The Interaction Between Patient Activation And Patient-clinician Relationship Quality: Implications For Ambulatory Heart Failure Care
HFSA ePoster Library. Venechuk G. 09/10/21; 343544; 304
Grace Venechuk
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Abstract
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Introduction: As ventricular assist device (VAD) technology continues to evolve, the target population for left and right sided support continues to grow. Therefore the degree of variability in cardiac anatomy and geometry in potential VAD recipients is likely to increase. Studying the cardiac geometry in relation to HeartMate 3 or HVAD device dimensions can help preoperative evaluation for ideal cannulation placement, and thereby potentially improve long term outcomes. Aims: Our project aims to develop a program that virtually models a VAD into the three-dimensional-rendering of the myocardium and chest using computed tomography images.
Methods and Results: We created a Python code process, a 4D retrospective cardiac CT dataset and then integrated a high definition 3D VAD model. Rather than creating a full-fledged viewer and analytics tool, which would be too costly and time-intensive, we created a tool that can be used to complement the capabilities of professional imaging processing software such as 3mensio or Visage (Figure 1 Demonstrates the program graphical user interface). The user can adjust the viewer to better isolate the heart prior to simulated VAD implant placement. The DICOM directories produced by our program can be opened directly in the software already used in daily clinical imaging to allow for more in-depth analysis of desired implant placement and cannula positioning, which can aid in pre-operative and intra-operative planning.
Conclusions: The next steps of the project will be to enable VAD implant model to be propagated across the 3D scan time series, increase computational efficiency and view clarity, as well as output the resulting DICOM directory so it can be opened in Visage or a similar tool. In a future version of the project, we will include an automatic placement feature and fluid dynamic analysis to validate the proposed implant location.
Methods and Results: We created a Python code process, a 4D retrospective cardiac CT dataset and then integrated a high definition 3D VAD model. Rather than creating a full-fledged viewer and analytics tool, which would be too costly and time-intensive, we created a tool that can be used to complement the capabilities of professional imaging processing software such as 3mensio or Visage (Figure 1 Demonstrates the program graphical user interface). The user can adjust the viewer to better isolate the heart prior to simulated VAD implant placement. The DICOM directories produced by our program can be opened directly in the software already used in daily clinical imaging to allow for more in-depth analysis of desired implant placement and cannula positioning, which can aid in pre-operative and intra-operative planning.
Conclusions: The next steps of the project will be to enable VAD implant model to be propagated across the 3D scan time series, increase computational efficiency and view clarity, as well as output the resulting DICOM directory so it can be opened in Visage or a similar tool. In a future version of the project, we will include an automatic placement feature and fluid dynamic analysis to validate the proposed implant location.
Introduction: As ventricular assist device (VAD) technology continues to evolve, the target population for left and right sided support continues to grow. Therefore the degree of variability in cardiac anatomy and geometry in potential VAD recipients is likely to increase. Studying the cardiac geometry in relation to HeartMate 3 or HVAD device dimensions can help preoperative evaluation for ideal cannulation placement, and thereby potentially improve long term outcomes. Aims: Our project aims to develop a program that virtually models a VAD into the three-dimensional-rendering of the myocardium and chest using computed tomography images.
Methods and Results: We created a Python code process, a 4D retrospective cardiac CT dataset and then integrated a high definition 3D VAD model. Rather than creating a full-fledged viewer and analytics tool, which would be too costly and time-intensive, we created a tool that can be used to complement the capabilities of professional imaging processing software such as 3mensio or Visage (Figure 1 Demonstrates the program graphical user interface). The user can adjust the viewer to better isolate the heart prior to simulated VAD implant placement. The DICOM directories produced by our program can be opened directly in the software already used in daily clinical imaging to allow for more in-depth analysis of desired implant placement and cannula positioning, which can aid in pre-operative and intra-operative planning.
Conclusions: The next steps of the project will be to enable VAD implant model to be propagated across the 3D scan time series, increase computational efficiency and view clarity, as well as output the resulting DICOM directory so it can be opened in Visage or a similar tool. In a future version of the project, we will include an automatic placement feature and fluid dynamic analysis to validate the proposed implant location.
Methods and Results: We created a Python code process, a 4D retrospective cardiac CT dataset and then integrated a high definition 3D VAD model. Rather than creating a full-fledged viewer and analytics tool, which would be too costly and time-intensive, we created a tool that can be used to complement the capabilities of professional imaging processing software such as 3mensio or Visage (Figure 1 Demonstrates the program graphical user interface). The user can adjust the viewer to better isolate the heart prior to simulated VAD implant placement. The DICOM directories produced by our program can be opened directly in the software already used in daily clinical imaging to allow for more in-depth analysis of desired implant placement and cannula positioning, which can aid in pre-operative and intra-operative planning.
Conclusions: The next steps of the project will be to enable VAD implant model to be propagated across the 3D scan time series, increase computational efficiency and view clarity, as well as output the resulting DICOM directory so it can be opened in Visage or a similar tool. In a future version of the project, we will include an automatic placement feature and fluid dynamic analysis to validate the proposed implant location.
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