Context-Aware Transformer GAN for Direct Generation of Attenuation and Scatter Corrected PET Data.

Mojtaba Jafaritadi, Emily Anaya, Garry Chinn, Jarrett Rosenberg, Tie Liang, Craig S Levin

Abstract read

In one paragraph

Article in IEEE transactions on radiation and plasma medical sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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5 · Who and what money

Authors and funding

6 authors.

Mojtaba JafaritadiDepartment of Radiology, Stanford University, Stanford, CA 94305 USA.ORCID 0000-0002-4085-4057

Emily AnayaDepartment of Electrical Engineering, Stanford University, Stanford, CA 94305 USA.ORCID 0000-0003-4171-0247

Garry ChinnDepartment of Radiology, Stanford University, Stanford, CA 94305 USA.ORCID 0000-0002-1467-3228

Jarrett RosenbergDepartment of Radiology, Stanford University, Stanford, CA 94305 USA.ORCID 0000-0003-2706-3290

Tie LiangDepartment of Radiology, Stanford University, Stanford, CA 94305 USA.

Craig S LevinDepartment of Radiology (and by courtesy, the Department of Physics, the Department of Electrical Engineering, and the Department of Bioengineering), Stanford University, Stanford, CA 94305 USA.ORCID 0000-0002-4575-5074

Funding

Translation and Validation of a Radiofrequency-Penetrable PET insert for Simultaneous PET/MRI imaging of Neurological DisordersR01EB031038 · NIBIB · STANFORD UNIVERSITY · PI LEVIN, CRAIG S · 2022 to 2025

$2.3M

NIBIB NIH HHS R01 EB031038

6 · The paper itself

Abstract

We present a context-aware generative deep learning framework to produce photon attenuation and scatter corrected (ASC) positron emission tomography (PET) images directly from nonattenuation and nonscatter corrected (NASC) images. We trained conditional generative adversarial networks (cGANs) on either single-modality (NASC) or multimodality (NASC+MRI) input data to map NASC images to pixel-wise continuously valued ASC PET images. We designed and evaluated four cGAN models including Pix2Pix, attention-guided cGAN (AG-Pix2Pix), vision transformer cGAN (ViT-GAN), and shifted window transformer cGAN (Swin-GAN). Retrospective

Indexed as

Attenuation correctiondeep learning (DL)generative adversarial networksPET/MRIpositron emission tomography (PET)scatter correctionshifted-window transformervision transformer

Identifiers

PMID41809058

DOI10.1109/trpms.2024.3397318

PMCPMC12971077

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.