Evidence map›Paper›PMID 36354200›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2022

Deep Learning Enhances Multiparametric Dynamic Volumetric Photoacoustic Computed Tomography In Vivo (DL-PACT).

Seongwook Choi, Jinge Yang, Soo Young Lee, Jiwoong Kim, Jihye Lee, Won Jong Kim, Seungchul Lee, Chulhong Kim

Open access · goldAbstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.

0numbers the graph read from it
0cells of the map it votes in
55citing papers in PubMed
6.8field-weighted citation impact, top 2% of its field
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

55 citing papers in PubMed, 88 citations in OpenAlex.

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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

8 authors at 1 institution in 1 country.

Seongwook ChoiDepartment of Electrical Engineering, Convergence IT Engineering, Mechanical Engineering, School of Interdisciplinary Bioscience and Bioengineering, Graduate School of Artificial Intelligence, and Medical Device Innovation Center, Pohang University of Science and Technology (POSTECH), 77 Cheongam-ro, Nam-gu, Pohang, Gyeongbuk, 37673, Republic of Korea.ORCID https://orcid.org/0000-0001-9774-058X
Jinge YangDepartment of Electrical Engineering, Convergence IT Engineering, Mechanical Engineering, School of Interdisciplinary Bioscience and Bioengineering, Graduate School of Artificial Intelligence, and Medical Device Innovation Center, Pohang University of Science and Technology (POSTECH), 77 Cheongam-ro, Nam-gu, Pohang, Gyeongbuk, 37673, Republic of Korea.
Soo Young LeeDepartment of Electrical Engineering, Convergence IT Engineering, Mechanical Engineering, School of Interdisciplinary Bioscience and Bioengineering, Graduate School of Artificial Intelligence, and Medical Device Innovation Center, Pohang University of Science and Technology (POSTECH), 77 Cheongam-ro, Nam-gu, Pohang, Gyeongbuk, 37673, Republic of Korea.
Jiwoong KimDepartment of Electrical Engineering, Convergence IT Engineering, Mechanical Engineering, School of Interdisciplinary Bioscience and Bioengineering, Graduate School of Artificial Intelligence, and Medical Device Innovation Center, Pohang University of Science and Technology (POSTECH), 77 Cheongam-ro, Nam-gu, Pohang, Gyeongbuk, 37673, Republic of Korea.
Jihye LeeDepartment of Chemistry, POSTECH-CATHOLIC Biomedical Engineering Institute, Pohang University of Science and Technology (POSTECH), 77 Cheongam-ro, Nam-gu, Pohang, Gyeongbuk, 37673, Republic of Korea.
Won Jong KimDepartment of Chemistry, POSTECH-CATHOLIC Biomedical Engineering Institute, Pohang University of Science and Technology (POSTECH), 77 Cheongam-ro, Nam-gu, Pohang, Gyeongbuk, 37673, Republic of Korea.
Seungchul LeeDepartment of Electrical Engineering, Convergence IT Engineering, Mechanical Engineering, School of Interdisciplinary Bioscience and Bioengineering, Graduate School of Artificial Intelligence, and Medical Device Innovation Center, Pohang University of Science and Technology (POSTECH), 77 Cheongam-ro, Nam-gu, Pohang, Gyeongbuk, 37673, Republic of Korea.
Chulhong KimDepartment of Electrical Engineering, Convergence IT Engineering, Mechanical Engineering, School of Interdisciplinary Bioscience and Bioengineering, Graduate School of Artificial Intelligence, and Medical Device Innovation Center, Pohang University of Science and Technology (POSTECH), 77 Cheongam-ro, Nam-gu, Pohang, Gyeongbuk, 37673, Republic of Korea.ORCID https://orcid.org/0000-0001-7249-1257
Pohang University of Science and Technology · KR

Funding

Artificial Intelligence Graduate School Program (POSTECH)Basic Science Research Program through the NRF funded by the Ministry of Education 2020R1A6A1A03047902BK21 FOUR (Fostering Outstanding Universities for Research) projectKorea Medical Device Development Fund grant funded by the Ministry of Trade, Industry and Energy 9991007019Korea Medical Device Development Fund grant funded by the Ministry of Trade, Industry and Energy KMDF_PR_20200901_0008National Research Foundation (NRF) of Korea funded by the Ministry of Education 2019H1A2A1076500National Research Foundation (NRF) of Korea funded by the Ministry of Education 2021M3C1C3097619National Research Foundation (NRF) of Korea funded by the Ministry of Education 2021M3C1C3097624NRF funded by the Ministry of Education 2019H1A2A1076500
6 · The paper itself

Abstract

Photoacoustic computed tomography (PACT) has become a premier preclinical and clinical imaging modality. Although PACT's image quality can be dramatically improved with a large number of ultrasound (US) transducer elements and associated multiplexed data acquisition systems, the associated high system cost and/or slow temporal resolution are significant problems. Here, a deep learning-based approach is demonstrated that qualitatively and quantitively diminishes the limited-view artifacts that reduce image quality and improves the slow temporal resolution. This deep learning-enhanced multiparametric dynamic volumetric PACT approach, called DL-PACT, requires only a clustered subset of many US transducer elements on the conventional multiparametric PACT. Using DL-PACT, high-quality static structural and dynamic contrast-enhanced whole-body images as well as dynamic functional brain images of live animals and humans are successfully acquired, all in a relatively fast and cost-effective manner. It is believed that the strategy can significantly advance the use of PACT technology for preclinical and clinical applications such as neurology, cardiology, pharmacology, endocrinology, and oncology.

Indexed as

deep learningmultiparametric imagingphotoacoustic imaging

Identifiers

PMID36354200
PMCPMC9811490
OpenAlexW4308679686

What OpenQuestion holds

Textmetadata
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.