The residual STL volume as a metric to evaluate accuracy and reproducibility of anatomic models for 3D printing: application in the validation of 3D-printable models of maxillofacial bone from reduced radiation dose CT images.

BackgroundThe effects of reduced radiation dose CT for the generation of maxillofacial bone STL models for 3D printing is currently unknown. Images of two full-face transplantation patients scanned with non-contrast 320-detector row CT were reconstructed at fractions of the acquisition radiation dose using noise simulation software and both filtered back-projection (FBP) and Adaptive Iterative Dose Reduction 3D (AIDR3D). The maxillofacial bone STL model segmented with thresholding from AIDR3D images at 100 % dose was considered the reference. For all other dose/reconstruction method combinations, a "residual STL volume" was calculated as the topologic subtraction of the STL model derived from that dataset from the reference and correlated to radiation dose.ResultsThe residual volume decreased with increasing radiation dose and was lower for AIDR3D compared to FBP reconstructions at all doses. As a fraction of the reference STL volume, the residual volume decreased from 2.9 % (20 % dose) to 1.4 % (50 % dose) in patient 1, and from 4.1 % to 1.9 %, respectively in patient 2 for AIDR3D reconstructions. For FBP reconstructions it decreased from 3.3 % (20 % dose) to 1.0 % (100 % dose) in patient 1, and from 5.5 % to 1.6 %, respectively in patient 2. Its morphology resembled a thin shell on the osseous surface with average thickness <0.1 mm.ConclusionThe residual volume, a topological difference metric of STL models of tissue depicted in DICOM images supports that reduction of CT dose by up to 80 % of the clinical acquisition in conjunction with iterative reconstruction yields maxillofacial bone models accurate for 3D printing

Predicting Parameters in Deep Learning

We demonstrate that there is significant redundancy in the parameterization of several deep learning models. Given only a few weight values for each feature it is possible to accurately predict the remaining values. Moreover, we show that not only can the parameter values be predicted, but many of them need not be learned at all. We train several different architectures by learning only a small number of weights and predicting the rest. In the best case we are able to predict more than 95% of the weights of a network without any drop in accuracy

Geometric Modeling of Cellular Materials for Additive Manufacturing in Biomedical Field: A Review

Advances in additive manufacturing technologies facilitate the fabrication of cellular materials that have tailored functional characteristics. The application of solid freeform fabrication techniques is especially exploited in designing scaffolds for tissue engineering. In this review, firstly, a classification of cellular materials from a geometric point of view is proposed; then, the main approaches on geometric modeling of cellular materials are discussed. Finally, an investigation on porous scaffolds fabricated by additive manufacturing technologies is pointed out. Perspectives in geometric modeling of scaffolds for tissue engineering are also proposed

Anatomical shape reconstruction and manufacturing: solving topological changes of lumen vessel trough geometric approach

Over the last years there has been an increasing growth of interest in Rapid Prototyping (RP) techniques applied to various fields of medicine. RP makes it possible, in vascular surgery, to produce accurate anatomic replicas of patient vessels. These replicas can help the customization of surgical invasive interventions such as in situ stent-graft insertion in carotid region. The main goal of this work is to obtain high quality in lumen reconstruction and manufacturing replicas by RP technique. This goal is achieved through the complete control of each phase of the generating process. We present a semi-automatic method for carotid lumen reconstruction based on Boundary Representation (BRep). All parameters influencing the quality of the shape reconstruction are presented and discussed: shape acquisition, shape reconstruction and shape manufacturing. The shape acquisition starts by extracting the points belonging to the boundary of the lumen vessel, from Computer Tomography (CT) images. These points, parameterised in a vector, are the input data of the shape reconstruction algorithm based on B-Spline interpolation. The B-Spline type for representing curves and surfaces were chosen because of their properties of continuity and local control. In the shape reconstruction stage we had to face problems due to the topological change on the vessel structure. For vessel regions where there are not changes of topology, we use the closed B-Spline curves (belonging to adjacent acquisition planes) as generating curves to build a B-Spline surface. For vessel regions with at least a change of topology (ex. bifurcation region) our algorithm split automatically the involved curves to obtain three rectangular B-Spline patches. Such patches are joined together to obtain the bifurcation vessel lumen. The set of lumen surfaces is then inserted in a Boundary Representation in order to get a valid solid. To analyse the quality of the reconstructed shapes, the final object is compared with the acquisition image. This solid is correctly tessellated in triangles to produce the data format used by the RP devices (STL)

Reverse Engineering Trimmed NURB Surfaces From Laser Scanned Data

A common reverse engineering problem is to convert several hundred thousand points collected from the surface of an object via a digitizing process, into a coherent geometric model that is easily transferred to a CAD software such as a solid modeler for either design improvement or manufacturing and analysis. These data are very dense and make data-set manipulation difficult and tedious. Many commercial solutions exist but involve time consuming interaction to go from points to surface meshes such as BSplines or NURBS (Non Uniform Rational BSplines). Our approach differs from current industry practice in that we produce a mesh with little or no interaction from the user. The user can produce degree 2 and higher BSpline surfaces and can choose the degree and number ofsegments as parameters to the system. The BSpline surface is both compact and curvature continuous. The former property reduces the large storage overhead, and the later implies a smooth can be created from noisy data. In addition, the nature ofthe BSpline allows one to easily and smoothly alter the surface, making re-engineering extremely feasible. The BSpline surface is created using the principle ofhigher orders least squares with smoothing functions at the edges. Both linear and cylindrical data sets are handled using an automated parameterization method. Also, because ofthe BSpline's continuous nature, a multiresolutional-triangulated mesh can quickly be produced. This last fact means that an STL file is simple to generate. STL files can also be easily used as input to the system.Mechanical Engineerin

Open CASCADE and rapid prototyping in human carotid lumen reconstruction

Image processing algorithms, CAD-CAM tools and rapid prototyping (RP) techniques are able to produce complex lumen artery replicas. This work presents a system for manufacturing the lumen of human carotid from computed tomography acquisition. The pipe-line of manufacturing process of a human carotid lumen replication is presented. Each stage of the pipe-line is briefly discussed. Technical details of the 3D surface reconstruction phase, based on the Open Cascade geometric modelling software, and the RP manufaturing process based on Fused Deposition Modelling are presented