Evidence map›Paper›PMID 31456382›Full record

ArticleJournal of Korean medical science2019

Advanced Sectioned Images of a Cadaver Head with Voxel Size of 0.04 mm.

Beom Sun Chung, Miran Han, Donghwan Har, Jin Seo Park

Abstract read
In one paragraph

Article in Journal of Korean medical science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Beom Sun ChungDepartment of Anatomy, Ajou University School of Medicine, Suwon, Korea.ORCID https://orcid.org/0000-0002-3644-9120
Miran HanDepartment of Radiology, Ajou University School of Medicine, Suwon, Korea.ORCID https://orcid.org/0000-0001-7752-5858
Donghwan HarCollege of ICT Engineering, Chung Ang University, Seoul, Korea.ORCID https://orcid.org/0000-0002-8676-8242
Jin Seo ParkDepartment of Anatomy, Dongguk University School of Medicine, Gyeongju, Korea. park93@dongguk.ac.kr.ORCID https://orcid.org/0000-0001-7956-4148

Funding

International Cooperative R&D program N0002249
6 · The paper itself

Abstract

backgroundThe sectioned images of a cadaver head made from the Visible Korean project have been used for research and educational purposes. However, the image resolution is insufficient to observe detailed structures suitable for experts. In this study, advanced sectioned images with higher resolution were produced for the identification of more detailed structures.

methodsThe head of a donated female cadaver was scanned for 3 Tesla magnetic resonance images and diffusion tensor images (DTIs). After the head was frozen, the head was sectioned serially at 0.04-mm intervals and photographed repeatedly using a digital camera.

resultsOn the resulting 4,000 sectioned images (intervals and pixel size, 0.04 mm³; color depth, 48 bits color; a file size, 288 Mbytes), minute brain structures, which can be observed not on previous sectioned images but on microscopic slides, were observed. The voxel size of this study (0.04 mm³) was very minute compared to our previous study (0.1 mm³; resolution, 4,368 × 2,912) and Visible Human Project of the USA (0.33 mm³; resolution, 2,048 × 2,048). Furthermore, the sectioned images were combined with tractography of the DTIs to elucidate the white matter with high resolution and the actual color of the tissue.

conclusionThe sectioned images will be used for diverse research, including the applications for the cross sectional anatomy and three-dimensional models for virtual experiments.

Indexed as

Diffusion Tensor ImagingMagnetic Resonance ImagingAgedAnatomy, Cross-SectionalBrainCadaverFemaleHumansImage Processing, Computer-AssistedCadaverCross-Sectional AnatomyDiffusion Tensor ImagingHeadVisible Human Project

Identifiers

PMID31456382
PMCPMC6717240

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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.