Evidence map›Paper›PMID 33624229›Full record

ReviewMolecular imaging and biology2021

X-ray Dark-Field Imaging (XDFI)-a Promising Tool for 3D Virtual Histopathology.

Daisuke Shimao, Naoki Sunaguchi, Tetsuya Yuasa, Masami Ando, Kensaku Mori, Rajiv Gupta, Shu Ichihara

Abstract readReview
PubMed Publisher
In one paragraph

Review in Molecular imaging and biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
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

7 authors.

Daisuke ShimaoDepartment of Radiological Technology, Hokkaido University of Science, Sapporo, Hokkaido, 006-8585, Japan.
Naoki SunaguchiGraduate School of Medicine, Nagoya University, Nagoya, Aichi, 461-8673, Japan.
Tetsuya YuasaGraduate School of Engineering and Science, Yamagata University, Yonezawa, Yamagata, 992-8510, Japan.
Masami AndoComprehensive Research Organization for Science and Society, Tsuchiura, Ibaraki, 300-0811, Japan.
Kensaku MoriGraduate School of Informatics, Nagoya University, Nagoya, 464-8603, Japan.
Rajiv GuptaDepartment of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA.
Shu IchiharaClinical Research Center, Department of Pathology, Nagoya Medical Center, Nagoya, 460-0001, Japan. shu-kkr@umin.ac.jp.ORCID 0000-0003-0279-2358

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

X-ray dark-field imaging (XDFI) utilizing a thin silicon crystal under Laue case enables visualizing three-dimensional (3D) morphological alterations of human tissue. XDFI uses refraction-contrast derived from phase shift rather than absorption as the main X-ray image contrast source to render 2D and 3D images of tissue specimens in unprecedented detail. The unique features of XDFI are its extremely high sensitivity (approximately 1000:1 compared to absorption for soft tissues under X-ray energy of around 20 keV, theoretically) and excellent resolution (8.5 μm) without requiring contrast medium or staining. Thus, XDFI-computed tomography can generate 3D virtual histological images equivalent to those of stained histological sections pathologists observe under low-power light microscopy as far as organs and tissues selected as samples in preliminary studies. This paper reviews the fundamental principles and the potential of XDFI, describes two optical setups for XDFI with examples, illustrates features of XDFI that are salient for histopathology, and presents XDFI examples of refraction-contrast images of atherosclerotic plaques, musculoskeletal tissue, neuronal tissue, and breast cancer specimens. Availability of this X-ray imaging in routine histopathological evaluations of tissue specimens would help guide clinical decision making by highlighting suspicious areas in unstained, thick sections for further sampling and analysis using conventional histopathological techniques. XDFI is a promising tool for 3D virtual histopathology.

Indexed as

AnimalsHumansImaging, Three-DimensionalPlaque, AtheroscleroticTomography, X-Ray ComputedX-RaysAbsorption contrastMesoscalePhase-contrastRefraction-contrastVirtual histopathologyX-ray dark-field imaging

Identifiers

PMID33624229

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.