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A deep neural network for parametric image reconstruction on a large axial field-of-view PET

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European Journal of Nuclear Medicine and Molecular Imaging Aims and scope Submit manuscript

Abstract

Purpose

The PET scanners with long axial field of view (AFOV) having ~ 20 times higher sensitivity than conventional scanners provide new opportunities for enhanced parametric imaging but suffer from the dramatically increased volume and complexity of dynamic data. This study reconstructed a high-quality direct Patlak Ki image from five-frame sinograms without input function by a deep learning framework based on DeepPET to explore the potential of artificial intelligence reducing the acquisition time and the dependence of input function in parametric imaging.

Methods

This study was implemented on a large AFOV PET/CT scanner (Biograph Vision Quadra) and twenty patients were recruited with 18F-fluorodeoxyglucose (18F-FDG) dynamic scans. During training and testing of the proposed deep learning framework, the last five-frame (25 min, 40–65 min post-injection) sinograms were set as input and the reconstructed Patlak Ki images by a nested EM algorithm on the vendor were set as ground truth. To evaluate the image quality of predicted Ki images, mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM) were calculated. Meanwhile, a linear regression process was applied between predicted and true Ki means on avid malignant lesions and tumor volume of interests (VOIs).

Results

In the testing phase, the proposed method achieved excellent MSE of less than 0.03%, high SSIM, and PSNR of ~ 0.98 and ~ 38 dB, respectively. Moreover, there was a high correlation (DeepPET: \({R}^{2}\)= 0.73, self-attention DeepPET: \({R}^{2}\)=0.82) between predicted Ki and traditionally reconstructed Patlak Ki means over eleven lesions.

Conclusions

The results show that the deep learning–based method produced high-quality parametric images from small frames of projection data without input function. It has much potential to address the dilemma of the long scan time and dependency on input function that still hamper the clinical translation of dynamic PET.

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Funding

This work was supported in part by the National Key Research and Development Program of China (No: 2020AAA0109502), by the National Natural Science Foundation of China (No: U1809204, 61701436), Zhejiang Provincial Natural Science Foundation of China (No: LY22F010007), by the Talent Program of Zhejiang Province (2021R51004), and by the Key Research and Development Program of Zhejiang Province (No: 2021C03029). Swiss National Science Foundation (SNF No. 188914) and Germaine de Staël Programm of Swiss Academy of Engineering Science (SATW).

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Authors and Affiliations

  1. College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, People’s Republic of China

    Y. Li

  2. College of Optical Science and Engineering, Zhejiang University, Hangzhou, People’s Republic of China

    Y. Li & H. Liu

  3. Department of Nuclear Medicine, Inselpital, Bern University Hospital, University of Bern, Bern, Switzerland

    J. Hu, S. Xue, R. Ma, A. Rominger & K. Shi

  4. Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland

    H. Sari

  5. Department of Engineering Physics, Tsinghua University, Beijing, China

    R. Ma

  6. LaTIM, INSERM, UMR 1101, University of Brest, Brest, France

    S. Kandarpa & D. Visvikis

  7. Computer Aided Medical Procedures and Augmented Reality, Institute of Informatics I16, Technical University of Munich, Munich, Germany

    K. Shi

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  1. Y. Li
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Contributions

All authors contributed to the study conception and design. H. Sari and S. Xue acquired and pre-processed the data, Y. Li trained network and analyzed data, J.Hu segmented the lesions, and R. Ma and S. Kandarpa assisted in network training. Y. Li drafted the manuscript and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to H. Liu.

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Ethics approval

The local Institutional Review Board approved the study (KEK 2019–02193), and written informed consent was obtained from all patients. The study was performed in accordance with the Declaration of Helsinki.

Conflict of interest

H. Sari is a full-time employee of Siemens Healthineers. The other authors have no conflicts of interest.

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Li, Y., Hu, J., Sari, H. et al. A deep neural network for parametric image reconstruction on a large axial field-of-view PET. Eur J Nucl Med Mol Imaging 50, 701–714 (2023). https://doi.org/10.1007/s00259-022-06003-4

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