Fast dynamic brain PET imaging using stochastic variational prediction for recurrent frame generation

Sanaat, Amirhossein; Mirsadeghi, Ehsan; Razeghi, Behrooz; Ginovart, Nathalie; Zaidi, Habib

doi:10.1002/mp.15063

Cited by 22 publications

(4 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The recent efforts were made to reduce the scanning time while allowing the parametric images to be drawn [17,19,20,[35][36][37][38]. As the proposed model predicts the entire frames of the dynamic PET given a few early frames alone, it provides the solution to reduce the scanning time while deriving the pharmacokinetics of interest in brain studies such as perfusion or binding perfusion.…”

Section: ) Reduced Time Protocolmentioning

confidence: 99%

Forecasting the Pharmacokinetics With Limited Early Frames in Dynamic Brain PET Imaging Using Neural Ordinary Differential Equation

Hong

Brendel

Erlandsson

et al. 2023

IEEE Trans. Radiat. Plasma Med. Sci.

View full text Add to dashboard Cite

In dynamic brain positron emission tomography (PET) studies, acquiring a time series of images, typically lasting more than an hour, is necessary to derive pharmacokinetic parameters. Analytically, these parameters are estimated by establishing kinetic models such as compartment models that consist of sets of ordinary differential equations (ODE), and by fitting the sparse time-activity curve (TAC) of the tracer. Yet, these models are simplified approximations of highly complex underlying processes, and sufficient samples of TAC are required throughout the entire acquisition, which is not only impractical but also hindered by patient involuntary motion and intrinsic noise. Therefore, recovering samples in missing timeframes is often required, which, in practice, is achieved by interpolation or extrapolation. Here, we introduce a novel deep-learning-based method that utilizes neural ODE (N-ODE) to predict TAC in the extended timeframes by mimicking analytical method in a datadriven manner. By training N-ODE to solve and fit sets of ODE such that the solution replicates the observed TAC, the N-ODE converges to the functional shapes that best describe the underlying pharmacokinetic processes. We customized N-ODE to predict the full-dynamic images (12 frames, 60min), hence pharmacokinetic parameters, given limited early-frame images (7 to 9 frames, 20 to 30min). For proof of concept, the proposed N-ODE was applied to simulated and clinical 18 F-PI-2620 brain PET. We demonstrated that the proposed N-ODE delivered promising performance, indicated by bias, variance, and mean absolute error as well as pharmacokinetic parameters such as rate constants, standardized uptake value ratio (SUVr), and binding potential (BPND).

show abstract

Section: ) Reduced Time Protocolmentioning

confidence: 99%

Forecasting the Pharmacokinetics With Limited Early Frames in Dynamic Brain PET Imaging Using Neural Ordinary Differential Equation

Hong

Brendel

Erlandsson

et al. 2023

IEEE Trans. Radiat. Plasma Med. Sci.

View full text Add to dashboard Cite

show abstract

“…The introduction of machine/deep learning algorithms in recent years has revolutionized medical imaging research, particularly in areas linked to human interpretation/intervention (e.g., segmentation, diagnostic, prognostic, radiomics, etc.) as well as other technical areas, including optimization of image acquisition, reconstruction, quantification, motion correction and image denoising (Akhavanallaf et al, 2021; Arabi et al, 2021; Arabi & Zaidi, 2021; Sanaat et al, 2020; Sanaat et al, 2022; Sanaat, Mirsadeghi, et al, 2021; Sanaat, Shiri, et al, 2021; Sanaat & Zaidi, 2020; Shiri et al, 2020; Zaidi & El Naqa, 2021).…”

Section: Introductionmentioning

confidence: 99%

Deep‐TOF‐PET: Deep learning‐guided generation of time‐of‐flight from non‐TOF brain PET images in the image and projection domains

Sanaat

Shiri

et al. 2022

Human Brain Mapping

Self Cite

View full text Add to dashboard Cite

We aim to synthesize brain time‐of‐flight (TOF) PET images/sinograms from their corresponding non‐TOF information in the image space (IS) and sinogram space (SS) to increase the signal‐to‐noise ratio (SNR) and contrast of abnormalities, and decrease the bias in tracer uptake quantification. One hundred forty clinical brain 18F‐FDG PET/CT scans were collected to generate TOF and non‐TOF sinograms. The TOF sinograms were split into seven time bins (0, ±1, ±2, ±3). The predicted TOF sinogram was reconstructed and the performance of both models (IS and SS) compared with reference TOF and non‐TOF. Wide‐ranging quantitative and statistical analysis metrics, including structural similarity index metric (SSIM), root mean square error (RMSE), as well as 28 radiomic features for 83 brain regions were extracted to evaluate the performance of the CycleGAN model. SSIM and RMSE of 0.99 ± 0.03, 0.98 ± 0.02 and 0.12 ± 0.09, 0.16 ± 0.04 were achieved for the generated TOF‐PET images in IS and SS, respectively. They were 0.97 ± 0.03 and 0.22 ± 0.12, respectively, for non‐TOF‐PET images. The Bland & Altman analysis revealed that the lowest tracer uptake value bias (−0.02%) and minimum variance (95% CI: −0.17%, +0.21%) were achieved for TOF‐PET images generated in IS. For malignant lesions, the contrast in the test dataset was enhanced from 3.22 ± 2.51 for non‐TOF to 3.34 ± 0.41 and 3.65 ± 3.10 for TOF PET in SS and IS, respectively. The implemented CycleGAN is capable of generating TOF from non‐TOF PET images to achieve better image quality.

show abstract

“…Deep learning (DL)-based methods aiming at resolution and sensitivity enhancement focused mainly on improving the overall performance of PET scanners (Gong et al 2020, Sanaat and Zaidi 2020, Sanaat et al 2021a, 2021b. Some studies improved PET's performance by DL-based positioning in monolithic crystals, clearly outperforming conventional event positioning algorithms (Sanaat et al 2020b).…”

Section: Introductionmentioning

confidence: 99%

Active-PET: a multifunctional PET scanner with dynamic gantry size featuring high-resolution and high-sensitivity imaging: a Monte Carlo simulation study

et al. 2022

Self Cite

View full text Add to dashboard Cite

Organ-specific PET scanners have been developed to provide both high spatial resolution and sensitivity, although the deployment of several dedicated PET scanners at the same center is costly and space-consuming. Active-PET is a multifunctional PET scanner design exploiting the advantages of two different types of detectors modules and mechanical arms mechanism enabling to reposition the detectors to allow the implementation of different geometries/configurations. Active-PET can be used for different applications, including brain, axilla, breast, prostate, whole-body, preclinical and pediatrics imaging, cell tracking, and image guidance for therapy. Monte Carlo techniques were used to simulate a PET scanner with two sets of high resolution and high sensitivity pixelated Lutetium Oxyorthoscilicate (LSO(Ce)) detector blocks (24 for each group, overall 48 detector modules for each ring), one with large pixel size (4×4 mm2) and crystal thickness (20 mm), and another one with small pixel size (2×2 mm2) and thickness (10 mm). Each row of detector modules is connected to a linear motor that can displace the detectors forward and backward along the radial axis to achieve variable gantry diameter in order to image the target subject at the optimal/desired resolution and/or sensitivity. At the center of the field-of-view, the highest sensitivity (15.98 kcps/MBq) was achieved by the scanner with a small gantry and high-sensitivity detectors while the best spatial resolution was obtained by the scanner with a small gantry and high-resolution detectors (2.2 mm, 2.3 mm, 2.5 mm FWHM for tangential, radial, and axial, respectively). The configuration with large-bore (combination of high-resolution and high-sensitivity detectors) achieved better performance and provided higher image quality compared to the Biograph mCT as reflected by the 3D Hoffman brain phantom simulation study. We introduced the concept of a non-static PET scanner capable of switching between large and small field-of-view as well as high-resolution and high-sensitivity imaging.

show abstract

Fast dynamic brain PET imaging using stochastic variational prediction for recurrent frame generation

Cited by 22 publications

References 41 publications

Forecasting the Pharmacokinetics With Limited Early Frames in Dynamic Brain PET Imaging Using Neural Ordinary Differential Equation

Forecasting the Pharmacokinetics With Limited Early Frames in Dynamic Brain PET Imaging Using Neural Ordinary Differential Equation

Deep‐TOF‐PET: Deep learning‐guided generation of time‐of‐flight from non‐TOF brain PET images in the image and projection domains

Active-PET: a multifunctional PET scanner with dynamic gantry size featuring high-resolution and high-sensitivity imaging: a Monte Carlo simulation study

Contact Info

Product

Resources

About