Pre-surgical Visualisation Made Easy by 3D Printed Patient-Specific Heart Models
3D printed models for health care are inching to become mainstream. Many success stories are reported in the literature. The medical imaging has become a keystone along with 3D printing to make it possible. Their capability to faithfully reproduce anatomical model is well demonstrated. Many diagnostic and treatment decisions are taken based on the model. Thus these models are used as qualitative reference collateral. While hard tissue based anatomy is lot easier to process, the soft tissue pose challenges due to overlapping contrast values. Heart, Lungs, Kidneys, liver, spleen, etc., are all soft tissue based anatomies that are difficult to process. Our focus is based upon infant Heart. Neonates suffer from Congenital Heart Diseases (CHD). Their occurrence is put at 8 in 1000 live births. Focus of our present research study is to investigate the usefulness of the 3D printed models in CHD treatment management. We worked with three tertiary medical research centres specialising in CHD cases. The study included random 16 live cases of different verity, from neonatal infants and up to 13 years of age, from simple defects to very complex cases. The CT (Computed Tomography) scan data of each case were processed using Mimics software to build the digital model. Each case was analysed for quality of input data/3D printed model quality and its role in clinical management. The results show that, 3D printed models are useful, particularly in combination defects such as Tetralogy of Fallot & Double Outlet Right Ventricle. The study also reemphasizes the need for such models.
Keywords3D printing Models Visualisation Congenital Heart Surgical Pre-plan
This research is supported by Imaginarium India Pvt. Ltd. and authors are very thankful to the resources used. We also wish to thank the tertiary care centres: Kokilaben Dhirubhai Ambani Hospital, Fortis Healthcare Ltd. both from Mumbai and Narayana Institute of Cardiac Sciences, Bengaluru for their support in live cases data and their expert clinicians for quality interactions.
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