A new tool for assessing Pectus Excavatum by a semi-automatic image processing pipeline calculating the classical severity indexes and a new marker: the Volumetric Correction Index

open6noBackground: In clinical assessment of Pectus Excavatum (PE), the indication to surgery is based not only on symptoms but also on quantitative markers calculated from Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) scans. According to clinical routine, these indexes are measured manually by radiologists with limited computer support. This process is time consuming and potentially subjected to inaccuracy and individual variability in measurements. Moreover, the existing indexes have limitations, since they are based on linear measurements performed on single slices rather than on volumetric data derived from all the thoracic scans. Results: In this paper we present an image processing pipeline aimed at providing radiologists with a computer-aid tool in support of diagnosis of PE patients developed in MATLAB® and conceived for MRI images. This framework has a dual purpose: (i) to automatize computation of clinical indexes with a view to ease and standardize pre-operative evaluation; (ii) to propose a new marker of pathological severity based on volumetric analysis and overcoming the limitations of existing axial slice-based indexes. Final designed framework is semi-automatic, requiring some user interventions at crucial steps: this is realized through a Graphical User Interface (GUI) that simplifies the interaction between the user and the tools. We tested our pipeline on 50 pediatric patients from Gaslini Children’s Hospital and performed manual computation of indexes, comparing the results between the proposed tool and gold-standard clinical practice. Automatic indexes provided by our algorithm have shown good agreement with manual measurements by two independent readers. Moreover, the new proposed Volumetric Correction Index (VCI) has exhibited good correlation with standardized markers of pathological severity, proving to be a potential innovative tool for diagnosis, treatment, and follow-up. Conclusions: Our pipeline represents an innovative image processing in PE evaluation, based on MRI images (radiation-free) and providing the clinician with a quick and accurate tool for automatically calculating the classical PE severity indexes and a new more comprehensive marker: the Volumetric Correction Index.openTro' R.; Martini S.; Stagnaro N.; Sambuceti V.; Torre M.; Fato M.M.Tro', R.; Martini, S.; Stagnaro, N.; Sambuceti, V.; Torre, M.; Fato, M. M

Development and Validation Methodology of the Nuss Procedure Surgical Planner

Pectus excavatum (PE) is a congenital chest wall deformity which is characterized, in most cases, by a deep depression of the sternum. A minimally invasive technique for the repair of PE (MIRPE), often referred to as the Nuss procedure, has been proven to be more advantageous than many other PE treatment techniques. The Nuss procedure consists of placement of a metal bar(s) underneath the sternum, thereby forcibly changing the geometry of the ribcage. Because of the prevalence of PE and the popularity of the Nuss procedure, the demand to perform this surgery is greater than ever. Therefore, a Nuss procedure surgical planner would be an invaluable planning tool ensuring an optimal physiological and aesthetic outcome. In this dissertation, the development and validation of the Nuss procedure planner is investigated. First, a generic model of the ribcage is developed to overcome the issue of missing cartilage when PE ribcages are segmented and facilitate the flexibility of the model to accommodate a range of deformity. Then, the CT data collected from actual patients with PE is used to create a set of patient specific finite element models. Based on finite element analyses performed over those models, a set force-displacement data set is created. This data is used to train an artificial neural network to generalize the data set. In order to evaluate the planning process, a methodology which uses an average shape of the chest for comparison with results of the Nuss procedure planner is developed. This method is based on a sample of normal chests obtained from the ODU male population using laser surface scanning and overcomes challenging issues such as hole-filling, scan registration and consistency. Additionally, this planning simulator is optimized so that it can be used for training purposes. Haptic feedback and inertial tracking is implemented, and the force-displacement model is approximated using a neural network approach and evaluated for real-time performance. The results show that it is possible to utilize this approximation of the force-displacement model for the Nuss procedure simulator. The detailed ribcage model achieves real-time performance

Proceedings, MSVSCC 2012

Proceedings of the 6th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 19, 2012 at VMASC in Suffolk, Virginia