ct-ffr for coronary in-stent stenosis based on isr-net algorithm

Conditions: Coronary Stent Occlusion

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.

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.

Eligibility criteria:
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 exclusion criteria: * previous coronary artery bypass grafting (cabg), artificial heart valve implantation, pacemaker or implantable defibrillator implantation; * there are persistent or active symptoms of clinical instability, including acute chest pain (sudden onset), cardiogenic shock, unstable blood pressure state (systolic blood pressure less than 90 mmhg), severe congestive heart failure (nyha cardiac function grade iii or iv) or acute pulmonary edema; * acute myocardial infarction occurred within 7 days before enrollment; * patients with other severe diseases are not suitable for clinical trials, such as complex congenital heart history, sick sinus syndrome, long qt syndrome, severe arrhythmia or tachycardia, severe asthma, severe or very severe chronic obstructive pulmonary disease, chronic renal function impairment (serum creatinine value \> 1.5 mg / dl or creatinine clearance rate \< 45ml / kg · 1.73m2); * there are contraindications to the use of disodium adenosine triphosphate; * allergic to iodized contrast media; * pregnancy or unknown pregnancy status; * the expected life is less than 2 months; * there are any other factors that the researchers believe are not suitable for inclusion or completion of this study. cta image exclusion criteria: * the coronary artery image was obviously misplaced; * coronary artery occlusion.

Keywords: in-stent stenosis ct derived fractional flow reserve artificial intelligence deep learning image processing