Spatial Normalization of 18F-Flutemetamol PET Images Using an Adaptive Principal-Component Template

Comparison of consistency in centiloid scale among different analytical methods in amyloid PET: the CapAIBL, VIZCalc, and Amyquant methods

OBJECTIVE

The Centiloid (CL) scale is a standardized measure for quantifying amyloid deposition in amyloid positron emission tomography (PET) imaging. We aimed to assess the agreement among 3 CL calculation methods: CapAIBL, VIZCalc, and Amyquant.

METHODS

This study included 192 participants (mean age: 71.5 years, range: 50-87 years), comprising 55 with Alzheimer's disease, 65 with mild cognitive impairment, 13 with non-Alzheimer's dementia, and 59 cognitively normal participants. All the participants were assessed using the three CL calculation methods. Spearman's rank correlation, linear regression, Friedman tests, Wilcoxon signed-rank tests, and Bland-Altman analysis were employed to assess data correlations, linear associations, method differences, and systematic bias, respectively.

RESULTS

Strong correlations (rho = 0.99, p < .001) were observed among the CL values calculated using the three methods. Scatter plots and regression lines visually confirmed these strong correlations and met the validation criteria. Despite the robust correlations, a significant difference in CL value between CapAIBL and Amyquant was observed (36.1 ± 39.7 vs. 34.9 ± 39.4; p < .001). In contrast, no significant differences were found between CapAIBL and VIZCalc or between VIZCalc and Amyquant. The Bland-Altman analysis showed no observable systematic bias between the methods.

CONCLUSIONS

The study demonstrated strong agreement among the three methods for calculating CL values. Despite minor variations in the absolute values of the Centiloid scores obtained using these methods, the overall agreement suggests that they are interchangeable.

Alzheimer's disease heterogeneity revealed by neuroanatomical normative modeling

INTRODUCTION

Overlooking the heterogeneity in Alzheimer's disease (AD) may lead to diagnostic delays and failures. Neuroanatomical normative modeling captures individual brain variation and may inform our understanding of individual differences in AD-related atrophy.

METHODS

We applied neuroanatomical normative modeling to magnetic resonance imaging from a real-world clinical cohort with confirmed AD (n = 86). Regional cortical thickness was compared to a healthy reference cohort (n = 33,072) and the number of outlying regions was summed (total outlier count) and mapped at individual- and group-levels.

RESULTS

The superior temporal sulcus contained the highest proportion of outliers (60%). Elsewhere, overlap between patient atrophy patterns was low. Mean total outlier count was higher in patients who were non-amnestic, at more advanced disease stages, and without depressive symptoms. Amyloid burden was negatively associated with outlier count.

DISCUSSION

Brain atrophy in AD is highly heterogeneous and neuroanatomical normative modeling can be used to explore anatomo-clinical correlations in individual patients.

Blood β-synuclein is related to amyloid PET positivity in memory clinic patients

INTRODUCTION

β-synuclein is an emerging blood biomarker to study synaptic degeneration in Alzheimer´s disease (AD), but its relation to amyloid-β (Αβ) pathology is unclear.

METHODS

We investigated the association of plasma β-synuclein levels with [18F] flutemetamol positron emission tomography (PET) in patients with AD dementia (n = 51), mild cognitive impairment (MCI-Aβ+ n = 18, MCI- Aβ- n = 30), non-AD dementias (n = 22), and non-demented controls (n = 5).

RESULTS

Plasma β-synuclein levels were higher in Aβ+ (AD dementia, MCI-Aβ+) than in Aβ- subjects (non-AD dementias, MCI-Aβ-) with good discrimination of Aβ+ from Aβ- subjects and prediction of Aβ status in MCI individuals. A positive correlation between plasma β-synuclein and Aβ PET was observed in multiple cortical regions across all lobes.

DISCUSSION

Plasma β-synuclein demonstrated discriminative properties for Aβ PET positive and negative subjects. Our data underline that β-synuclein is not a direct marker of Aβ pathology and suggest different longitudinal dynamics of synaptic degeneration versus amyloid deposition across the AD continuum.

HIGHLIGHTS

Blood and CSF β-synuclein levels are higher in Aβ+ than in Aβ- subjects. Blood β-synuclein level correlates with amyloid PET positivity in multiple regions. Blood β-synuclein predicts Aβ status in MCI individuals.

Longitudinal amyloid and tau PET imaging in Alzheimer's disease: A systematic review of methodologies and factors affecting quantification

Deposition of amyloid and tau pathology can be quantified in vivo using positron emission tomography (PET). Accurate longitudinal measurements of accumulation from these images are critical for characterizing the start and spread of the disease. However, these measurements are challenging; precision and accuracy can be affected substantially by various sources of errors and variability. This review, supported by a systematic search of the literature, summarizes the current design and methodologies of longitudinal PET studies. Intrinsic, biological causes of variability of the Alzheimer's disease (AD) protein load over time are then detailed. Technical factors contributing to longitudinal PET measurement uncertainty are highlighted, followed by suggestions for mitigating these factors, including possible techniques that leverage shared information between serial scans. Controlling for intrinsic variability and reducing measurement uncertainty in longitudinal PET pipelines will provide more accurate and precise markers of disease evolution, improve clinical trial design, and aid therapy response monitoring.

Evaluation of novel data-driven metrics of amyloid β deposition for longitudinal PET studies

PURPOSE

Positron emission tomography (PET) provides in vivo quantification of amyloid-β (Aβ) pathology. Established methods for assessing Aβ burden can be affected by physiological and technical factors. Novel, data-driven metrics have been developed to account for these sources of variability. We aimed to evaluate the performance of four of these amyloid PET metrics against conventional techniques, using a common set of criteria.

METHODS

Three cohorts were used for evaluation: Insight 46 (N=464, [18F]florbetapir), AIBL (N=277, [18F]flutemetamol), and an independent test-retest data (N=10, [18F]flutemetamol). Established metrics of amyloid tracer uptake included the Centiloid (CL) and where dynamic data was available, the non-displaceable binding potential (BPND). The four data-driven metrics computed were the amyloid load (Aβ load), the Aβ-PET pathology accumulation index (Aβ index), the Centiloid derived from non-negative matrix factorisation (CLNMF), and the amyloid pattern similarity score (AMPSS). These metrics were evaluated using reliability and repeatability in test-retest data, associations with BPND and CL, variability of the rate of change and sample size estimates to detect a 25% slowing in Aβ accumulation.

RESULTS

All metrics showed good reliability. Aβ load, Aβ index and CLNMF were strong associated with the BPND. The associations with CL suggest that cross-sectional measures of CLNMF, Aβ index and Aβ load are robust across studies. Sample size estimates for secondary prevention trial scenarios were the lowest for CLNMF and Aβ load compared to the CL.

CONCLUSION

Among the novel data-driven metrics evaluated, the Aβ load, the Aβ index and the CLNMF can provide comparable performance to more established quantification methods of Aβ PET tracer uptake. The CLNMF and Aβ load could offer a more precise alternative to CL, although further studies in larger cohorts should be conducted.

Validation of quantitative assessment of florbetaben PET scans as an adjunct to the visual assessment across 15 software methods

PURPOSE

Amyloid positron emission tomography (PET) with [18F]florbetaben (FBB) is an established tool for detecting Aβ deposition in the brain in vivo based on visual assessment of PET scans. Quantitative measures are commonly used in the research context and allow continuous measurement of amyloid burden. The aim of this study was to demonstrate the robustness of FBB PET quantification.

METHODS

This is a retrospective analysis of FBB PET images from 589 subjects. PET scans were quantified with 15 analytical methods using nine software packages (MIMneuro, Hermes BRASS, Neurocloud, Neurology Toolkit, statistical parametric mapping (SPM8), PMOD Neuro, CapAIBL, non-negative matrix factorization (NMF), AmyloidIQ) that used several metrics to estimate Aβ load (SUVR, centiloid, amyloid load, and amyloid index). Six analytical methods reported centiloid (MIMneuro, standard centiloid, Neurology Toolkit, SPM8 (PET only), CapAIBL, NMF). All results were quality controlled.

RESULTS

The mean sensitivity, specificity, and accuracy were 96.1 ± 1.6%, 96.9 ± 1.0%, and 96.4 ± 1.1%, respectively, for all quantitative methods tested when compared to histopathology, where available. The mean percentage of agreement between binary quantitative assessment across all 15 methods and visual majority assessment was 92.4 ± 1.5%. Assessments of reliability, correlation analyses, and comparisons across software packages showed excellent performance and consistent results between analytical methods.

CONCLUSION

This study demonstrated that quantitative methods using both CE marked software and other widely available processing tools provided comparable results to visual assessments of FBB PET scans. Software quantification methods, such as centiloid analysis, can complement visual assessment of FBB PET images and could be used in the future for identification of early amyloid deposition, monitoring disease progression and treatment effectiveness.

Quantification of amyloid beta and tau PET without a structural MRI

INTRODUCTION

Relying on magnetic resonance imaging (MRI) for quantification of positron emission tomography (PET) images may limit generalizability of the results. We evaluated several MRI-free approaches for amyloid beta (Aβ) and tau PET quantification relative to MRI-dependent quantification cross-sectionally and longitudinally.

METHODS

We compared baseline MRI-free and MRI-dependent measurements of Aβ PET ([18F]florbetapir [FBP], N = 1290, [18F]florbetaben [FBB], N = 290) and tau PET ([18F]flortaucipir [FTP], N = 768) images with respect to continuous and dichotomous agreement, effect sizes of Aβ+ impaired versus Aβ- unimpaired groups, and longitudinal standardized uptake value ratio (SUVR) slopes in a subset of individuals.

RESULTS

The best-performing MRI-free approaches had high continuous and dichotomous agreement with MRI-dependent SUVRs for Aβ PET and temporal flortaucipir (R² ≥0.95; ± agreement ≥92%) and for Alzheimer's disease-related effect sizes; agreement was slightly lower for entorhinal flortaucipir and longitudinal slopes.

DISCUSSION

There is no consistent loss of baseline or longitudinal AD-related signal with MRI-free Aβ and tau PET image quantification.

Development and clinical validation of CT-based regional modified Centiloid method for amyloid PET

Background

The standard Centiloid (CL) method was proposed to harmonize and quantify global ¹⁸F-labeled amyloid beta (Aβ) PET ligands using MRI as an anatomical reference. However, there is need for harmonizing and quantifying regional Aβ uptakes between ligands using CT as an anatomical reference. In the present study, we developed and validated a CT-based regional direct comparison of ¹⁸F-florbetaben (FBB) and ¹⁸F-flutemetamol (FMM) Centiloid (rdcCL).

Methods

For development of MRI-based or CT-based rdcCLs, the cohort consisted of 63 subjects (20 young controls (YC) and 18 old controls (OC), and 25 participants with Alzheimer’s disease dementia (ADD)). We performed a direct comparison of the FMM-FBB rdcCL method using MRI and CT images to define a common target region and the six regional VOIs of frontal, temporal, parietal, posterior cingulate, occipital, and striatal regions. Global and regional rdcCL scales were compared between MRI-based and CT-based methods. For clinical validation, the cohort consisted of 2245 subjects (627 CN, 933 MCI, and 685 ADD).

Results

Both MRI-based and CT-based rdcCL scales showed that FMM and FBB were highly correlated with each other, globally and regionally (R² = 0.96~0.99). Both FMM and FBB showed that CT-based rdcCL scales were highly correlated with MRI-based rdcCL scales (R² = 0.97~0.99). Regarding the absolute difference of rdcCLs between FMM and FBB, the CT-based method was not different from the MRI-based method, globally or regionally (p value = 0.07~0.95). In our clinical validation study, the global negative group showed that the regional positive subgroup had worse neuropsychological performance than the regional negative subgroup (p < 0.05). The global positive group also showed that the striatal positive subgroup had worse neuropsychological performance than the striatal negative subgroup (p < 0.05).

Conclusions

Our findings suggest that it is feasible to convert regional FMM or FBB rdcSUVR values into rdcCL scales without additional MRI scans. This allows a more easily accessible method for researchers that can be applicable to a variety of different conditions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13195-022-01099-0.

Evaluation of in vivo staging of amyloid deposition in cognitively unimpaired elderly aged 78-94

Amyloid-beta (Aβ) deposition is common in cognitively unimpaired (CU) elderly >85 years. This study investigated amyloid distribution and evaluated three published in vivo amyloid-PET staging schemes from a cognitively unimpaired (CU) cohort aged 84.9 ± 4.3 years (n = 75). SUV-based principal component analysis (PCA) was applied to 18F-flutemetamol PET data to determine an unbiased regional covariance pattern of tracer uptake across grey matter regions. PET staging schemes were applied to the data and compared to the PCA output. Concentration of p-tau181 was measured in blood plasma. The PCA revealed three distinct components accounting for 91.2% of total SUV variance. PC1 driven by the large common variance of uptake in neocortical and striatal regions was significantly positively correlated with global SUVRs, APOE4 status and p-tau181 concentration. PC2 represented mainly non-specific uptake in typical amyloid-PET reference regions, and PC3 the occipital lobe. Application of the staging schemes demonstrated that the majority of the CU cohort (up to 93%) were classified as having pathological amount and distribution of Aβ. Good correspondence existed between binary (+/-) classification and later amyloid stages, however, substantial differences existed between schemes for low stages with 8-17% of individuals being unstageable, i.e., not following the sequential progression of Aβ deposition. In spite of the difference in staging outcomes there was broad spatial overlap between earlier stages and PC1, most prominently in default mode network regions. This study critically evaluated the utility of in vivo amyloid staging from a single PET scan in CU elderly and found that early amyloid stages could not be consistently classified. The majority of the cohort had pathological Aβ, thus, it remains an open topic what constitutes abnormal brain Aβ in the oldest-old and what is the best method to determine that.

Quantification of amyloid PET for future clinical use: a state-of-the-art review

Amyloid-β (Aβ) pathology is one of the earliest detectable brain changes in Alzheimer's disease (AD) pathogenesis. The overall load and spatial distribution of brain Aβ can be determined in vivo using positron emission tomography (PET), for which three fluorine-18 labelled radiotracers have been approved for clinical use. In clinical practice, trained readers will categorise scans as either Aβ positive or negative, based on visual inspection. Diagnostic decisions are often based on these reads and patient selection for clinical trials is increasingly guided by amyloid status. However, tracer deposition in the grey matter as a function of amyloid load is an inherently continuous process, which is not sufficiently appreciated through binary cut-offs alone. State-of-the-art methods for amyloid PET quantification can generate tracer-independent measures of Aβ burden. Recent research has shown the ability of these quantitative measures to highlight pathological changes at the earliest stages of the AD continuum and generate more sensitive thresholds, as well as improving diagnostic confidence around established binary cut-offs. With the recent FDA approval of aducanumab and more candidate drugs on the horizon, early identification of amyloid burden using quantitative measures is critical for enrolling appropriate subjects to help establish the optimal window for therapeutic intervention and secondary prevention. In addition, quantitative amyloid measurements are used for treatment response monitoring in clinical trials. In clinical settings, large multi-centre studies have shown that amyloid PET results change both diagnosis and patient management and that quantification can accurately predict rates of cognitive decline. Whether these changes in management reflect an improvement in clinical outcomes is yet to be determined and further validation work is required to establish the utility of quantification for supporting treatment endpoint decisions. In this state-of-the-art review, several tools and measures available for amyloid PET quantification are summarised and discussed. Use of these methods is growing both clinically and in the research domain. Concurrently, there is a duty of care to the wider dementia community to increase visibility and understanding of these methods.

Automated semi-quantitative amyloid PET analysis technique without MR images for Alzheimer's disease

Objective

Although beta-amyloid (Aβ) positron emission tomography (PET) images are interpreted visually as positive or negative, approximately 10% are judged as equivocal in Alzheimer’s disease. Therefore, we aimed to develop an automated semi-quantitative analysis technique using ¹⁸F-flutemetamol PET images without anatomical images.

Methods

Overall, 136 cases of patients administered ¹⁸F-flutemetamol were enrolled. Of 136 cases, five PET images each with the highest and lowest values of standardized uptake value ratio (SUVr) of cerebral cortex-to-pons were used to create positive and negative templates. Using these templates, PET images of the remaining 126 cases were standardized, and SUVr images were produced with the pons as a reference region. The mean of SUVr values in the volume of interest delineated on the cerebral cortex was compared to those in the CortexID Suite (GE Healthcare). Furthermore, centiloid (CL) values were calculated for the 126 cases using data from the Centiloid Project (http://www.gaain.org/centiloid-project) and both templates. ¹⁸F-flutemetamol-PET was interpreted visually as positive/negative based on Aβ deposition in the cortex. However, the criterion "equivocal" was added for cases with focal or mild Aβ accumulation that were difficult to categorize. Optimal cutoff values of SUVr and CL maximizing sensitivity and specificity for Aβ detection were determined by receiver operating characteristic (ROC) analysis using the visual evaluation as a standard of truth.

Results

SUVr calculated by our method and CortexID were highly correlated (R² = 0.9657). The 126 PET images comprised 84 negative and 42 positive cases of Aβ deposition by visual evaluation, of which 11 and 10 were classified as equivocal, respectively. ROC analyses determined the optimal cutoff values, sensitivity, and specificity for SUVr as 0.544, 89.3%, and 92.9%, respectively, and for CL as 12.400, 94.0%, and 92.9%, respectively. Both semi-quantitative analyses showed that 12 and 9 of the 21 equivocal cases were negative and positive, respectively, under the optimal cutoff values.

Conclusions

This semi-quantitative analysis technique using ¹⁸F-flutemetamol-PET calculated SUVr and CL automatically without anatomical images. Moreover, it objectively and homogeneously interpreted positive or negative Aβ burden in the brain as a supplemental tool for the visual reading of equivocal cases in routine clinical practice.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12149-022-01769-x.

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 (Aβ) 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 Aβ- 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 (SAβ_L) images. The association of SAβ_L with various cognitive and functional test scores was evaluated using Spearman analysis, as well as the differences in SAβ_L with cognitive test scores for 49 subjects with 2 time-point scans and sensitivity analysis.

RESULTS

SAβ_L derived from both SN and HRN showed higher association with memory-related cognitive test scores compared to SUVr. However, for longitudinal scans, only SAβ_L estimated from multimodal SN consistently performed better than SUVr for all memory-related cognitive test scores.

CONCLUSIONS

Our proposed method of quantifying Aβ 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.

Spatial normalization and quantification approaches of PET imaging for neurological disorders

Quantification approaches of positron emission tomography (PET) imaging provide user-independent evaluation of pathophysiological processes in living brains, which have been strongly recommended in clinical diagnosis of neurological disorders. Most PET quantification approaches depend on spatial normalization of PET images to brain template; however, the spatial normalization and quantification approaches have not been comprehensively reviewed. In this review, we introduced and compared PET template-based and magnetic resonance imaging (MRI)-aided spatial normalization approaches. Tracer-specific and age-specific PET brain templates were surveyed between 1999 and 2021 for ¹⁸F-FDG, ¹¹C-PIB, ¹⁸F-Florbetapir, ¹⁸F-THK5317, and etc., as well as adaptive PET template methods. Spatial normalization-based PET quantification approaches were reviewed, including region-of-interest (ROI)-based and voxel-wise quantitative methods. Spatial normalization-based ROI segmentation approaches were introduced, including manual delineation on template, atlas-based segmentation, and multi-atlas approach. Voxel-wise quantification approaches were reviewed, including voxel-wise statistics and principal component analysis. Certain concerns and representative examples of clinical applications were provided for both ROI-based and voxel-wise quantification approaches. At last, a recipe for PET spatial normalization and quantification approaches was concluded to improve diagnosis accuracy of neurological disorders in clinical practice.

Unified spatial normalization method of brain PET images using adaptive probabilistic brain atlas

PURPOSE

A unique advantage of the brain positron emission tomography (PET) imaging is the ability to image different biological processes with different radiotracers. However, the diversity of the brain PET image patterns also makes their spatial normalization challenging. Since structural MR images are not always available in the clinical practice, this study proposed a PET-only spatial normalization method based on adaptive probabilistic brain atlas.

METHODS

The proposed method (atlas-based method) consists of two parts, an adaptive probabilistic brain atlas generation algorithm, and a probabilistic framework for registering PET image to the generated atlas. To validate this method, the results of MRI-based method and template-based method (a widely used PET-only method) were treated as the gold standard and control, respectively. A total of 286 brain PET images, including seven radiotracers (FDG, PIB, FBB, AV-45, AV-1451, AV-133, [¹⁸F]altanserin) and four groups of subjects (Alzheimer disease, Parkinson disease, frontotemporal dementia, and healthy control), were spatially normalized using the three methods. The results were then quantitatively compared by using correlation analysis, meta region of interest (meta-ROI) standardized uptake value ratio (SUVR) analysis, and statistical parametric mapping (SPM) analysis.

RESULTS

The Pearson correlation coefficient between the images computed by atlas-based method and the gold standard was 0.908 ± 0.005. The relative error of meta-ROI SUVR computed by atlas-based method was 2.12 ± 0.18%. Compared with template-based method, atlas-based method was also more consistent with the gold standard in SPM analysis.

CONCLUSION

The proposed method provides a unified approach to spatially normalize brain PET images of different radiotracers accurately without MR images. A free MATLAB toolbox for this method has been provided.

Evaluation of semi-quantitative measures of 18F-flutemetamol PET for the clinical diagnosis of Alzheimer's disease

BACKGROUND

¹⁸F-flutemetamol positron emission tomography (PET) is used to assess cortical amyloid-β burden in patients with cognitive impairment to support a clinical diagnosis. Visual classification is the most widely used method in clinical practice although semi-quantification is beneficial to obtain an objective and continuous measure of the Aβ burden. The aims were: first to evaluate the correspondence between standardized uptake value ratios (SUVRs) from three different software, Centiloids and visual classification, second to estimate thresholds for supporting visual classification and last to assess differences in semi-quantitative measures between clinical diagnoses.

METHODS

This observational study included 195 patients with cognitive impairment who underwent ¹⁸F-flutemetamol PET. PET images were semi-quantified with SyngoVia, CortexID suite, and PMOD. Receiver operating characteristics curves were used to compare visual classification with composite SUVR normalized to pons (SUVRpons) and cerebellar cortex (SUVRcer), and Centiloids. We explored correlations and differences between semi-quantitative measures as well as differences in SUVR between two clinical diagnosis groups: Alzheimer's disease-group and non-Alzheimer's disease-group.

RESULTS

PET images from 191 patients were semi-quantified with SyngoVia and CortexID and 86 PET-magnetic resonance imaging pairs with PMOD. All receiver operating characteristics curves showed a high area under the curve (>0.98). Thresholds for a visually positive PET was for SUVRcer: 1.87 (SyngoVia) and 1.64 (CortexID) and for SUVRpons: 0.54 (SyngoVia) and 0.55 (CortexID). The threshold on the Centiloid scale was 39.6 Centiloids. All semi-quantitative measures showed a very high correlation between different software and normalization methods. Composite SUVRcer was significantly different between SyngoVia and PMOD, SyngoVia and CortexID but not between PMOD and CortexID. Composite SUVRpons were significantly different between all three software. There were significant differences in the mean rank of SUVRpons, SUVRcer, and Centiloid between Alzheimer's disease-group and non-Alzheimer's disease-group.

CONCLUSIONS

SUVR from different software performed equally well in discriminating visually positive and negative ¹⁸F-Flutemetamol PET images. Thresholds should be considered software-specific and cautiously be applied across software without preceding validation to categorize scans as positive or negative. SUVR and Centiloid may be used alongside a thorough clinical evaluation to support a clinical diagnosis.

rPOP: Robust PET-only processing of community acquired heterogeneous amyloid-PET data

The reference standard for amyloid-PET quantification requires structural MRI (sMRI) for preprocessing in both multi-site research studies and clinical trials. Here we describe rPOP (robust PET-Only Processing), a MATLAB-based MRI-free pipeline implementing non-linear warping and differential smoothing of amyloid-PET scans performed with any of the FDA-approved radiotracers (¹⁸F-florbetapir/FBP, ¹⁸F-florbetaben/FBB or ¹⁸F-flutemetamol/FLUTE). Each image undergoes spatial normalization based on weighted PET templates and data-driven differential smoothing, then allowing users to perform their quantification of choice. Prior to normalization, users can choose whether to automatically reset the origin of the image to the center of mass or proceed with the pipeline with the image as it is. We validate rPOP with n = 740 (514 FBP, 182 FBB, 44 FLUTE) amyloid-PET scans from the Imaging Dementia-Evidence for Amyloid Scanning - Brain Health Registry sub-study (IDEAS-BHR) and n = 1,518 scans from the Alzheimer's Disease Neuroimaging Initiative (n = 1,249 FBP, n = 269 FBB), including heterogeneous acquisition and reconstruction protocols. After running rPOP, a standard quantification to extract Standardized Uptake Value ratios and the respective Centiloids conversion was performed. rPOP-based amyloid status (using an independent pathology-based threshold of ≥24.4 Centiloid units) was compared with either local visual reads (IDEAS-BHR, n = 663 with complete valid data and reads available) or with amyloid status derived from an MRI-based PET processing pipeline (ADNI, thresholds of >20/>18 Centiloids for FBP/FBB). Finally, within the ADNI dataset, we tested the linear associations between rPOP- and MRI-based Centiloid values. rPOP achieved accurate warping for N = 2,233/2,258 (98.9%) in the first pass. Of the N = 25 warping failures, 24 were rescued with manual reorientation and origin reset prior to warping. We observed high concordance between rPOP-based amyloid status and both visual reads (IDEAS-BHR, Cohen's k = 0.72 [0.7-0.74], ∼86% concordance) or MRI-pipeline based amyloid status (ADNI, k = 0.88 [0.87-0.89], ∼94% concordance). rPOP- and MRI-pipeline based Centiloids were strongly linearly related (R²:0.95, p<0.001), with this association being significantly modulated by estimated PET resolution (β= -0.016, p<0.001). rPOP provides reliable MRI-free amyloid-PET warping and quantification, leveraging widely available software and only requiring an attenuation-corrected amyloid-PET image as input. The rPOP pipeline enables the comparison and merging of heterogeneous datasets and is publicly available at https://github.com/leoiacca/rPOP.

AMYQ: An index to standardize quantitative amyloid load across PET tracers

INTRODUCTION

Positron emission tomography (PET) amyloid quantification methods require magnetic resonance imaging (MRI) for spatial registration and a priori reference region to scale the images. Furthermore, different tracers have distinct thresholds for positivity. We propose the AMYQ index, a new measure of amyloid burden, to overcome these limitations.

METHODS

We selected 18F-amyloid scans from ADNI and Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL) with the corresponding T1-MRI. A subset also had neuropathological data. PET images were normalized, and the AMYQ was calculated based on an adaptive template. We compared AMYQ with the Centiloid scale on clinical and neuropathological diagnostic performance.

RESULTS

AMYQ was related with amyloid neuropathological burden and had excellent diagnostic performance to discriminate controls from patients with Alzheimer's disease (AD) (area under the curve [AUC] = 0.86). AMYQ had a high agreement with the Centiloid scale (intraclass correlation coefficient [ICC] = 0.88) and AUC between 0.94 and 0.99 to discriminate PET positivity when using different Centiloid cutoffs.

DISCUSSION

AMYQ is a new MRI-independent index for standardizing and quantifying amyloid load across tracers.

Selecting software pipelines for change in flortaucipir SUVR: Balancing repeatability and group separation

Since tau PET tracers were introduced, investigators have quantified them using a wide variety of automated methods. As longitudinal cohort studies acquire second and third time points of serial within-person tau PET data, determining the best pipeline to measure change has become crucial. We compared a total of 415 different quantification methods (each a combination of multiple options) according to their effects on a) differences in annual SUVR change between clinical groups, and b) longitudinal measurement repeatability as measured by the error term from a linear mixed-effects model. Our comparisons used MRI and Flortaucipir scans of 97 Mayo Clinic study participants who clinically either: a) were cognitively unimpaired, or b) had cognitive impairments that were consistent with Alzheimer's disease pathology. Tested methods included cross-sectional and longitudinal variants of two overarching pipelines (FreeSurfer 6.0, and an in-house pipeline based on SPM12), three choices of target region (entorhinal, inferior temporal, and a temporal lobe meta-ROI), five types of partial volume correction (PVC) (none, two-compartment, three-compartment, geometric transfer matrix (GTM), and a tau-specific GTM variant), seven choices of reference region (cerebellar crus, cerebellar gray matter, whole cerebellum, pons, supratentorial white matter, eroded supratentorial WM, and a composite of eroded supratentorial WM, pons, and whole cerebellum), two choices of region masking (GM or GM and WM), and two choices of statistic (voxel-wise mean vs. median). Our strongest findings were: 1) larger temporal-lobe target regions greatly outperformed entorhinal cortex (median sample size estimates based on a hypothetical clinical trial were 520-526 vs. 1740); 2) longitudinal processing pipelines outperformed cross-sectional pipelines (median sample size estimates were 483 vs. 572); and 3) reference regions including supratentorial WM outperformed traditional cerebellar and pontine options (median sample size estimates were 370 vs. 559). Altogether, our results favored longitudinally SUVR methods and a temporal-lobe meta-ROI that includes adjacent (juxtacortical) WM, a composite reference region (eroded supratentorial WM + pons + whole cerebellum), 2-class voxel-based PVC, and median statistics.

Non-negative matrix factorisation improves Centiloid robustness in longitudinal studies

BACKGROUND

Centiloid was introduced to harmonise β-Amyloid (Aβ) PET quantification across different tracers, scanners and analysis techniques. Unfortunately, Centiloid still suffers from some quantification disparities in longitudinal analysis when normalising data from different tracers or scanners. In this work, we aim to reduce this variability using a different analysis technique applied to the existing calibration data.

METHOD

All PET images from the Centiloid calibration dataset, along with 3762 PET images from the AIBL study were analysed using the recommended SPM pipeline. The PET images were SUVR normalised using the whole cerebellum. All SUVR normalised PiB images from the calibration dataset were decomposed using non-negative matrix factorisation (NMF). The NMF coefficients related to the first component were strongly correlated with global SUVR and were subsequently used as a surrogate for Aβ retention. For each tracer of the calibration dataset, the components of the NMF were computed in a way such that the coefficients of the first component would match those of the corresponding PiB. Given the strong correlations between the SUVR and the NMF coefficients on the calibration dataset, all PET images from AIBL were subsequently decomposed using the computed NMF, and their coefficients transformed into Centiloids.

RESULTS

Using the AIBL data, the correlation between the standard Centiloid and the novel NMF-based Centiloid was high in each tracer. The NMF-based Centiloids showed a reduction of outliers, and improved longitudinal consistency. Furthermore, it removed the effects of switching tracers from the longitudinal variance of the Centiloid measure, when assessed using a linear mixed effects model.

CONCLUSION

We here propose a novel image driven method to perform the Centiloid quantification. The methods is highly correlated with standard Centiloids while improving the longitudinal reliability when switching tracers. Implementation of this method across multiple studies may lend to more robust and comparable data for future research.

Derivation and utility of an Aβ-PET pathology accumulation index to estimate Aβ load

OBJECTIVE

To evaluate a novel β-amyloid (Aβ)-PET-based quantitative measure (Aβ accumulation index [Aβ index]), including the assessment of its ability to discriminate between participants based on Aβ status using visual read, CSF Aβ₄₂/Aβ₄₀, and post-mortem neuritic plaque burden as standards of truth.

METHODS

One thousand one hundred twenty-one participants (with and without cognitive impairment) were scanned with Aβ-PET: Swedish BioFINDER, n = 392, [¹⁸F]flutemetamol; Alzheimer's Disease Neuroimaging Initiative (ADNI), n = 692, [¹⁸F]florbetapir; and a phase 3 end-of-life study, n = 100, [¹⁸F]flutemetamol. The relationships between Aβ index and standardized uptake values ratios (SUVR) from Aβ-PET were assessed. The diagnostic performances of Aβ index and SUVR were compared with visual reads, CSF Aβ₄₂/Aβ₄₀, and Aβ histopathology used as reference standards.

RESULTS

Strong associations were observed between Aβ index and SUVR (R ²: BioFINDER 0.951, ADNI 0.943, end-of-life, 0.916). Both measures performed equally well in differentiating Aβ-positive from Aβ-negative participants, with areas under the curve (AUCs) of 0.979 to 0.991 to detect abnormal visual reads, AUCs of 0.961 to 0.966 to detect abnormal CSF Aβ₄₂/Aβ₄₀, and AUCs of 0.820 to 0.823 to detect abnormal Aβ histopathology. Both measures also showed a similar distribution across postmortem-based Aβ phases (based on anti-Aβ 4G8 antibodies). Compared to models using visual read alone, the addition of the Aβ index resulted in a significant increase in AUC and a decrease in Akaike information criterion to detect abnormal Aβ histopathology.

CONCLUSION

The proposed Aβ index showed a tight association to SUVR and carries an advantage over the latter in that it does not require the definition of regions of interest or the use of MRI. Aβ index may thus prove simpler to implement in clinical settings and may also facilitate the comparison of findings using different Aβ-PET tracers.

CLASSIFICATION OF EVIDENCE

This study provides Class III evidence that the Aβ accumulation index accurately differentiates Aβ-positive from Aβ-negative participants compared to Aβ-PET visual reads, CSF Aβ₄₂/Aβ₄₀, and Aβ histopathology.

PET amyloid in normal aging: direct comparison of visual and automatic processing methods

Assessment of amyloid deposits is a critical step for the identification of Alzheimer disease (AD) signature in asymptomatic elders. Whether the different amyloid processing methods impacts on the quality of clinico-radiological correlations is still unclear. We directly compared in 155 elderly controls with extensive neuropsychological testing at baseline and 4.5 years follow-up three approaches: (i) operator-dependent standard visual reading, (ii) operator-independent automatic SUVR with four different reference regions, and (iii) novel operator and region of reference-independent automatic Aβ-index. The coefficient of variance was used to examine inter-individual variability for each processing method. Using visually-established amyloid positivity as the gold standard, the area under the receiver operating characteristic curve (ROC) was computed. Linear regression models were used to assess the association between changes in continuous cognitive score and amyloid uptake values. In SUVR analyses, the coefficient of variance varied from 1.718 to 1.762 according to the area of reference and was of − 3.045 for the Aβ-index method. Compared to the visual rating, Aβ-index method showed the largest area under the ROC curve [0.9568 (95% CI 0.9252, 0.98833)]. The best cut-off score was of − 0.3359 with sensitivity and specificity values of 0.97 and 0.83, respectively. Only the Aß-index was related to more severe decrement of cognitive performances [regression coefficient: 9.103 (95% CI 1.148, 17.058)]. The Aβ-index is considered as preferred option in asymptomatic elders, since it is operator-independent, avoids the selection of reference area, is closer to established visual scoring and correlates with the evolution of cognitive performances.

Validation of a spatial normalization method using a principal component derived adaptive template for [18F]florbetaben PET

Quantification may help in the context of amyloid-β positron emission tomography (PET). Quantification typically requires that PET images be spatially normalized, a process that can be subject to bias. We herein aimed to test whether a principal component approach (PCA) previously applied to [¹⁸F]flutemetamol PET extends to [¹⁸F]florbetaben. PCA was applied to [¹⁸F]florbetaben PET data for 132 subjects (70 Alzheimer dementia, 62 controls) and used to generate an adaptive synthetic template. Spatial normalization of [¹⁸F]florbetaben data using this approach was compared to that achieved using SPM12's magnetic resonance (MR) imaging driven algorithm. The two registration methods showed high agreement and minimal difference in standardized uptake value ratios (SUVR) (R² = 0.997 using cerebellum as reference region and 0.996 using the pons). Our method allows for robust and accurate registration of [¹⁸F]florbetaben images to template space, without the need for an MR image, and may prove of value in clinical and research settings.

Development of Convolutional Neural Networks to identify bone metastasis for prostate cancer patients in bone scintigraphy

OBJECTIVE

The main aim of this work is to build a robust Convolutional Neural Network (CNN) algorithm that efficiently and quickly classifies bone scintigraphy images, by determining the presence or absence of prostate cancer metastasis.

METHODS

CNN, widely applied in medical image classification, was used for bone scintigraphy image classification. The retrospective study included 778 sequential male patients who underwent whole-body bone scans. A nuclear medicine physician classified all the cases into 3 categories: (1) normal, (2) malignant, and (3) degenerative, which were used as the gold standard.

RESULTS

An efficient CNN architecture was built, based on CNN exploration performance, achieving high prediction accuracy. The results showed that the method is sufficiently precise when it comes to differentiating a bone metastasis from other either degenerative changes or normal tissue (overall classification accuracy = 91.42% ± 1.64%). To strengthen the outcomes of this study the authors further compared the best performing CNN method to other popular CNN architectures for medical imaging, like ResNet50, VGG16 and GoogleNet, as reported in the literature.

CONCLUSIONS

The prediction results reveal the efficacy of the proposed CNN-based approach and its ability for an easier and more precise interpretation of whole-body images in bone metastasis diagnosis for prostate cancer patients in nuclear medicine. This leads to marked effects on the diagnostic accuracy and decision-making regarding the treatment to be applied.

Impact of Tracer Retention Levels on Visual Analysis of Cerebral [18F]- Florbetaben Pet Images

BACKGROUND

To compare visual and semi-quantitative analysis of brain [¹⁸F]Florbetaben PET images in Mild Cognitive Impairment (MCI) patients and relate this finding to the degree of ß-amyloid burden.

METHODS

A sample of 71 amnestic MCI patients (age 74 ± 7.3 years, Mini Mental State Examination 24.2 ± 5.3) underwent cerebral [¹⁸F]Florbetaben PET/CT. Images were visually scored as positive or negative independently by three certified readers blinded to clinical and neuropsychological assessment. Amyloid positivity was also assessed by semiquantitative approach by means of a previously published threshold (SUVr ≥ 1.3). Fleiss kappa coefficient was used to compare visual analysis (after consensus among readers) and semi-quantitative analysis. Statistical significance was taken at P<0.05.

RESULTS

After the consensus reading, 43/71 (60.6%) patients were considered positive. Cases that were interpreted as visually positive had higher SUVr than visually negative patients (1.48 ± 0.19 vs 1.11 ± 0.09) (P<0.05). Agreement between visual analysis and semi-quantitative analysis was excellent (k=0.86, P<0.05). Disagreement occurred in 7/71 patients (9.9%) (6 false positives and 1 false negative). Agreement between the two analyses was 90.0% (18/20) for SUVr < 1.1, 83% (24/29) for SUVr between 1.1 and 1.5, and 100% (22/22) for SUVr > 1.5 indicating lowest agreement for the group with intermediate amyloid burden.

CONCLUSION

Inter-rater agreement of visual analysis of amyloid PET images is high. Agreement between visual analysis and SUVr semi-quantitative analysis decreases in the range of 1.1<SUVr <=1.5, where the clinical scenario is more challenging.

Less agreeable, better preserved? A PET amyloid and MRI study in a community-based cohort

The relationship between personality profiles and brain integrity in old age is still a matter of debate. We examined the association between Big Five factor and facet scores and MRI brain volume changes on a 54-month follow-up in 65 elderly controls with 3 neurocognitive assessments (baseline, 18 months, and 54 months), structural brain MRI (baseline and 54 months), brain amyloid PET during follow-up, and APOE genotyping. Personality was assessed with the Neuroticism Extraversion Openness Personality Inventory-Revised. Regression models were used to identify predictors of volume loss including time, age, sex, personality, amyloid load, presence of APOE ε4 allele, and cognitive evolution. Lower agreeableness factor scores (and 4 of its facets) were associated with lower volume loss in the hippocampus, entorhinal cortex, amygdala, mesial temporal lobe, and precuneus bilaterally. Higher openness factor scores (and 2 of its facets) were also associated with lower volume loss in the left hippocampus. Our findings persisted when adjusting for confounders in multivariable models. These data suggest that the combination of low agreeableness and high openness is an independent predictor of better preservation of brain volume in areas vulnerable to neurodegeneration.

Personality Factors' Impact on the Structural Integrity of Mentalizing Network in Old Age: A Combined PET-MRI Study

The mentalizing network (MN) treats social interactions based on our understanding of other people's intentions and includes the medial prefrontal cortex (mPFC), temporoparietal junction (TPJ), posterior cingulate cortex (PCC), precuneus (PC), and amygdala. Not all elders are equally affected by the aging-related decrease of mentalizing abilities. Personality has recently emerged as a strong determinant of functional connectivity in MN areas. However, its impact on volumetric changes across the MN in brain aging is still unknown. To address this issue, we explored the determinants of volume decrease in MN components including amyloid burden, personality, and APOE genotyping in a previously established cohort of 130 healthy elders with a mean follow-up of 54 months. Personality was assessed with the Neuroticism Extraversion Openness Personality Inventory-Revised. Regression models corrected for multiple comparisons were used to identify predictors of volume loss including time, age, sex, personality, amyloid load, presence of APOE epsilon 4 allele, and cognitive evolution. In cases with higher Agreeableness scores, there were lower volume losses in PCC, PC, and amygdala bilaterally. This was also the case for the right mPFC in elders displaying lower Agreeableness and Conscientiousness. In multiple regression models, the effect of Agreeableness was still observed in left PC and right amygdala and that of Conscientiousness was still observed in right mPFC volume loss (26.3% of variability, significant age and sex). Several Agreeableness (Modesty) and Conscientiousness (order, dutifulness, achievement striving, and self-discipline) facets were positively related to increased volume loss in cortical components of the MN. In conclusion, these data challenge the beneficial role of higher levels of Agreeableness and Conscientiousness in old age, showing that they are associated with an increased rate of volume loss within the MN.

Amyloid Load, Hippocampal Volume Loss, and Diffusion Tensor Imaging Changes in Early Phases of Brain Aging

Background and Purpose

Amyloid imaging, gray matter (GM) morphometry and diffusion tensor imaging (DTI) have all been used as predictive biomarkers in dementia. Our objective was to define the imaging profile of healthy elderly controls as a function of their cognitive trajectories and explore whether amyloid burden and white matter (WM) microstructure changes are associated with subtle decrement of neuropsychological performances in old age.

Materials and Methods

We performed a 4.5-year longitudinal study in 133 elderly individuals who underwent cognitive testing at inclusion and follow-up, amyloid PET, MRI including DTI sequences at inclusion, and APOE epsilon 4 genotyping. All cases were assessed using a continuous cognitive score (CCS) taking into account the global evolution of neuropsychological performances. Data processing included region of interest analysis of amyloid PET analysis, GM densities and tract-based spatial statistics (TBSS)-DTI. Regression models were built to explore the association between the CCS and imaging parameters controlling for significant demographic and clinical covariates.

Results

Amyloid uptake was not related to the cognitive outcome. In contrast, GM densities in bilateral hippocampus were associated with worst CCS at follow-up. In addition, radial and axial diffusivities in left hippocampus were negatively associated with CCS. Amyloid load was associated with decreased VBM and increased radial and axial diffusivity in the same area. These associations persisted when adjusting for gender and APOE4 genotype. Importantly, they were absent in amygdala and neocortical areas studied.

Conclusion

The progressive decrement of neuropsychological performances in normal aging is associated with volume loss and WM microstructure changes in hippocampus long before the emergence of clinically overt symptoms. Higher amyloid load in hippocampus is compatible with cognitive preservation in cases with better preservation of GM densities and WM microstructure in this area.

Voxel-based statistical analysis and quantification of amyloid PET in the Japanese Alzheimer's disease neuroimaging initiative (J-ADNI) multi-center study

Background

Amyloid PET plays a vital role in detecting the accumulation of in vivo amyloid-β (Aβ). The quantification of Aβ accumulation has been widely performed using the region of interest (ROI)-based mean cortical standardized uptake value ratio (mcSUVR). However, voxel-based statistical analysis has not been well studied. The purpose of this study was to examine the feasibility of analyzing amyloid PET scans by voxel-based statistical analysis. The results were then compared to those with the ROI-based mcSUVR. In total, 166 subjects who underwent ¹¹C-PiB PET in the J-ADNI multi-center study were analyzed. Additionally, 18 Aβ-negative images were collected from other studies to form a normal database. The PET images were spatially normalized to the standard space using an adaptive template method without MRI. The mcSUVR was measured using a pre-defined ROI. Voxel-wise Z-scores within the ROI were calculated using the normal database, after which Z-score maps were generated. A receiver operating characteristic (ROC) analysis was performed to evaluate whether Z-sum (sum of the Z-score) and mcSUVR could be used to classify the scans into positive and negative using the central visual read as the reference standard. PET scans that were equivocal were regarded as positive.

Results

Sensitivity and specificity were respectively 90.8% and 100% by Z-sum and 91.8% and 98.5% by mcSUVR. Most of the equivocal scans were subsequently classified by both Z-sum and mcSUVR as false negatives. Z-score maps correctly delineated abnormal Aβ accumulation over the same regions as the visual read.

Conclusions

We examined the usefulness of voxel-based statistical analysis for amyloid PET. This method provides objective Z-score maps and Z-sum values, which were observed to be helpful as an adjunct to visual interpretation especially for cases with mild or limited Aβ accumulation. This approach could improve the Aβ detection sensitivity, reduce inter-reader variability, and allow for detailed monitoring of Aβ deposition.

Trial registration

The number of the J-ADNI study is UMIN000001374

A Fully Automatic Technique for Precise Localization and Quantification of Amyloid-β PET Scans

Spatial heterogeneity in the accumulation of amyloid-β plaques throughout the brain during asymptomatic as well as clinical stages of Alzheimer disease calls for precise localization and quantification of this protein using PET imaging. To address this need, we have developed and evaluated a technique that quantifies the extent of amyloid-β pathology on a millimeter-by-millimeter scale in the brain with unprecedented precision using data from PET scans. Methods: An intermodal and intrasubject registration with normalized mutual information as the cost function was used to transform all FreeSurfer neuroanatomic labels into PET image space, which were subsequently used to compute regional SUV ratio (SUVR). We have evaluated our technique using postmortem histopathologic staining data from 52 older participants as the standard-of-truth measurement. Results: Our method resulted in consistently and significantly higher SUVRs in comparison to the conventional method in almost all regions of interest. A 2-way ANOVA revealed a significant main effect of method as well as a significant interaction effect of method on the relationship between computed SUVR and histopathologic staining score. Conclusion: These findings suggest that processing the amyloid-β PET data in subjects' native space can improve the accuracy of the computed SUVRs, as they are more closely associated with the histopathologic staining data than are the results of the conventional approach.

Clinical impact of [18F]flutemetamol PET among memory clinic patients with an unclear diagnosis

Purpose

To investigate the impact of amyloid PET with [¹⁸F]flutemetamol on diagnosis and treatment management in a cohort of patients attending a tertiary memory clinic in whom, despite extensive cognitive assessment including neuropsychological testing, structural imaging, CSF biomarker analysis and in some cases [¹⁸F]FDG PET, the diagnosis remained unclear.

Methods

The study population consisted of 207 patients with a clinical diagnosis prior to [¹⁸F]flutemetamol PET including mild cognitive impairment (MCI; n = 131), Alzheimer’s disease (AD; n = 41), non-AD (n = 10), dementia not otherwise specified (dementia NOS; n = 20) and subjective cognitive decline (SCD; n = 5).

Results

Amyloid positivity was found in 53% of MCI, 68% of AD, 20% of non-AD, 20% of dementia NOS, and 60% of SCD patients. [¹⁸F]Flutemetamol PET led, overall, to a change in diagnosis in 92 of the 207 patients (44%). A high percentage of patients with a change in diagnosis was observed in the MCI group (n = 67, 51%) and in the dementia NOS group (n = 11; 55%), followed by the non-AD and AD (30% and 20%, respectively). A significant increase in cholinesterase inhibitor treatment was observed after [¹⁸F]flutemetamol PET (+218%, 34 patients before and 108 patients after).

Conclusion

The present study lends support to the clinical value of amyloid PET in patients with an uncertain diagnosis in the tertiary memory clinic setting.

Biomarkers for the diagnosis of Alzheimer's disease, dementia Lewy body, frontotemporal dementia and vascular dementia

Background

Dementia is a chronic brain disorder classified by four distinct diseases that impact cognition and mental degeneration. Each subgroup exhibits similar brain deficiencies and mutations. This review will focus on four dementia subgroups: Alzheimer’s disease, vascular dementia, frontotemporal dementia and dementia Lewy body.

Aim

The aim of this systematic review is to create a concise overview of unique similarities within dementia used to locate and identify new biomarker methods in diagnosing dementia.

Methods

123 300 articles published after 2010 were identified from PubMed, JSTOR, WorldCat Online Computer Library and PALNI (Private Academic Library Network of Indiana) using the following search items (in title or abstract): ‘Neurodegenerative Diseases’ OR ‘Biomarkers’ OR ‘Alzheimer’s Disease’ OR ‘Frontal Temporal Lobe Dementia’ OR ‘Vascular Dementia’ OR ‘Dementia Lewy Body’ OR ‘Cerebral Spinal Fluid’ OR ‘Mental Cognitive Impairment’. 47 studies were included in the qualitative synthesis.

Results

Evidence suggested neuroimaging with amyloid positron emission tomography (PET) scanning and newly found PET tracers to be more effective in diagnosing Alzheimer’s and amnesiac mental cognitive impairment than carbon-11 Pittsburgh compound-B radioisotope tracer. Newly created methods to make PET scans more accurate and practical in clinical settings signify a major shift in diagnosing dementia and neurodegenerative diseases.

Conclusion

Vast improvements in neuroimaging techniques have led to newly discovered biomarkers and diagnostics. Neuroimaging with amyloid PET scanning surpasses what had been considered the dominant method of neuroimaging and MRI. Newly created methods to make PET scans more accurate and practical in clinical settings signify a major shift in diagnosing dementia pathology. Continued research and studies must be conducted to improve current findings and streamline methods to further subcategorise neurodegenerative disorders and diagnosis.