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Claes P.,Medical Imaging Research Center | Claes P.,University of Melbourne | Walters M.,Cranio Maxillo Facial Unit | Clement J.,University of Melbourne
International Journal of Oral and Maxillofacial Surgery | Year: 2012

The capacity to process three-dimensional facial surfaces to objectively assess outcomes of craniomaxillofacial care is urgently required. Available surface registration techniques depart from conventional facial anthropometrics by not including anatomical relationship in their analysis. Current registrations rely on the manual selection of areas or points that have not moved during surgery, introducing subjectivity. An improved technique is proposed based on the concept of an anthropometric mask (AM) combined with robust superimposition. The AM is the equivalent to landmark definitions, as used in traditional anthropometrics, but described in a spatially dense way using (∼10.000) quasi-landmarks. A robust superimposition is performed to align surface images facilitating accurate measurement of spatial differences between corresponding quasi-landmarks. The assessment describes magnitude and direction of change objectively and can be displayed graphically. The technique was applied to three patients, without any modification and prior knowledge: a 4-year-old boy with Treacher-Collins syndrome in a resting and smiling pose; surgical correction for hemimandibular hypoplasia; and mandibular hypoplasia with staged orthognathic procedures. Comparisons were made with a reported closest-point (CP) strategy. Contrasting outcomes were found where the CP strategy resulted in anatomical implausibility whilst the AM technique was parsimonious to expected differences. © 2011 International Association of Oral and Maxillofacial Surgeons. Source

George J.,Medical Imaging Research Center
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention | Year: 2012

18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) has become the de facto standard for current clinical therapy follow up evaluations. In pursuit of robust biomarkers for predicting early therapy response, an efficient marker quantification procedure is certainly a necessity. Among various PET derived markers, the clinical investigations indicated that the total lesion metabolic activity (TLA) of a tumor lesion has a good prognostic value in several longitudinal studies. We utilize a fuzzy multi-class modeling using a stochastic expectation maximization (SEM) algorithm to fit a finite mixture model (FMM) to the PET image. We then propose a direct estimation formula for TLA and SUVmean from this multi-class statistical model. In order to evaluate our proposition, a realistic liver lesion is simulated and reconstructed. All results were evaluated with reference to the ground truth knowledge. Our experimental study conveys that the proposed method is robust enough to handle background heterogeneities in realistic scenarios. Source

Bagheri M.H.,Medical Imaging Research Center | Hosseini M.M.,Medical Imaging Research Center | Emami M.J.,Bone and Joint Disease Research Center | Foroughi A.A.,Medical Imaging Research Center
European Journal of Radiology | Year: 2012

Objective: The aim of the present study was to evaluate the metallic artifacts in MRI of the orthopedic patients after removal of metallic implants. Subjects and methods: From March to August 2009, 40 orthopedic patients operated for removal of orthopedic metallic implants were studied by post-operative MRI from the site of removal of implants. A grading scale of 0-3 was assigned for artifact in MR images whereby 0 was considered no artifact; and I-III were considered mild, moderate, and severe metallic artifacts, respectively. These grading records were correlated with other variables including the type, size, number, and composition of metallic devices; and the site and duration of orthopedic devices stay in the body. Results: Metallic susceptibly artifacts were detected in MRI of 18 of 40 cases (45%). Screws and pins in removed hardware were the most important factors for causing artifacts in MRI. The artifacts were found more frequently in the patients who had more screws and pins in the removed implants. Gender, age, site of implantation of the device, length of the hardware, composition of the metallic implants (stainless steel versus titanium), and duration of implantation of the hardware exerted no effect in producing metallic artifacts after removal of implants. Short TE sequences of MRI (such as T1 weighted) showed fewer artifacts. Conclusion: Susceptibility of metallic artifacts is a frequent phenomenon in MRI of patients upon removal of metallic orthopedic implants. © 2010 Elsevier Ireland Ltd. All rights reserved. Source

Monnin P.,University of Lausanne | Bosmans H.,Medical Imaging Research Center | Verdun F.R.,University of Lausanne | Marshall N.W.,Medical Imaging Research Center
Physics in Medicine and Biology | Year: 2016

A version of cascaded systems analysis was developed specifically with the aim of studying quantum noise propagation in x-ray detectors. Signal and quantum noise propagation was then modelled in four types of x-ray detectors used for digital mammography: four flat panel systems, one computed radiography and one slot-scan silicon wafer based photon counting device. As required inputs to the model, the two dimensional (2D) modulation transfer function (MTF), noise power spectra (NPS) and detective quantum efficiency (DQE) were measured for six mammography systems that utilized these different detectors. A new method to reconstruct anisotropic 2D presampling MTF matrices from 1D radial MTFs measured along different angular directions across the detector is described; an image of a sharp, circular disc was used for this purpose. The effective pixel fill factor for the FP systems was determined from the axial 1D presampling MTFs measured with a square sharp edge along the two orthogonal directions of the pixel lattice. Expectation MTFs were then calculated by averaging the radial MTFs over all possible phases and the 2D EMTF formed with the same reconstruction technique used for the 2D presampling MTF. The quantum NPS was then established by noise decomposition from homogenous images acquired as a function of detector air kerma. This was further decomposed into the correlated and uncorrelated quantum components by fitting the radially averaged quantum NPS with the radially averaged EMTF2. This whole procedure allowed a detailed analysis of the influence of aliasing, signal and noise decorrelation, x-ray capture efficiency and global secondary gain on NPS and detector DQE. The influence of noise statistics, pixel fill factor and additional electronic and fixed pattern noises on the DQE was also studied. The 2D cascaded model and decompositions performed on the acquired images also enlightened the observed quantum NPS and DQE anisotropy. © 2016 Institute of Physics and Engineering in Medicine. Source

Monnin P.,University of Lausanne | Bosmans H.,Medical Imaging Research Center | Verdun F.R.,University of Lausanne | Marshall N.W.,Medical Imaging Research Center
Physics in Medicine and Biology | Year: 2014

Given the adverse impact of image noise on the perception of important clinical details in digital mammography, routine quality control measurements should include an evaluation of noise. The European Guidelines, for example, employ a second-order polynomial fit of pixel variance as a function of detector air kerma (DAK) to decompose noise into quantum, electronic and fixed pattern (FP) components and assess the DAK range where quantum noise dominates. This work examines the robustness of the polynomial method against an explicit noise decomposition method. The two methods were applied to variance and noise power spectrum (NPS) data from six digital mammography units. Twenty homogeneously exposed images were acquired with PMMA blocks for target DAKs ranging from 6.25 to 1600 μGy. Both methods were explored for the effects of data weighting and squared fit coefficients during the curve fitting, the influence of the additional filter material (2 mm Al versus 40 mm PMMA) and noise de-trending. Finally, spatial stationarity of noise was assessed. Data weighting improved noise model fitting over large DAK ranges, especially at low detector exposures. The polynomial and explicit decompositions generally agreed for quantum and electronic noise but FP noise fraction was consistently underestimated by the polynomial method. Noise decomposition as a function of position in the image showed limited noise stationarity, especially for FP noise; thus the position of the region of interest (ROI) used for noise decomposition may influence fractional noise composition. The ROI area and position used in the Guidelines offer an acceptable estimation of noise components. While there are limitations to the polynomial model, when used with care and with appropriate data weighting, the method offers a simple and robust means of examining the detector noise components as a function of detector exposure. © 2014 Institute of Physics and Engineering in Medicine. Source

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