Early-Frame [18F]Florbetaben PET/MRI for Cerebral Blood Flow Quantification in Patients with Cognitive Impairment: Comparison to an [15O]Water Gold Standard.

Cerebral blood flow (CBF) may be estimated from early-frame PET imaging of lipophilic tracers, such as amyloid agents, enabling measurement of this important biomarker in participants with dementia and memory decline. Although previous methods could map relative CBF, quantitative measurement in absolute units (mL/100 g/min) remained challenging and has not been evaluated against the gold standard method of [15O]water PET. The purpose of this study was to develop and validate a minimally invasive quantitative CBF imaging method combining early [18F]florbetaben (eFBB) with phase-contrast MRI using simultaneous PET/MRI. Methods: Twenty participants (11 men and 9 women; 8 cognitively normal, 9 with mild cognitive impairment, and 3 with dementia; 10 ß-amyloid negative and 10 ß-amyloid positive; 69 ± 9 y old) underwent [15O]water PET, phase-contract MRI, and eFBB imaging in a single session on a 3-T PET/MRI scanner. Quantitative CBF images were created from the first 2 min of brain activity after [18F]florbetaben injection combined with phase-contrast MRI measurement of total brain blood flow. These maps were compared with [15O]water CBF using concordance correlation (CC) and Bland-Altman statistics for gray matter, white matter, and individual regions derived from the automated anatomic labeling (AAL) atlas. Results: The 2 methods showed similar results in gray matter ([15O]water, 55.2 ± 14.7 mL/100 g/min; eFBB, 55.9 ± 14.2 mL/100 g/min; difference, 0.7 ± 2.4 mL/100 g/min; P = 0.2) and white matter ([15O]water, 21.4 ± 5.6 mL/100 g/min; eFBB, 21.2 ± 5.3 mL/100 g/min; difference, -0.2 ± 1.0 mL/100 g/min; P = 0.4). The intrasubject CC for AAL-derived regions was high (0.91 ± 0.04). Intersubject CC in different AAL-derived regions was similarly high, ranging from 0.86 for midfrontal regions to 0.98 for temporal regions. There were no significant differences in performance between the methods in the amyloid-positive and amyloid-negative groups as well as participants with different cognitive statuses. Conclusion: We conclude that eFBB PET/MRI can provide robust CBF measurements, highlighting the capability of simultaneous PET/MRI to provide measurements of both CBF and amyloid burden in a single imaging session in participants with memory disorders.

Predicting FDG-PET Images From Multi-Contrast MRI Using Deep Learning in Patients With Brain Neoplasms.

BACKGROUND: 18 F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is valuable for determining presence of viable tumor, but is limited by geographical restrictions, radiation exposure, and high cost. PURPOSE: To generate diagnostic-quality PET equivalent imaging for patients with brain neoplasms by deep learning with multi-contrast MRI. STUDY TYPE: Retrospective. SUBJECTS: Patients (59 studies from 51 subjects; age 56 ± 13 years; 29 males) who underwent 18 F-FDG PET and MRI for determining recurrent brain tumor. FIELD STRENGTH/SEQUENCE: 3T; 3D GRE T1, 3D GRE T1c, 3D FSE T2-FLAIR, and 3D FSE ASL, 18 F-FDG PET imaging. ASSESSMENT: Convolutional neural networks were trained using four MRIs as inputs and acquired FDG PET images as output. The agreement between the acquired and synthesized PET was evaluated by quality metrics and Bland-Altman plots for standardized uptake value ratio. Three physicians scored image quality on a 5-point scale, with score ≥3 as high-quality. They assessed the lesions on a 5-point scale, which was binarized to analyze diagnostic consistency of the synthesized PET compared to the acquired PET. STATISTICAL TESTS: The agreement in ratings between the acquired and synthesized PET were tested with Gwet's AC and exact Bowker test of symmetry. Agreement of the readers was assessed by Gwet's AC. P = 0.05 was used as the cutoff for statistical significance. RESULTS: The synthesized PET visually resembled the acquired PET and showed significant improvement in quality metrics (+21.7% on PSNR, +22.2% on SSIM, -31.8% on RSME) compared with ASL. A total of 49.7% of the synthesized PET were considered as high-quality compared to 73.4% of the acquired PET which was statistically significant, but with distinct variability between readers. For the positive/negative lesion assessment, the synthesized PET had an accuracy of 87% but had a tendency to overcall. CONCLUSION: The proposed deep learning model has the potential of synthesizing diagnostic quality FDG PET images without the use of radiotracers. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Exposure to Metalworking Fluids and Cancer Incidence in the United Auto Workers-General Motors Cohort.

In previous studies, investigators have reported increased risks of specific cancers associated with exposure to metalworking fluids (MWFs). In this report we broadly examine the incidence of 14 types of cancer, with a focus on digestive, respiratory, and hormonal cancers, in the United Auto Workers-General Motors (UAW-GM) cohort, a cohort of workers exposed to MWFs (1973-2015). The cohort included 39,132 workers followed for cancer incidence. Cox models yielded estimates of adjusted hazard ratios, with categorical variables for lagged cumulative exposure to 3 types of MWF (straight, soluble, and synthetic). We fitted penalized splines to examine the shape of the exposure-response relationships. There were 7,809 incident cancer cases of interest. Oil-based straight and soluble MWFs were each modestly associated with all cancers combined. Exposure-response patterns were consistent with prior reports from this cohort, and results for splined exposures generally reflected their categorically modeled counterparts. We found significantly increased incidence of stomach and kidney cancer with higher levels of straight MWF exposure and increased rectal and prostate cancer with increasing water-based synthetic MWF exposure. Only non-Hodgkin lymphoma and prostate cancer were associated with soluble MWF. All results for colon and lung cancers were null. Our results provide updated evidence for associations between MWF exposure and incidence of several types of cancer.

The Impact of Job Loss on Self-injury Mortality in a Cohort of Autoworkers: Application of a Novel Causal Approach.

BACKGROUND: Recent increases in national rates of suicide and fatal overdose have been linked to a deterioration of economic and social stability. The American auto industry experienced comparable pressures beginning in the 1980s with the emergence of a competitive global market. METHODS: Using the United Autoworkers-General Motors (GM) cohort as a case study, we examine the impact of employment loss on these self-injury mortality events. For 29,538 autoworkers employed on or after 1 January 1970, we apply incremental propensity score interventions, a novel causal inference approach, to examine how proportional shifts in the odds of leaving active GM employment affect the cumulative incidence of self-injury mortality. RESULTS: Cumulative incidence of self-injury mortality was 0.87% (255 cases) at the observed odds of leaving active GM employment (δ = 1) over a 45-year period. A 10% decrease in the odds of leaving active GM employment (δ = 0.9) results in an estimated 8% drop in self-injury mortality (234 cases) while a 10% increase (δ = 1.1) results in a 19% increase in self-injury mortality (303 cases). CONCLUSIONS: These results are consistent with the hypothesis that leaving active employment at GM increases the risk of death due to suicide or drug overdose.

Assessment of motion and model bias on the detection of dopamine response to behavioral challenge.

Compartmental modeling analysis of 11C-raclopride (RAC) PET data can be used to measure the dopaminergic response to intra-scan behavioral tasks. Bias in estimates of binding potential (BPND) and its dynamic changes (ΔBPND) can arise both when head motion is present and when the compartmental model used for parameter estimation deviates from the underlying biology. The purpose of this study was to characterize the effects of motion and model bias within the context of a behavioral task challenge, examining the impacts of different mitigation strategies. Seventy healthy adults were administered bolus plus constant infusion RAC during a simultaneous PET/magnetic resonance (MR) scan with a reward task experiment. BPND and ΔBPND were estimated using an extension of the Multilinear Reference Tissue Model (E-MRTM2) and a new method (DE-MRTM2) was proposed to selectively discount the contribution of the initial uptake period. Motion was effectively corrected with a standard frame-based approach, which performed equivalently to a more complex reconstruction-based approach. DE-MRTM2 produced estimates of ΔBPND in putamen and nucleus accumbens that were significantly different from those estimated from E-MRTM2, while also decoupling ΔBPND values from first-pass k2' estimation and removing skew in the spatial bias distribution of parametric ΔBPND estimates within the striatum.

Metalworking Fluid Exposure and Stroke Mortality Among US Autoworkers.

Although air pollution is an important risk factor for stroke, few studies have considered the impact of workplace exposure to particulate matter (PM). We examined implications of exposure to PM composed of metalworking fluids (MWFs) for stroke mortality in the United Autoworkers-General Motors cohort. Cox proportional hazards models with age as the timescale were used to estimate the association of cumulative straight, soluble, and synthetic MWF exposure with stroke mortality, controlling for sex, race, plant, calendar year, and hire year. Among 38,553 autoworkers followed during 1941-1995, we identified 114 ischemic stroke deaths and 113 hemorrhagic stroke deaths. Overall stroke mortality risk was increased among workers in the middle exposure category for straight MWF (hazard ratio (HR) = 1.31, 95% confidence interval (CI): 0.87, 1.98) and workers in the highest exposure category for synthetic MWF (HR = 1.94, 95% CI: 1.13, 3.16) compared with workers who had no direct exposure. Ischemic stroke mortality risk was increased among workers in the highest exposure categories for straight MWF (HR = 1.45, 95% CI: 0.83, 2.52) and synthetic MWF (HR = 2.39, 95% CI: 1.39, 4.50). We observed no clear relationship between MWF exposure and hemorrhagic stroke mortality. Our results support a potentially important role for occupational PM exposures in stroke mortality and indicate the need for further studies of PM exposure and stroke in varied occupational settings.

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.

Racial disparities in alcohol-related liver disease mortality in a 75 year follow-up study of Michigan autoworkers.

BACKGROUND: Suicide, drug overdose, and alcohol-related liver disease (ALD) mortality have been rising in the United States. While suicide and overdose have received a great deal of attention, far less public health concern has focused on chronic ALD. To address this gap, we examine ALD mortality rates, by race, in a cohort of autoworkers to describe trends over the past 75 years, from the peak in automobile manufacturing employment through its decline. METHODS: Based on the United Autoworkers-General Motors (UAW-GM) cohort we estimated temporal trends in age-adjusted ALD mortality rates from 1941 through 2015 at three automobile manufacturing plants in Michigan. We compared these rates to county, state, and U.S. rates, directly standardized to the 2000 U.S. census, to assess the roles of race and employment on ALD mortality. RESULTS: The overall age-adjusted ALD mortality rate among 41,097 male autoworkers peaked at 46.1 per 100,000 in the 1970s, followed by a gradual decline and a recent rise. Rates were slightly higher for black than white men until early 2000s, when rates increased only for white men. ALD mortality rates in the study cohort tracked national, state, and county rates for white men until the most recent time period, but were lower throughout the study period for black men, especially in the 1970s and 1980s. CONCLUSIONS: Employment in automobile manufacturing may have offered some protection against death from ALD for black men, and loss of those manufacturing jobs may have impacted white men without a college degree more in recent decades.

True ultra-low-dose amyloid PET/MRI enhanced with deep learning for clinical interpretation.

PURPOSE: While sampled or short-frame realizations have shown the potential power of deep learning to reduce radiation dose for PET images, evidence in true injected ultra-low-dose cases is lacking. Therefore, we evaluated deep learning enhancement using a significantly reduced injected radiotracer protocol for amyloid PET/MRI. METHODS: Eighteen participants underwent two separate 18F-florbetaben PET/MRI studies in which an ultra-low-dose (6.64 ± 3.57 MBq, 2.2 ± 1.3% of standard) or a standard-dose (300 ± 14 MBq) was injected. The PET counts from the standard-dose list-mode data were also undersampled to approximate an ultra-low-dose session. A pre-trained convolutional neural network was fine-tuned using MR images and either the injected or sampled ultra-low-dose PET as inputs. Image quality of the enhanced images was evaluated using three metrics (peak signal-to-noise ratio, structural similarity, and root mean square error), as well as the coefficient of variation (CV) for regional standard uptake value ratios (SUVRs). Mean cerebral uptake was correlated across image types to assess the validity of the sampled realizations. To judge clinical performance, four trained readers scored image quality on a five-point scale (using 15% non-inferiority limits for proportion of studies rated 3 or better) and classified cases into amyloid-positive and negative studies. RESULTS: The deep learning-enhanced PET images showed marked improvement on all quality metrics compared with the low-dose images as well as having generally similar regional CVs as the standard-dose. All enhanced images were non-inferior to their standard-dose counterparts. Accuracy for amyloid status was high (97.2% and 91.7% for images enhanced from injected and sampled ultra-low-dose data, respectively) which was similar to intra-reader reproducibility of standard-dose images (98.6%). CONCLUSION: Deep learning methods can synthesize diagnostic-quality PET images from ultra-low injected dose simultaneous PET/MRI data, demonstrating the general validity of sampled realizations and the potential to reduce dose significantly for amyloid imaging.

Persistently Elevated mTOR Complex 1-S6 Kinase 1 Disrupts DARPP-32-Dependent D1 Dopamine Receptor Signaling and Behaviors.

BACKGROUND: The serine-threonine kinase mTORC1 (mechanistic target of rapamycin complex 1) is essential for normal cell function but is aberrantly activated in the brain in both genetic-developmental and sporadic diseases and is associated with a spectrum of neuropsychiatric symptoms. The underlying molecular mechanisms of cognitive and neuropsychiatric symptoms remain controversial. METHODS: The present study examines behaviors in transgenic models that express Rheb, the most proximal known activator of mTORC1, and profiles striatal phosphoproteomics in a model with persistently elevated mTORC1 signaling. Biochemistry, immunohistochemistry, electrophysiology, and behavior approaches are used to examine the impact of persistently elevated mTORC1 on D1 dopamine receptor (D1R) signaling. The effect of persistently elevated mTORC1 was confirmed using D1-Cre to elevate mTORC1 activity in D1R neurons. RESULTS: We report that persistently elevated mTORC1 signaling blocks canonical D1R signaling that is dependent on DARPP-32 (dopamine- and cAMP-regulated neuronal phosphoprotein). The immediate downstream effector of mTORC1, ribosomal S6 kinase 1 (S6K1), phosphorylates and activates DARPP-32. Persistent elevation of mTORC1-S6K1 occludes dynamic D1R signaling downstream of DARPP-32 and blocks multiple D1R responses, including dynamic gene expression, D1R-dependent corticostriatal plasticity, and D1R behavioral responses including sociability. Candidate biomarkers of mTORC1-DARPP-32 occlusion are increased in the brain of human disease subjects in association with elevated mTORC1-S6K1, supporting a role for this mechanism in cognitive disease. CONCLUSIONS: The mTORC1-S6K1 intersection with D1R signaling provides a molecular framework to understand the effects of pathological mTORC1 activation on behavioral symptoms in neuropsychiatric disease.

Suicide, overdose and worker exit in a cohort of Michigan autoworkers.

BACKGROUND: In recent decades, suicide and fatal overdose rates have increased in the US, particularly for working-age adults with no college education. The coincident decline in manufacturing has limited stable employment options for this population. Erosion of the Michigan automobile industry provides a striking case study. METHODS: We used individual-level data from a retrospective cohort study of 26 804 autoworkers in the United Autoworkers-General Motors cohort, using employment records from 1970 to 1994 and mortality follow-up from 1970 to 2015. We estimated HRs for suicide or fatal overdose in relation to leaving work, measured as active or inactive employment status and age at worker exit. RESULTS: There were 257 deaths due to either suicide (n=202) or overdose (n=55); all but 21 events occurred after leaving work. The hazard rate for suicide was 16.1 times higher for inactive versus active workers (95% CI 9.8 to 26.5). HRs for suicide were elevated for all younger age groups relative to those leaving work after age 55. Those 30-39 years old at exit had the highest HR for suicide, 1.9 (95% CI 1.2 to 3.0). When overdose was included, the rate increased by twofold for both 19- to 29-year-olds and 30- to 39-year-olds at exit. Risks remained elevated when follow-up was restricted to 5 years after exit. CONCLUSIONS: Autoworkers who left work had a higher risk of suicide or overdose than active employees. Those who left before retirement age had higher rates than those who left after, suggesting that leaving work early may increase the risk.

Generalization of deep learning models for ultra-low-count amyloid PET/MRI using transfer learning.

PURPOSE: We aimed to evaluate the performance of deep learning-based generalization of ultra-low-count amyloid PET/MRI enhancement when applied to studies acquired with different scanning hardware and protocols. METHODS: Eighty simultaneous [18F]florbetaben PET/MRI studies were acquired, split equally between two sites (site 1: Signa PET/MRI, GE Healthcare, 39 participants, 67 ± 8 years, 23 females; site 2: mMR, Siemens Healthineers, 64 ± 11 years, 23 females) with different MRI protocols. Twenty minutes of list-mode PET data (90-110 min post-injection) were reconstructed as ground-truth. Ultra-low-count data obtained from undersampling by a factor of 100 (site 1) or the first minute of PET acquisition (site 2) were reconstructed for ultra-low-dose/ultra-short-time (1% dose and 5% time, respectively) PET images. A deep convolution neural network was pre-trained with site 1 data and either (A) directly applied or (B) trained further on site 2 data using transfer learning. Networks were also trained from scratch based on (C) site 2 data or (D) all data. Certified physicians determined amyloid uptake (+/-) status for accuracy and scored the image quality. The peak signal-to-noise ratio, structural similarity, and root-mean-squared error were calculated between images and their ground-truth counterparts. Mean regional standardized uptake value ratios (SUVR, reference region: cerebellar cortex) from 37 successful site 2 FreeSurfer segmentations were analyzed. RESULTS: All network-synthesized images had reduced noise than their ultra-low-count reconstructions. Quantitatively, image metrics improved the most using method B, where SUVRs had the least variability from the ground-truth and the highest effect size to differentiate between positive and negative images. Method A images had lower accuracy and image quality than other methods; images synthesized from methods B-D scored similarly or better than the ground-truth images. CONCLUSIONS: Deep learning can successfully produce diagnostic amyloid PET images from short frame reconstructions. Data bias should be considered when applying pre-trained deep ultra-low-count amyloid PET/MRI networks for generalization.

Ultra-low-dose PET reconstruction using generative adversarial network with feature matching and task-specific perceptual loss.

PURPOSE: Our goal was to use a generative adversarial network (GAN) with feature matching and task-specific perceptual loss to synthesize standard-dose amyloid Positron emission tomography (PET) images of high quality and including accurate pathological features from ultra-low-dose PET images only. METHODS: Forty PET datasets from 39 participants were acquired with a simultaneous PET/MRI scanner following injection of 330 ± 30 MBq of the amyloid radiotracer 18F-florbetaben. The raw list-mode PET data were reconstructed as the standard-dose ground truth and were randomly undersampled by a factor of 100 to reconstruct 1% low-dose PET scans. A 2D encoder-decoder network was implemented as the generator to synthesize a standard-dose image and a discriminator was used to evaluate them. The two networks contested with each other to achieve high-visual quality PET from the ultra-low-dose PET. Multi-slice inputs were used to reduce noise by providing the network with 2.5D information. Feature matching was applied to reduce hallucinated structures. Task-specific perceptual loss was designed to maintain the correct pathological features. The image quality was evaluated by peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and root mean square error (RMSE) metrics with and without each of these modules. Two expert radiologists were asked to score image quality on a 5-point scale and identified the amyloid status (positive or negative). RESULTS: With only low-dose PET as input, the proposed method significantly outperformed Chen et al.'s method (Chen et al. Radiology. 2018;290:649-656) (which shows the best performance in this task) with the same input (PET-only model) by 1.87 dB in PSNR, 2.04% in SSIM, and 24.75% in RMSE. It also achieved comparable results to Chen et al.'s method which used additional magnetic resonance imaging (MRI) inputs (PET-MR model). Experts' reading results showed that the proposed method could achieve better overall image quality and maintain better pathological features indicating amyloid status than both PET-only and PET-MR models proposed by Chen et al. CONCLUSION: Standard-dose amyloid PET images can be synthesized from ultra-low-dose images using GAN. Applying adversarial learning, feature matching, and task-specific perceptual loss are essential to ensure image quality and the preservation of pathological features.

Ultra-Low-Dose 18F-Florbetaben Amyloid PET Imaging Using Deep Learning with Multi-Contrast MRI Inputs.

Purpose To reduce radiotracer requirements for amyloid PET/MRI without sacrificing diagnostic quality by using deep learning methods. Materials and Methods Forty data sets from 39 patients (mean age ± standard deviation [SD], 67 years ± 8), including 16 male patients and 23 female patients (mean age, 66 years ± 6 and 68 years ± 9, respectively), who underwent simultaneous amyloid (fluorine 18 [18F]-florbetaben) PET/MRI examinations were acquired from March 2016 through October 2017 and retrospectively analyzed. One hundredth of the raw list-mode PET data were randomly chosen to simulate a low-dose (1%) acquisition. Convolutional neural networks were implemented with low-dose PET and multiple MR images (PET-plus-MR model) or with low-dose PET alone (PET-only) as inputs to predict full-dose PET images. Quality of the synthesized images was evaluated while Bland-Altman plots assessed the agreement of regional standard uptake value ratios (SUVRs) between image types. Two readers scored image quality on a five-point scale (5 = excellent) and determined amyloid status (positive or negative). Statistical analyses were carried out to assess the difference of image quality metrics and reader agreement and to determine confidence intervals (CIs) for reading results. Results The synthesized images (especially from the PET-plus-MR model) showed marked improvement on all quality metrics compared with the low-dose image. All PET-plus-MR images scored 3 or higher, with proportions of images rated greater than 3 similar to those for the full-dose images (-10% difference [eight of 80 readings], 95% CI: -15%, -5%). Accuracy for amyloid status was high (71 of 80 readings [89%]) and similar to intrareader reproducibility of full-dose images (73 of 80 [91%]). The PET-plus-MR model also had the smallest mean and variance for SUVR difference to full-dose images. Conclusion Simultaneously acquired MRI and ultra-low-dose PET data can be used to synthesize full-dose-like amyloid PET images. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Catana in this issue.

Corrections to "Direct Patlak Reconstruction From Dynamic PET Data Using the Kernel Method With MRI Information Based on Structural Similarity".

In the above paper [1], there are typos in Algorithm 1 table. The correct version of Algorithm 1 is given below.

An Efficient Approach to Perform MR-assisted PET Data Optimization in Simultaneous PET/MR Neuroimaging Studies.

A main advantage of PET is that it provides quantitative measures of the radiotracer concentration, but its accuracy is confounded by several factors, including attenuation, subject motion, and limited spatial resolution. Using the information from one simultaneously acquired morphological MR sequence with embedded navigators, we propose an efficient method called MR-assisted PET data optimization (MaPET) to perform attenuation correction (AC), motion correction, and anatomy-aided reconstruction. Methods: For attenuation correction, voxel-wise linear attenuation coefficient maps were generated using an SPM8-based approach method on the MR volume. The embedded navigators were used to derive head motion estimates for event-based PET motion correction. The anatomy provided by the MR volume was incorporated into the PET image reconstruction using a kernel-based method. Region-based analyses were carried out to assess the quality of images generated through various stages of PET data optimization. Results: The optimized PET images reconstructed with MaPET was superior in image quality compared to images reconstructed using only attenuation correction, with high SNR and low coefficient of variation (5.08 and 0.229 in a composite cortical region compared to 3.12 and 0.570). The optimized images were also shown using the Cohen's d metric to achieve a greater effect size in distinguishing cortical regions with hypometabolism from regions of preserved metabolism in each individual for different diagnosis groups. Conclusion: We have shown the spatiotemporally correlated data acquired using a single MR sequence can be used for PET attenuation, motion and partial volume effects corrections and the MaPET method may enable more accurate assessment of pathological changes in dementia and other brain disorders.

Direct Patlak Reconstruction From Dynamic PET Data Using the Kernel Method With MRI Information Based on Structural Similarity.

Positron emission tomography (PET) is a functional imaging modality widely used in oncology, cardiology, and neuroscience. It is highly sensitive, but suffers from relatively poor spatial resolution, as compared with anatomical imaging modalities, such as magnetic resonance imaging (MRI). With the recent development of combined PET/MR systems, we can improve the PET image quality by incorporating MR information into image reconstruction. Previously, kernel learning has been successfully embedded into static and dynamic PET image reconstruction using either PET temporal or MRI information. Here, we combine both PET temporal and MRI information adaptively to improve the quality of direct Patlak reconstruction. We examined different approaches to combine the PET and MRI information in kernel learning to address the issue of potential mismatches between MRI and PET signals. Computer simulations and hybrid real-patient data acquired on a simultaneous PET/MR scanner were used to evaluate the proposed methods. Results show that the method that combines PET temporal information and MRI spatial information adaptively based on the structure similarity index has the best performance in terms of noise reduction and resolution improvement.

MR-assisted PET motion correction in simultaneous PET/MRI studies of dementia subjects.

BACKGROUND: Subject motion in positron emission tomography (PET) studies leads to image blurring and artifacts; simultaneously acquired magnetic resonance imaging (MRI) data provides a means for motion correction (MC) in integrated PET/MRI scanners. PURPOSE: To assess the effect of realistic head motion and MR-based MC on static [18 F]-fluorodeoxyglucose (FDG) PET images in dementia patients. STUDY TYPE: Observational study. POPULATION: Thirty dementia subjects were recruited. FIELD STRENGTH/SEQUENCE: 3T hybrid PET/MR scanner where EPI-based and T1 -weighted sequences were acquired simultaneously with the PET data. ASSESSMENT: Head motion parameters estimated from high temporal resolution MR volumes were used for PET MC. The MR-based MC method was compared to PET frame-based MC methods in which motion parameters were estimated by coregistering 5-minute frames before and after accounting for the attenuation-emission mismatch. The relative changes in standardized uptake value ratios (SUVRs) between the PET volumes processed with the various MC methods, without MC, and the PET volumes with simulated motion were compared in relevant brain regions. STATISTICAL TESTS: The absolute value of the regional SUVR relative change was assessed with pairwise paired t-tests testing at the P = 0.05 level, comparing the values obtained through different MR-based MC processing methods as well as across different motion groups. The intraregion voxelwise variability of regional SUVRs obtained through different MR-based MC processing methods was also assessed with pairwise paired t-tests testing at the P = 0.05 level. RESULTS: MC had a greater impact on PET data quantification in subjects with larger amplitude motion (higher than 18% in the medial orbitofrontal cortex) and greater changes were generally observed for the MR-based MC method compared to the frame-based methods. Furthermore, a mean relative change of ∼4% was observed after MC even at the group level, suggesting the importance of routinely applying this correction. The intraregion voxelwise variability of regional SUVRs was also decreased using MR-based MC. All comparisons were significant at the P = 0.05 level. DATA CONCLUSION: Incorporating temporally correlated MR data to account for intraframe motion has a positive impact on the FDG PET image quality and data quantification in dementia patients. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:1288-1296.