Evidence map›Paper›PMID 42590487›Full record

ArticleSensors (Basel, Switzerland)2026

Trans

Minmin Yang, Yunhui Zhu, Huantao Ren, Senem Velipasalar

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2026. 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

4 authors.

Minmin YangDepartment of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY 13244, USA.
Yunhui ZhuDepartment of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY 13244, USA.
Huantao RenDepartment of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY 13244, USA.
Senem VelipasalarDepartment of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY 13244, USA.ORCID 0000-0002-1430-1555

Funding

New York State Department of Economic Development C080116
6 · The paper itself

Abstract

Cone-beam computed tomography (CBCT) with sparse projection views offers reduced radiation dose and faster scans but introduces severe streak artifacts and spatial coverage gaps. We address these challenges within a unified framework. First, we replace conventional UNet/ResNet encoders with TransUNet, a hybrid CNN-Transformer architecture that jointly models local details and long-range spatial context. It is adapted to CBCT reconstruction by concatenating multi-scale feature maps and introducing a lightweight attenuation-prediction head. Trans-CBCT outperforms the best baseline by 1.17 dB in PSNR and by 0.0163 in SSIM on LUNA16 with only six projection views. Second, we incorporate a neighbor-aware Point Transformer with explicit 3D positional encodings and a neighbor-aware attention module aggregating information from each point's

Indexed as

CBCT reconstructioncone-beam computed tomographyneighbor-aware attentionneighbor-aware point transformerTrans2-CBCTtransunet

Identifiers

PMID42590487
PMCPMC13468996

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.