Recommendations in pre-procedural imaging assessment for TAVI intervention: SIC-SIRM position paper part 2 (CT and MR angiography, standard medical reporting, future perspectives).
Riccardo MaranoGianluca PontoneEustachio AgricolaBrunilda AlushiAntonio BartorelliMatteo CameliNazario CarrabbaAntonio EspositoRiccardo FalettiMarco FranconeNicola GaleaPaolo GolinoMarco GuglielmoAnna PalmisanoSonia PetronioMaria PetullàSilvia PradellaFlavio RibichiniFrancesco RomeoVincenzo RussoSalvatore ScanduraNicolò SchicchiCarmen SpaccarotellaFabrizio TomaiCiro IndolfiMaurizio CentonzePublished in: La Radiologia medica (2022)
Non-invasive cardiovascular imaging owns a pivotal role in the preoperative assessment of patient candidates for transcatheter aortic valve implantation (TAVI), providing a wide range of crucial information to select the patients who will benefit the most and have the procedure done safely. This document has been developed by a joined group of experts of the Italian Society of Cardiology and the Italian Society of Medical and Interventional Radiology and aims to produce an updated consensus statement about the pre-procedural imaging assessment in candidate patients for TAVI intervention. The writing committee consisted of members and experts of both societies who worked jointly to develop a more integrated approach in the field of cardiac and vascular radiology. Part 2 of the document will cover CT and MR angiography, standard medical reporting, and future perspectives.
Keyphrases
- transcatheter aortic valve implantation
- aortic stenosis
- aortic valve
- aortic valve replacement
- ejection fraction
- computed tomography
- high resolution
- contrast enhanced
- transcatheter aortic valve replacement
- healthcare
- randomized controlled trial
- artificial intelligence
- magnetic resonance
- left ventricular
- dual energy
- newly diagnosed
- magnetic resonance imaging
- prognostic factors
- patients undergoing
- positron emission tomography
- machine learning
- minimally invasive
- heart failure
- case report
- deep learning
- clinical practice
- atrial fibrillation
- clinical evaluation