# Virtual Prototyping: Computational Device Placements within Detailed Human Heart Models
## 1 Introduction  
The process for developing novel medical devices, procedural delivery systems, and/or next generation devices can be long, arduous, and extremely costly. For example, many steps must be taken to ensure that the device will fit correctly within a specified patient population. Typically, animal and cadaver studies are conducted to assist in determining such parameters for a device to fit into the relative anatomies [1]. While these studies offer many benefits, they are costly and time-consuming, and anatomies can vary greatly from living patients. Thus, medical device innovators are seeking novel methodologies, such as computational modeling, to gain further insights into device designs [2, 3]. This has initiated an increased use of computational modeling by medical device innovators to assist in the design, refinement, and/or validation of their devices [4]. Software packages from companies such as Materialise (Mimics and 3-Matic suites; Leuven, Belgium) have greatly aided in the creation (segmentation) of computational 3D models. Digital Imaging and Communications in Medicine (DICOM) files such as those obtained from computed tomography (CT) or magnetic resonance imaging (MRI) from cadaveric or clinical cases can be uploaded to these software packages for segmentation. Anatomical measurements can be abstracted from these 3D models, which is largely beneficial for improving the understanding of a wide variety of humananatomies. These measurements can also be utilized to quantify the statistical distributions of specific anatomical features within various patient populations [5,6]. In addition, medical device models can be computationally implanted into the anatomical models and subsequently 3D printed, thereby offering additional unique insights relative to the interactions between a given device and a patient's specific anatomy. Performing these implants in several anatomical models demonstrates how these devices would likely fit within varied patient anatomies. In other words, the generated 3D printed models grant further knowledge about implant site anatomies, clinical procedures, and optimal device placements, further enabling refinement of the device design to better fit an initial target population before performing expensive and time-consuming cadaver and/or animal studies. These anatomy-device models are also useful for explaining novel medical technologies to physicians. The 3D printed models help facilitate discussions between physicians and device engineers. The function of the model can be educational (explaining how the device functions) or beneficial for gaining iterative device prototype feedback (demonstrating a prototype device computationally placed in a heart and receiving feedback from physicians on how to improve the next iteration of the device). Additionally, 3D prints of computational device placements within anatomical models offer benefits outside of medical device design. These 3D printed models can be utilized by clinical care teams to better communicate the details of a surgical procedure or as intervention amongst themselves and/or with a patient or their family. For example, investigators seeking to quantify these benefits have found that 3D printed anatomical models significantly improved patient understanding of the planned procedure and relevant anatomy [7], thus bridging the gap of knowledge between clinical care teams and their patients. Here we demonstrate the ability to computationally place devices within varied and detailed 3D human cardiac models. These devices were placed in a variety of locations within anatomical models, as a means to study the device-tissue interface at differing implant sites. Devices were also placed in a number of different computational models with varied anatomies, allowing for the determination of ideal device placement locations, as well as for the characterization of variances in anatomical delivery pathways.