Accurate and robust reconstruction of proximal femur from sparse intraoperative data and dense point distribution model for surgical navigation

Constructing a 3D surface model from sparse-point data is a nontrivial task. Here, we report an accurate and robust approach for reconstructing a surface model of the proximal femur from sparse-point data and a dense-point distribution model (DPDM). The problem is formulated as a three-stage optimal estimation process. The first stage, affine registration, is to iteratively estimate a scale and a rigid transformation between the mean surface model of the DPDM and the sparse input points. The estimation results of the first stage are used to establish point correspondences for the second stage, statistical instantiation, which stably instantiates a surface model from the DPDM using a statistical approach. This surface model is then fed to the third stage, kernel-based deformation, which further refines the surface model. Handling outliers is achieved by consistently employing the least trimmed squares (LTS) approach with a roughly estimated outlier rate in all three stages. If an optimal value of the outlier rate is preferred, we propose a hypothesis testing procedure to automatically estimate it. We present here our validations using four experiments, which include 1 leave-one-out experiment, 2 experiment on evaluating the present approach for handling pathology, 3 experiment on evaluating the present approach for handling outliers, and 4 experiment on reconstructing surface models of seven dry cadaver femurs using clinically relevant data without noise and with noise added. Our validation results demonstrate the robust performance of the present approach in handling outliers, pathology, and noise. An average 95-percentile error of 1.7-2.3 mm was found when the present approach was used to reconstruct surface models of the cadaver femurs from sparse-point data with noise added.

Proposed minimal standards for the description of genera, species and subspecies of the Pasteurellaceae

Principles and guidelines are presented to ensure a solid scientific standard of papers dealing with the taxonomy of taxa of Pasteurellaceae Pohl 1981. The classification of the Pasteurellaceae is in principle based on a polyphasic approach. DNA sequencing of certain genes is very important for defining the borders of a taxon. However, the characteristics that are common to all members of the taxon and which might be helpful for separating it from related taxa must also be identified. Descriptions have to be based on as many strains as possible (inclusion of at least five strains is highly desirable), representing different sources with respect to geography and ecology, to allow proper characterization both phenotypically and genotypically, to establish the extent of diversity of the cluster to be named. A genus must be monophyletic based on 16S rRNA gene sequence-based phylogenetic analysis. Only in very rare cases is it acceptable that monophyly can not be achieved by 16S rRNA gene sequence comparison. Recently, the monophyly of genera has been confirmed by sequence comparison of housekeeping genes. In principle, a new genus should be recognized by a distinct phenotype, and characters that separate the new genus from its neighbours should be given clearly. Due to the overall importance of accurate classification of species, at least two genotypic methods are needed to show coherence and for separation at the species level. The main criterion for the classification of a novel species is that it forms a monophyletic group based on 16S rRNA gene sequence-based phylogenetic analysis. However, some groups might also include closely related species. In these cases, more sensitive tools for genetic recognition of species should be applied, such as DNA-DNA hybridizations. The comparison of housekeeping gene sequences has recently been used for genotypic definition of species. In order to separate species, phenotypic characters must also be identified to recognize them, and at least two phenotypic differences from existing species should be identified if possible. We recommend the use of the subspecies category only for subgroups associated with disease or similar biological characteristics. At the subspecies level, the genotypic groups must always be nested within the boundaries of an existing species. Phenotypic cohesion must be documented at the subspecies level and separation between subspecies and related species must be fully documented, as well as association with particular disease and host. An overview of methods previously used to characterize isolates of the Pasteurellaceae has been given. Genotypic and phenotypic methods are separated in relation to tests for investigating diversity and cohesion and to separate taxa at the level of genus as well as species and subspecies.

ROBREG: Stata module providing robust regression estimators

robreg provides a number of robust estimators for linear regression models. Among them are the high breakdown-point and high efficiency MM-estimator, the Huber and bisquare M-estimator, and the S-estimator, each supporting classic or robust standard errors. Furthermore, basic versions of the LMS/LQS (least median of squares) and LTS (least trimmed squares) estimators are provided. Note that the moremata package, also available from SSC, is required.

Comparison of partial least squares regression and principal component regression for pelvic shape prediction.

This paper studied two different regression techniques for pelvic shape prediction, i.e., the partial least square regression (PLSR) and the principal component regression (PCR). Three different predictors such as surface landmarks, morphological parameters, or surface models of neighboring structures were used in a cross-validation study to predict the pelvic shape. Results obtained from applying these two different regression techniques were compared to the population mean model. In almost all the prediction experiments, both regression techniques unanimously generated better results than the population mean model, while the difference on prediction accuracy between these two regression methods is not statistically significant (α=0.01).

A systematic review of the survival and complication rates of implant-supported fixed dental prostheses (FDPs) after a mean observation period of at least 5 years

The objective of this systematic review was to assess the 5- and 10-year survival of implant-supported fixed dental prostheses (FDPs) and to describe the incidence of biological and technical complications.

Comparison of bacterial plaque samples from titanium implant and tooth surfaces by different methods

Studies have shown similarities in the microflora between titanium implants or tooth sites when samples are taken by gingival crevicular fluid (GCF) sampling methods. The purpose of the present study was to study the microflora from curette and GCF samples using the checkerboard DNA-DNA hybridization method to assess the microflora of patients who had at least one oral osseo-integrated implant and who were otherwise dentate. Plaque samples were taken from tooth/implant surfaces and from sulcular gingival surfaces with curettes, and from gingival fluid using filter papers. A total of 28 subjects (11 females) were enrolled in the study. The mean age of the subjects was 64.1 years (SD+/-4.7). On average, the implants studied had been in function for 3.7 years (SD+/-2.9). The proportion of Streptococcus oralis (P<0.02) and Fusobacterium periodonticum (P<0.02) was significantly higher at tooth sites (curette samples). The GCF samples yielded higher proportions for 28/40 species studies (P-values varying between 0.05 and 0.001). The proportions of Tannerella forsythia (T. forsythensis), and Treponema denticola were both higher in GCF samples (P<0.02 and P<0.05, respectively) than in curette samples (implant sites). The microbial composition in gingival fluid from samples taken at implant sites differed partly from that of curette samples taken from implant surfaces or from sulcular soft tissues, providing higher counts for most bacteria studied at implant surfaces, but with the exception of Porphyromonas gingivalis. A combination of GCF and curette sampling methods might be the most representative sample method.

A systematic review of the survival and complication rates of resin-bonded bridges after an observation period of at least 5 years

OBJECTIVES: The objectives of this systematic review were to assess the 5-year survival of resin-bonded bridges (RBBs) and to describe the incidence of technical and biological complications. METHODS: An electronic Medline search complemented by manual searching was conducted to identify prospective and retrospective cohort studies on RBBs with a mean follow-up time of at least 5 years. Patients had to have been examined clinically at the follow-up visit. Assessment of the identified studies and data extraction were performed independently by two reviewers. Failure and complication rates were analyzed using random-effects Poissons regression models to obtain summary estimates of 5-year proportions. RESULTS: The search provided 6110 titles and 214 abstracts. Full-text analysis was performed for 93 articles, resulting in 17 studies that met the inclusion criteria. Meta-analysis of these studies indicated an estimated survival of RBBs of 87.7% (95% confidence interval (CI): 81.6-91.9%) after 5 years. The most frequent complication was debonding (loss of retention), which occurred in 19.2% (95% CI: 13.8-26.3%) of RBBs over an observation period of 5 years. The annual debonding rate for RBBs placed on posterior teeth (5.03%) tended to be higher than that for anterior-placed RBBs (3.05%). This difference, however, did not reach statistical significance (P=0.157). Biological complications, like caries on abutments and RBBs lost due to periodontitis, occurred in 1.5% of abutments and 2.1% of RBBs, respectively. CONCLUSION: Despite the high survival rate of RBBs, technical complications like debonding are frequent. This in turn means that a substantial amount of extra chair time may be needed following the incorporation of RBBs. There is thus an urgent need for studies with a follow-up time of 10 years or more, to evaluate the long-term outcomes.

In vivo biochemical 7.0 Tesla magnetic resonance: preliminary results of dGEMRIC, zonal T2, and T2* mapping of articular cartilage

INTRODUCTION: Ultra-high-field whole-body systems (7.0 T) have a high potential for future human in vivo magnetic resonance imaging (MRI). In musculoskeletal MRI, biochemical imaging of articular cartilage may benefit, in particular. Delayed gadolinium-enhanced MRI of cartilage (dGEMRIC) and T2 mapping have shown potential at 3.0 T. Although dGEMRIC, allows the determination of the glycosaminoglycan content of articular cartilage, T2 mapping is a promising tool for the evaluation of water and collagen content. In addition, the evaluation of zonal variation, based on tissue anisotropy, provides an indicator of the nature of cartilage ie, hyaline or hyaline-like articular cartilage.Thus, the aim of our study was to show the feasibility of in vivo dGEMRIC, and T2 and T2* relaxation measurements, at 7.0 T MRI; and to evaluate the potential of T2 and T2* measurements in an initial patient study after matrix-associated autologous chondrocyte transplantation (MACT) in the knee. MATERIALS AND METHODS: MRI was performed on a whole-body 7.0 T MR scanner using a dedicated circular polarization knee coil. The protocol consisted of an inversion recovery sequence for dGEMRIC, a multiecho spin-echo sequence for standard T2 mapping, a gradient-echo sequence for T2* mapping and a morphologic PD SPACE sequence. Twelve healthy volunteers (mean age, 26.7 +/- 3.4 years) and 4 patients (mean age, 38.0 +/- 14.0 years) were enrolled 29.5 +/- 15.1 months after MACT. For dGEMRIC, 5 healthy volunteers (mean age, 32.4 +/- 11.2 years) were included. T1 maps were calculated using a nonlinear, 2-parameter, least squares fit analysis. Using a region-of-interest analysis, mean cartilage relaxation rate was determined as T1 (0) for precontrast measurements and T1 (Gd) for postcontrast gadopentate dimeglumine [Gd-DTPA(2-)] measurements. T2 and T2* maps were obtained using a pixelwise, monoexponential, non-negative least squares fit analysis; region-of-interest analysis was carried out for deep and superficial cartilage aspects. Statistical evaluation was performed by analyses of variance. RESULTS: Mean T1 (dGEMRIC) values for healthy volunteers showed slightly different results for femoral [T1 (0): 1259 +/- 277 ms; T1 (Gd): 683 +/- 141 ms] compared with tibial cartilage [T1 (0): 1093 +/- 281 ms; T1 (Gd): 769 +/- 150 ms]. Global mean T2 relaxation for healthy volunteers showed comparable results for femoral (T2: 56.3 +/- 15.2 ms; T2*: 19.7 +/- 6.4 ms) and patellar (T2: 54.6 +/- 13.0 ms; T2*: 19.6 +/- 5.2 ms) cartilage, but lower values for tibial cartilage (T2: 43.6 +/- 8.5 ms; T2*: 16.6 +/- 5.6 ms). All healthy cartilage sites showed a significant increase from deep to superficial cartilage (P < 0.001). Within healthy cartilage sites in MACT patients, adequate values could be found for T2 (56.6 +/- 13.2 ms) and T2* (18.6 +/- 5.3 ms), which also showed a significant stratification. Within cartilage repair tissue, global mean values showed no difference, with 55.9 +/- 4.9 ms for T2 and 16.2 +/- 6.3 ms for T2*. However, zonal assessment showed only a slight and not significant increase from deep to superficial cartilage (T2: P = 0.174; T2*: P = 0.150). CONCLUSION: In vivo T1 dGEMRIC assessment in healthy cartilage, and T2 and T2* mapping in healthy and reparative articular cartilage, seems to be possible at 7.0 T MRI. For T2 and T2*, zonal variation of articular cartilage could also be evaluated at 7.0 T. This zonal assessment of deep and superficial cartilage aspects shows promising results for the differentiation of healthy and affected articular cartilage. In future studies, optimized protocol selection, and sophisticated coil technology, together with increased signal at ultra-high-field MRI, may lead to advanced biochemical cartilage imaging.

T2 mapping in the knee after microfracture at 3.0 T: correlation of global T2 values and clinical outcome - preliminary results

OBJECTIVE: The aim of our study was to correlate global T2 values of microfracture repair tissue (RT) with clinical outcome in the knee joint. METHODS: We assessed 24 patients treated with microfracture in the knee joint. Magnetic resonance (MR) examinations were performed on a 3T MR unit, T2 relaxation times were obtained with a multi-echo spin-echo technique. T2 maps were obtained using a pixel wise, mono-exponential non-negative least squares fit analysis. Slices covering the cartilage RT were selected and region of interest analysis was done. An individual T2 index was calculated with global mean T2 of the RT and global mean T2 of normal, hyaline cartilage. The Lysholm score and the International Knee Documentation Committee (IKDC) knee evaluation forms were used for the assessment of clinical outcome. Bivariate correlation analysis and a paired, two tailed t test were used for statistics. RESULTS: Global T2 values of the RT [mean 49.8ms, standards deviation (SD) 7.5] differed significantly (P<0.001) from global T2 values of normal, hyaline cartilage (mean 58.5ms, SD 7.0). The T2 index ranged from 61.3 to 101.5. We found the T2 index to correlate with outcome of the Lysholm score (r(s)=0.641, P<0.001) and the IKDC subjective knee evaluation form (r(s)=0.549, P=0.005), whereas there was no correlation with the IKDC knee form (r(s)=-0.284, P=0.179). CONCLUSION: These findings indicate that T2 mapping is sensitive to assess RT function and provides additional information to morphologic MRI in the monitoring of microfracture.

3D volumetric intensity reconsturction from 2D x-ray images using partial least squares regression

Reconstruction of shape and intensity from 2D x-ray images has drawn more and more attentions. Previously introduced work suffers from the long computing time due to its iterative optimization characteristics and the requirement of generating digitally reconstructed radiographs within each iteration. In this paper, we propose a novel method which uses a patient-specific 3D surface model reconstructed from 2D x-ray images as a surrogate to get a patient-specific volumetric intensity reconstruction via partial least squares regression. No DRR generation is needed. The method was validated on 20 cadaveric proximal femurs by performing a leave-one-out study. Qualitative and quantitative results demonstrated the efficacy of the present method. Compared to the existing work, the present method has the advantage of much shorter computing time and can be applied to both DXA images as well as conventional x-ray images, which may hold the potentials to be applied to clinical routine task such as total hip arthroplasty (THA).

Impact of different cultivation methods on the metabolic profile of apples studied by 1H HR-MAS NMR spectroscopy

The cultivation of dessert apples has to meet the consumer's increasing demand for high fruit quality and a sustainable mostly residue-free production while ensuring a competitive agricultural productivity. It is therefore of great interest to know the impact of different cultivation methods on the fruit quality and the chemical composition, respectively. Previous studies have demonstrated the feasibility of High Resolution Magic Angle Spinning (HR-MAS) NMR spectroscopy directly performed on apple tissue as analytical tool for metabonomic studies. In this study, HR-MAS NMR spectroscopy is applied to apple tissue to analyze the metabolic profiles of apples grown under 3 different cultivation methods. Golden Delicious apples were grown applying organic (Bio), integrated (IP) and low-input (LI) plant protection strategies. A total of 70 1H HR-MAS NMR spectra were analyzed by means of principle component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). Apples derived from Bio-production could be well separated from the two other cultivation methods applying both, PCA and PLS-DA. Apples obtained from integrated (IP) and low-input (LI) production discriminated when taking the third PLS-component into account. The identified chemical composition and the compounds responsible for the separation, i.e. the PLS-loadings, are discussed. The results are compared with fruit quality parameters assessed by conventional methods. The present study demonstrates the potential of HR-MAS NMR spectroscopy of fruit tissue as analytical tool for finding markers for specific fruit production conditions like the cultivation method.

Independent measurement of biogenic silica in sediments by FTIR spectroscopy and PLS regression

We present an independent calibration model for the determination of biogenic silica (BSi) in sediments, developed from analysis of synthetic sediment mixtures and application of Fourier transform infrared spectroscopy (FTIRS) and partial least squares regression (PLSR) modeling. In contrast to current FTIRS applications for quantifying BSi, this new calibration is independent from conventional wet-chemical techniques and their associated measurement uncertainties. This approach also removes the need for developing internal calibrations between the two methods for individual sediments records. For the independent calibration, we produced six series of different synthetic sediment mixtures using two purified diatom extracts, with one extract mixed with quartz sand, calcite, 60/40 quartz/calcite and two different natural sediments, and a second extract mixed with one of the natural sediments. A total of 306 samples—51 samples per series—yielded BSi contents ranging from 0 to 100 %. The resulting PLSR calibration model between the FTIR spectral information and the defined BSi concentration of the synthetic sediment mixtures exhibits a strong cross-validated correlation ( R2cv = 0.97) and a low root-mean square error of cross-validation (RMSECV = 4.7 %). Application of the independent calibration to natural lacustrine and marine sediments yields robust BSi reconstructions. At present, the synthetic mixtures do not include the variation in organic matter that occurs in natural samples, which may explain the somewhat lower prediction accuracy of the calibration model for organic-rich samples.