PET Denoising and Uncertainty Estimation Based on NVAE Model Using Quantile Regression Loss

Cui, Juan; Xie, Yutong; Joshi, Anand A.; Gong, Kuang; Kim, Kyungsang; Son, Young-Don; Kim, Jong-Hoon; Leahy, Richard M.; Liu, Huafeng; Li, Quanzheng

doi:10.1007/978-3-031-16440-8_17

Cited by 6 publications

(3 citation statements)

References 18 publications

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“…From the viewpoint of personal information protection, federated learning, which enables decentralized learning without the need to export clinical data, is beginning to be applied to PET image denoising [165,166]. In addition, uncertainty estimation [167,168] and noise-aware networks [169][170][171] can provide additional value to conventional denoising methods. The advancement of PET state-of-the-art scanners, represented currently by total-body PET scanners [172], will pave the way for further applications of deep learning.…”

Section: Emerging Approachesmentioning

confidence: 99%

Deep learning-based PET image denoising and reconstruction: a review

Hashimoto,

Onishi,

Ote

et al. 2024

Radiol Phys Technol

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This review focuses on positron emission tomography (PET) imaging algorithms and traces the evolution of PET image reconstruction methods. First, we provide an overview of conventional PET image reconstruction methods from filtered backprojection through to recent iterative PET image reconstruction algorithms, and then review deep learning methods for PET data up to the latest innovations within three main categories. The first category involves post-processing methods for PET image denoising. The second category comprises direct image reconstruction methods that learn mappings from sinograms to the reconstructed images in an end-to-end manner. The third category comprises iterative reconstruction methods that combine conventional iterative image reconstruction with neural-network enhancement. We discuss future perspectives on PET imaging and deep learning technology.

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Section: Emerging Approachesmentioning

confidence: 99%

Deep learning-based PET image denoising and reconstruction: a review

Hashimoto,

Onishi,

Ote

et al. 2024

Radiol Phys Technol

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“…The third approach is performing image post-processing with DL methods after reconstruction to improve quality or denoising. One of the most attractive applications of DL is image denoising from low-dose PET to standard-dose PET [275][276][277][278][279][280], as it may reduce the radiation exposure of patients in PET studies. The advantage of this approach is that image pairs can be easily obtained by removing events from list-mode data of standard-dose acquisitions.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

Recent advances in combined Positron Emission Tomography and Magnetic Resonance Imaging

Galve,

Rodriguez-Vila,

Herraiz

et al. 2024

J. Inst.

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Hybrid imaging modalities combine two or more medical imaging techniques offering exciting new possibilities to image the structure, function and biochemistry of the human body in far greater detail than has previously been possible to improve patient diagnosis. In this context, simultaneous Positron Emission Tomography and Magnetic Resonance (PET/MR) imaging offers great complementary information, but it also poses challenges from the point of view of hardware and software compatibility. The PET signal may interfere with the MR magnetic field and vice-versa, posing several challenges and constrains in the PET instrumentation for PET/MR systems. Additionally, anatomical maps are needed to properly apply attenuation and scatter corrections to the resulting reconstructed PET images, as well motion estimates to minimize the effects of movement throughout the acquisition. In this review, we summarize the instrumentation implemented in modern PET scanners to overcome these limitations, describing the historical development of hybrid PET/MR scanners. We pay special attention to the methods used in PET to achieve attenuation, scatter and motion correction when it is combined with MR, and how both imaging modalities may be combined in PET image reconstruction algorithms.

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“…PET denoising is an inverse problem; to estimate the reconstruction uncertainty [28] proposed Nouveau variational autoencoder-based model using quantile regression loss.…”

Section: Introductionmentioning

confidence: 99%

Whole-body PET image denoising for reduced acquisition time

Kruzhilov¹,

Kudin²,

Vetoshkin³

et al. 2023

Preprint

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This paper evaluates the performance of supervised and unsupervised deep learning models for denoising positron emission tomography (PET) images in the presence of reduced acquisition times. Our experiments consider 212 studies (56908 images), and evaluate the models using 2D (RMSE, SSIM) and 3D (SUVpeak and SUVmax error for the regions of interest) metrics. It was shown that, in contrast to previous studies, supervised models (ResNet, Unet, SwinIR) outperform unsupervised models (pix2pix GAN and CycleGAN with ResNet backbone and various auxiliary losses) in the reconstruction of 2D PET images. Moreover, a hybrid approach of supervised CycleGAN shows the best results in SUVmax estimation for denoised images, and the SUVmax estimation error for denoised images is comparable with the PET reproducibility error.

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PET Denoising and Uncertainty Estimation Based on NVAE Model Using Quantile Regression Loss

Cited by 6 publications

References 18 publications

Deep learning-based PET image denoising and reconstruction: a review

Deep learning-based PET image denoising and reconstruction: a review

Recent advances in combined Positron Emission Tomography and Magnetic Resonance Imaging

Whole-body PET image denoising for reduced acquisition time

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