Influence of head positioning during cone-beam CT imaging on the accuracy of virtual 3D models.

OBJECTIVE: Cone beam computed tomography (CBCT) images are being increasingly used to acquire three-dimensional (3D) models of the skull for additive manufacturing purposes. However, the accuracy of such models remains a challenge, especially in the orbital area. The aim of this study is to assess the impact of four different CBCT imaging positions on the accuracy of the resulting 3D models in the orbital area. METHODS: An anthropomorphic head phantom was manufactured by submerging a dry human skull in silicon to mimic the soft tissue attenuation and scattering properties of the human head. The phantom was scanned on a ProMax 3D MAX CBCT scanner using 90 and 120 kV for four different field of view positions: standard; elevated; backwards tilted; and forward tilted. All CBCT images were subsequently converted into 3D models and geometrically compared with a "gold-standard" optical scan of the dry skull. RESULTS: Mean absolute deviations of the 3D models ranged between 0.15 ± 0.11 mm and 0.56 ± 0.28 mm. The elevated imaging position in combination with 120 kV tube voltage resulted in an improved representation of the orbital walls in the resulting 3D model without compromising the accuracy. CONCLUSIONS: Head positioning during CBCT imaging can influence the accuracy of the resulting 3D model. The accuracy of such models may be improved by positioning the region of interest (e.g. the orbital area) in the focal plane (Figure 2a) of the CBCT X-ray beam.

Deep Learning Method for Mandibular Canal Segmentation in Dental Cone Beam Computed Tomography Volumes.

Accurate localisation of mandibular canals in lower jaws is important in dental implantology, in which the implant position and dimensions are currently determined manually from 3D CT images by medical experts to avoid damaging the mandibular nerve inside the canal. Here we present a deep learning system for automatic localisation of the mandibular canals by applying a fully convolutional neural network segmentation on clinically diverse dataset of 637 cone beam CT volumes, with mandibular canals being coarsely annotated by radiologists, and using a dataset of 15 volumes with accurate voxel-level mandibular canal annotations for model evaluation. We show that our deep learning model, trained on the coarsely annotated volumes, localises mandibular canals of the voxel-level annotated set, highly accurately with the mean curve distance and average symmetric surface distance being 0.56 mm and 0.45 mm, respectively. These unparalleled accurate results highlight that deep learning integrated into dental implantology workflow could significantly reduce manual labour in mandibular canal annotations.

Document retrieval on repetitive string collections.

Most of the fastest-growing string collections today are repetitive, that is, most of the constituent documents are similar to many others. As these collections keep growing, a key approach to handling them is to exploit their repetitiveness, which can reduce their space usage by orders of magnitude. We study the problem of indexing repetitive string collections in order to perform efficient document retrieval operations on them. Document retrieval problems are routinely solved by search engines on large natural language collections, but the techniques are less developed on generic string collections. The case of repetitive string collections is even less understood, and there are very few existing solutions. We develop two novel ideas, interleaved LCPs and precomputed document lists, that yield highly compressed indexes solving the problem of document listing (find all the documents where a string appears), top-k document retrieval (find the k documents where a string appears most often), and document counting (count the number of documents where a string appears). We also show that a classical data structure supporting the latter query becomes highly compressible on repetitive data. Finally, we show how the tools we developed can be combined to solve ranked conjunctive and disjunctive multi-term queries under the simple [Formula: see text] model of relevance. We thoroughly evaluate the resulting techniques in various real-life repetitiveness scenarios, and recommend the best choices for each case.

A graph-theoretical approach for motif discovery in protein sequences.

Motif recognition is a challenging problem in bioinformatics due to the diversity of protein motifs. Many existing algorithms identify motifs of a given length, thus being either not applicable or not efficient when searching simultaneously for motifs of various lengths. Searching for gapped motifs, although very important, is a highly time-consuming task due to the combinatorial explosion of possible combinations implied by the consideration of long gaps. We introduce a new graph theoretical approach to identify motifs of various lengths, both with and without gaps. We compare our approach with two widely used methods: MEME and GLAM2 analyzing both the quality of the results and the required computational time. Our method provides results of a slightly higher level of quality than MEME but at a much faster rate, i.e., one eighth of MEME's query time. By using similarity indexing, we drop the query times down to an average of approximately one sixth of the ones required by GLAM2, while achieving a slightly higher level of quality of the results. More precisely, for sequence collections smaller than 50000 bytes GLAM2 is 13 times slower, while being at least as fast as our method on larger ones. The source code of our C++ implementation is freely available in GitHub: https://github.com/hirvolt1/debruijn-motif.

The impact of manual threshold selection in medical additive manufacturing.

PURPOSE: Medical 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. METHOD: One 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"). RESULTS: The 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). CONCLUSIONS: This 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.