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

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 propo...

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Bibliographic Details
Published in:European journal of nuclear medicine and molecular imaging 2022-11, Vol.49 (13), p.4464-4477
Main Authors: 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
Format: Article
Language:English
Subjects:
Advanced Image Analyses (Radiomics and Artificial Intelligence)
Cardiology
Coders
Computed tomography
Data acquisition
Datasets
Deep learning
Encoders-Decoders
Feasibility studies
Field of view
Image processing
Image quality
Image reconstruction
Imaging
Medical imaging
Medicine
Medicine & Public Health
Nuclear Medicine
Oncology
Original Article
Orthopedics
Patients
Positron emission
Positron emission tomography
Radiology
Signal to noise ratio
Visual field
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Summary: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  
ISSN:1619-7070
1619-7089
DOI:10.1007/s00259-022-05861-2