Dataset Information

Single-cell tracking with PET using a novel trajectory reconstruction algorithm.

ABSTRACT: Virtually all biomedical applications of positron emission tomography (PET) use images to represent the distribution of a radiotracer. However, PET is increasingly used in cell tracking applications, for which the "imaging" paradigm may not be optimal. Here, we investigate an alternative approach, which consists in reconstructing the time-varying position of individual radiolabeled cells directly from PET measurements. As a proof of concept, we formulate a new algorithm for reconstructing the trajectory of one single moving cell directly from list-mode PET data. We model the trajectory as a 3-D B-spline function of the temporal variable and use nonlinear optimization to minimize the mean-square distance between the trajectory and the recorded list-mode coincidence events. Using Monte Carlo simulations (GATE), we show that this new algorithm can track a single source moving within a small-animal PET system with 3 mm accuracy provided that the activity of the cell [Bq] is greater than four times its velocity [mm/s]. The algorithm outperforms conventional ML-EM as well as the "minimum distance" method used for positron emission particle tracking (PEPT). The new method was also successfully validated using experimentally acquired PET data. In conclusion, we demonstrated the feasibility of a new method for tracking a single moving cell directly from PET list-mode data, at the whole-body level, for physiologically relevant activities and velocities.

SUBMITTER: Lee KS

PROVIDER: S-EPMC4392854 | biostudies-literature | 2015 Apr

REPOSITORIES: biostudies-literature

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Single-cell tracking with PET using a novel trajectory reconstruction algorithm.

Lee Keum Sil KS Kim Tae Jin TJ Pratx Guillem G

IEEE transactions on medical imaging 20141121 4

Virtually all biomedical applications of positron emission tomography (PET) use images to represent the distribution of a radiotracer. However, PET is increasingly used in cell tracking applications, for which the "imaging" paradigm may not be optimal. Here, we investigate an alternative approach, which consists in reconstructing the time-varying position of individual radiolabeled cells directly from PET measurements. As a proof of concept, we formulate a new algorithm for reconstructing the tr ...[more]

PMID: 25423651

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Project description:INTRODUCTION:11C-acetate (ACE)-PET/CT is used for staging of high-risk prostate cancer. PET data is reconstructed with iterative algorithms, such as VUEPointHD ViP (VPHD) and VUEPoint HD Sharp IR (SharpIR), the latter with additional resolution recovery. It is expected that the resolution recovery algorithm should render more accurate maximum and mean standardized uptake values (SUVmax and SUVmean) and functional tumor volumes (FTV) than the ordinary OSEM. Performing quantitative analysis, choice of volume-of-interest delineation algorithm (SUV threshold) may influence FTV. Optimizing PET acquisition time is justified if image quality and quantitation do not deteriorate. The aim of this study is to identify the optimal reconstruction algorithm, SUV threshold and acquisition time for ACE-PET/CT. METHODS:ACE-PET/CT data acquired with a General Electric Discovery 690 PET/CT from 16 consecutive high-risk prostate cancer patients was reconstructed with VPHD and SharpIR. Forty pelvic lymph nodes (LNs) and 14 prostate glands were delineated with 42% and estimated threshold. SUVmax, SUVmean, FTV and total lesion uptake were measured. Default acquisition time was four minutes per bed position. In a subset of lesions, acquisition times of one, two and four minutes were evaluated. Structural tumor volumes (STV) of the LNs were measured with CT for correlation with functional volumetric parameters. To validate SUV quantification under different conditions with SharpIR 42%, recovery coefficients (RCs) of SUVmean and FTV were calculated from a phantom with 18F-fluoro-deoxy-glucose (FDG)-filled volumes 0.1-9.2cm3 and signal-to-background (S/B) ratios 4.3-15.9. RESULTS:With SharpIR, SUVmax and SUVmean were higher and FTV lower compared with VPHD, regardless of threshold method, in both prostates and LNs. Total lesion uptake determined with both threshold methods was lower with SharpIR compared with VPHD with both threshold methods, except in subgroup analysis of prostate targets where estimated threshold returned higher values. Longer acquisition times returned higher FTV for both threshold methods, regardless of reconstruction algorithm. The FTV difference was most pronounced with one minute's acquisition per bed position, which also produced visually the highest noise. SUV parameters were unaffected by varying acquisition times. FTV with SharpIR 42% showed the best correspondence with STV. SharpIR 42% gave higher RCs of SUVmean and FTV with increasing phantom size and S/B-ratio, as expected. CONCLUSIONS:Delineation with SharpIR 42% seems to provide the most accurate combined information from SUVmax, SUVmean, FTV and total lesion uptake. Acquisition time may be shortened to two minutes per bed position with preserved image quality.

Dataset Information

Single-cell tracking with PET using a novel trajectory reconstruction algorithm.

Publications

Single-cell tracking with PET using a novel trajectory reconstruction algorithm.

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