Evidence map›Paper›PMID 36583827›Full record

ArticleRadiological physics and technology2023

Characteristics of the deep learning-based virtual monochromatic image with fast kilovolt-switching CT: a phantom study.

Yuna Katsuyama, Tsukasa Kojima, Takashi Shirasaka, Masatoshi Kondo, Toyoyuki Kato

Abstract read
PubMed Publisher
In one paragraph

Article in Radiological physics and technology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

5 authors.

Yuna KatsuyamaDivision of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Fukuoka, Japan. katsuyama.yuna.198@m.kyushu-u.ac.jp.
Tsukasa KojimaDivision of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Fukuoka, Japan.
Takashi ShirasakaDivision of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Fukuoka, Japan.
Masatoshi KondoDivision of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Fukuoka, Japan.
Toyoyuki KatoDivision of Radiology, Department of Medical Technology, Kyushu University Hospital, Fukuoka, Fukuoka, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeWe assessed the physical properties of virtual monochromatic images (VMIs) obtained with different energy levels in various contrast settings and radiation doses using deep learning-based spectral computed tomography (DL-Spectral CT) and compared the results with those from single-energy CT (SECT) imaging. MATERIALS AND

methodsA Catphan

resultsRegardless of the radiation dose, the noise was the lowest at 70 keV VMI. The NPS showed that the noise amplitude at all spatial frequencies was the lowest among other VMI and 120 kVp images. The spatial resolution was higher for 70 keV VMI compared to the other VMIs, except for high-contrast objects. The d' of 70 keV VMI was the highest among the VMI and 120 kVp images at all radiation doses and contrast settings. The d' of the 70 keV VMIs at the minimum dose was higher than that at the maximum dose in any other image.

conclusionThe physical properties of the DL-Spectral CT VMIs varied with the energy level. The 70 keV VMI had the highest detectability by far among the VMI and 120-kVp images. DL-Spectral CT may be useful to reduce radiation doses.

Indexed as

Deep LearningPhantoms, ImagingRadiation DosageRadiographic Image Interpretation, Computer-AssistedRetrospective StudiesSignal-To-Noise RatioTomography, X-Ray ComputedComputed tomographyDeep learningDetectabilityDual energy

Identifiers

PMID36583827

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.