Data · dataset · 2026
Artificial intelligence iterative reconstruction for quality improvement in renal artery CTA
Listed in ScienceDB
Objective To observe the value of artificial intelligence iterative reconstruction (AIIR) in improving the image quality of renal artery computed tomography angiography (CTA).
Description
Methods
A retrospective analysis was conducted on 50 patients who underwent low-dose (120 kVp) renal artery CTA. The CTA images were reconstructed using both hybrid iterative reconstruction (HIR) and the AIIR. The subjective and objective image quality scores of the two groups were compared to evaluate the value of AIIR for renal artery CTA image quality improvement.Results Compared to the HIR group, the AIIR-reconstructed renal artery CTA images demonstrated more visible renal artery branches, along with significantly higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) (all P < 0.001).
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The subjective image quality scores for the AIIR group were also significantly higher (all P < 0.001). The effective radiation dose for the 120 kVp renal artery CTA examination was (7.45±1.16) mSv.Conclusion The AIIR is effective in improving the image quality of renal artery CTA, It has considerable clinical application value.
Links
Where it is published
- DOI doi.org/10.57760/sciencedb.zhfsyxyfhzz.00853 ↗
DOI / persistent id · from scidb cn
Catalogue records · 1
- OAI-PMH record scidb.cn/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=10.57760%2… ↗
metadata API · from scidb cn
Topics
- From keywords
- Computer Science & AI · Earth & Environmental Science · Engineering · Humanities · Life Sciences · Social Science
- Inferred from text
- Artificial intelligence 69% · Computed tomography 75% · Image 75%
Provenance · 1 source records, 14 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ScienceDB | 10.57760/sciencedb.zhfsyxyfhzz.00853 | 12 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].anzsrc:group:4602 | enrichment · scidb cn | taxonomy-embedding@1.0.0 | title+keywords+description (69%) |
| concepts[field].local:field:computer-science-ai | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:engineering | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:humanities | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:social-science | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[modality].local:modality:ct | enrichment · scidb cn | keyword-concept-rules@1.0.0 | title+description (75%) |
| concepts[modality].local:modality:image | enrichment · scidb cn | keyword-concept-rules@1.0.0 | title+description (75%) |
| description | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/description |
| license_text | source · scidb cn | connector:scidb_cn@1.0.0 | |
| publication_date | source · scidb cn | connector:scidb_cn@1.0.0 | |
| title | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/title |