Accurate Total-Body Ki Parametric Imaging with Shortened Dynamic 18F-FDG PET Scan Durations via Effective Data Processing

Abstract Background: Total-body dynamic PET (dPET) imaging using 18F-fluorodeoxyglucose (18F-FDG) has received widespread attention in clinical oncology. However, the conventionally required scan duration of approximately one hour seriously limits the application and promotion of this imaging technique. In this study, using Patlak analysis-based Ki parametric imaging as the evaluation standard, we investigated the possibility and feasibility of shortening the total-body dynamic scan duration to 30 mins post-injection (PI) with the help of a novel Patlak data processing algorithm.Methods: Total-body dPET images acquired by uEXPLORER (United Imaging Healthcare Inc.) using 18F-FDG of 15 patients with different types of tumors were analyzed in this study. Dynamic images were reconstructed into 25 frames with a specific temporal dividing protocol for the scan data acquired one hour PI. Patlak analysis-based Ki parametric imaging was carried out based on the imaging data corresponding to the first 30 mins PI, during which a Patlak data processing method based on third-order Hermite interpolation (THI) was applied. The resulting Ki images and standard Ki images were compared in terms of visual imaging effect and Ki estimation accuracy to evaluate the performance of the proposed data processing algorithm for parametric imaging with dPET with a shortened scan duration.Results: With the help of Patlak data processing, acceptable Ki parametric images were obtained from dPET data acquired with a shortened scan duration. Compared to Ki images obtained from unprocessed Patlak data, the resulting images from the proposed method contained less image noise, leading to remarkably improved imaging quality. Moreover, box plot analysis showed that that 30-min Ki images obtained from processed Patlak data have higher accuracy regarding tumor lesion Ki values.Conclusion: Acceptable Ki parametric images can be acquired from dynamic imaging data corresponding to the first 30 mins PI. Patlak data processing can help achieve higher Ki imaging quality and higher accuracy regarding tumor lesion Ki values. Clinically, it is possible to shorten the dynamic scan duration of 18F-FDG PET to 30 mins to facilitate the usage of such imaging techniques on uEXPLORER scanners.

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Valuable Characteristic of Patlak Parametric Imaging Based on Total-Body Dynamic PET Imaging: Higher Contrast For Tumor Lesions With Respect To Hypermetabolic Tissues

10.21203/rs.3.rs-669813/v1 ◽

2021 ◽

Author(s):

Zixiang Chen ◽

Yanhua Duan ◽

Chenwei Li ◽

Ying Wang ◽

Yongfeng Yang ◽

...

Keyword(s):

Parametric Imaging ◽

Dynamic Pet ◽

Parametric Images ◽

Positron Emission ◽

Tumor Tissues ◽

Valuable Characteristic ◽

Clinical Detection ◽

Detection And Diagnosis ◽

Total Body ◽

Body Dynamic

Abstract Purpose To demonstrate the characteristics of high-contrast tumor lesions on total-body dynamic positron emission tomography (dPET) parametric images qualitatively and quantitatively. Method We reported the results of Patlak parametric images based on total-body dPET images of four patients with different types of tumor lesions. The contrast-to-noise ratios (CNRs) of the target tumor lesions were calculated with respect to hypermetabolic tissues, including the liver and ventricles, both on static PET and parametric images. Results Visual comparisons between the last frame of total-body dPET images and the generated parametric images illustrated the higher contrast of tumor lesions relative to other tissues in the patients. Visualization of the tumor lesions was reserved, while that of the livers and ventricles was diminished. The parametric images resulted in higher CNR values for the tumor lesions with respect to livers and ventricles compared to those given by dynamic PET images. The results were consistent in all the cases analyzed in this study. Conclusion Patlak parametric imaging provides the valuable characteristic of higher contrast for tumor lesions than hypermetabolic tissues, which helps in the clinical detection and diagnosis of tumor tissues.

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Use of population input functions for reduced scan duration whole-body Patlak 18F-FDG PET imaging

EJNMMI Physics ◽

10.1186/s40658-021-00357-8 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Joyce van Sluis ◽

Maqsood Yaqub ◽

Adrienne H. Brouwers ◽

Rudi A. J. O. Dierckx ◽

Walter Noordzij ◽

...

Keyword(s):

Rate Constants ◽

Arterial Input Function ◽

Input Function ◽

Dynamic Imaging ◽

Whole Body ◽

Time Interval ◽

Time Activity ◽

Dynamic Scan ◽

Patlak Analysis ◽

Body Dynamic

Abstract Whole-body Patlak images can be obtained from an acquisition of first 6 min of dynamic imaging over the heart to obtain the arterial input function (IF), followed by multiple whole-body sweeps up to 60 min pi. The use of a population-averaged IF (PIF) could exclude the first dynamic scan and minimize whole-body sweeps to 30–60 min pi. Here, the effects of (incorrect) PIFs on the accuracy of the proposed Patlak method were assessed. In addition, the extent of mitigating these biases through rescaling of the PIF to image-derived IF values at 30–60 min pi was evaluated. Methods Using a representative IF and rate constants from the literature, various tumour time-activity curves (TACs) were simulated. Variations included multiplication of the IF with a positive and negative gradual linear bias over 60 min of 5, 10, 15, 20, and 25% (generating TACs using an IF different from the PIF); use of rate constants (K1, k3, and both K1 and k2) multiplied by 2, 1.5, and 0.75; and addition of noise (μ = 0 and σ = 5, 10 and 15%). Subsequent Patlak analysis using the original IF (representing the PIF) was used to obtain the influx constant (Ki) for the differently simulated TACs. Next, the PIF was scaled towards the (simulated) IF value using the 30–60-min pi time interval, simulating scaling of the PIF to image-derived values. Influence of variabilities in IF and rate constants, and rescaling the PIF on bias in Ki was evaluated. Results Percentage bias in Ki observed using simulated modified IFs varied from − 16 to 16% depending on the simulated amplitude and direction of the IF modifications. Subsequent scaling of the PIF reduced these Ki biases in most cases (287 out of 290) to < 5%. Conclusions Simulations suggest that scaling of a (possibly incorrect) PIF to IF values seen in whole-body dynamic imaging from 30 to 60 min pi can provide accurate Ki estimates. Consequently, dynamic Patlak imaging protocols may be performed for 30–60 min pi making whole-body Patlak imaging clinically feasible.

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Total-Body Dynamic Reconstruction and Parametric Imaging on the uEXPLORER

Journal of Nuclear Medicine ◽

10.2967/jnumed.119.230565 ◽

2019 ◽

Vol 61 (2) ◽

pp. 285-291 ◽

Cited By ~ 25

Author(s):

Xuezhu Zhang ◽

Zhaoheng Xie ◽

Eric Berg ◽

Martin S. Judenhofer ◽

Weiping Liu ◽

...

Keyword(s):

Parametric Imaging ◽

Dynamic Reconstruction ◽

Total Body ◽

Body Dynamic

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Disrupted metabolic connectivity in dopaminergic and cholinergic networks at different stages of dementia from 18F-FDG PET brain persistent homology network

Scientific Reports ◽

10.1038/s41598-021-84722-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Tun-Wei Hsu ◽

Jong-Ling Fuh ◽

Da-Wei Wang ◽

Li-Fen Chen ◽

Chia-Jung Chang ◽

...

Keyword(s):

Algebraic Topology ◽

Fdg Pet ◽

Persistent Homology ◽

Network Connectivity ◽

Brain Regions ◽

Imaging Data ◽

Cholinergic Pathways ◽

Positron Emission ◽

Metabolic Connectivity ◽

Neurochemical Changes

AbstractDementia is related to the cellular accumulation of β-amyloid plaques, tau aggregates, or α-synuclein aggregates, or to neurotransmitter deficiencies in the dopaminergic and cholinergic pathways. Cellular and neurochemical changes are both involved in dementia pathology. However, the role of dopaminergic and cholinergic networks in metabolic connectivity at different stages of dementia remains unclear. The altered network organisation of the human brain characteristic of many neuropsychiatric and neurodegenerative disorders can be detected using persistent homology network (PHN) analysis and algebraic topology. We used 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) imaging data to construct dopaminergic and cholinergic metabolism networks, and used PHN analysis to track the evolution of these networks in patients with different stages of dementia. The sums of the network distances revealed significant differences between the network connectivity evident in the Alzheimer’s disease and mild cognitive impairment cohorts. A larger distance between brain regions can indicate poorer efficiency in the integration of information. PHN analysis revealed the structural properties of and changes in the dopaminergic and cholinergic metabolism networks in patients with different stages of dementia at a range of thresholds. This method was thus able to identify dysregulation of dopaminergic and cholinergic networks in the pathology of dementia.

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Ultra-low-activity total-body dynamic PET imaging allows equal performance to full-activity PET imaging for investigating kinetic metrics of 18F-FDG in healthy volunteers

European Journal of Nuclear Medicine and Molecular Imaging ◽

10.1007/s00259-020-05173-3 ◽

2021 ◽

Author(s):

Guobing Liu ◽

Pengcheng Hu ◽

Haojun Yu ◽

Hui Tan ◽

Yiqiu Zhang ◽

...

Keyword(s):

Healthy Volunteers ◽

Pet Imaging ◽

Dynamic Pet ◽

Full Activity ◽

18F Fdg ◽

Total Body ◽

Body Dynamic ◽

Low Activity

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Total-body 18F-FDG PET/CT scan in oncology patients: how fast could it be?

European Journal of Nuclear Medicine and Molecular Imaging ◽

10.1007/s00259-021-05357-5 ◽

2021 ◽

Author(s):

Pengcheng Hu ◽

Yiqiu Zhang ◽

Haojun Yu ◽

Shuguang Chen ◽

Hui Tan ◽

...

Keyword(s):

Ct Scan ◽

Fdg Pet ◽

Oncology Patients ◽

Pet Ct ◽

18F Fdg ◽

Total Body ◽

Fdg Pet Ct ◽

Pet Ct Scan

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Characterizing intra-tumor regions on quantitative ultrasound parametric images to predict breast cancer response to chemotherapy at pre-treatment

Scientific Reports ◽

10.1038/s41598-021-94004-y ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Hamidreza Taleghamar ◽

Hadi Moghadas-Dastjerdi ◽

Gregory J. Czarnota ◽

Ali Sadeghi-Naini

Keyword(s):

Breast Cancer ◽

Treatment Response ◽

Quantitative Ultrasound ◽

Parametric Imaging ◽

Term Survival ◽

Adaptive Boosting ◽

Tumor Margin ◽

Parametric Images ◽

Response To Chemotherapy ◽

Pre Treatment

AbstractThe efficacy of quantitative ultrasound (QUS) multi-parametric imaging in conjunction with unsupervised classification algorithms was investigated for the first time in characterizing intra-tumor regions to predict breast tumor response to chemotherapy before the start of treatment. QUS multi-parametric images of breast tumors were generated using the ultrasound radiofrequency data acquired from 181 patients diagnosed with locally advanced breast cancer and planned for neo-adjuvant chemotherapy followed by surgery. A hidden Markov random field (HMRF) expectation maximization (EM) algorithm was applied to identify distinct intra-tumor regions on QUS multi-parametric images. Several features were extracted from the segmented intra-tumor regions and tumor margin on different parametric images. A multi-step feature selection procedure was applied to construct a QUS biomarker consisting of four features for response prediction. Evaluation results on an independent test set indicated that the developed biomarker coupled with a decision tree model with adaptive boosting (AdaBoost) as the classifier could predict the treatment response of patient at pre-treatment with an accuracy of 85.4% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.89. In comparison, the biomarkers consisted of the features derived from the entire tumor core (without consideration of the intra-tumor regions), and the entire tumor core and the tumor margin could predict the treatment response of patients with an accuracy of 74.5% and 76.4%, and an AUC of 0.79 and 0.76, respectively. Standard clinical features could predict the therapy response with an accuracy of 69.1% and an AUC of 0.6. Long-term survival analyses indicated that the patients predicted by the developed model as responders had a significantly better survival compared to the non-responders. Similar findings were observed for the two response cohorts identified at post-treatment based on standard clinical and pathological criteria. The results obtained in this study demonstrated the potential of QUS multi-parametric imaging integrated with unsupervised learning methods in identifying distinct intra-tumor regions in breast cancer to characterize its responsiveness to chemotherapy prior to the start of treatment.

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