Evidence map›Paper›PMID 39739291›Full record

ArticleInternational journal of computer assisted radiology and surgery2025

Fundamental study on improving the quality of X-ray fluorescence computed tomography images by applying deep image prior to projection images as a pre-denoising method.

Sota Kusakari, Kazuki Sato, Yuta Tsushima, Masahiro Matsuoka, Tenta Sasaya, Naoki Sunaguchi, Keisuke Matsubara, Hidekazu Kawashima, Kazuyuki Hyodo, Tetsuya Yuasa and 1 more

Abstract read
PubMed Publisher
In one paragraph

Article in International journal of computer assisted radiology and surgery, 2025. 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
–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

0 citing papers in PubMed.

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

11 authors.

Sota KusakariGraduate School of Science and Technology, Hirosaki University, Hirosaki, Japan.ORCID http://orcid.org/0009-0007-9902-6866
Kazuki SatoGraduate School of Science and Technology, Hirosaki University, Hirosaki, Japan.
Yuta TsushimaGraduate School of Science and Technology, Hirosaki University, Hirosaki, Japan.
Masahiro MatsuokaGraduate School of Science and Engineering, Yamagata University, Yonezawa, Japan.
Tenta SasayaGraduate School of Science and Engineering, Yamagata University, Yonezawa, Japan.
Naoki SunaguchiDepartment of Radiological and Medical Laboratory Science, Nagoya University Graduate School of Medicine, Nagoya, Japan.
Keisuke MatsubaraDepartment of Management Science and Engineering, Faculty of Systems Science and Technology, Akita Prefectural University, Yurihonjo, Japan.
Hidekazu KawashimaRadioisotope Research Center, Kyoto Pharmaceutical University, Kyoto, Japan.
Kazuyuki HyodoHigh Energy Accelerator Research Organization, Tsukuba, Japan.
Tetsuya YuasaGraduate School of Science and Engineering, Yamagata University, Yonezawa, Japan.
Tsutomu ZeniyaGraduate School of Science and Technology, Hirosaki University, Hirosaki, Japan. zeniya@hirosaki-u.ac.jp.ORCID http://orcid.org/0000-0002-0499-9021

Funding

JSPS KAKENHI JP17H04116JSPS KAKENHI JP22K04133JSPS KAKENHI JP22K19501
6 · The paper itself

Abstract

purposeWe are developing a three-dimensional X-ray fluorescence computed tomography (3D XFCT) system using non-radioactive-labeled compounds for preclinical studies as a new modality that provides images of biological functions. Improvements in image quality and detection limits are required for the in vivo imaging. The aim of this study was to improve the quality of XFCT images by applying a deep image prior (DIP), which is a type of convolutional neural network, to projection images as a pre-denoising method, and then compare with DIP post-denoising.

methodsDIP can restore images using only the projection images acquired by XFCT. The projected images were processed with DIP for denoising. Three-dimensional images were reconstructed using the ordered subsets expectation maximization method for XFCT systems with multi-pinhole collimators. To evaluate the effectiveness of DIP pre-denoising, we constructed an XFCT system using synchrotron radiation and performed imaging experiments on a physical phantom and a mouse brain sample. The proposed method was compared with the DIP post-denoising and other denoising methods.

resultsThe proposed DIP pre-denoising method reduced noise and significantly improved the image quality and was superior to the DIP post-denoising and other methods. The contrast-to-noise ratio improved by 3.7 to 4.6 times with almost no deterioration in spatial resolution, and the detection limit improved from 0.069 to 0.035 mg/mL. There was a strong linear relationship between the iodine concentration and pixel values. Finally, image quality of the mouse brain improved.

conclusionsThrough experiments using phantoms and mouse brains, this study demonstrated that the application of DIP to projection images as a pre-denoising method can significantly improve the image quality and detection limit of 3D XFCT without degrading the spatial resolution. DIP was more effective when applied as pre-denoising than as post-denoising and can contribute to in vivo 3D imaging in the future.

Indexed as

BrainImage Processing, Computer-AssistedImaging, Three-DimensionalTomography, X-Ray ComputedAnimalsMiceNeural Networks, ComputerPhantoms, ImagingSignal-To-Noise RatioBiofunctional imagingDeep image priorDenoisingX-ray fluorescence computed tomography

Identifiers

PMID39739291

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.