Our aim was to identify sub-groups of right ventricular outflow tract morphology that would be suitable for percutaneous pulmonary valve implantation and to document their prevalence in our patient population. Eighty-three consecutive patients with right ventricular outflow tract dysfunction (5-41 years, 76% tetralogy of Fallot) referred to our center for cardiovascular magnetic resonance were studied. A morphological classification was created according to visual assessment of three-dimensional reconstructions and detailed measurement. Diagnosis, right ventricular outflow tract type, surgical history and treatment outcomes were documented. Right ventricular outflow tract morphology was heterogeneous; nevertheless, 5 patterns were visually identified. Type I, a pyramidal morphology, was most prevalent (49%) and related to the presence of a transannular patch. Other types (II-V) were seen more commonly in patients with conduits. Two patients had unclassifiable morphology. Ninety-five percent of patients were assigned to the correct morphological classification by visual assessment alone. Percutaneous pulmonary valve implantation was performed successfully in 10 patients with Type II-V morphology and in 1 patient with unclassifiable morphology. Percutaneous implantation was not performed in patients with Type I morphology. Only right ventricular outflow tract diameters < 22 mm in diameter were suitable for the current device. We have created a morphological classification of the RVOT in patients referred for assessment of RVOT dysfunction. Though only 13% of our patients underwent percutaneous implantation, > 50% of outflow tract morphologies may be suitable for this approach, in particular with the development of new devices appropriate for larger outflow.

Compliant vascular phantoms are desirable for in-vitro patient-specific experiments and device testing. TangoPlus FullCure 930® is a commercially available rubber-like material that can be used for PolyJet rapid prototyping. This work aims to gather preliminary data on the distensibility of this material, in order to assess the feasibility of its use in the context of experimental cardiovascular modelling.

Current computational tools for planning and simulation in plastic and reconstructive surgery lack sufficient precision and are time-consuming, thus resulting in limited adoption. Although computer-assisted surgical planning systems help to improve clinical outcomes, shorten operation time and reduce cost, they are often too complex and require extensive manual input, which ultimately limits their use in doctor-patient communication and clinical decision making. Here, we present the first large-scale clinical 3D morphable model, a machine-learning-based framework involving supervised learning for diagnostics, risk stratification, and treatment simulation. The model, trained and validated with 4,261 faces of healthy volunteers and orthognathic (jaw) surgery patients, diagnoses patients with 95.5% sensitivity and 95.2% specificity, and simulates surgical outcomes with a mean accuracy of 1.1 ± 0.3 mm. We demonstrate how this model could fully-automatically aid diagnosis and provide patient-specific treatment plans from a 3D scan alone, to help efficient clinical decision making and improve clinical understanding of face shape as a marker for primary and secondary surgery.

The process of restenosis after a stenting procedure is related to local biomechanical environment. Arterial wall stresses caused by the interaction of the stent with the vascular wall and possibly stress induced stent strut fracture are two important parameters. The knowledge of these parameters after stent deployment in a patient derived 3D reconstruction of a diseased coronary artery might give insights in the understanding of the process of restenosis. 3D reconstruction of a mildly stenosed coronary artery was carried out based on a combination of biplane angiography and intravascular ultrasound. Finite element method computations were performed to simulate the deployment of a stent inside the reconstructed coronary artery model at inflation pressure of 1.0 MPa. Strut thickness of the stent was varied to investigate stresses in the stent and the vessel wall. Deformed configurations, pressure-lumen area relationship and stress distribution in the arterial wall and stent struts were studied. The simulations show how the stent pushes the arterial wall towards the outside allowing the expansion of the occluded artery. Higher stresses in the arterial wall are present behind the stent struts and in regions where the arterial wall was thin. Values of 200 MPa for the peak stresses in the stent strut were detected near the connecting parts between the stent struts, and they were only just below the fatigue stress. Decreasing strut thickness might reduce arterial damage without increasing stresses in the struts significantly. The method presented in this paper can be used to predict stresses in the stent struts and the vessel wall, and thus evaluate whether a specific stent design is optimal for a specific patient.

Microfocus computed tomography (micro-CT) is an imaging method that provides three dimensional (3D) digital datasets with comparable resolution to light microscopy. Although it has traditionally been used for non-destructive testing in engineering, aerospace industries and in preclinical animal studies, new applications are rapidly becoming available in the clinical setting including post-mortem fetal imaging and pathological specimen analysis. Printing three dimensional models from imaging datasets for educational purposes is well established in the medical literature, but typically using low resolution (0.7 mm voxel size) data acquired from computed tomography (CT) or magnetic resonance (MR) examinations. With higher resolution imaging (voxel sizes below 1 micron, <0.001mm) at micro-CT, smaller structures can be better characterised, and datasets post-processed to create accurate anatomical models for review and handling. In this review, we provide examples of how 3D printing of micro-CT imaged specimen...