Abstract: The present disclosure provides to co-register an external image of a vessel to IVUS images captured within the vessel and provides graphical user interfaces to facilitate modification to the co-registration. Notably, the present disclosure provides for the co-registration even when the IVUS images are captured before the external image or before co-registration is initiated with the external image.

Abstract: This disclosure provides methods for vascular imaging co-registration and using the co-registered imaging data in guiding live fluoroscopy. Extravascular imaging data includes an extravascular contrast image showing the portion of the blood vessel with contrast showing a visualized anatomical landmark while intravascular imaging data is obtained during a translation procedure that includes one or more intravascular images showing a detected anatomical landmark. The starting location and the ending location of the imaging element on the extravascular imaging data is marked, and the predicted location of the detected anatomical landmark on the extravascular imaging data is marked. The predicted location of the detected anatomical landmark is then aligned with the visualized anatomical landmark.

Abstract: In some examples, a processor of a system evaluates a therapy program based on a score determined based on a volume of tissue expected to be activated (“VTA”) by therapy delivery according to the therapy program. The score may be determined using an efficacy map comprising a plurality of voxels that are each assigned a value. In some examples, the efficacy map is selected from a plurality of stored efficacy maps based on a patient condition, one or more patient symptoms, or both the patient condition and one or more patient symptoms. In addition, in some examples, voxels of the efficacy map are assigned respective values that are associated with a clinical rating scale.

Abstract: In some examples, a processor of a system evaluates a therapy program based on a score determined based on a volume of tissue expected to be activated (“VTA”) by therapy delivery according to the therapy program. The score may be determined using an efficacy map comprising a plurality of voxels that are each assigned a value. In some examples, the efficacy map is selected from a plurality of stored efficacy maps based on a patient condition, one or more patient symptoms, or both the patient condition and one or more patient symptoms. In addition, in some examples, voxels of the efficacy map are assigned respective values that are associated with a clinical rating scale.

Abstract: In some examples, a processor of a system evaluates a therapy program based on a score determined based on a volume of tissue expected to be activated (“VTA”) by therapy delivery according to the therapy program. The score may be determined using an efficacy map comprising a plurality of voxels that are each assigned a value. In some examples, the efficacy map is selected from a plurality of stored efficacy maps based on a patient condition, one or more patient symptoms, or both the patient condition and one or more patient symptoms. In addition, in some examples, voxels of the efficacy map are assigned respective values that are associated with a clinical rating scale.

Abstract: In some examples, a processor of a system evaluates a therapy program based on a score determined based on a volume of tissue expected to be activated (“VTA”) by therapy delivery according to the therapy program. The score may be determined using an efficacy map comprising a plurality of voxels that are each assigned a value. In some examples, the efficacy map is selected from a plurality of stored efficacy maps based on a patient condition, one or more patient symptoms, or both the patient condition and one or more patient symptoms. In addition, in some examples, voxels of the efficacy map are assigned respective values that are associated with a clinical rating scale.