Evidence map›Paper›PMID 40386576›Full record

ArticleArXiv2025

DUAL PROMPTING FOR DIVERSE COUNT-LEVEL PET DENOISING.

Xiaofeng Liu, Yongsong Huang, Thibault Marin, Samira Vafay Eslahi, Amal Tiss, Yanis Chemli, Keith A Johnson, Georges El Fakhri, Jinsong Ouyang

Abstract readPreprint
PubMed Full text at PMC
In one paragraph

Article in ArXiv, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Xiaofeng LiuDept. of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA.
Yongsong HuangDept. of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA.
Thibault MarinDept. of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA.
Samira Vafay EslahiDept. of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
Amal TissDept. of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
Yanis ChemliDept. of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA.
Keith A JohnsonDept. of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
Georges El FakhriDept. of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA.
Jinsong OuyangDept. of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA.

Funding

Vascular factors, physical activity, and inflammation as modulators of neurodegenerative and cognitive trajectories (Project 2)P01AG036694 · NIA · MASSACHUSETTS GENERAL HOSPITAL · PI REISA A. SPERLING · 2010 to 2026
$50.2M
Unified Joint Statistical Reconstruction of PET & MRP41EB022544 · NIBIB · YALE UNIVERSITY · PI Georges El Fakhri · 2017 to 2026
$14.8M
Postgraduate Training Program in Medical Imaging (PTPMI)T32EB013180 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI Ciprian Catana, Pedram Heidari · 2011 to 2026
$3.7M
Methods for Quantitative Neuroimaging of Tau Burden inPre-symptomatic ADR01AG076153 · NIA · YALE UNIVERSITY · PI Georges El Fakhri, Marc David Normandin · 2022 to 2026
$3.0M
Multi-Center Academic-Industrial Partnership for Personalized Al-Enabled High Count PETR01CA275188 · NCI · YALE UNIVERSITY · PI RAMSEY D. BADAWI, Georges El Fakhri · 2023 to 2026
$2.5M
Deep Learning for Individualized Treatment Effect Inference with Multimodal DepictionR21EB034911 · NIBIB · YALE UNIVERSITY · PI Xiaofeng Liu · 2024 to 2026
$653k
NCI NIH HHS R01 CA275188NIA NIH HHS P01 AG036694NIA NIH HHS R01 AG076153NIBIB NIH HHS P41 EB022544NIBIB NIH HHS R21 EB034911NIBIB NIH HHS T32 EB013180
6 · The paper itself

Abstract

The to-be-denoised positron emission tomography (PET) volumes are inherent with diverse count levels, which imposes challenges for a unified model to tackle varied cases. In this work, we resort to the recently flourished prompt learning to achieve generalizable PET denoising with different count levels. Specifically, we propose dual prompts to guide the PET denoising in a divide-and-conquer manner, i.e., an explicitly count-level prompt to provide the specific prior information and an implicitly general denoising prompt to encode the essential PET denoising knowledge. Then, a novel prompt fusion module is developed to unify the heterogeneous prompts, followed by a prompt-feature interaction module to inject prompts into the features. The prompts are able to dynamically guide the noise-conditioned denoising process. Therefore, we are able to efficiently train a unified denoising model for various count levels, and deploy it to different cases with personalized prompts. We evaluated on 1940 low-count PET 3D volumes with uniformly randomly selected 13-22% fractions of events from 97

Indexed as

Count LevelImage DenoisingPositron Emission TomographyPrompt Learning

Identifiers

PMID40386576
PMCPMC12083701

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.