Citations

Abstract Given the prevalence of dementia and the development of pathology-specific disease-modifying therapies, high-value biomarker strategies to inform medical decision-making are critical. In vivo tau-PET is an ideal target as a biomarker for Alzheimer’s disease diagnosis and treatment outcome measure. However, tau-PET is not currently widely accessible to patients compared to other neuroimaging methods. In this study, we present a convolutional neural network (CNN) model that imputes tau-PET images from more widely available cross-modality imaging inputs. Participants (n = 1192) with brain T1-weighted MRI (T1w), fluorodeoxyglucose (FDG)-PET, amyloid-PET and tau-PET were included. We found that a CNN model can impute tau-PET images with high accuracy, the highest being for the FDG-based model followed by amyloid-PET and T1w. In testing implications of artificial intelligence-imputed tau-PET, only the FDG-based model showed a significant improvement of performance in classifying tau positivity and diagnostic groups compared to the original input data, suggesting that application of the model could enhance the utility of the metabolic images. The interpretability experiment revealed that the FDG- and T1w-based models utilized the non-local input from physically remote regions of interest to estimate the tau-PET, but this was not the case for the Pittsburgh compound B-based model. This implies that the model can learn the distinct biological relationship between FDG-PET, T1w and tau-PET from the relationship between amyloid-PET and tau-PET. Our study suggests that extending neuroimaging’s use with artificial intelligence to predict protein specific pathologies has great potential to inform emerging care models.

AbstractPurposeDental amalgam contains mercury and is commonly used in dental restorations. The impact of MRI on mercury excretion from dental amalgam is not well understood across clinical field strengths, especially 7T. We investigated the effects of MRI exposure on mercury excretion using fresh, lab‐created dental amalgam restorations and in extracted teeth with old, pre‐existing restorations.MethodsDonated, unfilled human teeth (n = 120) were restored with amalgam before being stored in saline, artificial saliva, or a dry box prior to MRI scanning. The teeth were placed in individual tubes of fresh artificial saliva and scanned at 1.5T, 3T, or 7T or left unscanned as controls. Mercury concentrations were measured 24–30 h later. Donated teeth with pre‐existing restorations (n = 40) were stored in artificial saliva, scanned at 7T or left unscanned as controls, and mercury concentration tested.ResultsFor teeth extracted and restored in a laboratory, no significant difference was found (F = 2.42, P = 0.072) between mean mercury concentrations of unscanned teeth (13.72 μg/L) and teeth scanned at 1.5T (10.88 μg/L), 3T (12.65 μg/L), or 7T (8.88 μg/L). For teeth extracted with previously placed restorations, no significant difference (P = 0.288) was found between unscanned controls (4.28 μg/L) and teeth scanned at 7T (6.63 μg/L).ConclusionMRI of dental amalgam does not significantly increase mercury excretion at 1.5T, 3T, or 7T compared to unscanned teeth. This holds true for controlled laboratory restorations as well as for those placed and lived with prior to extraction and scanning, demonstrating no added risk to the clinical patient or research subject.

AbstractBackgroundIn vivo tau‐positron emission tomography (PET) is an attractive biomarker for Alzheimer’s disease (AD) diagnosis and treatment. However, tau‐PET is less widely available than other modalities. In this study, we tested cross‐modality synthesis of tau‐PET brain images from fluorodeoxyglucose F‐18 (FDG)‐PET using a deep convolutional neural network (CNN).MethodParticipants (n=1,192) who had brain FDG‐PET with 18F‐FDG and tau‐PET with Flortaucipir (F‐18‐AV‐1451) were included for training and testing. This cohort spanned normal aging (ages 26‐98), pre‐clinical, and clinical AD and related disorders including the FTD and DLB spectrum. External validation was done using ADNI (n=288). The PET scans were co‐registered to the corresponding MRI and subsequently warped to Mayo Clinic Adult Lifespan Template (MCALT) space. Tau‐PET images were SUVR‐normalized to the cerebellar crus, and FDG to the pons. A 3D dense‐U‐net model was utilized as an architecture. Cross‐validation experiments were conducted using 5‐fold validations (60% training set, 20% validation set, and 20% test set) with mean squared error as the loss function.ResultOur dense‐U‐net model successfully synthesized tau‐PET from metabolic images with good correlation and low prediction error for regional SUVRs (Figure 1A‐C). The model showed a robust prediction ability, performing accurately in an independent, external ADNI cohort (Figure 1D‐F). The model‐imputed tau‐PET significantly improved performance in classifying tau positivity (mean AUROC(±SD)=0.78±0.04 and 0.85±0.03 for FDG‐PET and synthesized tau‐PET, respectively) and diagnostic groups (cognitively unimpaired with abnormal amyloid‐PET vs. cognitively impaired with abnormal amyloid‐PET) compared to the original input FDG data (mean AUROC(±SD)=0.89±0.04, 0.85±0.05 0.91±0.04 for actual tau‐PET, FDG‐PET and synthesized tau‐PET, respectively) (Figure 2), suggesting enhanced clinical utility for metabolic images. The ADNI cohort also showed similar results (for tau positivity: AUROC=0.66 and 0.78 for FDG‐PET and synthesized tau‐PET, respectively; for CU A+ vs. CI A+: AUROC=0.86, 0.62, and 0.73 for actual tau‐PET, FDG‐PET, and synthesized tau‐PET; Figure 3).ConclusionWe showed that using a CNN model to predict tau‐PET from FDG‐PET is feasible. The synthesized tau‐PET can augment the value of FDG‐PET, facilitating the multi‐modal diagnosis of AD.

Objectives The aim of this study is to introduce the infrapedicular approach to CT-guided spine interventions, a specialized technique that can safely expand the scope of spine lesions amenable to treatment, and to document the feasibility in a variety of procedural scenarios to the extent possible with a retrospective case series. Methods Data from 24 cases performed at a single institution over a 10-year period were retrospectively reviewed to assess the technical feasibility and safety profile of the technique. Results The infrapedicular approach enabled a technically satisfactory procedural result in 24 cases (mean age 63.9 years, range 35–83 years). Two peri-procedural complications occurred, including a small pneumothorax and a cerebrospinal fluid leak, both of which resolved with conservative treatment. No lasting injurious effects or additional complications were identified. The infrapedicular approach was found to be particularly useful in multiple technically challenging scenarios: it facilitates access to lesions in the inferior vertebral body, allows biopsy, cement augmentation, or ablation of high thoracic lesions difficult to treat due to limitations of steep angulation of fluoroscopy and CT scanners, and enables treatment of large lesions by using multiple overlapping probes.

Abstract PET imaging plays an essential role in achieving earlier and more specific diagnoses of dementia syndromes, important for clinical prognostication and optimal medical management. This has become especially vital with the recent development of pathology-specific disease-modifying therapy for Alzheimer disease, which will continue to evolve and require methods to select appropriate treatment candidates. Techniques that began as research tools such as amyloid and tau PET have now entered clinical use, making nuclear medicine physicians and radiologists essential members of the care team. This review discusses recent changes in the understanding of dementia and examines the roles of nuclear medicine imaging in clinical practice. Within this framework, multiple cases will be shown to illustrate a systematic approach of FDG PET interpretation and integration of PET imaging of specific molecular pathology including dopamine transporters, amyloid, and tau. The approach presented here incorporates contemporary understanding of both common and uncommon dementia syndromes, intended as an updated practical guide to assist with the sophisticated interpretation of nuclear medicine examinations in the context of this rapidly and continually developing area of imaging.

Abstract Normal brain aging is accompanied by patterns of functional and structural change. Alzheimer's disease (AD), a representative neurodegenerative disease, has been linked to accelerated brain aging at respective age ranges. Here, we developed a deep learning-based brain age prediction model using fluorodeoxyglucose (FDG) PET and structural MRI and tested how the brain age gap relates to degenerative cognitive syndromes including mild cognitive impairment, AD, frontotemporal dementia, and Lewy body dementia. Occlusion analysis, performed to facilitate interpretation of the model, revealed that the model learns an age- and modality-specific pattern of brain aging. The elevated brain age gap in dementia cohorts was highly correlated with the cognitive impairment and AD biomarker. However, regions generating brain age gaps were different for each diagnosis group of which the AD continuum showed similar patterns to normal aging in the CU.