scholarly journals A New Deformation Pose Estimation Algorithm for the Fully Automatic Design of Patient Specific Knee Prosthesis: Preliminary Results

10.29007/v8d7 ◽  
2020 ◽  
Author(s):  
Charles Garraud ◽  
Arnaud Clavé ◽  
Jérôme Ogor ◽  
Eric Stindel ◽  
Guillaume Dardenne
Keyword(s):  
Knee Prosthesis ◽  
Synthetic Data ◽  
Estimation Algorithm ◽  
Source Model ◽  
Patient Specific ◽  
Shape Models ◽  
Statistical Shape ◽  
Fully Automatic ◽  
The Mean ◽  
Partial View

In the context of automatic landmarks localization with statistical shape models for the design of customized TKA prosthesis, the first step consists of registering a model, represented by the mean mesh of some healthy femoral bones, towards the segmented femur of the patient. The most complex aspect of the mesh-to-mesh correspondence in this case lies in the fact the source (model) and the target mesh can differ largely (partial view of the femur, anatomy that lies away from the mean) which makes common correspondence approaches inefficient. In this paper, we introduce a contribution to an algorithm from the field of object recognition that produces a reliable registration. By adding the concept of global deformability in the algorithm, we are able to improve the precision of the algorithm (mean mesh-to-mesh distance improved from 2.77mm to 0.79mm) and its robustness to anatomy far off the mean (better standard deviation and Hausdorff distance) on synthetic data . The next step will be to assess it in its application field i.e. the automatic localization of knee landmarks for the design of patient-specific knee prosthesis.

scholarly journals Multi-object Segmentation of Head Bones

2009 ◽  
Author(s):  
Dagmar Kainmueller ◽  
Hans Lamecker ◽  
Heiko Seim ◽  
Stefan Zachow
Keyword(s):  
Image Data ◽  
Special Focus ◽  
Heuristic Model ◽  
3D Segmentation ◽  
Mandibular Bone ◽  
Shape Models ◽  
Statistical Shape ◽  
Fully Automatic ◽  
Ct Data ◽  
Manual Correction

We present a fully automatic method for 3D segmentation of the mandibular bone from CT data. The method includes an adaptation of statistical shape models of the mandible, the skull base and the midfacial bones, followed by a simultaneous graph-based optimization of adjacent deformable models. The adaptation of the models to the image data is performed according to a heuristic model of the typical intensity distribution in the vincinity of the bone boundary, with special focus on an accurate discrimination of adjacent bones in joint regions. An evaluation of our method based on 18 CT scans shows that a manual correction of the automatic segmentations is not necessary in approx. 60% of the axial slices that contain the mandible.

Computed Tomography-Based Patient-Specific Instrumentation Loses Accuracy with Significant Varus Preoperative Misalignment

2020 ◽  
Author(s):  
Vicente Jesús León-Muñoz ◽  
Mirian López-López ◽  
Alonso José Lisón-Almagro ◽  
Francisco Martínez-Martínez ◽  
Fernando Santonja-Medina
Keyword(s):  
Computed Tomography ◽  
Deformity Correction ◽  
Patient Specific ◽  
Control Group ◽  
X Rays ◽  
Varus Alignment ◽  
Patient Specific Instrumentation ◽  
Conventional Instrumentation ◽  
The Mean ◽  
Valgus Alignment

AbstractPatient-specific instrumentation (PSI) has been introduced to simplify and make total knee arthroplasty (TKA) surgery more precise, effective, and efficient. We performed this study to determine whether the postoperative coronal alignment is related to preoperative deformity when computed tomography (CT)-based PSI is used for TKA surgery, and how the PSI approach compares with deformity correction obtained with conventional instrumentation. We analyzed pre-and post-operative full length standing hip-knee-ankle (HKA) X-rays of the lower limb in both groups using a convention > 180 degrees for valgus alignment and < 180 degrees for varus alignment. For the PSI group, the mean (± SD) pre-operative HKA angle was 172.09 degrees varus (± 6.69 degrees) with a maximum varus alignment of 21.5 degrees (HKA 158.5) and a maximum valgus alignment of 14.0 degrees. The mean post-operative HKA was 179.43 degrees varus (± 2.32 degrees) with a maximum varus alignment of seven degrees and a maximum valgus alignment of six degrees. There has been a weak correlation among the values of the pre- and postoperative HKA angle. The adjusted odds ratio (aOR) of postoperative alignment outside the range of 180 ± 3 degrees was significantly higher with a preoperative varus misalignment of 15 degrees or more (aOR: 4.18; 95% confidence interval: 1.35–12.96; p = 0.013). In the control group (conventional instrumentation), this loss of accuracy occurs with preoperative misalignment of 10 degrees. Preoperative misalignment below 15 degrees appears to present minimal influence on postoperative alignment when a CT-based PSI system is used. The CT-based PSI tends to lose accuracy with preoperative varus misalignment over 15 degrees.

scholarly journals Quality Control in 3D Printing: Accuracy Analysis of 3D-Printed Models of Patient-Specific Anatomy

Materials ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1021
Author(s):  
Bernhard Dorweiler ◽  
Pia Elisabeth Baqué ◽  
Rayan Chaban ◽  
Ahmed Ghazy ◽  
Oroa Salem
Keyword(s):  
Wall Thickness ◽  
Large Scale ◽  
Fused Deposition Modeling ◽  
Vascular Anatomy ◽  
3D Models ◽  
Patient Specific ◽  
Stl File ◽  
Fused Deposition ◽  
3D Printed ◽  
The Mean

As comparative data on the precision of 3D-printed anatomical models are sparse, the aim of this study was to evaluate the accuracy of 3D-printed models of vascular anatomy generated by two commonly used printing technologies. Thirty-five 3D models of large (aortic, wall thickness of 2 mm, n = 30) and small (coronary, wall thickness of 1.25 mm, n = 5) vessels printed with fused deposition modeling (FDM) (rigid, n = 20) and PolyJet (flexible, n = 15) technology were subjected to high-resolution CT scans. From the resulting DICOM (Digital Imaging and Communications in Medicine) dataset, an STL file was generated and wall thickness as well as surface congruency were compared with the original STL file using dedicated 3D engineering software. The mean wall thickness for the large-scale aortic models was 2.11 µm (+5%), and 1.26 µm (+0.8%) for the coronary models, resulting in an overall mean wall thickness of +5% for all 35 3D models when compared to the original STL file. The mean surface deviation was found to be +120 µm for all models, with +100 µm for the aortic and +180 µm for the coronary 3D models, respectively. Both printing technologies were found to conform with the currently set standards of accuracy (<1 mm), demonstrating that accurate 3D models of large and small vessel anatomy can be generated by both FDM and PolyJet printing technology using rigid and flexible polymers.

scholarly journals A flexible framework for sequential estimation of model parameters in computational hemodynamics

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Christopher J. Arthurs ◽  
Nan Xiao ◽  
Philippe Moireau ◽  
Tobias Schaeffter ◽  
C. Alberto Figueroa
Keyword(s):  
Unscented Kalman Filter ◽  
Three Dimensional ◽  
Synthetic Data ◽  
Sequential Estimation ◽  
Wall Material ◽  
Patient Specific ◽  
Model Parameters ◽  
Computational Framework ◽  
Lumped Parameter ◽  
Estimation Of Model Parameters

AbstractA major challenge in constructing three dimensional patient specific hemodynamic models is the calibration of model parameters to match patient data on flow, pressure, wall motion, etc. acquired in the clinic. Current workflows are manual and time-consuming. This work presents a flexible computational framework for model parameter estimation in cardiovascular flows that relies on the following fundamental contributions. (i) A Reduced-Order Unscented Kalman Filter (ROUKF) model for data assimilation for wall material and simple lumped parameter network (LPN) boundary condition model parameters. (ii) A constrained least squares augmentation (ROUKF-CLS) for more complex LPNs. (iii) A “Netlist” implementation, supporting easy filtering of parameters in such complex LPNs. The ROUKF algorithm is demonstrated using non-invasive patient-specific data on anatomy, flow and pressure from a healthy volunteer. The ROUKF-CLS algorithm is demonstrated using synthetic data on a coronary LPN. The methods described in this paper have been implemented as part of the CRIMSON hemodynamics software package.

A framework for the merging of pre-existing and correspondenceless 3D statistical shape models

2014 ◽  
Vol 18 (7) ◽  
pp. 1044-1058 ◽  
Author(s):  
Marco Pereañez ◽  
Karim Lekadir ◽  
Constantine Butakoff ◽  
Corné Hoogendoorn ◽  
Alejandro F. Frangi
Keyword(s):  
Statistical Shape Models ◽  
Shape Models ◽  
Statistical Shape
Sign in / Sign up

Export Citation Format

Share Document