Tunable and real‐time automatic interventional x‐ray collimation from semi‐supervised deep feature extraction
The algorithm's modular design ensured that users' collimation requirements were met without disruption to the interventional workflow or procedure time, while exhibiting strong potential to reduce radiation risk to patients and operators. Evaluation in more varied clinical settings could support translation of this technology into the clinic.
Authors
- Damini Rijhwani
- Pedro Nicolas Lylyk (ORCID: https://orcid.org/0000-0001-7044-4369)
- Alexander Haak
- Carlos Bleise (ORCID: https://orcid.org/0000-0003-2292-3263)
- Daniël Ruijters (ORCID: https://orcid.org/0000-0002-9931-4047)
- Brian C. Lee (ORCID: https://orcid.org/0000-0002-1849-5801)
- Ayushi Sinha (ORCID: https://orcid.org/0000-0001-8606-6585)
- S. Lang (ORCID: https://orcid.org/0000-0001-8091-0570)
- Shaun van Oorde‐Grainger
Institutions
- Philips (Netherlands) (NL)
- Philips (United States) (US)
Publication Details
- Journal
- Medical Physics
- Published
- 2024-12-06
- DOI
- https://doi.org/10.1002/mp.17522
- Citations
- 3
- Primary Topic
- Advanced X-ray and CT Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.55
Funders
- Philips Research Americas