Coronary Stent Occlusion
Conditions
Keywords
in-stent stenosis, CT derived fractional flow reserve, artificial intelligence, deep learning, image processing
Brief summary
CT-FFR(CT-derived flow reserve fraction) usually could not been measured accurately for in-stent lesions due to the serious interference with the metal structs. ISR-Net is a new algorithm in assessing the flow of coronary in-stent stenosis. We compare the CT-FFR value of in-stent lesions with the invasive FFR measured by pressure wire to evaluate the accuracy of ISR-Net algorithm. The research results are of great significance to solve the bottleneck problem of CT-FFR and expand its application scope.
Detailed description
CT-FFR is an important noninvasive examination to evaluate the function of coronary artery disease. It can help clinicians make clinical decisions and reduce patients' invasive coronary angiography (ICA). The image quality of coronary CT angiography (CCTA) is the basis of CT-FFR measurement. Because metal stents seriously interfere with the imaging of CCTA, it is very difficult to measure the CT-FFR value of lesions in stents. However, a large number of patients need imaging follow-up evaluation after stenting. In the previous research, the investigators creatively invented a new algorithm ISR-Net and conducted a retrospective analysis. It is preliminarily proved that the algorithm can more accurately display the stenosis lesions in the stent than the previous imaging software, making it possible to calculate the CT-FFR of the lesions in the stent. At present, the algorithm has applied for a national invention patent. In order to transform to clinical application, further clinical verification is needed. This study will evaluate the accuracy of ISR-Net algorithm in assessing the function of coronary stent stenosis by carrying out prospective clinical trials and taking the blood flow reserve fraction (FFR) measured by pressure wire as the gold standard. At the same time, the standard process of CT-FFR measurement of in stent lesions was established. The research results are of great significance to solve the bottleneck problem of CT-FFR and expand its application scope.
Interventions
DIAGNOSTIC_TESTCT-FFR measurement
Patients were scanned with ≥ 64 row CT according to standard operating specifications. The software obtains the coronary CT angiography image file through the data communication interface. Based on the image processing algorithm, the centerline and contour of the target vessel can be extracted, and then the target vessel can be reconstructed to obtain the three-dimensional size information of the vessel; Based on hydrodynamics calculation and analysis, the fractional flow reserve (FFR) of each position of the target vessel is measured.
PROCEDUREinvasive FFR
Insert the pressure guide wire into the finger guide tube and push the pressure guide wire until the pressure sensor just comes out of the orifice of guiding catheter; Equalize PD and PA values;Push the pressure guide wire to the distal end of the lesion, and record the measured blood vessel and position;Record the resting Pd / PA of the pressure guide wire;Nitroglycerin and adenosine triphosphate were administered intravenously according to standard catheter laboratory specifications to achieve maximum hyperemia;Record the FFR value of the in-stent lesions.
Sponsors
Beijing Hospital
Study design
Observational model
CASE_CONTROL
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 90 Years
Healthy volunteers
No
Inclusion criteria
General Inclusion Criteria: * Over 18 years old; * Be able to understand the purpose of the test and sign the informed consent form; * Previous intracoronary stent implantation; * According to the comprehensive clinical evaluation, coronary angiography and FFR were proposed; CTA image Inclusion Criteria: * The coronary CT angiography images showed that the reference vessel diameter of the stenosis segment in the stent was ≥ 2mm; * The stenosis degree of coronary stent diameter ≥ 30% and ≤ 90% by visual inspection.
Exclusion criteria
General
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| To predict the sensitivity, specificity and accuracy of CT-FFR in the functional sense of in stent lesions based on ISR-Net algorithm. | one month |
Secondary
| Measure | Time frame |
|---|---|
| To predict the functional accuracy of in stent lesions, PPV, NPV and area under ROC curve (AUC) | one month |
Countries
China
Outcome results
None listed