MRI-based Correction for PET Photon Attenuation in Simultaneous PET/MRI Using Ultrashort Echo Time Methods

Positron emission tomography (PET) is a functional imaging modality that allows clinicians to visualize complex physiological processes such as metabolism, proliferation, perfusion, and receptor binding. Magnetic resonance imaging (MRI) is a versatile imaging modality that provides detailed anatomical images as well as functional information. Hybrid PET/MRI systems have been recently proposed as a means to combine the high-sensitivity functional information provided by PET with the high-resolution anatomical information provided by MRI. Furthermore, PET/MRI systems have the capability to provide complementary functional information acquired from both modalities. These systems have garnered significant clinical interest particularly in neurological imaging due to these capabilities. A major drawback of PET/MRI systems is the lack of an accurate, clinically feasible MRI-based method for performing PET photon attenuation correction. The current vendor-provided methods lack accuracy, and more accurate methods proposed in literature are not clinically feasible due to long computation times. The inaccuracies of the vendor-provided methods result from misidentification of tissues, particularly bone, or the assumption of homogenous attenuation coefficients inside each tissue. Therefore, the goal of this work was to develop an MR-based attenuation correction method that addresses both of these challenges in a clinically feasible framework. To achieve this goal, we propose an ultrashort echo-time method that acquires all necessary data using one sequence and produces the necessary attenuation maps quickly. The proposed sequence utilizes a dual flip-angle, dual echo-time ultrashort echo time (UTE) acquisition to segment all tissues of interest to attenuation correction in the head and neck. Next, continuous-valued attenuation coefficients are assigned to all imaging voxels through a conversion from MR relaxation rate R1. The capability of the method to generate accurate PET images was assessed by comparison to the gold standard CT-based method in a large number of subjects. The results show that the proposed method is significantly more accurate in the whole brain as well as in several smaller regions of interest when compared to the corresponding vendor-provided method. The proposed method has been fully automated and can be easily incorporated into the PET/MRI clinical work-flow.Doctor of Philosoph

MR-based attenuation correction for PET/MRI neurological studies with continuous-valued attenuation coefficients for bone through a conversion from R2* to CT-Hounsfield units

AIM: MR-based correction for photon attenuation in PET/MRI remains challenging, particularly for neurological applications requiring quantitation of data. Existing methods are either not sufficiently accurate or are limited by the computation time required. The goal of this study was to develop an MR-based attenuation correction method that accurately separates bone tissue from air and provides continuous-valued attenuation coefficients for bone. MATERIALS AND METHODS: PET/MRI and CT datasets were obtained from 98 subjects (mean age [±SD]: 66yrs [±9.8], 57 females) using an IRB-approved protocol and with informed consent. Subjects were injected with 352±29MBq of (18)F-Florbetapir tracer, and PET acquisitions were begun either immediately or 50min after injection. CT images of the head were acquired separately using a PET/CT system. Dual echo ultrashort echo-time (UTE) images and two-point Dixon images were acquired. Regions of air were segmented via a threshold of the voxel-wise multiplicative inverse of the UTE echo 1 image. Regions of bone were segmented via a threshold of the R2* image computed from the UTE echo 1 and UTE echo 2 images. Regions of fat and soft tissue were segmented using fat and water images decomposed from the Dixon images. Air, fat, and soft tissue were assigned linear attenuation coefficients (LACs) of 0, 0.092, and 0.1cm(-1), respectively. LACs for bone were derived from a regression analysis between corresponding R2* and CT values. PET images were reconstructed using the gold standard CT method and the proposed CAR-RiDR method. RESULTS: The RiDR segmentation method produces mean Dice coefficient±SD across subjects of 0.75±0.05 for bone and 0.60±0.08 for air. The CAR model for bone LACs greatly improves accuracy in estimating CT values (28.2%±3.0 mean error) compared to the use of a constant CT value (46.9%±5.8, p<10(-6)). Finally, the CAR-RiDR method provides a low whole-brain mean absolute percent-error (MAPE±SD) in PET reconstructions across subjects of 2.55%±0.86. Regional PET errors were also low and ranged from 0.88% to 3.79% in 24 brain ROIs. CONCLUSION: We propose an MR-based attenuation correction method (CAR-RiDR) for quantitative PET neurological imaging. The proposed method employs UTE and Dixon images and consists of two novel components: 1) accurate segmentation of air and bone using the inverse of the UTE1 image and the R2* image, respectively and 2) estimation of continuous LAC values for bone using a regression between R2* and CT-Hounsfield units. From our analysis, we conclude that the proposed method closely approaches (<3% error) the gold standard CT-scaled method in PET reconstruction accuracy

Probabilistic Air Segmentation and Sparse Regression Estimated Pseudo CT for PET/MR Attenuation Correction

A probabilistic air segmentation and sparse regression method was developed for PET attenuation correction with a mean whole-brain PET error of 2.42% ± 1.0 by estimating continuous pseudo CT images from T1-weighted MR and atlas CT images

Cardiovascular and metabolic health is associated with functional brain connectivity in middle-aged and older adults: Results from the Human Connectome Project-Aging study

Several cardiovascular and metabolic indicators, such as cholesterol and blood pressure have been associated with altered neural and cognitive health as well as increased risk of dementia and Alzheimer\u27s disease in later life. In this cross-sectional study, we examined how an aggregate index of cardiovascular and metabolic risk factor measures was associated with correlation-based estimates of resting-state functional connectivity (FC) across a broad adult age-span (36-90+ years) from 930 volunteers in the Human Connectome Project Aging (HCP-A). Increased (i.e., worse) aggregate cardiometabolic scores were associated with reduced FC globally, with especially strong effects in insular, medial frontal, medial parietal, and superior temporal regions. Additionally, at the network-level, FC between core brain networks, such as default-mode and cingulo-opercular, as well as dorsal attention networks, showed strong effects of cardiometabolic risk. These findings highlight the lifespan impact of cardiovascular and metabolic health on whole-brain functional integrity and how these conditions may disrupt higher-order network integrity

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

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

Probabilistic Air Segmentation and Sparse Regression Estimated Pseudo CT for PET/MR Attenuation Correction

PURPOSE: To develop a positron emission tomography (PET) attenuation correction method for brain PET/magnetic resonance (MR) imaging by estimating pseudo computed tomographic (CT) images from T1-weighted MR and atlas CT images. MATERIALS AND METHODS: In this institutional review board–approved and HIPAA-compliant study, PET/MR/CT images were acquired in 20 subjects after obtaining written consent. A probabilistic air segmentation and sparse regression (PASSR) method was developed for pseudo CT estimation. Air segmentation was performed with assistance from a probabilistic air map. For nonair regions, the pseudo CT numbers were estimated via sparse regression by using atlas MR patches. The mean absolute percentage error (MAPE) on PET images was computed as the normalized mean absolute difference in PET signal intensity between a method and the reference standard continuous CT attenuation correction method. Friedman analysis of variance and Wilcoxon matched-pairs tests were performed for statistical comparison of MAPE between the PASSR method and Dixon segmentation, CT segmentation, and population averaged CT atlas (mean atlas) methods. RESULTS: The PASSR method yielded a mean MAPE ± standard deviation of 2.42% ± 1.0, 3.28% ± 0.93, and 2.16% ± 1.75, respectively, in the whole brain, gray matter, and white matter, which were significantly lower than the Dixon, CT segmentation, and mean atlas values (P < .01). Moreover, 68.0% ± 16.5, 85.8% ± 12.9, and 96.0% ± 2.5 of whole-brain volume had within ±2%, ±5%, and ±10% percentage error by using PASSR, respectively, which was significantly higher than other methods (P < .01). CONCLUSION: PASSR outperformed the Dixon, CT segmentation, and mean atlas methods by reducing PET error owing to attenuation correction. (©) RSNA, 201

Preliminary evidence for cerebral capillary shunting in adults with sickle cell anemia

Elevated flow velocities in adults with sickle cell anemia (SCA) may cause rapid erythrocyte transit through capillaries. This phenomenon could present as dural venous sinus hyperintensity on arterial spin labeling (ASL)-MRI and could be indicative of capillary shunting. Here, the prevalence of ASL venous hyperintensities and association with relevant physiology in adults with SCA was investigated. SCA (n = 46) and age-matched control (n = 16) volunteers were recruited for 3.0 T MRI. Pseudo-continuous ASL-MRI was acquired for cerebral blood flow (CBF) calculation and venous hyperintensity determination; venous signal intensity and a categorical venous score (three raters; 0 = no hyperintensity, 1 = focal hyperintensity, and 2 = diffuse hyperintensity) were recorded. Flow velocity in cervical internal carotid artery segments was determined from phase contrast data (v enc = 40 cm/s) and whole-brain oxygen extraction fraction (OEF) was determined from T 2-relaxation-under-spin-tagging MRI. Cerebral metabolic rate of oxygen was calculated as the product of OEF, CBF, and blood oxygen content. ASL venous hyperintensities were significantly (p < 0.001) more prevalent in SCA (65%) relative to control (6%) participants and were associated with elevated flow velocities (p = 0.03). CBF (p < 0.001), but not OEF, increased with increasing hyperintensity score. Prospective trials that evaluate this construct as a possible marker of impaired oxygen delivery and stroke risk may be warranted

Associations between age, sex, APOE genotype, and regional vascular physiology in typically aging adults

Altered blood flow in the human brain is characteristic of typical aging. However, numerous factors contribute to inter-individual variation in patterns of blood flow throughout the lifespan. To better understand the mechanisms behind such variation, we studied how sex and APOE genotype, a primary genetic risk factor for Alzheimer's disease (AD), influence associations between age and brain perfusion measures. We conducted a cross-sectional study of 562 participants from the Human Connectome Project - Aging (36 to >90 years of age). We found widespread associations between age and vascular parameters, where increasing age was associated with regional decreases in cerebral blood flow (CBF) and increases in arterial transit time (ATT). When grouped by sex and APOE genotype, interactions between group and age demonstrated that females had relatively greater CBF and lower ATT compared to males. Females carrying the APOE ε4 allele showed the strongest association between CBF decline and ATT incline with age. This demonstrates that sex and genetic risk for AD modulate age-associated patterns of cerebral perfusion measures