pet imaging
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2022 ◽  
Vol 9 (1) ◽  
Roberto Fedrigo ◽  
Dan J. Kadrmas ◽  
Patricia E. Edem ◽  
Lauren Fougner ◽  
Ivan S. Klyuzhin ◽  

Abstract Background Positron emission tomography (PET) with prostate specific membrane antigen (PSMA) have shown superior performance in detecting metastatic prostate cancers. Relative to [18F]fluorodeoxyglucose ([18F]FDG) PET images, PSMA PET images tend to visualize significantly higher-contrast focal lesions. We aim to evaluate segmentation and reconstruction algorithms in this emerging context. Specifically, Bayesian or maximum a posteriori (MAP) image reconstruction, compared to standard ordered subsets expectation maximization (OSEM) reconstruction, has received significant interest for its potential to reach convergence with minimal noise amplifications. However, few phantom studies have evaluated the quantitative accuracy of such reconstructions for high contrast, small lesions (sub-10 mm) that are typically observed in PSMA images. In this study, we cast 3 mm–16-mm spheres using epoxy resin infused with a long half-life positron emitter (sodium-22; 22Na) to simulate prostate cancer metastasis. The anthropomorphic Probe-IQ phantom, which features a liver, bladder, lungs, and ureters, was used to model relevant anatomy. Dynamic PET acquisitions were acquired and images were reconstructed with OSEM (varying subsets and iterations) and BSREM (varying β parameters), and the effects on lesion quantitation were evaluated. Results The 22Na lesions were scanned against an aqueous solution containing fluorine-18 (18F) as the background. Regions-of-interest were drawn with MIM Software using 40% fixed threshold (40% FT) and a gradient segmentation algorithm (MIM’s PET Edge+). Recovery coefficients (RCs) (max, mean, peak, and newly defined “apex”), metabolic tumour volume (MTV), and total tumour uptake (TTU) were calculated for each sphere. SUVpeak and SUVapex had the most consistent RCs for different lesion-to-background ratios and reconstruction parameters. The gradient-based segmentation algorithm was more accurate than 40% FT for determining MTV and TTU, particularly for lesions $$\le$$ ≤  6 mm in diameter (R2 = 0.979–0.996 vs. R2 = 0.115–0.527, respectively). Conclusion An anthropomorphic phantom was used to evaluate quantitation for PSMA PET imaging of metastatic prostate cancer lesions. BSREM with β = 200–400 and OSEM with 2–5 iterations resulted in the most accurate and robust measurements of SUVmean, MTV, and TTU for imaging conditions in 18F-PSMA PET/CT images. SUVapex, a hybrid metric of SUVmax and SUVpeak, was proposed for robust, accurate, and segmentation-free quantitation of lesions for PSMA PET.

Keisuke Matsubara ◽  
Masanobu Ibaraki ◽  
Mitsutaka Nemoto ◽  
Hiroshi Watabe ◽  
Yuichi Kimura

2022 ◽  
MariaGiovanna Trivieri ◽  
Philip M Robson ◽  
Vittoria Vergani ◽  
Gina LaRocca ◽  
Angelica M Romero-Daza ◽  

Objectives: To evaluate an extended hybrid MR/PET imaging strategy in cardiac sarcoidosis (CS) employing qualitative and quantitative assessment of PET tracer uptake, and to evaluate its association with cardiac-related outcomes. Background: Invasive endomyocardial biopsy is the gold standard to diagnose CS, but it has poor sensitivity due to the patchy distribution of disease. Imaging with hybrid late gadolinium enhancement (LGE) MR and 18F-fluorodexyglucose (18F-FDG) PET allows simultaneous assessment of myocardial injury and disease activity and has shown promise for improved diagnosis of active CS based on the combined positive imaging outcome, MR(+)PET(+). Methods: 148 patients with suspected CS were enrolled for hybrid MR/PET imaging. Patients were classified based on presence/absence of LGE (MR+/MR-), presence/absence of 18F-FDG (PET+/PET-), and pattern of 18F-FDG uptake (focal/diffuse) into the following categories: MR(+)PET(+)FOCAL, MR(+)PET(+)DIFFUSE, MR(+)PET(-), MR(-)PET(+)FOCAL, MR(-)PET(+)DIFFUSE, MR(-)PET(-). Patients classified as MR(+)PET(+)FOCAL were designated as having active CS [aCS(+)], while all others were considered as having inactive or absent CS and designated aCS(-). Quantitative values of standard uptake value (SUVmax), target-to-background ratio (TBRmax), target-to-normal-myocardium ratio (TNMRmax) and T2 were measured. Occurrence of a cardiac-related clinical outcome was defined as any of the following during the 6-month period after imaging: cardiac arrest, ventricular arrhythmia, complete heart block, need for cardiac resynchronization/defibrillator/pacemaker/monitoring device (CRT-D, ICD/WCD, or ILR). MR/PET imaging results were compared to the presence of the composite clinical outcome. Results: Patients designated aCS(+) had more than 4-fold increased odds of meeting the clinical endpoint compared to aCS(-) (unadjusted odds ratio 4.8; 95% CI 2.0-11.4; p<0.001). TNMRmax achieved an area under the receiver operating characteristic curve of 0.90 for separating aCS(+) from aCS(-). Conclusions: Hybrid MR/PET imaging with an extended image-based classification of CS was statistically associated with clinical outcomes in CS. TNMRmax had high sensitivity and excellent specificity for quantifying the imaging-based classification of active CS.

2022 ◽  
Vol 13 ◽  
Roos J. Jutten ◽  
Dorene M. Rentz ◽  
Jessie F. Fu ◽  
Danielle V. Mayblyum ◽  
Rebecca E. Amariglio ◽  

Introduction: We investigated whether monthly assessments of a computerized cognitive composite (C3) could aid in the detection of differences in practice effects (PE) in clinically unimpaired (CU) older adults, and whether diminished PE were associated with Alzheimer's disease (AD) biomarkers and annual cognitive decline.Materials and Methods:N = 114 CU participants (age 77.6 ± 5.0, 61% female, MMSE 29 ± 1.2) from the Harvard Aging Brain Study completed the self-administered C3 monthly, at-home, on an iPad for one year. At baseline, participants underwent in-clinic Preclinical Alzheimer's Cognitive Composite-5 (PACC5) testing, and a subsample (n = 72, age = 77.8 ± 4.9, 59% female, MMSE 29 ± 1.3) had 1-year follow-up in-clinic PACC5 testing available. Participants had undergone PIB-PET imaging (0.99 ± 1.6 years before at-home baseline) and Flortaucipir PET imaging (n = 105, 0.62 ± 1.1 years before at-home baseline). Linear mixed models were used to investigate change over months on the C3 adjusting for age, sex, and years of education, and to extract individual covariate-adjusted slopes over the first 3 months. We investigated the association of 3-month C3 slopes with global amyloid burden and tau deposition in eight predefined regions of interest, and conducted Receiver Operating Characteristic analyses to examine how accurately 3-month C3 slopes could identify individuals that showed &gt;0.10 SD annual decline on the PACC-5.Results: Overall, individuals improved on all C3 measures over 12 months (β = 0.23, 95% CI [0.21–0.25], p &lt; 0.001), but improvement over the first 3 months was greatest (β = 0.68, 95% CI [0.59–0.77], p &lt; 0.001), suggesting stronger PE over initial repeated exposures. However, lower PE over 3 months were associated with more global amyloid burden (r = −0.20, 95% CI [−0.38 – −0.01], p = 0.049) and tau deposition in the entorhinal cortex (r = −0.38, 95% CI [−0.54 – −0.19], p &lt; 0.001) and inferior-temporal lobe (r = −0.23, 95% CI [−0.41 – −0.02], p = 0.03). 3-month C3 slopes exhibited good discriminative ability to identify PACC-5 decliners (AUC 0.91, 95% CI [0.84–0.98]), which was better than baseline C3 (p &lt; 0.001) and baseline PACC-5 scores (p = 0.02).Conclusion: While PE are commonly observed among CU adults, diminished PE over monthly cognitive testing are associated with greater AD biomarker burden and cognitive decline. Our findings imply that unsupervised computerized testing using monthly retest paradigms can provide rapid detection of diminished PE indicative of future cognitive decline in preclinical AD.

Blood ◽  
2022 ◽  
Vol 139 (2) ◽  
pp. 154-155
Anne-Ségolène Cottereau

2022 ◽  
pp. jnumed.121.263222
Daniele Bertoglio ◽  
Nicolas Halloin ◽  
Stef De Lombaerde ◽  
Aleksandar Jankovski ◽  
Jeroen Verhaeghe ◽  

Tao Sun ◽  
Yaping Wu ◽  
Yan Bai ◽  
Zhenguo Wang ◽  
Chushu Shen ◽  

Abstract As a non-invasive imaging tool, Positron Emission Tomography (PET) plays an important role in brain science and disease research. Dynamic acquisition is one way of brain PET imaging. Its wide application in clinical research has often been hindered by practical challenges, such as patient involuntary movement, which could degrade both image quality and the accuracy of the quantification. This is even more obvious in scans of patients with neurodegeneration or mental disorders. Conventional motion compensation methods were either based on images or raw measured data, were shown to be able to reduce the effect of motion on the image quality. As for a dynamic PET scan, motion compensation can be challenging as tracer kinetics and relatively high noise can be present in dynamic frames. In this work, we propose an image-based inter-frame motion compensation approach specifically designed for dynamic brain PET imaging. Our method has an iterative implementation that only requires reconstructed images, based on which the inter-frame subject movement can be estimated and compensated. The method utilized tracer-specific kinetic modelling and can deal with simple and complex movement patterns. The synthesized phantom study showed that the proposed method can compensate for the simulated motion in scans with 18F-FDG, 18F-Fallypride and 18F-AV45. Fifteen dynamic 18F-FDG patient scans with motion artifacts were also processed. The quality of the recovered image was superior to the one of the non-corrected images and the corrected images with other image-based methods. The proposed method enables retrospective image quality control for dynamic brain PET imaging, hence facilitates the applications of dynamic PET in clinics and research.

Hanyi Fang ◽  
Samantha Rossano ◽  
Xingxing Wang ◽  
Nabeel Nabulsi ◽  
Brian Kelley ◽  

JCI Insight ◽  
2022 ◽  
Vol 7 (1) ◽  
Alvaro A. Ordonez ◽  
Matthew F.L. Parker ◽  
Robert J. Miller ◽  
Donika Plyku ◽  
Camilo A. Ruiz-Bedoya ◽  

2022 ◽  
Vol Publish Ahead of Print ◽  
Chenhao Jia ◽  
Meiqi Wu ◽  
Tzu-Chen Yen ◽  
Yanfeng Li ◽  
Ruixue Cui

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