The impact of manual threshold selection in medical additive manufacturing

Maureen Van Eijnatten,Kalle Karhu,Jan Wolff,Juha Koivisto,Tymour Forouzanfar

doi:10.1007/s11548-016-1490-4

Abstract

PurposeMedical additive manufacturing requires standard tessellation language (STL) models. Such models are commonly derived from computed tomography (CT) images using thresholding. Threshold selection can be performed manually or automatically. The aim of this study was to assess the impact of manual and default threshold selection on the reliability and accuracy of skull STL models using different CT technologies.MethodOne female and one male human cadaver head were imaged using multi-detector row CT, dual-energy CT, and two cone-beam CT scanners. Four medical engineers manually thresholded the bony structures on all CT images. The lowest and highest selected mean threshold values and the default threshold value were used to generate skull STL models. Geometric variations between all manually thresholded STL models were calculated. Furthermore, in order to calculate the accuracy of the manually and default thresholded STL models, all STL models were superimposed on an optical scan of the dry female and male skulls (“gold standard”).ResultsThe intra- and inter-observer variability of the manual threshold selection was good (intra-class correlation coefficients >0.9). All engineers selected grey values closer to soft tissue to compensate for bone voids. Geometric variations between the manually thresholded STL models were 0.13 mm (multi-detector row CT), 0.59 mm (dual-energy CT), and 0.55 mm (cone-beam CT). All STL models demonstrated inaccuracies ranging from −0.8 to +1.1 mm (multi-detector row CT), −0.7 to +2.0 mm (dual-energy CT), and −2.3 to +4.8 mm (cone-beam CT).ConclusionsThis study demonstrates that manual threshold selection results in better STL models than default thresholding. The use of dual-energy CT and cone-beam CT technology in its present form does not deliver reliable or accurate STL models for medical additive manufacturing. New approaches are required that are based on pattern recognition and machine learning algorithms.

Highlights

Additive manufacturing (AM), known as three-dimensional (3D) printing, refers to a process where a series of successive layers are laid down to create a 3D construct
The geometric variations between the highest and lowest thresholded standard tessellation language (STL) models were larger in the STL models derived from dual-energy computed tomography (DECT) and conebeam computed tomography (CBCT) when compared with the multi-detector row computed tomography (MDCT)-derived STL models (Fig. 5)
When compared to the “gold standard”, all manually and automatically thresholded STL models demonstrated inaccuracies ranging from −0.8 to +1.1 mm, −0.7 to +2.0 mm, and −2.3 to +4.8 mm for all STL models derived from MDCT, DECT, and CBCT, respectively (Fig. 6a–k)

Summary

Introduction

Additive manufacturing (AM), known as three-dimensional (3D) printing, refers to a process where a series of successive layers are laid down to create a 3D construct. AM combined with advanced medical imaging technologies such as computed tomography (CT) and magnetic resonance imaging (MRI) has resulted in a paradigm shift in medicine from traditional serial production to patientspecific constructs. This combination of technologies offers new possibilities for the fabrication of implants, saw guides and drill guides that are designed to meet the specific anatomical needs of patients [1]. The three-step medical AM process begins with image acquisition (Fig. 1, Step 1), which is commonly performed using a multi-detector row computed tomography (MDCT) scanner. Conebeam computed tomography (CBCT) is being increasingly used in dentistry and maxillofacial surgery due to its low costs and reduced radiation dose when compared with MDCT scanners [3]

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