A Review on Cross-modality Synthesis from MRI to PET

Manjooran, George Prince; Malakkaran, Antony J; Joseph, Ashia; Babu, Harishma M; S, Meharban M

doi:10.1109/icsccc51823.2021.9478170

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…Likewise, Figure 3 (right), Figure 4 suggest that the model prediction is unbiased with respect to brain region and true uptake. It is difficult to compare the results directly with related studies due to the small test set and intrinsic differences in datasets, validation schemes, synthetic resolution, and use of scale variant metrics, however, a PSNR of 26.3 is considered high given the target resolution ( Manjooran et al, 2021 ). Combined, these observations suggest that the model is both accurate and robust in its prediction of healthy uptake, which is necessary for the images to act as personalized baselines.…”

Section: Discussionmentioning

confidence: 99%

A zero-dose synthetic baseline for the personalized analysis of [18F]FDG-PET: Application in Alzheimer’s disease

Hinge

Henriksen²,

Lindberg³

et al. 2022

Front. Neurosci.

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PurposeBrain 2-Deoxy-2-[18F]fluoroglucose ([18F]FDG-PET) is widely used in the diagnostic workup of Alzheimer’s disease (AD). Current tools for uptake analysis rely on non-personalized templates, which poses a challenge as decreased glucose uptake could reflect neuronal dysfunction, or heterogeneous brain morphology associated with normal aging. Overcoming this, we propose a deep learning method for synthesizing a personalized [18F]FDG-PET baseline from the patient’s own MRI, and showcase its applicability in detecting AD pathology.MethodsWe included [18F]FDG-PET/MRI data from 123 patients of a local cohort and 600 patients from ADNI. A supervised, adversarial model with two connected Generative Adversarial Networks (GANs) was trained on cognitive normal (CN) patients with transfer-learning to generate full synthetic baseline volumes (sbPET) (192 × 192 × 192) which reflect healthy uptake conditioned on brain anatomy. Synthetic accuracy was measured by absolute relative %-difference (Abs%), relative %-difference (RD%), and peak signal-to-noise ratio (PSNR). Lastly, we deployed the sbPET images in a fully personalized method for localizing metabolic abnormalities.ResultsThe model achieved a spatially uniform Abs% of 9.4%, RD% of 0.5%, and a PSNR of 26.3 for CN subjects. The sbPET images conformed to the anatomical information dictated by the MRI and proved robust in presence of atrophy. The personalized abnormality method correctly mapped the pathology of AD subjects while showing little to no anomalies for CN subjects.ConclusionThis work demonstrated the feasibility of synthesizing fully personalized, healthy-appearing [18F]FDG-PET images. Using these, we showcased a promising application in diagnosing AD, and theorized the potential value of sbPET images in other neuroimaging routines.

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Section: Discussionmentioning

confidence: 99%

A zero-dose synthetic baseline for the personalized analysis of [18F]FDG-PET: Application in Alzheimer’s disease

Hinge

Henriksen²,

Lindberg³

et al. 2022

Front. Neurosci.

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“…Therefore, edge features can be completed through feature stitching to obtain a complete feature map. It is for these reasons that U-net networks are widely used in the segmentation of images ( Li et al, 2018 ; Li et al, 2022 ; Yao & Jin, 2022 ) and generation of images ( Kim, Yoo & Jung, 2020 ; Manjooran et al, 2021 ).…”

Section: Preliminaries Informationmentioning

confidence: 99%

Remote sensing image analysis and prediction based on improved Pix2Pix model for water environment protection of smart cities

Wang

et al. 2023

PeerJ Computer Science

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Background As an important part of smart cities, smart water environmental protection has become an important way to solve water environmental pollution problems. It is proposed in this article to develop a water quality remote sensing image analysis and prediction method based on the improved Pix2Pix (3D-GAN) model to overcome the problems associated with water environment prediction of smart cities based on remote sensing image data having low accuracy in predicting image information, as well as being difficult to train. Methods Firstly, due to inversion differences and weather conditions, water quality remote sensing images are not perfect, which leads to the creation of time series data that cannot be used directly in prediction modeling. Therefore, a method for preprocessing time series of remote sensing images has been proposed in this article. The original remote sensing image was unified by pixel substitution, the image was repaired by spatial weight matrix, and the time series data was supplemented by linear interpolation. Secondly, in order to enhance the ability of the prediction model to process spatial-temporal data and improve the prediction accuracy of remote sensing images, the convolutional gated recurrent unit network is concatenated with the U-net network as the generator of the improved Pix2Pix model. At the same time, the channel attention mechanism is introduced into the convolutional gated recurrent unit network to enhance the ability of extracting image time series information, and the residual structure is introduced into the downsampling of the U-net network to avoid gradient explosion or disappearance. After that, the remote sensing images of historical moments are superimposed on the channels as labels and sent to the discriminator for adversarial training. The improved Pix2Pix model no longer translates images, but can predict two dimensions of space and one dimension of time, so it is actually a 3D-GAN model. Third, remote sensing image inversion data of chlorophyll-a concentrations in the Taihu Lake basin are used to verify and predict the water environment at future moments. Results The results show that the mean value of structural similarity, peak signal-to-noise ratio, cosine similarity, and mutual information between the predicted value of the proposed method and the real remote sensing image is higher than that of existing methods, which indicates that the proposed method is effective in predicting water environment of smart cities.

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Applications of Generative Adversarial Networks (GANs) in Positron Emission Tomography (PET) imaging: A review

Apostolopoulos

Παπαθανασίου

Apostolopoulos

et al. 2022

Eur J Nucl Med Mol Imaging

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A Review on Cross-modality Synthesis from MRI to PET

Cited by 3 publications

References 8 publications

A zero-dose synthetic baseline for the personalized analysis of [18F]FDG-PET: Application in Alzheimer’s disease

A zero-dose synthetic baseline for the personalized analysis of [18F]FDG-PET: Application in Alzheimer’s disease

Remote sensing image analysis and prediction based on improved Pix2Pix model for water environment protection of smart cities

Applications of Generative Adversarial Networks (GANs) in Positron Emission Tomography (PET) imaging: A review

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