New Challenges for PET Image Reconstruction for Total-Body Imaging

Efthimiou, Nikos

doi:10.1016/j.cpet.2020.06.002

Cited by 21 publications

(13 citation statements)

References 52 publications

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“…Although the current study did not consider all the challenging issues such as larger and Due to the huge number of LORs received in LAFOV PET, the storage and processing of the acquisition data is daunting [28,31]. For example, the 2-m LAFOV system has roughly 10 times of data to process compared to a 20-cm PET system.…”

Section: Discussionmentioning

confidence: 99%

“…Although the Sinogram of LAFOV PET did not consider all the rich information in the acquisition of this powerful scanner leading to downgraded measurement data, the preliminary results confirmed that the deep neural network can reconstruct PET images with corrections of attenuation and scattering directly from Sinogram without the input of CT. This potential of AI in complex reconstruction with various corrections may bring benefits for the reconstruction of LAFOV PET considering the increased complexity in the reconstruction of LAFOV PET [28]. The LAFOV of total-body PET increases the probability of the detection of LORs to increase the sensitivity.…”

Section: Discussionmentioning

confidence: 99%

“…The increased Compton scattering and the ratio between multiple over single scattered photons is another critical bottleneck for the reconstruction of LAFOV PET [28]. The fraction of multiple scatters changes heterogeneously in LAFOV PET [31].…”

Section: Discussionmentioning

confidence: 99%

“…But with more oblique LORs used, there could be a 40-fold increase [31]. Remarkably, the prompts count rate peaked at 10 million events, a few orders of magnitude larger than for a traditional PET scanner [28]. Conventional PET reconstruction algorithms are inefficient in the processing of the vast data of LAFOV PET reconstruction.…”

Section: Discussionmentioning

confidence: 99%

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An encoder-decoder network for direct image reconstruction on sinograms of a long axial field of view PET

Xue

et al. 2022

Eur J Nucl Med Mol Imaging

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Deep learning is an emerging reconstruction method for positron emission tomography (PET) that can tackle complex PET corrections in an integrated procedure. This study optimized the direct PET reconstruction from sinogram on a long axial field of view (LAFOV) PET. Methods:This study developed a new deep learning architecture to reduce the biases during direct reconstruction from sinograms to images. This architecture is based on an encoder-decoder network and perceptual loss is adopted with pre-trained convolutional layers. It is trained and tested on data of 80 patients acquired from recent Siemens Biograph Vision Quadra PET/CT. The patients were randomly split into a training dataset of 60 patients, the validation dataset of 10 patients, and the test data set of 10 patients. The 3D sinograms were converted into 2D sinogram slices and were used as input to the network, and the vendor reconstructed images were considered as ground truths.The proposed method was compared with DeepPET, a benchmark deep learning method for PET reconstruction. Results:Compared to the DeepPET, the proposed network significantly reduced the root-mean-squared error (rRMSE) from 0.63 to 0.6 (p<0.01), and the structural similarity index (SSIM) and peak signal-tonoise ratio (PSNR) were improved from 0.93 to 0.95(p<0.01) and from 82.02 to 82.36(p<0.01), respectively. The reconstruction time was approximately 10s per patient, which was shortened by 36 times compared with the reconstruction using the conventional method. The errors of average standardized uptake values (SUV) for lesions between ground truth and the predicted result was reduced from 33.5% to 18.7% (P=0.03), and the error of max SUV was reduced from 32.7% to 21.8% (P=0.02). Conclusion:The results demonstrated the feasibility of using deep learning to reconstruct images with acceptable image quality and short reconstruction time. It showed that the proposed method can improve the quality of deep learning-based reconstructed images without additional CT images for attenuation and scattering corrections. This learning-based approach may provide the potential to accommodate more complex corrections for LAFOV PET.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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An encoder-decoder network for direct image reconstruction on sinograms of a long axial field of view PET

Xue

et al. 2022

Eur J Nucl Med Mol Imaging

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“…Currently, routine PET imaging with devices of about 20 cm AFOV [3,4], in a single bed position, enables the diagnosis of individual organs only, and the diagnosis of the whole-body requires a combination of series of sequential images obtained from many patient positions in the scanner, thus only a timedependent scan of the total-body is available [5]. With the advent of the total-body PET (TB-PET), precision medicine will be enhanced with a new toolbox that allows for the simultaneous molecular imaging of the whole human body, providing concurrent imaging of metabolic rate in near and distant organs [6][7][8][9][10][11][12][13][14][15][16]. Thanks to the high sensitivity, TB-PET enables the extreme reduction of the whole-body imaging duration or the lessen of the radiopharmaceutical dose [17], thus opening perspectives for application of PET to the wider group of patients (e.g.…”

Section: Introductionmentioning

confidence: 99%

Simulating NEMA characteristics of the modular total-body J-PET scanner -- an economic total-body PET from plastic scintillators

Moskal,

Kowalski,

Shopa

et al. 2021

Preprint

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The purpose of the presented research is estimation of the performance characteristics of the economic Total-Body Jagiellonian-PET system (TB-J-PET) constructed from plastic scintillators. The characteristics are estimated according to the NEMA NU-2-2018 standards utilizing the GATE package. The simulated detector consists of 24 modules, each built out of 32 plastic scintillator strips (each with cross section of 6 mm times 30 mm and length of 140 cm or 200 cm) arranged in two layers in regular 24-sided polygon circumscribing a circle with the diameter of 78.6 cm.For the TB-J-PET with an axial field-of-view (AFOV) of 200 cm, a spatial resolutions of 3.7 mm (transversal) and 4.9 mm (axial) are achieved. The noise equivalent count rate (NECR) peak of 630 kcps is expected at 30 kBq/cc activity concentration and the sensitivity at the center amounts to 38 cps/kBq. The scatter fraction is estimated to 36.2 %. The values of scatter fraction and spatial resolution are comparable to those obtained for the state-of-the-art clinical PET scanners and the first total-body tomographs: uExplorer and PennPET. With respect to the standard PET systems with AFOV in the range from 16 cm to 26 cm, the TB-J-PET is characterized by an increase in NECR approximately by factor of 4 and by the increase of the wholebody sensitivity by factor of 12.6 to 38. The time-of-flight resolution for the TB-J-PET is expected to be at the level of CRT = 240 ps (FWHM). For the TB-J-PET with an

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A Simulation Study to Estimate Optimum LOR Angular Acceptance for the Image Reconstruction with the Total-Body J-PET

Dadgar

Parzych

Ardebili

2021

Medical Image Understanding and Analysis

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New Challenges for PET Image Reconstruction for Total-Body Imaging

Cited by 21 publications

References 52 publications

An encoder-decoder network for direct image reconstruction on sinograms of a long axial field of view PET

An encoder-decoder network for direct image reconstruction on sinograms of a long axial field of view PET

Simulating NEMA characteristics of the modular total-body J-PET scanner -- an economic total-body PET from plastic scintillators

A Simulation Study to Estimate Optimum LOR Angular Acceptance for the Image Reconstruction with the Total-Body J-PET

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