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Motion Correction in Intravascular Ultrasound Sequences Based on Deep Learning and Topological Constraints

Motion Correction in Intravascular Ultrasound Sequences Based on Deep Learning and Topological Constraints

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500115210
Enrollment
Unknown
Registered
2025-12-23
Start date
2026-01-01
Completion date
Unknown
Last updated
2026-01-27

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Coronary Heart Disease

Interventions

Dataset A:No intervention.
Dataset B:No intervention.

Sponsors

Hangzhou Cancer Hospital
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: 1. The data were obtained from the internationally recognized public IVUS-2011 dataset. 2. All data have been anonymized and contain no patient-identifiable information. 3. The image quality meets the analysis requirements, with no significant signal loss.

Exclusion criteria

Exclusion criteria: 1. Image damage (e.g., missing frames); 2. Missing data annotations (e.g., absence of vascular structure reference standards).

Design outcomes

Primary

MeasureTime frame
Spectral leakage;Signal-to-noise ratio;—

Countries

China

Contacts

Public ContactYuan Yang

Hangzhou Cancer Hospital

yuanyang9608@163.com+86 571 56006035

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026