiomic features, with risk factors of age, prostate-specific antigen (PSA), PSA density and volume, of 45 different lesion inputs were input in different combinations into four ML models - Decision Tree (DT), Support Vector Machine (SVM), k-Nearest-Neighbour (kNN), Ensembles model (EM). RESULTS: SUVmax yielded the highest accuracy in discriminating detected lesions. Among the 4 ML models, kNN yielded the highest accuracies of 0.929 using either quantitative parameters or radiomic features with risk factors as input. CONCLUSIONS: ML models' performance is dependent on the input combinations and risk factors further improve ML classification accuracy.

Characterisation of partial volume effect and region-based correction in small animal positron emission tomography (PET) of the rat brain.

Accurate quantification of PET imaging data is required for a useful interpretation of the measured radioactive tracer concentrations. The partial volume effect (PVE) describes signal dilution and mixing due to spatial resolution and sampling limitations, which introduces bias in quantitative results. In the present study we investigated the magnitude of PVE for volumes of interest (VOIs) in the rat brain and the effect of positron range. In simulated (11)C-raclopride studies we examined the influence of PVE on time activity curves in striatal and cerebellar VOIs and binding potential estimation. The performance of partial volume correction (PVC) was studied using the region-based geometric transfer matrix (GTM) method including the question of whether a spatially variant point spread function (PSF) is necessary for PVC of a rat brain close to the centre of the field of view. Furthermore, we determined the effect of spillover from activity outside the brain. The results confirmed that PVE is significant in rat brain PET and showed that positron range is an important factor that needs to be included in the PSF. There was considerable bias in time activity curves for the simulated (11)C-raclopride studies and significant underestimation of binding potential even for very small centred VOIs. Good activity recovery was achieved with the GTM PVC using a spatially invariant simulated PSF when no activity was present outside the brain. PVC using a simple Gaussian fit point spread function was not sufficiently accurate. Spillover from regions outside the brain had a significant impact on measured activity concentrations and reduced the accuracy of PVC with the GTM method using rat brain regions alone, except for the smallest VOI size but at the cost of increased noise. Voxel-based partial volume correction methods which inherently compensate for spillover from outside the brain might be a more suitable choice.

[18F]Fallypride PET measurement of striatal and extrastriatal dopamine D 2/3 receptor availability in recently abstinent alcoholics.

Positron emission tomography (PET) shows reduced binding of the dopamine D(2/3) antagonist [(11) C]raclopride in striatum of withdrawn psychostimulant abusers, but not consistently in patients with alcohol dependence (AD). We make first use of the high affinity ligand [(18) F]fallypride to obtain serial measures of D(2/3) receptor availability in striatal and extrastriatal regions of AD patients undergoing detoxification. Seventeen patients (mean age 44 ± 5y) with AD and 14 age-matched healthy volunteers participated. Each patient underwent [(18) F]fallypride PET upon hospital admission, and again 1-2 weeks later; two patients achieving abstinence, and two with substantial harm reduction had additional PET follow-up at 1 year. Dynamic 180-minute PET recordings were used for volume of interest (VOI)-based and voxel-wise analysis of [(18) F]fallypride binding potential (BP(ND) ). Mean baseline BP(ND) in striatum of the AD patients (15.7 ± 3.6) was unaltered during short-term follow-up, and did not differ from that in healthy controls (16.8 ± 3.0); however, BP(ND) was 10-20% lower in thalamus, hippocampus, and insular and temporal cortex of the AD patients (P < 0.05). Age-dependent declines in BP(ND) were very small in controls, but more pronounced and widespread in the AD group. Striatal and thalamic BP(ND) increased by 30% in four patients with long-term abstinence or reduced alcohol consumption. VOI-based [(18) F]fallypride PET analyses revealed group differences in D(2/3) receptor availability primarily in extra-striatal regions. Age-related loss of dopamine D(2/3) receptors was more pronounced in AD patients. Receptor availability was unaltered by acute withdrawal, but increased in the subgroup of patients with long-term follow-up, suggesting reversibility of receptor changes.

Validation of deep learning-based nonspecific estimates for amyloid burden quantification with longitudinal data.

PURPOSE: To validate our previously proposed method of quantifying amyloid-beta (Aß) load using nonspecific (NS) estimates generated with convolutional neural networks (CNNs) using [18F]Florbetapir scans from longitudinal and multicenter ADNI data. METHODS: 188 paired MR (T1-weighted and T2-weighted) and PET images were downloaded from the ADNI3 dataset, of which 49 subjects had 2 time-point scans. 40 Aß- subjects with low specific uptake were selected for training. Multimodal ScaleNet (SN) and monomodal HighRes3DNet (HRN), using either T1-weighted or T2-weighted MR images as inputs) were trained to map structural MR to NS-PET images. The optimized SN and HRN networks were used to estimate the NS for all scans and then subtracted from SUVr images to determine the specific amyloid load (SAßL) images. The association of SAßL with various cognitive and functional test scores was evaluated using Spearman analysis, as well as the differences in SAßL with cognitive test scores for 49 subjects with 2 time-point scans and sensitivity analysis. RESULTS: SAßL derived from both SN and HRN showed higher association with memory-related cognitive test scores compared to SUVr. However, for longitudinal scans, only SAßL estimated from multimodal SN consistently performed better than SUVr for all memory-related cognitive test scores. CONCLUSIONS: Our proposed method of quantifying Aß load using NS estimated from CNN correlated better than SUVr with cognitive decline for both static and longitudinal data, and was able to estimate NS of [18F]Florbetapir. We suggest employing multimodal networks with both T1-weighted and T2-weighted MR images for better NS estimation.

Comparison of Three Automated Approaches for Classification of Amyloid-PET Images.

Automated amyloid-PET image classification can support clinical assessment and increase diagnostic confidence. Three automated approaches using global cut-points derived from Receiver Operating Characteristic (ROC) analysis, machine learning (ML) algorithms with regional SUVr values, and deep learning (DL) network with 3D image input were compared under various conditions: number of training data, radiotracers, and cohorts. 276 [11C]PiB and 209 [18F]AV45 PET images from ADNI database and our local cohort were used. Global mean and maximum SUVr cut-points were derived using ROC analysis. 68 ML models were built using regional SUVr values and one DL network was trained with classifications of two visual assessments - manufacturer's recommendations (gray-scale) and with visually guided reference region scaling (rainbow-scale). ML-based classification achieved similarly high accuracy as ROC classification, but had better convergence between training and unseen data, with a smaller number of training data. Naïve Bayes performed the best overall among the 68 ML algorithms. Classification with maximum SUVr cut-points yielded higher accuracy than with mean SUVr cut-points, particularly for cohorts showing more focal uptake. DL networks can support the classification of definite cases accurately but performed poorly for equivocal cases. Rainbow-scale standardized image intensity scaling and improved inter-rater agreement. Gray-scale detects focal accumulation better, thus classifying more amyloid-positive scans. All three approaches generally achieved higher accuracy when trained with rainbow-scale classification. ML yielded similarly high accuracy as ROC, but with better convergence between training and unseen data, and further work may lead to even more accurate ML methods.