Organ phantoms are widely used for evaluating medical technologies, training clinical practitioners, as well as surgical planning. In the context of cardiovascular disease, a patient-specific cardiac phantom can play an important role for interventional cardiology procedures. However, phantoms with complicated structures are difficult to fabricate by conventional manufacturing methods. The emergence of three-dimensional (3D) printing with soft materials provides the opportunity to produce phantoms with complex geometries and realistic properties. In this work, the aim was to explore the use of a direct 3D printing technique to produce multimodal imaging cardiac phantoms and to test the physical properties of the new materials used, namely the Poro-Lay series and TangoPlus. The cardiac phantoms were first modeled using real data segmented from a patient chest computer tomography (CT) scan and then printed with the novel materials. They were then tested quantitatively in terms of stiffness and ultrasound (US) acoustic values and qualitatively with US, CT, and magnetic resonance imaging systems. From the stiffness measurements, Lay-fomm 40 had the closest Young's modulus to real myocardium with an error of 29–54%, while TangoPlus had the largest difference. From the US acoustics measurements, Lay-fomm 40 also demonstrated the closest soft tissue-mimicking properties with both the smallest attenuation and impedance differences. Furthermore, the imaging results show that the phantoms are compatible with multiple imaging modalities and thus have potential to be used for interventional procedure simulation and testing of novel interventional devices. In conclusion, direct 3D printing with Poro-Lay and TangoPlus is a promising method for manufacture of multimodal imaging phantoms with complicated structures, especially for soft patient-specific phantoms.

Interventional cardiology procedure is an important type of minimally invasive surgery that deals with the catheter-based treatment of cardiovascular diseases, such as coronary artery diseases, strokes, peripheral arterial diseases, and aortic diseases. Ultrasound imaging, also called echocardiography, is a typical imaging tool that monitors catheter puncturing. Localising a medical device accurately during cardiac interventions can help improve the procedure’s safety and reliability under ultrasound imaging. However, external device tracking and image-based tracking methods can only provide a partial solution. Thus, we proposed a hybrid framework, with the combination of both methods to localise the catheter tip target in an automatic way. The external device used was an electromagnetic tracking system from North Digital Inc (NDI), and the ultrasound image analysis was based on UNet, a deep learning network for semantic segmentation. From the external method, the tip’s location was determined precisely, and the deep learning platform segmented the exact catheter tip automatically. This novel hybrid localisation framework combines the advantages of external electromagnetic (EM) tracking and the deep learning-based image method, which offers a new solution to identify the moving medical device in low-resolution ultrasound images.

Radiofrequency ablation (RFA) is a treatment used in the management of various arrhythmias including atrial fibrillation. Enhanced training for electrophysiologists through the use of physical simulators has a significant role in improving patient outcomes. The requirements for a high-fidelity simulator for cardiac RFA are challenging and not fully met by any research or commercial simulator at present. In this study, we have produced and evaluated a 3D-printed, bi-atrial model contained in a custom-made enclosure for RFA simulation using a new soft tissue-mimicking polymer, Layfomm-40, combined with thermochromic pigment and barium sulphate in an acrylic paint carrier. We evaluated the conductive properties of Layfomm-40, its sensitivity to RFA, and its visibility in X-ray imaging, and carried a full simulation of RFA in the cardiac catheterization laboratory by an electrophysiologist. We demonstrated that a patient-specific 3D-printed Layfomm-40 bi-atrial model coated with a custom thermochromic/barium sulphate paint was compatible with the CARTO3 electroanatomic mapping system and could be effectively imaged using X-ray fluoroscopy. We demonstrated the effective delivery and visualization of radiofrequency ablation lesions in this model. The simulator meets nearly all the requirements for high-fidelity physical simulation of RFA. The use of such simulators is likely to have impact on the training of electrophysiologists and the evaluation of novel RFA devices.

Interventional cardiology procedure is an important type of minimally invasive surgery that deals with the catheter-based treatment of cardiovascular diseases, such as coronary artery diseases, strokes, peripheral arterial diseases and aortic diseases. Ultrasound imaging, also called echocardiography, is a typical imaging tool that monitors catheter puncturing. Localising a medical device accurately during cardiac interventions can help improve the procedure’s safety and reliability under ultrasound imaging. However, external device tracking and image-based tracking methods can only provide a partial solution. Thus, we proposed a hybrid framework, with the combination of both methods to localise the catheter tip target in an automatic way. The external device used was an electromagnetic tracking system from North Digital Inc (NDI) and the ultrasound image analysis was based on UNet, a deep learning network for semantic segmentation. From the external method, the tip’s location was determined precisely, and the deep learning platform segmented the exact catheter tip automatically. This novel hybrid localisation framework combines the advantages of external electromagnetic (EM) tracking and deep-learning-based image method, which offers a new solution to identify the moving medical device in low-resolution ultrasound images. Featured Application This framework can be applied to other medical-device localisation fields to help doctors identify a moving target in low-resolution ultrasound images.