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An encoder-decoder network for direct image reconstruction on sinograms of a long axial field of view PET.
Ma, Ruiyao; Hu, Jiaxi; Sari, Hasan; Xue, Song; Mingels, Clemens; Viscione, Marco; Kandarpa, Venkata Sai Sundar; Li, Wei Bo; Visvikis, Dimitris; Qiu, Rui; Rominger, Axel; Li, Junli; Shi, Kuangyu.
Afiliación
  • Ma R; Department of Engineering Physics, Tsinghua University, and Key Laboratory of Particle & Radiation Imaging (Tsinghua University), Ministry of Education, Beijing, 100084, China.
  • Hu J; Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
  • Sari H; Institute of Radiation Medicine, Helmholtz Zentrum München German Research Center for Environmental Health (GmbH), Bavaria, Neuherberg, Germany.
  • Xue S; Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
  • Mingels C; Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
  • Viscione M; Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland.
  • Kandarpa VSS; Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
  • Li WB; Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
  • Visvikis D; Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
  • Qiu R; LaTIM, INSERM, UMR1101, University of Brest, Brest, France.
  • Rominger A; Institute of Radiation Medicine, Helmholtz Zentrum München German Research Center for Environmental Health (GmbH), Bavaria, Neuherberg, Germany.
  • Li J; LaTIM, INSERM, UMR1101, University of Brest, Brest, France.
  • Shi K; Department of Engineering Physics, Tsinghua University, and Key Laboratory of Particle & Radiation Imaging (Tsinghua University), Ministry of Education, Beijing, 100084, China. qiurui@tsinghua.edu.cn.
Eur J Nucl Med Mol Imaging ; 49(13): 4464-4477, 2022 11.
Article en En | MEDLINE | ID: mdl-35819497
ABSTRACT

PURPOSE:

Deep learning is an emerging reconstruction method for positron emission tomography (PET), which can tackle complex PET corrections in an integrated procedure. This paper optimizes the direct PET reconstruction from sinogram on a long axial field of view (LAFOV) PET.

METHODS:

This paper proposes a novel deep learning architecture to reduce the biases during direct reconstruction from sinograms to images. This architecture is based on an encoder-decoder network, where the perceptual loss is used with pre-trained convolutional layers. It is trained and tested on data of 80 patients acquired from recent Siemens Biograph Vision Quadra long axial FOV (LAFOV) PET/CT. The patients are randomly split into a training dataset of 60 patients, a validation dataset of 10 patients, and a test dataset of 10 patients. The 3D sinograms are converted into 2D sinogram slices and used as input to the network. In addition, the vendor reconstructed images are considered as ground truths. Finally, the proposed method is compared with DeepPET, a benchmark deep learning method for PET reconstruction.

RESULTS:

Compared with DeepPET, the proposed network significantly reduces the root-mean-squared error (NRMSE) from 0.63 to 0.6 (p < 0.01) and increases the structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) from 0.93 to 0.95 (p < 0.01) and from 82.02 to 82.36 (p < 0.01), respectively. The reconstruction time is approximately 10 s per patient, which is shortened by 23 times compared with the conventional method. The errors of mean standardized uptake values (SUVmean) for lesions between ground truth and the predicted result are reduced from 33.5 to 18.7% (p = 0.03). In addition, the error of max SUV is reduced from 32.7 to 21.8% (p = 0.02).

CONCLUSION:

The results demonstrate the feasibility of using deep learning to reconstruct images with acceptable image quality and short reconstruction time. It is shown that the proposed method can improve the quality of deep learning-based reconstructed images without additional CT images for attenuation and scattering corrections. This study demonstrated the feasibility of deep learning to rapidly reconstruct images without additional CT images for complex corrections from actual clinical measurements on LAFOV PET. Despite improving the current development, AI-based reconstruction does not work appropriately for untrained scenarios due to limited extrapolation capability and cannot completely replace conventional reconstruction currently.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Procesamiento de Imagen Asistido por Computador / Tomografía Computarizada por Tomografía de Emisión de Positrones Límite: Humans Idioma: En Revista: Eur J Nucl Med Mol Imaging Asunto de la revista: MEDICINA NUCLEAR Año: 2022 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Procesamiento de Imagen Asistido por Computador / Tomografía Computarizada por Tomografía de Emisión de Positrones Límite: Humans Idioma: En Revista: Eur J Nucl Med Mol Imaging Asunto de la revista: MEDICINA NUCLEAR Año: 2022 Tipo del documento: Article País de afiliación: China