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Movement correction method for human brain PET images: application to quantitative analysis of dynamic 18F-FDDNP scans.

ABSTRACT: Head movement during a PET scan (especially a dynamic scan) can affect both the qualitative and the quantitative aspects of an image, making it difficult to accurately interpret the results. The primary objective of this study was to develop a retrospective image-based movement correction (MC) method and evaluate its implementation on dynamic 2-(1-{6-[(2-(18)F-fluoroethyl)(methyl)amino]-2-naphthyl}ethylidene)malononitrile ((18)F-FDDNP) PET images of cognitively intact controls and patients with Alzheimer's disease (AD).Dynamic (18)F-FDDNP PET images, used for in vivo imaging of beta-amyloid plaques and neurofibrillary tangles, were obtained from 12 AD patients and 9 age-matched controls. For each study, a transmission scan was first acquired for attenuation correction. An accurate retrospective MC method that corrected for transmission-emission and emission-emission misalignments was applied to all studies. No restriction was assumed for zero movement between the transmission scan and the first emission scan. Logan analysis, with the cerebellum as the reference region, was used to estimate various regional distribution volume ratio (DVR) values in the brain before and after MC. Discriminant analysis was used to build a predictive model for group membership, using data with and without MC.MC improved the image quality and quantitative values in (18)F-FDDNP PET images. In this subject population, no significant difference in DVR value was observed in the medial temporal (MTL) region of controls and patients with AD before MC. However, after MC, significant differences in DVR values in the frontal, parietal, posterior cingulate, MTL, lateral temporal (LTL), and global regions were seen between the 2 groups (P < 0.05). In controls and patients with AD, the variability of regional DVR values (as measured by the coefficient of variation) decreased on average by more than 18% after MC. Mean DVR separation between controls and patients with AD was higher in frontal, MTL, LTL, and global regions after MC. Group classification by discriminant analysis based on (18)F-FDDNP DVR values was markedly improved after MC.The streamlined and easy-to-use MC method presented in this work significantly improves the image quality and the measured tracer kinetics of (18)F-FDDNP PET images. The proposed MC method has the potential to be applied to PET studies on patients having other disorders (e.g., Down syndrome and Parkinson's disease) and to brain PET scans with other molecular imaging probes.

SUBMITTER: Wardak M

PROVIDER: S-EPMC2929579 | biostudies-literature | 2010 Feb

REPOSITORIES: biostudies-literature

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Movement correction method for human brain PET images: application to quantitative analysis of dynamic 18F-FDDNP scans.

Wardak Mirwais M Wong Koon-Pong KP Shao Weber W Dahlbom Magnus M Kepe Vladimir V Satyamurthy Nagichettiar N Small Gary W GW Barrio Jorge R JR Huang Sung-Cheng SC

Journal of nuclear medicine : official publication, Society of Nuclear Medicine 20100115 2

<h4>Unlabelled</h4>Head movement during a PET scan (especially a dynamic scan) can affect both the qualitative and the quantitative aspects of an image, making it difficult to accurately interpret the results. The primary objective of this study was to develop a retrospective image-based movement correction (MC) method and evaluate its implementation on dynamic 2-(1-{6-[(2-(18)F-fluoroethyl)(methyl)amino]-2-naphthyl}ethylidene)malononitrile ((18)F-FDDNP) PET images of cognitively intact controls ...[more]

PMID: 20080894

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Project description:PurposeThe purpose of this study was to explore the application of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) image radiomics in the identification of spine multiple myeloma (MM) and bone metastasis (BM), and whether this method could improve the classification diagnosis performance compared with traditional methods.MethodsThis retrospective study collected a total of 184 lesions from 131 patients between January 2017 and January 2021. All images were visually evaluated independently by two physicians with 20 years of experience through the double-blind method, while the maximum standardized uptake value (SUVmax) of each lesion was recorded. A total of 279 radiomics features were extracted from the region of interest (ROI) of CT and PET images of each lesion separately by manual method. After the reliability test, the least absolute shrinkage and selection operator (LASSO) regression and 10-fold cross-validation were used to perform dimensionality reduction and screening of features. Two classification models of CT and PET were derived from CT images and PET images, respectively and constructed using the multivariate logistic regression algorithm. In addition, the ComModel was constructed by combining the PET model and the conventional parameter SUVmax. The performance of the three classification diagnostic models, as well as the human experts and SUVmax, were evaluated and compared, respectively.ResultsA total of 8 and 10 features were selected from CT and PET images for the construction of radiomics models, respectively. Satisfactory performance of the three radiomics models was achieved in both the training and the validation groups (Training: AUC: CT: 0.909, PET: 0.949, ComModel: 0.973; Validation: AUC: CT: 0.897, PET: 0.929, ComModel: 0.948). Moreover, the PET model and ComModel showed significant improvement in diagnostic performance between the two groups compared to the human expert (Training: P = 0.01 and P = 0.001; Validation: P = 0.018 and P = 0.033), and no statistical difference was observed between the CT model and human experts (P = 0.187 and P = 0.229, respectively).ConclusionThe radiomics model constructed based on 18F-FDG PET/CT images achieved satisfactory diagnostic performance for the classification of MM and bone metastases. In addition, the radiomics model showed significant improvement in diagnostic performance compared to human experts and PET conventional parameter SUVmax.

Project description:INTRODUCTION:Standardized uptake value ratios (SUVRs) calculated from cerebral cortical areas can be used to categorize 18F-Florbetaben (FBB) PET scans by applying appropriate cutoffs. The objective of this work was first to generate FBB SUVR cutoffs using visual assessment (VA) as standard of truth (SoT) for a number of reference regions (RR) (cerebellar gray matter (GCER), whole cerebellum (WCER), pons (PONS), and subcortical white matter (SWM)). Secondly, to validate the FBB PET scan categorization performed by SUVR cutoffs against the categorization made by post-mortem histopathological confirmation of the A? presence. Finally, to evaluate the added value of SUVR cutoff categorization to VA. METHODS:SUVR cutoffs were generated for each RR using FBB scans from 143 subjects who were visually assessed by 3 readers. SUVR cutoffs were validated in 78 end-of life subjects using VA from 8 independent blinded readers (3 expert readers and 5 non-expert readers) and histopathological confirmation of the presence of neuritic beta-amyloid plaques as SoT. Finally, the number of correctly or incorrectly classified scans according to pathology results using VA and SUVR cutoffs was compared. RESULTS:Composite SUVR cutoffs generated were 1.43 (GCER), 0.96 (WCER), 0.78 (PONS) and 0.71 (SWM). Accuracy values were high and consistent across RR (range 83-94% for histopathology, and 85-94% for VA). SUVR cutoff performed similarly as VA but did not improve VA classification of FBB scans read either by expert readers or the majority read but provided higher accuracy than some non-expert readers. CONCLUSION:The accurate scan classification obtained in this study supports the use of VA as SoT to generate site-specific SUVR cutoffs. For an elderly end of life population, VA and SUVR cutoff categorization perform similarly in classifying FBB scans as A?-positive or A?-negative. These results emphasize the additional contribution that SUVR cutoff classification may have compared with VA performed by non-expert readers.

Project description:18F-NaF, a PET radiotracer of bone turnover, has shown potential as an imaging biomarker for assessing the response of bone metastases to therapy. This study aimed to evaluate the repeatability of 18F-NaF PET-derived SUV imaging metrics in individual bone lesions from patients in a multicenter study.MethodsThirty-five castration-resistant prostate cancer patients with multiple metastases underwent 2 whole-body (test-retest) 18F-NaF PET/CT scans 3 ± 2 d apart from 1 of 3 imaging sites. A total of 411 bone lesions larger than 1.5 cm3 were automatically segmented using an SUV threshold of 15 g/mL. Two levels of analysis were performed: lesion-level, in which measures were extracted from individual-lesion regions of interest (ROI), and patient-level, in which all lesions within a patient were grouped into a patient ROI for analysis. Uptake was quantified with SUVmax, SUVmean, and SUVtotal Test-retest repeatability was assessed using Bland-Altman analysis, intraclass correlation coefficient (ICC), coefficient of variation, critical percentage difference, and repeatability coefficient. The 95% limit of agreement (LOA) of the ratio between test and retest measurements was calculated.ResultsAt the lesion level, the coefficient of variation for SUVmax, SUVmean, and SUVtotal was 14.1%, 6.6%, and 25.5%, respectively. At the patient level, it was slightly smaller: 12.0%, 5.3%, and 18.5%, respectively. ICC was excellent (>0.95) for all SUV metrics. Lesion-level 95% LOA for SUVmax, SUVmean, and SUVtotal was (0.76, 1.32), (0.88, 1.14), and (0.63, 1.71), respectively. Patient-level 95% LOA was slightly narrower, at (0.79, 1.26), (0.89, 1.10), and (0.70, 1.44), respectively. We observed significant differences in the variance and sample mean of lesion-level and patient-level measurements between imaging sites.ConclusionThe repeatability of SUVmax, SUVmean, and SUVtotal for 18F-NaF PET/CT was similar between lesion- and patient-level ROIs. We found significant differences in lesion-level and patient-level distributions between sites. These results can be used to establish 18F-NaF PET-based criteria for assessing treatment response at the lesion and patient levels. 18F-NaF PET demonstrates repeatability levels useful for clinically quantifying the response of bone lesions to therapy.

Project description:BACKGROUND:A long dynamic scanning protocol may be required to accurately measure longitudinal changes in amyloid load. However, such a protocol results in a lower patient comfort and scanning efficiency compared to static scans. A compromise can be achieved by implementing dual-time-window protocols. This study aimed to optimize these protocols for quantitative [18F]flutemetamol and [18F]florbetaben studies. METHODS:Rate constants for subjects across the Alzheimer's disease spectrum (i.e., non-displaceable binding potential (BPND) in the range 0.02-0.77 and 0.02-1.04 for [18F]flutemetamol and [18F]florbetaben, respectively) were established based on clinical [18F]flutemetamol (N?=?6) and [18F]florbetaben (N?=?20) data, and used to simulate tissue time-activity curves (TACs) of 110?min using a reference tissue and plasma input model. Next, noise was added (N?=?50) and data points corresponding to different intervals were removed from the TACs, ranging from 0 (i.e., 90-90?=?full-kinetic curve) to 80 (i.e., 10-90) minutes, creating a dual-time-window. Resulting TACs were fitted using the simplified reference tissue method (SRTM) to estimate the BPND, outliers (??1.5?×?BPND max) were removed and the bias was assessed using the distribution volume ratio (DVR?=?BPND?+?1). To this end, acceptability curves, which display the fraction of data below a certain bias threshold, were generated and the area under those curves were calculated. RESULTS:[18F]Flutemetamol and [18F]florbetaben data demonstrated an increased bias in amyloid estimate for larger intervals and higher noise levels. An acceptable bias (??3.1%) in DVR could be obtained with all except the 10-90 and 20-90-min intervals. Furthermore, a reduced fraction of acceptable data and most outliers were present for these two largest intervals (maximum percentage outliers 48 and 32 for [18F]flutemetamol and [18F]florbetaben, respectively). CONCLUSIONS:The length of the interval inversely correlates with the accuracy of the BPND estimates. Consequently, a dual-time-window protocol of 0-30 and 90-110?min (=maximum of 60?min interval) allows for accurate estimation of BPND values for both tracers. [18F]flutemetamol: EudraCT 2007-000784-19, registered 8 February 2007, [18F]florbetaben: EudraCT 2006-003882-15, registered 2006.

Dataset Information

Movement correction method for human brain PET images: application to quantitative analysis of dynamic 18F-FDDNP scans.

Publications

Movement correction method for human brain PET images: application to quantitative analysis of dynamic 18F-FDDNP scans.

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