Prediction of stent expansion using finite element modeling and machine learning

A non-transitory computer-readable medium storing computer-executable instructions that, when executed, cause a processor to perform operations, comprising:

• accessing an intravascular optical coherence tomography (IVOCT) image of a blood vessel;
• determining one or more pre-stent label volumes associated with the blood vessel;
• determining one or more treatment variables associated with the blood vessel;
• generating one or more FEM-mimic simulations by applying a first deep learning model to the one or more pre-stent label volumes and the one or more treatment variables, wherein the one or more FEM-mimic simulations include a displacement field showing changes in positions of blood vessel components in response to insertion and expansion of a stent at the one or more treatment variables and one or more inflated label volumes formed using the displacement field and the one or more pre-stent label volumes;
• and
• generating one or more stress/strain maps from one or more of the displacement field and the one or more inflated label volumes.

A stent prediction apparatus, comprising:

• a memory configured to store a pre-stent intravascular image of a blood vessel of a patient, one or more treatment variables relating to the pre-stent intravascular image, and one or more pre-stent label volumes of the pre-stent intravascular image;
• a first deep learning model configured to generate one or more FEM-mimic simulations from the one or more treatment variables and the one or more pre-stent label volumes by:
• applying the first deep learning model to the one or more pre-stent label volumes and the one or more treatment variables to form a displacement field showing changes in positions of blood vessel components in response to insertion and expansion of a stent at the one or more treatment variables;
• and
• determining one or more inflated label volumes using the displacement field and the one or more pre-stent label volumes;
• and
• a second deep learning model configured to generate one or more stress/strain maps from one or more of the displacement field and the one or more inflated label volumes.