Evidence map›Paper›PMID 38174207›Full record

ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2023

Fast Reconstruction for Deep Learning PET Head Motion Correction.

Tianyi Zeng, Jiazhen Zhang, Eléonore V Lieffrig, Zhuotong Cai, Fuyao Chen, Chenyu You, Mika Naganawa, Yihuan Lu, John A Onofrey

Abstract read
In one paragraph

Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Tianyi ZengDepartment of Radiology & Biomedical Imaging.
Jiazhen ZhangDepartment of Biomedical Engineering, Yale University, New Haven, CT, USA.
Eléonore V LieffrigDepartment of Radiology & Biomedical Imaging.
Zhuotong CaiDepartment of Radiology & Biomedical Imaging.
Fuyao ChenDepartment of Biomedical Engineering, Yale University, New Haven, CT, USA.
Chenyu YouDepartment of Electrical Engineering, Yale University, New Haven, CT, USA.
Mika NaganawaDepartment of Radiology & Biomedical Imaging.
Yihuan LuUnited Imaging Healthcare, Shanghai, China.
John A OnofreyDepartment of Radiology & Biomedical Imaging.

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Data-driven Head Motion Correction in PET Imaging Using Deep LearningR21EB028954 · NIBIB · YALE UNIVERSITY · PI ONOFREY, JOHN AARON · 2020 to 2022
$866k
NCATS NIH HHS UL1 TR001863NIBIB NIH HHS R21 EB028954
6 · The paper itself

Abstract

Head motion correction is an essential component of brain PET imaging, in which even motion of small magnitude can greatly degrade image quality and introduce artifacts. Building upon previous work, we propose a new head motion correction framework taking fast reconstructions as input. The main characteristics of the proposed method are: (i) the adoption of a high-resolution short-frame fast reconstruction workflow; (ii) the development of a novel encoder for PET data representation extraction; and (iii) the implementation of data augmentation techniques. Ablation studies are conducted to assess the individual contributions of each of these design choices. Furthermore, multi-subject studies are conducted on an

Indexed as

Brain PETData-driven motion correctionDeep LearningPET fast reconstruction

Identifiers

PMID38174207
PMCPMC10758999

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Registered trials

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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.