Evidence map›Paper›PMID 40181814›Full record

ArticleConference proceedings. International Conference on Image Formation in X-Ray Computed Tomography2024

Noise Controlled CT Super-Resolution with Conditional Diffusion Model.

Yuang Wang, Siyeop Yoon, Rui Hu, Baihui Yu, Duhgoon Lee, Rajiv Gupta, Li Zhang, Zhiqiang Chen, Dufan Wu

Abstract read
PubMed Full text at PMC
In one paragraph

Article in Conference proceedings. International Conference on Image Formation in X-Ray Computed Tomography, 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
–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

9 authors.

Yuang WangDepartment of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston MA 02114, USA.
Siyeop YoonDepartment of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston MA 02114, USA.
Rui HuDepartment of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston MA 02114, USA.
Baihui YuDepartment of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston MA 02114, USA.
Duhgoon LeeNeurologica Corp., Danvers MA 01923, USA.
Rajiv GuptaDepartment of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston MA 02114, USA.
Li ZhangDepartment of Engineering Physics, Tsinghua University, Beijing 100084, China.
Zhiqiang ChenDepartment of Engineering Physics, Tsinghua University, Beijing 100084, China.
Dufan WuDepartment of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston MA 02114, USA.

Funding

Optimizing Mobile Photon-Counting CT Image Quality via Deep Learning for Neuro Intensive Care UnitR01EB035394 · NIBIB · OHIO STATE UNIVERSITY · PI Rajiv Gupta, Dufan Wu · 2024 to 2026
$1.3M
Dual Energy CT-enabled Asymptomatic Pulmonary Embolism Detection on Non-contrast CTR21EB031939 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI WU, DUFAN · 2021 to 2021
$449k
NIBIB NIH HHS R01 EB035394NIBIB NIH HHS R21 EB031939
6 · The paper itself

Abstract

Improving the spatial resolution of CT images is a meaningful yet challenging task, often accompanied by the issue of noise amplification. This article introduces an innovative framework for noise-controlled CT super-resolution utilizing the conditional diffusion model. The model is trained on hybrid datasets, combining noise-matched simulation data with segmented details from real data. Experimental results with real CT images validate the effectiveness of our proposed framework, showing its potential for practical applications in CT imaging.

Indexed as

Conditional Diffusion ModelNoise ControllingSuper-Resolution

Identifiers

PMID40181814
PMCPMC11967985

What OpenQuestion holds

Textmetadata
LicenceTDM
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.