Non-invasive quantification of 18F-florbetaben with total-body EXPLORER PET.

BACKGROUND: Kinetic modeling of 18F-florbetaben provides important quantification of brain amyloid deposition in research and clinical settings but its use is limited by the requirement of arterial blood data for quantitative PET. The total-body EXPLORER PET scanner supports the dynamic acquisition of a full human body simultaneously and permits noninvasive image-derived input functions (IDIFs) as an alternative to arterial blood sampling. This study quantified brain amyloid burden with kinetic modeling, leveraging dynamic 18F-florbetaben PET in aorta IDIFs and the brain in an elderly cohort. METHODS: 18F-florbetaben dynamic PET imaging was performed on the EXPLORER system with tracer injection (300 MBq) in 3 individuals with Alzheimer's disease (AD), 3 with mild cognitive impairment, and 9 healthy controls. Image-derived input functions were extracted from the descending aorta with manual regions of interest based on the first 30 s after injection. Dynamic time-activity curves (TACs) for 110 min were fitted to the two-tissue compartment model (2TCM) using population-based metabolite corrected IDIFs to calculate total and specific distribution volumes (VT, Vs) in key brain regions with early amyloid accumulation. Non-displaceable binding potential ([Formula: see text] was also calculated from the multi-reference tissue model (MRTM). RESULTS: Amyloid-positive (AD) patients showed the highest VT and VS in anterior cingulate, posterior cingulate, and precuneus, consistent with [Formula: see text] analysis. [Formula: see text]and VT from kinetic models were correlated (r² = 0.46, P < 2[Formula: see text] with a stronger positive correlation observed in amyloid-positive participants, indicating reliable model fits with the IDIFs. VT from 2TCM was highly correlated ([Formula: see text]= 0.65, P < 2[Formula: see text]) with Logan graphical VT estimation. CONCLUSION: Non-invasive quantification of amyloid binding from total-body 18F-florbetaben PET data is feasible using aorta IDIFs with high agreement between kinetic distribution volume parameters compared to [Formula: see text]in amyloid-positive and amyloid-negative older individuals.

Recommendations for quantitative cerebral perfusion MRI using multi-timepoint arterial spin labeling: Acquisition, quantification, and clinical applications.

Accurate assessment of cerebral perfusion is vital for understanding the hemodynamic processes involved in various neurological disorders and guiding clinical decision-making. This guidelines article provides a comprehensive overview of quantitative perfusion imaging of the brain using multi-timepoint arterial spin labeling (ASL), along with recommendations for its acquisition and quantification. A major benefit of acquiring ASL data with multiple label durations and/or post-labeling delays (PLDs) is being able to account for the effect of variable arterial transit time (ATT) on quantitative perfusion values and additionally visualize the spatial pattern of ATT itself, providing valuable clinical insights. Although multi-timepoint data can be acquired in the same scan time as single-PLD data with comparable perfusion measurement precision, its acquisition and postprocessing presents challenges beyond single-PLD ASL, impeding widespread adoption. Building upon the 2015 ASL consensus article, this work highlights the protocol distinctions specific to multi-timepoint ASL and provides robust recommendations for acquiring high-quality data. Additionally, we propose an extended quantification model based on the 2015 consensus model and discuss relevant postprocessing options to enhance the analysis of multi-timepoint ASL data. Furthermore, we review the potential clinical applications where multi-timepoint ASL is expected to offer significant benefits. This article is part of a series published by the International Society for Magnetic Resonance in Medicine (ISMRM) Perfusion Study Group, aiming to guide and inspire the advancement and utilization of ASL beyond the scope of the 2015 consensus article.

Neural Underpinnings of Learning in Dementia Populations: A Review of Motor Learning Studies Combined with Neuroimaging.

The intent of this review article is to serve as an overview of current research regarding the neural characteristics of motor learning in Alzheimer disease (AD) as well as prodromal phases of AD: at-risk populations, and mild cognitive impairment. This review seeks to provide a cognitive framework to compare various motor tasks. We will highlight the neural characteristics related to cognitive domains that, through imaging, display functional or structural changes because of AD progression. In turn, this motivates the use of motor learning paradigms as possible screening techniques for AD and will build upon our current understanding of learning abilities in AD populations.

Dynamic Magnetic Resonance Vascular Fingerprinting During Hypercapnia for Quantitative and Multiparametric Cerebrovascular Reactivity Measures.

Magnetic resonance fingerprinting (MRF) represents a potential paradigm shift in MR image acquisition, reconstruction, and analysis using computational biophysical modelling in parallel to image acquisition. Its flexibility allows for examination of cerebrovascular metrics through MR vascular fingerprinting (MRvF), and this has been extended even further to produce quantitative cerebral blood volume (CBV), microvascular vessel radius, and tissue oxygen saturation (SO2) maps of the whole brain simultaneously every few seconds. This allows for observation of rapid physiological changes like cerebrovascular reactivity (CVR), which is the ability of vessels to dilate in response to a vasoactive stimulus. Here we demonstrated a novel protocol in which a rapid, spin- and gradient-echo pulse sequence allowed for dynamic, and simultaneous acquisition of MRvF and blood oxygen level dependent (BOLD) measures. By combining this with a tailored hypercapnic (5% CO2) breathing paradigm we were able to show how these quantitative CBV, radius, and SO2 parameters changed in response to a stimulus and directly compare those to a colocalized, traditionally used BOLD CVR. We also compared these measures to another traditionally utilized technique in cerebral blood flow CVR from an arterial spin labelling sequence. These imaging, processing, and analysis techniques will allow for further investigation into the magnitude and rate of CVR based on BOLD and MRvF-based metrics and enable investigations to better understand vascular function in healthy aging and cerebrovascular diseases.Clinical Relevance- The development of dynamic magnetic resonance vascular fingerprinting has the potential to enable rapid, quantitative, and multiparametric functional imaging biomarkers of cerebrovascular diseases like vascular cognitive impairment, dementia, and Alzheimer's disease.

Characterizing systemic physiological effects on the blood oxygen level dependent signal of resting-state fMRI in time-frequency space using wavelets.

Systemic physiological dynamics, such as heart rate variability (HRV) and respiration volume per time (RVT), are known to account for significant variance in the blood oxygen level dependent (BOLD) signal of resting-state functional magnetic resonance imaging (rsfMRI). However, synchrony between these cardiorespiratory changes and the BOLD signal could be due to neuronal (i.e., autonomic activity inducing changes in heart rate and respiration) or vascular (i.e., cardiorespiratory activity facilitating hemodynamic changes and thus the BOLD signal) effects and the contributions of these effects may differ spatially, temporally, and spectrally. In this study, we characterize these brain-body dynamics using a wavelet analysis in rapidly sampled rsfMRI data with simultaneous pulse oximetry and respiratory monitoring of the Human Connectome Project. Our time-frequency analysis across resting-state networks (RSNs) revealed differences in the coherence of the BOLD signal and heartbeat interval (HBI)/RVT dynamics across frequencies, with unique profiles per network. Somatomotor (SMN), visual (VN), and salience (VAN) networks demonstrated the greatest synchrony with both systemic physiological signals when compared to other networks; however, significant coherence was observed in all RSNs regardless of direct autonomic involvement. Our phase analysis revealed distinct frequency profiles of percentage of time with significant coherence between BOLD and systemic physiological signals for different phase offsets across RSNs, suggesting that the phase offset and temporal order of signals varies by frequency. Lastly, our analysis of temporal variability of coherence provides insight on potential influence of autonomic state on brain-body communication. Overall, the novel wavelet analysis enables an efficient characterization of the dynamic relationship between cardiorespiratory activity and the BOLD signal in spatial, temporal, and spectral dimensions to inform our understanding of autonomic states and improve our interpretation of the BOLD signal.

Cortical oxygen extraction fraction using quantitative BOLD MRI and cerebral blood flow during vasodilation.

Introduction: We aimed to demonstrate non-invasive measurements of regional oxygen extraction fraction (OEF) from quantitative BOLD MRI modeling at baseline and after pharmacological vasodilation. We hypothesized that OEF decreases in response to vasodilation with acetazolamide (ACZ) in healthy conditions, reflecting compensation in regions with increased cerebral blood flow (CBF), while cerebral metabolic rate of oxygen (CMRO2) remained unchanged. We also aimed to assess the relationship between OEF and perfusion in the default mode network (DMN) regions that have shown associations with vascular risk factors and cerebrovascular reactivity in different neurological conditions. Material and methods: Eight healthy subjects (47 ± 13 years, 6 female) were scanned on a 3 T scanner with a 32-channel head coil before and after administration of 15 mg/kg ACZ as a pharmacological vasodilator. The MR imaging acquisition protocols included: 1) A Gradient Echo Slice Excitation Profile Imaging Asymmetric Spin Echo scan to quantify OEF, deoxygenated blood volume, and reversible transverse relaxation rate (R2 ') and 2) a multi-post labeling delay arterial spin labeling scan to measure CBF. To assess changes in each parameter due to vasodilation, two-way t-tests were performed for all pairs (baseline versus vasodilation) in the DMN brain regions with Bonferroni correction for multiple comparisons. The relationships between CBF versus OEF and CBF versus R2' were analyzed and compared across DMN regions using linear, mixed-effect models. Results: During vasodilation, CBF significantly increased in the medial frontal cortex (P=0.004), posterior cingulate gyrus (pCG) (P=0.004), precuneus cortex (PCun) (P=0.004), and occipital pole (P=0.001). Concurrently, a significant decrease in OEF was observed only in the pCG (8.8%, P=0.003) and PCun (8.7%,P=0.001). CMRO2 showed a trend of increased values after vasodilation, but these differences were not significant after correction for multiple comparisons. Although R2' showed a slightly decreasing trend, no statistically significant changes were found in any regions in response to ACZ. The CBF response to ACZ exhibited a stronger negative correlation with OEF (ß=-0.104±0.027; t=-3.852,P<0.001), than with R2' (ß=-0.016±0.006; t=-2.692,P=0.008). Conclusion: Quantitative BOLD modeling can reliably measure OEF across multiple physiological conditions and captures vascular changes with higher sensitivity than R2' values. The inverse correlation between OEF and CBF across regions in DMN, suggests that these two measurements, in response to ACZ vasodilation, are reliable indicators of tissue health in this healthy cohort.

Modeling venous bias in resting state functional MRI metrics.

Resting-state (rs) functional magnetic resonance imaging (fMRI) is used to detect low-frequency fluctuations in the blood oxygen-level dependent (BOLD) signal across brain regions. Correlations between temporal BOLD signal fluctuations are commonly used to infer functional connectivity. However, because BOLD is based on the dilution of deoxyhemoglobin, it is sensitive to veins of all sizes, and its amplitude is biased by draining veins. These biases affect local BOLD signal location and amplitude, and may also influence BOLD-derived connectivity measures, but the magnitude of this venous bias and its relation to vein size and proximity is unknown. Here, veins were identified using high-resolution quantitative susceptibility maps and utilized in a biophysical model to investigate systematic venous biases on common local rsfMRI-derived measures. Specifically, we studied the impact of vein diameter and distance to veins on the amplitude of low-frequency fluctuations (ALFF), fractional ALFF (fALFF), Hurst exponent (HE), regional homogeneity (ReHo), and eigenvector centrality values in the grey matter. Values were higher across all distances in smaller veins, and decreased with increasing vein diameter. Additionally, rsfMRI values associated with larger veins decrease with increasing distance from the veins. ALFF and ReHo were the most biased by veins, while HE and fALFF exhibited the smallest bias. Across all metrics, the amplitude of the bias was limited in voxel-wise data, confirming that venous structure is not the dominant source of contrast in these rsfMRI metrics. Finally, the models presented can be used to correct this venous bias in rsfMRI metrics.

Multimodal comparisons of QSM and PET in neurodegeneration and aging.

Quantitative susceptibility mapping (QSM) has been used to study susceptibility changes that may occur based on tissue composition and mineral deposition. Iron is a primary contributor to changes in magnetic susceptibility and of particular interest in applications of QSM to neurodegeneration and aging. Iron can contribute to neurodegeneration through inflammatory processes and via interaction with aggregation of disease-related proteins. To better understand the local susceptibility changes observed on QSM, its signal has been studied in association with other imaging metrics such as positron emission tomography (PET). The associations of QSM and PET may provide insight into the pathophysiology of disease processes, such as the role of iron in aging and neurodegeneration, and help to determine the diagnostic utility of QSM as an indirect indicator of disease processes typically evaluated with PET. In this review we discuss the proposed mechanisms and summarize prior studies of the associations of QSM and amyloid PET, tau PET, TSPO PET, FDG-PET, 15O-PET, and F-DOPA PET in evaluation of neurologic diseases with a focus on aging and neurodegeneration.

Non-invasive quantification of 18F-florbetaben with total-body EXPLORER PET.

Purpose: Kinetic modeling of 18F-florbetaben provides important quantification of brain amyloid deposition in research and clinical settings but its use is limited by the requirement of arterial blood data for quantitative PET. The total-body EXPLORER PET scanner supports the dynamic acquisition of a full human body simultaneously and permits noninvasive image-derived input functions (IDIFs) as an alternative to arterial blood sampling. This study quantified brain amyloid burden with kinetic modeling, leveraging dynamic 18F-florbetaben PET in aorta IDIFs and the brain in an elderly cohort. Methods: 18F-florbetaben dynamic PET imaging was performed on the EXPLORER system with tracer injection (300 MBq) in 3 individuals with Alzheimer's disease (AD), 3 with mild cognitive impairment, and 9 healthy controls. Image-derived input functions were extracted from the descending aorta with manual regions of interest based on the first 30 seconds after injection. Dynamic time-activity curves (TACs) for 110 minutes were fitted to the two-tissue compartment model (2TCM) using population-based metabolite corrected IDIFs to calculate total and specific distribution volumes (VT, Vs) in key brain regions with early amyloid accumulation. Non-displaceable binding potential (BPND) was also calculated from the multi-reference tissue model (MRTM). Results: Amyloid-positive (AD) patients showed the highest VT and VS in anterior cingulate, posterior cingulate, and precuneus, consistent with BPND analysis. BPND and VT from kinetic models were correlated (r2 = 0.46, P<2e-16) with a stronger positive correlation observed in amyloid-positive participants, indicating reliable model fits with the IDIFs. VT from 2TCM was highly correlated (r2 = 0.65, P< 2e-16) with Logan graphical VT estimation. Conclusion: Non-invasive quantification of amyloid binding from total-body 18F-florbetaben PET data is feasible using aorta IDIFs with high agreement between kinetic distribution volume parameters compared to BPND in amyloid-positive and negative older individuals.

Using arterial spin labeling to measure cerebrovascular reactivity in Moyamoya disease: Insights from simultaneous PET/MRI.

Cerebrovascular reactivity (CVR) reflects the CBF change to meet different physiological demands. The reference CVR technique is PET imaging with vasodilators but is inaccessible to most patients. DSC can measure transit time to evaluate patients suspected of stroke, but the use of gadolinium may cause side-effects. Arterial spin labeling (ASL) is a non-invasive MRI technique for CBF measurements. Here, we investigate the effectiveness of ASL with single and multiple post labeling delays (PLD) to replace PET and DSC for CVR and transit time mapping in 26 Moyamoya patients. Images were collected using simultaneous PET/MRI with acetazolamide. CVR, CBF, arterial transit time (ATT), and time-to-maximum (Tmax) were measured in different flow territories. Results showed that CVR was lower in occluded regions than normal regions (by 68 ± 12%, 52 ± 5%, and 56 ± 9%, for PET, single- and multi-PLD PCASL, respectively, all p < 0.05). Multi-PLD PCASL correlated slightly higher with PET (CCC = 0.36 and 0.32 in affected and unaffected territories respectively). Vasodilation caused ATT to reduce by 4.5 ± 3.1% (p < 0.01) in occluded regions. ATT correlated significantly with Tmax (R2 > 0.35, p < 0.01). Therefore, multi-PLD ASL is recommended for CVR studies due to its high agreement with the reference PET technique and the capability of measuring transit time.

18F-FSPG PET/CT Imaging of System xC- Transporter Activity in Patients with Primary and Metastatic Brain Tumors.

Background The PET tracer (4S)-4-(3-[18F]fluoropropyl)-l-glutamate (18F-FSPG) targets the system xC- cotransporter, which is overexpressed in various tumors. Purpose To assess the role of 18F-FSPG PET/CT in intracranial malignancies. Materials and Methods Twenty-six patients (mean age, 54 years ± 12; 17 men; 48 total lesions) with primary brain tumors (n = 17) or brain metastases (n = 9) were enrolled in this prospective, single-center study (ClinicalTrials.gov identifier: NCT02370563) between November 2014 and March 2016. A 30-minute dynamic brain 18F-FSPG PET/CT scan and a static whole-body (WB) 18F-FSPG PET/CT scan at 60-75 minutes were acquired. Moreover, all participants underwent MRI, and four participants underwent fluorine 18 (18F) fluorodeoxyglucose (FDG) PET imaging. PET parameters and their relative changes were obtained for all lesions. Kinetic modeling was used to estimate the 18F-FSPG tumor rate constants using the dynamic and dynamic plus WB PET data. Imaging parameters were correlated to lesion outcomes, as determined with follow-up MRI and/or pathologic examination. The Mann-Whitney U test or Student t test was used for group mean comparisons. Receiver operating characteristic curve analysis was used for performance comparison of different decision measures. Results 18F-FSPG PET/CT helped identify all 48 brain lesions. The mean tumor-to-background ratio (TBR) on the whole-brain PET images at the WB time point was 26.6 ± 24.9 (range: 2.6-150.3). When 18F-FDG PET was performed, 18F-FSPG permitted visualization of non-18F-FDG-avid lesions or allowed better lesion differentiation from surrounding tissues. In participants with primary brain tumors, the predictive accuracy of the relative changes in influx rate constant Ki and maximum standardized uptake value to discriminate between poor and good lesion outcomes were 89% and 81%, respectively. There were significant differences in the 18F-FSPG uptake curves of lesions with good versus poor outcomes in the primary brain tumor group (P < .05) but not in the brain metastases group. Conclusion PET/CT imaging with (4S)-4-(3-[18F]fluoropropyl)-l-glutamate (18F-FSPG) helped detect primary brain tumors and brain metastases with a high tumor-to-background ratio. Relative changes in 18F-FSPG uptake with multi-time-point PET appear to be helpful in predicting lesion outcomes. Clinical trial registration no. NCT02370563 © RSNA, 2022 Online supplemental material is available for this article.

Reproducibility of cerebrovascular reactivity measurements: A systematic review of neuroimaging techniques.

Cerebrovascular reactivity (CVR), the capacity of the brain to increase cerebral blood flow (CBF) to meet changes in physiological demand, is an important biomarker to evaluate brain health. Typically, this brain "stress test" is performed by using a medical imaging modality to measure the CBF change between two states: at baseline and after vasodilation. However, since there are many imaging modalities and many ways to augment CBF, a wide range of CVR values have been reported. An understanding of CVR reproducibility is critical to determine the most reliable methods to measure CVR as a clinical biomarker. This review focuses on CVR reproducibility studies using neuroimaging techniques in 32 articles comprising 427 total subjects. The literature search was performed in PubMed, Embase, and Scopus. The review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). We identified 5 factors of the experimental subjects (such as sex, blood characteristics, and smoking) and 9 factors of the measuring technique (such as the imaging modality, the type of the vasodilator, and the quantification method) that have strong effects on CVR reproducibility. Based on this review, we recommend several best practices to improve the reproducibility of CVR quantification in neuroimaging studies.

Convolutional Neural Net Learning Can Achieve Production-Level Brain Segmentation in Structural Magnetic Resonance Imaging.

Deep learning implementations using convolutional neural nets have recently demonstrated promise in many areas of medical imaging. In this article we lay out the methods by which we have achieved consistently high quality, high throughput computation of intra-cranial segmentation from whole head magnetic resonance images, an essential but typically time-consuming bottleneck for brain image analysis. We refer to this output as "production-level" because it is suitable for routine use in processing pipelines. Training and testing with an extremely large archive of structural images, our segmentation algorithm performs uniformly well over a wide variety of separate national imaging cohorts, giving Dice metric scores exceeding those of other recent deep learning brain extractions. We describe the components involved to achieve this performance, including size, variety and quality of ground truth, and appropriate neural net architecture. We demonstrate the crucial role of appropriately large and varied datasets, suggesting a less prominent role for algorithm development beyond a threshold of capability.

Cerebrovascular reactivity measurements using simultaneous 15O-water PET and ASL MRI: Impacts of arterial transit time, labeling efficiency, and hematocrit.

Cerebrovascular reactivity (CVR) reflects the capacity of the brain to meet changing physiological demands and can predict the risk of cerebrovascular diseases. CVR can be obtained by measuring the change in cerebral blood flow (CBF) during a brain stress test where CBF is altered by a vasodilator such as acetazolamide. Although the gold standard to quantify CBF is PET imaging, the procedure is invasive and inaccessible to most patients. Arterial spin labeling (ASL) is a non-invasive and quantitative MRI method to measure CBF, and a consensus guideline has been published for the clinical application of ASL. Despite single post labeling delay (PLD) pseudo-continuous ASL (PCASL) being the recommended ASL technique for CBF quantification, it is sensitive to variations to the arterial transit time (ATT) and labeling efficiency induced by the vasodilator in CVR studies. Multi-PLD ASL controls for the changes in ATT, and velocity selective ASL is in theory insensitive to both ATT and labeling efficiency. Here we investigate CVR using simultaneous 15O-water PET and ASL MRI data from 19 healthy subjects. CVR and CBF measured by the ASL techniques were compared using PET as the reference technique. The impacts of blood T1 and labeling efficiency on ASL were assessed using individual measurements of hematocrit and flow velocity data of the carotid and vertebral arteries measured using phase-contrast MRI. We found that multi-PLD PCASL is the ASL technique most consistent with PET for CVR quantification (group mean CVR of the whole brain = 42±19% and 40±18% respectively). Single-PLD ASL underestimated the CVR of the whole brain significantly by 15±10% compared with PET (p<0.01, paired t-test). Changes in ATT pre- and post-acetazolamide was the principal factor affecting ASL-based CVR quantification. Variations in labeling efficiency and blood T1 had negligible effects.

Reliability of arterial spin labeling derived cerebral blood flow in periventricular white matter.

We aimed to assess the reliability of cerebral blood flow (CBF) measured using arterial spin labeled (ASL) perfusion magnetic resonance imaging (MRI) from the periventricular white matter (PVWM) by computing its repeatability and comparing to [15O]-water Positron Emission Tomography (PET) as a reference. Simultaneous PET/MRI perfusion data were acquired twice in the same session, about 15 min apart, from 16 subjects (age: 41.4 ± 12.0 years, 9 female). ASL protocols used pseudocontinuous labeling (pCASL) with background-suppressed 3-dimensional readouts, and included both single and multiple post labeling delay (PLD) acquisitions, each acquired twice, with the latter providing both CBF and arterial transit time (ATT) maps. The reliability of ASL derived PVWM CBF was evaluated using intra-session repeatability assessed by the within-subject coefficient of variation (wsCV) of the PVWM CBF values obtained from the two scans, correlation with concurrently-acquired PET CBF values, and by comparing them with that measured in other commonly used regions of interest (ROIs) such as whole brain (WB), gray matter (GM) and white matter (WM). The wsCVs for PVWM CBF with single and multi-PLD acquisitions were 5.7 (95% CI: (3.4,7.7)) % and 6.1 (95% CI: (3.8,8.3))%, which were similar to those obtained from WB, GM and WM CBF even though the PVWM region is the most weakly perfused region of brain parenchyma. Correlations between relative PVWM CBF derived from ASL and from [15O]-water PET were also comparable to the other ROIs. Finally, the ATT of the PVWM region was found to be 1.27 ± 0.27s, which was not an outlier for the arterial circulation of the brain. These findings suggest that PVWM CBF can be reliably measured with the current state-of-the-art ASL methods.

Quantification of brain oxygen extraction and metabolism with [15O]-gas PET: A technical review in the era of PET/MRI.

Oxygen extraction fraction (OEF) and the cerebral metabolic rate of oxygen (CMRO2) are key cerebral physiological parameters to identify at-risk cerebrovascular patients and understand brain health and function. PET imaging with [15O]-oxygen tracers, either through continuous or bolus inhalation, provides non-invasive assessment of OEF and CMRO2. Numerous tracer delivery, PET acquisition, and kinetic modeling approaches have been adopted to map brain oxygenation. The purpose of this technical review is to critically evaluate different methods for [15O]-gas PET and its impact on the accuracy and reproducibility of OEF and CMRO2 measurements. We perform a meta-analysis of brain oxygenation PET studies in healthy volunteers and compare between continuous and bolus inhalation techniques. We also describe OEF metrics that have been used to detect hemodynamic impairment in cerebrovascular disease. For these patients, advanced techniques to accelerate the PET scans and potential synthesis with MRI to avoid arterial blood sampling would facilitate broader use of [15O]-oxygen PET for brain physiological assessment.

Simultaneous FDG-PET/MRI detects hippocampal subfield metabolic differences in AD/MCI.

The medial temporal lobe is one of the most well-studied brain regions affected by Alzheimer's disease (AD). Although the spread of neurofibrillary pathology in the hippocampus throughout the progression of AD has been thoroughly characterized and staged using histology and other imaging techniques, it has not been precisely quantified in vivo at the subfield level using simultaneous positron emission tomography (PET) and magnetic resonance imaging (MRI). Here, we investigate alterations in metabolism and volume using [18F]fluoro-deoxyglucose (FDG) and simultaneous time-of-flight (TOF) PET/MRI with hippocampal subfield analysis of AD, mild cognitive impairment (MCI), and healthy subjects. We found significant structural and metabolic changes within the hippocampus that can be sensitively assessed at the subfield level in a small cohort. While no significant differences were found between groups for whole hippocampal SUVr values (p = 0.166), we found a clear delineation in SUVr between groups in the dentate gyrus (p = 0.009). Subfield analysis may be more sensitive for detecting pathological changes using PET-MRI in AD compared to global hippocampal assessment.

Predicting PET Cerebrovascular Reserve with Deep Learning by Using Baseline MRI: A Pilot Investigation of a Drug-Free Brain Stress Test.

Background Cerebrovascular reserve (CVR) may be measured by using an acetazolamide test to clinically evaluate patients with cerebrovascular disease. However, acetazolamide use may be contraindicated and/or undesirable in certain clinical settings. Purpose To predict CVR images generated from acetazolamide vasodilation with a deep learning network by using only images before acetazolamide administration. Materials and Methods Simultaneous oxygen 15 (15O)-labeled water PET/MRI before and after acetazolamide injection were retrospectively analyzed for patients with Moyamoya disease and healthy control participants from April 2017 to May 2019. Inputs to deep learning models were perfusion-based images (arterial spin labeling [ASL]), structural scans (T2 fluid-attenuated inversion-recovery, T1), and brain location. Two models, that is, 15O-labeled water PET cerebral blood flow (CBF) and MRI (PET-plus-MRI model) before acetazolamide administration and only MRI (MRI-only model) before acetazolamide administration, were trained and tested with sixfold cross-validation. The models learned to predict a voxelwise relative CBF change (rΔCBF) map by using rΔCBF measured with PET due to acetazolamide as ground truth. Quantitative analysis included image quality metrics (peak signal-to-noise ratio, root mean square error, and structural similarity index), as well as comparison between the various methods by using correlation and Bland-Altman analyses. Identification of vascular territories with impaired rΔCBF was evaluated by using receiver operating characteristic metrics. Results Thirty-six participants were included: 24 patients with Moyamoya disease (mean age ± standard deviation, 41 years ± 12; 17 women) and 12 age-matched healthy control participants (mean age, 39 years ± 16; nine women). The rΔCBF maps predicted by both deep learning models demonstrated better image quality metrics than did ASL (all P < .001 in patients) and higher correlation coefficient with PET than with ASL (PET-plus-MRI model, 0.704; MRI-only model, 0.690 vs ASL, 0.432; both P < .001 in patients). Both models also achieved high diagnostic performance in identifying territories with impaired rΔCBF (area under receiver operating characteristic curve, 0.95 for PET-plus-MRI model [95% confidence interval: 0.90, 0.99] and 0.95 for MRI-only model [95% confidence interval: 0.91, 0.98]). Conclusion By using only images before acetazolamide administration, PET-plus-MRI and MRI-only deep learning models predicted cerebrovascular reserve images without the need for vasodilator injection. © RSNA, 2020 Online supplemental material is available for this article.

Elevated brain oxygen extraction fraction measured by MRI susceptibility relates to perfusion status in acute ischemic stroke.

Recent clinical trials of new revascularization therapies in acute ischemic stroke have highlighted the importance of physiological imaging to identify optimal treatments for patients. Oxygen extraction fraction (OEF) is a hallmark of at-risk tissue in stroke, and can be quantified from the susceptibility effect of deoxyhemoglobin molecules in venous blood on MRI phase scans. We measured OEF within cerebral veins using advanced quantitative susceptibility mapping (QSM) MRI reconstructions in 20 acute stroke patients. Absolute OEF was elevated in the affected (29.3 ± 3.4%) versus the contralateral hemisphere (25.5 ± 3.1%) of patients with large diffusion-perfusion lesion mismatch (P = 0.032). In these patients, OEF negatively correlated with relative CBF measured by dynamic susceptibility contrast MRI (P = 0.004), suggesting compensation for reduced flow. Patients with perfusion-diffusion match or no hypo-perfusion showed less OEF difference between hemispheres. Nine patients received longitudinal assessment and showed OEF ratio (affected to contralateral) of 1.2 ± 0.1 at baseline that normalized (decreased) to 1.0 ± 0.1 at follow-up three days later (P = 0.03). Our feasibility study demonstrates that QSM MRI can non-invasively quantify OEF in stroke patients, relates to perfusion status, and is sensitive to OEF changes over time. Clinical trial registration: Longitudinal MRI examinations of patients with brain ischemia and blood brain barrier permeability; clinicaltrials.org :NCT02077582.