Caselli, Richard J.

AbstractBackgroundCross‐modality translation between MRI and PET presents significant challenges due to the distinct mechanisms underlying those modalities. Currently, the leading method for this translation is CycleGAN. Previous studies have proved a strong correlation between BBBMs and brain amyloid status measured by PET. However, the impact of blood‐based biomarkers (BBBMs) on PET image synthesis has not yet been thoroughly evaluated. In this study, we propose Plasma CycleGAN, a generative model based on CycleGAN to synthesize PET images from MRI, while also incorporating BBBMs and other clinical covariates. We show that Plasma CycleGAN outperforms the state‐of‐the‐art CycleGAN for MRI‐to‐PET translation. Our method is the first to integrate BBBMs in a conditional cross modality translation from MRI to PET.MethodOur model was built on CycleGAN architecture, while the backbone was improved by integrating BBBMs as additional conditions in the input domain. To be specific, we introduce a masked perturbation in the MRI image, whose voxel‐level magnitudes are learnable parameters in the neural network. The dataset we used to evaluate our model is downloaded from the ADNI database. The cohort contains 160 subjects with matching BBBM, T1‐weighted MRI images, amyloid PET, and clinical features, including age, sex, weights, etc. The synthesized PET images are compared with the ground truth PET images for performance evaluation using traditional metrics like structural similarity index (SSIM), peak‐signal‐to‐noise ratio (PSNR) and mean square error (MSE). In addition, the Pearson correlation coefficients (PCCs) of the masked SUVR ratios were calculated.ResultWe evaluated the baseline CycleGAN model using MRI input, as well as combined inputs of MRI+BBBM and other clinical features. Our model achieved an SSIM of 0.72±0.16 and PSNR of 22.8±2.3 in PET image synthesis task using MRI+ Aβ42/40, both yielding the best results among all input combinations. MRI+sex achieved the best result using MSE measurement. PCCs of all experimental results ranged from 0.44 to 0.5.ConclusionWe introduce Plasma CycleGAN, the first generative model to integrate BBBMs and clinical features in PET image synthesis from MRI images. In PET image synthesis task, our proposed integrated model outperforms the baseline CycleGAN model.

Background: Alzheimer’s disease (AD) is the most common type of age-related dementia, affecting 6.2 million people aged 65 or older according to CDC data. It is commonly agreed that discovering an effective AD diagnosis biomarker could have enormous public health benefits, potentially preventing or delaying up to 40% of dementia cases. Tau neurofibrillary tangles are the primary driver of downstream neurodegeneration and subsequent cognitive impairment in AD, resulting in structural deformations such as hippocampal atrophy that can be observed in magnetic resonance imaging (MRI) scans. Objective: To build a surface-based model to 1) detect differences between APOE subgroups in patterns of tau deposition and hippocampal atrophy, and 2) use the extracted surface-based features to predict cognitive decline. Methods: Using data obtained from different institutions, we develop a surface-based federated Chow test model to study the synergistic effects of APOE, a previously reported significant risk factor of AD, and tau on hippocampal surface morphometry. Results: We illustrate that the APOE-specific morphometry features correlate with AD progression and better predict future AD conversion than other MRI biomarkers. For example, a strong association between atrophy and abnormal tau was identified in hippocampal subregion cornu ammonis 1 (CA1 subfield) and subiculum in e4 homozygote cohort. Conclusion: Our model allows for identifying MRI biomarkers for AD and cognitive decline prediction and may uncover a corner of the neural mechanism of the influence of APOE and tau deposition on hippocampal morphology.

Background: Amyloid-β (Aβ) plaques and tau protein tangles in the brain are the defining ‘A’ and ‘T’ hallmarks of Alzheimer’s disease (AD), and together with structural atrophy detectable on brain magnetic resonance imaging (MRI) scans as one of the neurodegenerative (‘N’) biomarkers comprise the “ATN framework” of AD. Current methods to detect Aβ/tau pathology include cerebrospinal fluid (invasive), positron emission tomography (PET; costly and not widely available), and blood-based biomarkers (promising but mainly still in development). Objective: To develop a non-invasive and widely available structural MRI-based framework to quantitatively predict the amyloid and tau measurements. Methods: With MRI-based hippocampal multivariate morphometry statistics (MMS) features, we apply our Patch Analysis-based Surface Correntropy-induced Sparse coding and max-pooling (PASCS-MP) method combined with the ridge regression model to individual amyloid/tau measure prediction. Results: We evaluate our framework on amyloid PET/MRI and tau PET/MRI datasets from the Alzheimer’s Disease Neuroimaging Initiative. Each subject has one pair consisting of a PET image and MRI scan, collected at about the same time. Experimental results suggest that amyloid/tau measurements predicted with our PASCP-MP representations are closer to the real values than the measures derived from other approaches, such as hippocampal surface area, volume, and shape morphometry features based on spherical harmonics. Conclusion: The MMS-based PASCP-MP is an efficient tool that can bridge hippocampal atrophy with amyloid and tau pathology and thus help assess disease burden, progression, and treatment effects.

BackgroundAdvanced diffusion‐based MRI biomarkers may provide insight into microstructural and perfusion changes associated with neurodegeneration and cognitive decline.PurposeTo assess longitudinal microstructural and perfusion changes using apparent diffusion coefficient (ADC) and intravoxel incoherent motion diffusion‐weighted imaging (IVIM‐DWI) parameters in cognitively impaired (CI) and healthy control (HC) groups.Study TypeProspective/longitudinal.PopulationTwelve CI patients (75% female) and 13 HC subjects (69% female).Field Strength/Sequence3 T; Spin‐Echo‐IVIM‐DWI.AssessmentTwo MRI scans were performed with a 12‐month interval. ADC and IVIM‐DWI metrics (diffusion coefficient [D] and perfusion fraction [f]) were generated from monoexponential and biexponential fits, respectively. Additionally, voxel‐based correlations were evaluated between change in Montreal Cognitive Assessment (ΔMoCA) and baseline imaging parameters.Statistical TestsAnalysis of covariance with sex and age as covariates was performed for main effects of group and time (false discovery rate [FDR] corrected) with post hoc comparisons using Bonferroni correction. Partial‐η2 and Hedges' g were used for effect‐size analysis. Spearman's correlations (FDR corrected) were used for the relationship between ΔMoCA score and imaging. P < 0.05 was considered statistically significant.ResultsSignificant differences were found for the main effects of group (HC vs. CI) and time. For group effects, higher ADC, IVIM‐D, and IVIM‐f were observed in the CI group compared to HC (ADC: 1.23 ± 0.08.10−3 vs. 1.09 ± 0.07.10−3 mm2/sec; IVIM‐D: 0.82 ± 0.01.10−3 vs. 0.73 ± 0.01.10−3 mm2/sec; and IVIM‐f: 0.317 ± 0.008 vs. 0.253 ± 0.009). Significantly higher ADC, IVIM‐D, and IVIM‐f values were observed in the CI group after 12 months (ADC: 1.45 ± 0.05.10−3 vs. 1.50 ± 0.07.10−3 mm2/sec; IVIM‐D: 0.87 ± 0.01.10−3 vs. 0.94 ± 0.02.10−3 mm2/sec; and IVIM‐f: 0.303 ± 0.007 vs. 0.332 ± 0.008), but not in the HC group at large effect size. ADC, IVIM‐D, and IVIM‐f negatively correlated with ΔMoCA score (ρ = −0.49, −0.51, and −0.50, respectively).Data ConclusionThese findings demonstrate that longitudinal differences between CI and HC cohorts can be measured using IVIM‐based metrics.Level of Evidence2Technical Efficacy Stage2

BackgroundAlzheimer's disease (AD) is a progressive neurodegenerative disease that affects aging populations. Current MRI techniques are often limited in their sensitivity to underlying neuropathological changes.PurposeTo characterize differences in voxel‐based morphometry (VBM), apparent diffusion coefficient (ADC), and intravoxel incoherent motion diffusion‐weighted imaging (IVIM‐DWI) metrics in aging populations. Additionally, to investigate the connection between cognitive assessments and neuroimaging metrics.Study TypeProspective/cross‐sectional.PopulationIn all, 49 subjects, including 13 with AD dementia, 12 with mild cognitive impairment (MCI), and 24 healthy controls (HC).Field Strength/Sequence3T/magnetization‐prepared rapid acquisition gradient echo (MP‐RAGE) and IVIM‐DWIAssessmentAll participants completed a cognitive screening battery prior to MRI. IVIM‐DWI maps (pure diffusion coefficient [D], pseudodiffusion coefficient [D*], and perfusion fraction [f]) were generated from a biexponential fit of diffusion MRI data. VBM was performed on the standard T1‐weighted MP‐RAGE structural images. Group‐wise templates were used to compare across groups.Statistical TestsAnalysis of covariance (ANCOVA) with gender and age as covariates (familywise error [FWE] corrected, post‐hoc comparisons using Bonferroni correction) for group comparisons. Partial‐η2 and Hedges' g were used for effect‐size analysis. Spearman's correlations (false discovery rate [FDR]‐corrected) for the relationship between cognitive scores and imaging.ResultsClusters of significant group‐wise differences were found mainly in the temporal lobe, hippocampus, and amygdala using all VBM and IVIM methods (P < 0.05 FWE). While VBM showed significant changes between MCI and AD groups and between HC and AD groups, no significant clusters were observed between HC and MCI using VBM. ADC and IVIM‐D demonstrated significant changes, at P < 0.05 FWE, between HC and MCI, notably in the amygdala and hippocampus. Several voxel‐based correlations were observed between neuroimaging metrics and cognitive tests within the cognitively impaired groups (P < 0.05 FDR).Data ConclusionThese findings suggest that IVIM‐DWI metrics may be earlier biomarkers for AD‐related changes than VBM. The use of these techniques may provide novel insight into subvoxel neurodegenerative processes.Level of Evidence2Technical Efficacy Stage2 J. MAGN. RESON. IMAGING 2020;52:1811–1826.