GapFill-Recon Net: A Cascade Network for simultaneously PET Gap Filling and Image Reconstruction

Huang, Yanchao; Zhu, Huobiao; Duan, Xiaoman; Hong, Xiaotong; Sun, Hao; Lv, Wenbing; Lu, Lijun; Feng, Qianjin

doi:10.1016/j.cmpb.2021.106271

Cited by 6 publications

(1 citation statement)

References 37 publications

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“…The peak signal-to-noise ratio (PSNR), root mean squared error (RMSE) and structural similarity index (SSIM) outperformed ordered subset expectation maximisation (OSEM) and filtered back projection (FBP) reconstruction for the synthetic evaluation data providing a strong proof of concept. Huang et al [ 46 ] used a fully convolutional network for reconstruction and incorporated a pre-processing neural network for filling crystal spacing gaps in sinogram data. Fully convolutional generative adversarial networks (GAN) were investigated by Liu et al [ 47 ] using a conditional GAN and Hu et al [ 48 ] using a cycle consistent GAN [ 49 ] with a VGG19 network [ 50 ] trained with clinical data for perceptual loss.…”

Section: Review Of Deep Learning-based Image Reconstructionmentioning

confidence: 99%

Deep learning-based image reconstruction and post-processing methods in positron emission tomography for low-dose imaging and resolution enhancement

Pain

Egan

Chen

2022

Eur J Nucl Med Mol Imaging

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Image processing plays a crucial role in maximising diagnostic quality of positron emission tomography (PET) images. Recently, deep learning methods developed across many fields have shown tremendous potential when applied to medical image enhancement, resulting in a rich and rapidly advancing literature surrounding this subject. This review encapsulates methods for integrating deep learning into PET image reconstruction and post-processing for low-dose imaging and resolution enhancement. A brief introduction to conventional image processing techniques in PET is firstly presented. We then review methods which integrate deep learning into the image reconstruction framework as either deep learning-based regularisation or as a fully data-driven mapping from measured signal to images. Deep learning-based post-processing methods for low-dose imaging, temporal resolution enhancement and spatial resolution enhancement are also reviewed. Finally, the challenges associated with applying deep learning to enhance PET images in the clinical setting are discussed and future research directions to address these challenges are presented.

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Section: Review Of Deep Learning-based Image Reconstructionmentioning

confidence: 99%