Evidence map›Paper›PMID 38032506›Full record

ArticleRadiological physics and technology2024

Deep learning-based attenuation correction method in

Masahiro Miyai, Ryohei Fukui, Masahiro Nakashima, Sachiko Goto

Abstract read
PubMed Publisher
In one paragraph

Article in Radiological physics and technology, 2024. 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
0.5field-weighted citation impact, top 32% 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

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

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

4 authors at 2 institutions in 1 country.

Masahiro MiyaiDepartment of Radiological Technology, Graduate School of Health Sciences, Okayama University, 2-5-1 Shikata-Cho, Kita-Ku, Okayama-Shi, Okayama, 700-8558, Japan. miya0210@hp.kawasaki-m.ac.jp.ORCID http://orcid.org/0009-0008-2276-8519
Ryohei FukuiDepartment of Radiological Technology, Faculty of Health Sciences, Okayama University, 2-5-1 Shikata-Cho, Kita-Ku, Okayama-shi, Okayama, 700-8558, Japan.
Masahiro NakashimaDivision of Radiological Technology, Okayama University Hospital, 2-5-1 Shikata-Cho, Kita-Ku, Okayama-shi, Okayama, 700-8558, Japan.
Sachiko GotoDepartment of Radiological Technology, Faculty of Health Sciences, Okayama University, 2-5-1 Shikata-Cho, Kita-Ku, Okayama-shi, Okayama, 700-8558, Japan.
Okayama University Hospital · JPOkayama University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aimed to evaluate a deep learning-based attenuation correction (AC) method to generate pseudo-computed tomography (CT) images from non-AC single-photon emission computed tomography images (SPECT

Indexed as

Deep LearningTechnetium Tc 99m Aggregated AlbuminTechnetium Tc 99m PentetateHumansLiverTomography, Emission-Computed, Single-PhotonTomography, X-Ray ComputedTechnetium Tc 99m Aggregated Albumintechnetium Tc 99m DTPA-galactosyl-human serum albuminTechnetium Tc 99m Pentetate99mTc-GSAAttenuation correctionDeep learningPseudo-CT imageSingle-photon emission computed tomography

Identifiers

PMID38032506
OpenAlexW4389180310

What OpenQuestion holds

Textmetadata
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.