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Koehl NJ, Shah S, Tenekam ID, Khamiakova T, Sauwen N, Vingerhoets S, Coppenolle H, Holm R. Lipid Based Formulations in Hard Gelatin and HPMC Capsules: a Physical Compatibility Study. Pharm Res 2021; 38:1439-1454. [PMID: 34378150 DOI: 10.1007/s11095-021-03088-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Accepted: 07/26/2021] [Indexed: 11/30/2022]
Abstract
PURPOSE To investigate the compatibility between hard gelatin and HPMC capsules with a range of different isotropic lipid based formulations containing multiple excipients. METHODS The miscibility was investigated for 350 systems applying five different oils (Labrafac ™ lipophile WL1349, Maisine® CC, Captex 300 EP/NF, olive oil, and Capmul MCM EP/NF), five different surfactans (Labrasol ® ALF, Labrafil M 2125 CS, Kolliphor ® ELP, Kolliphor ® HS 15, Tween 80) and three different cosolvents (propylene glycol, polyethylene glycol 400, and Transcutol ® HP). For the isotropic systems capsule compatibility was investigated in both gelatin and HPMC capsules at 25°C at 40% and 60% relative humidity by examining physical damages to the capsules and weight changes after storage. RESULTS The miscibility of lipid based vehicles was best when the formulation contained monoglycerides and surfactants with a hydrophilic-lipophilic balance value <12. Gelatin capsules in general resulted in a better compatibility when compared to HPMC capsules for the evaluated formulations. Addition of water to the formulation improved the capsule compatibility for both capsule types. The expected capsule mass change could partly be predicted in binary systems using the provided data of the single excipients weighted for its formulation proportion. CONCLUSIONS The capsule compatibility was driven by the components incorporated into the formulations, where more was compatible with gelatin than HPMC capsules. Prediction of the mass change from individual excipient contributions can provide a good first estimate if a vehicle is compatible with a capsule, however, this needs to be proved experimentally.
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Affiliation(s)
- Niklas J Koehl
- Drug Product Development, Janssen Research and Development, Johnson & Johnson, Turnhoutseweg 30, 2340, Beerse, Belgium
| | - Sanket Shah
- Drug Product Development, Janssen Research and Development, Johnson & Johnson, Turnhoutseweg 30, 2340, Beerse, Belgium
| | - Ingrid Djouka Tenekam
- Drug Product Development, Janssen Research and Development, Johnson & Johnson, Turnhoutseweg 30, 2340, Beerse, Belgium
| | - Tatsiana Khamiakova
- Quantitative Sciences, Janssen Research and Development, Johnson and Johnson, Turnhoutseweg 30, 2340, Beerse, Belgium
| | - Nicolas Sauwen
- Open Analytics NV, Jupiterstraat 20, 2600, Antwerp, Belgium
| | - Sien Vingerhoets
- Drug Product Development, Janssen Research and Development, Johnson & Johnson, Turnhoutseweg 30, 2340, Beerse, Belgium
| | - Hans Coppenolle
- Quantitative Sciences, Janssen Research and Development, Johnson and Johnson, Turnhoutseweg 30, 2340, Beerse, Belgium
| | - René Holm
- Drug Product Development, Janssen Research and Development, Johnson & Johnson, Turnhoutseweg 30, 2340, Beerse, Belgium. .,Department of Physics, Chemistry and Pharmacy, University of Southern Denmark, Campusvej 55, 5230, Odense M, Denmark.
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Sauwen N, Acou M, Bharath HN, Sima DM, Veraart J, Maes F, Himmelreich U, Achten E, Van Huffel S. The successive projection algorithm as an initialization method for brain tumor segmentation using non-negative matrix factorization. PLoS One 2017; 12:e0180268. [PMID: 28846686 PMCID: PMC5573288 DOI: 10.1371/journal.pone.0180268] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2016] [Accepted: 06/13/2017] [Indexed: 11/19/2022] Open
Abstract
Non-negative matrix factorization (NMF) has become a widely used tool for additive parts-based analysis in a wide range of applications. As NMF is a non-convex problem, the quality of the solution will depend on the initialization of the factor matrices. In this study, the successive projection algorithm (SPA) is proposed as an initialization method for NMF. SPA builds on convex geometry and allocates endmembers based on successive orthogonal subspace projections of the input data. SPA is a fast and reproducible method, and it aligns well with the assumptions made in near-separable NMF analyses. SPA was applied to multi-parametric magnetic resonance imaging (MRI) datasets for brain tumor segmentation using different NMF algorithms. Comparison with common initialization methods shows that SPA achieves similar segmentation quality and it is competitive in terms of convergence rate. Whereas SPA was previously applied as a direct endmember extraction tool, we have shown improved segmentation results when using SPA as an initialization method, as it allows further enhancement of the sources during the NMF iterative procedure.
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Affiliation(s)
- Nicolas Sauwen
- KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Centre for Dynamical Systems, Signal Processing and Data Analytics, Leuven, Belgium
- imec, Leuven, Belgium
| | - Marjan Acou
- Ghent University Hospital, Department of Radiology, Ghent, Belgium
| | - Halandur N. Bharath
- KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Centre for Dynamical Systems, Signal Processing and Data Analytics, Leuven, Belgium
- imec, Leuven, Belgium
| | - Diana M. Sima
- KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Centre for Dynamical Systems, Signal Processing and Data Analytics, Leuven, Belgium
- imec, Leuven, Belgium
- Icometrix, R&D Department, Leuven, Belgium
| | - Jelle Veraart
- University of Antwerp, iMinds Vision Lab, Department of Physics, Antwerp, Belgium
| | - Frederik Maes
- KU Leuven, Department of Electrical Engineering (ESAT), PSI Centre for Processing Speech and Images, Leuven, Belgium
| | - Uwe Himmelreich
- KU Leuven, Department of Imaging and Pathology, Biomedical MRI/MoSAIC, Leuven, Belgium
| | - Eric Achten
- Ghent University Hospital, Department of Radiology, Ghent, Belgium
| | - Sabine Van Huffel
- KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Centre for Dynamical Systems, Signal Processing and Data Analytics, Leuven, Belgium
- imec, Leuven, Belgium
- * E-mail:
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Sauwen N, Acou M, Sima DM, Veraart J, Maes F, Himmelreich U, Achten E, Huffel SV. Semi-automated brain tumor segmentation on multi-parametric MRI using regularized non-negative matrix factorization. BMC Med Imaging 2017; 17:29. [PMID: 28472943 PMCID: PMC5418702 DOI: 10.1186/s12880-017-0198-4] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2016] [Accepted: 04/11/2017] [Indexed: 12/19/2022] Open
Abstract
Background Segmentation of gliomas in multi-parametric (MP-)MR images is challenging due to their heterogeneous nature in terms of size, appearance and location. Manual tumor segmentation is a time-consuming task and clinical practice would benefit from (semi-) automated segmentation of the different tumor compartments. Methods We present a semi-automated framework for brain tumor segmentation based on non-negative matrix factorization (NMF) that does not require prior training of the method. L1-regularization is incorporated into the NMF objective function to promote spatial consistency and sparseness of the tissue abundance maps. The pathological sources are initialized through user-defined voxel selection. Knowledge about the spatial location of the selected voxels is combined with tissue adjacency constraints in a post-processing step to enhance segmentation quality. The method is applied to an MP-MRI dataset of 21 high-grade glioma patients, including conventional, perfusion-weighted and diffusion-weighted MRI. To assess the effect of using MP-MRI data and the L1-regularization term, analyses are also run using only conventional MRI and without L1-regularization. Robustness against user input variability is verified by considering the statistical distribution of the segmentation results when repeatedly analyzing each patient’s dataset with a different set of random seeding points. Results Using L1-regularized semi-automated NMF segmentation, mean Dice-scores of 65%, 74 and 80% are found for active tumor, the tumor core and the whole tumor region. Mean Hausdorff distances of 6.1 mm, 7.4 mm and 8.2 mm are found for active tumor, the tumor core and the whole tumor region. Lower Dice-scores and higher Hausdorff distances are found without L1-regularization and when only considering conventional MRI data. Conclusions Based on the mean Dice-scores and Hausdorff distances, segmentation results are competitive with state-of-the-art in literature. Robust results were found for most patients, although careful voxel selection is mandatory to avoid sub-optimal segmentation.
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Affiliation(s)
- Nicolas Sauwen
- Department of Electrical Engineering (ESAT), STADIUS Centre for Dynamical Systems, Signal Processing and Data Analytics, KULeuven, Kasteelpark Arenberg, Leuven, Belgium. .,imec, Kapeldreef 75, Leuven, 3001, Belgium.
| | - Marjan Acou
- Department of Radiology, Ghent University Hospital, De Pintelaan 185, Ghent, 9000, Belgium
| | - Diana M Sima
- Department of Electrical Engineering (ESAT), STADIUS Centre for Dynamical Systems, Signal Processing and Data Analytics, KULeuven, Kasteelpark Arenberg, Leuven, Belgium.,imec, Kapeldreef 75, Leuven, 3001, Belgium
| | - Jelle Veraart
- Department of Physics, iMinds Vision Lab, University of Antwerp, Edegemsesteenweg 200-240, Antwerp, 2610, Belgium
| | - Frederik Maes
- Department of Electrical Engineering (ESAT), PSI Centre for Processing Speech and Images, KULeuven, Kasteelpark Arenberg 10, Leuven, 3001, Belgium
| | - Uwe Himmelreich
- Department of Imaging and Pathology, Biomedical MRI/MoSAIC, KULeuven, Herestraat 49, Leuven, 3000, Belgium
| | - Eric Achten
- Department of Radiology, Ghent University Hospital, De Pintelaan 185, Ghent, 9000, Belgium
| | - Sabine Van Huffel
- Department of Electrical Engineering (ESAT), STADIUS Centre for Dynamical Systems, Signal Processing and Data Analytics, KULeuven, Kasteelpark Arenberg, Leuven, Belgium.,imec, Kapeldreef 75, Leuven, 3001, Belgium
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Sauwen N, Acou M, Van Cauter S, Sima DM, Veraart J, Maes F, Himmelreich U, Achten E, Van Huffel S. Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric MRI. Neuroimage Clin 2016; 12:753-764. [PMID: 27812502 PMCID: PMC5079350 DOI: 10.1016/j.nicl.2016.09.021] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2016] [Revised: 09/27/2016] [Accepted: 09/29/2016] [Indexed: 12/03/2022]
Abstract
Tumor segmentation is a particularly challenging task in high-grade gliomas (HGGs), as they are among the most heterogeneous tumors in oncology. An accurate delineation of the lesion and its main subcomponents contributes to optimal treatment planning, prognosis and follow-up. Conventional MRI (cMRI) is the imaging modality of choice for manual segmentation, and is also considered in the vast majority of automated segmentation studies. Advanced MRI modalities such as perfusion-weighted imaging (PWI), diffusion-weighted imaging (DWI) and magnetic resonance spectroscopic imaging (MRSI) have already shown their added value in tumor tissue characterization, hence there have been recent suggestions of combining different MRI modalities into a multi-parametric MRI (MP-MRI) approach for brain tumor segmentation. In this paper, we compare the performance of several unsupervised classification methods for HGG segmentation based on MP-MRI data including cMRI, DWI, MRSI and PWI. Two independent MP-MRI datasets with a different acquisition protocol were available from different hospitals. We demonstrate that a hierarchical non-negative matrix factorization variant which was previously introduced for MP-MRI tumor segmentation gives the best performance in terms of mean Dice-scores for the pathologic tissue classes on both datasets. Unsupervised classification algorithms are applied for brain tumor segmentation on multi-parametric MRI datasets. Reported mean Dice-scores are in the range of state-of-the-art segmentation algorithms. Hierarchical NMF obtained the best segmentation results in terms of mean Dice-scores for most of the tissue classes.
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Key Words
- 1H MRSI, proton magnetic resonance spectroscopic imaging
- ADC, apparent diffusion coefficient
- Cho, total choline
- Clustering
- Cre, total creatine
- DKI, diffusion kurtosis imaging
- DSC-MRI, dynamic susceptibility-weighted contrast-enhanced magnetic resonance imaging
- DTI, diffusion tensor imaging
- DWI, diffusion-weighted imaging
- FA, fractional anisotropy
- FCM, fuzzy C-means clustering
- FLAIR, fluid-attenuated inversion recovery
- GBM, glioblastoma multiforme
- GMM, Gaussian mixture modelling
- Glioma
- Glx, glutamine + glutamate
- Gly, glycine
- HALS, hierarchical alternating least squares
- HGG, high-grade glioma
- LGG, low-grade glioma
- Lac, lactate
- Lip, lipids
- MD, mean diffusivity
- MK, mean kurtosis
- MP-MRI, multi-parametric magnetic resonance imaging
- Multi-parametric MRI
- NAA, N-acetyl-aspartate
- NMF, non-negative matrix factorization
- NNLS, non-negative linear least-squares
- Non-negative matrix factorization
- PWI, perfusion-weighted imaging
- ROI, region of interest
- SC, spectral clustering
- SPA, successive projection algorithm
- Segmentation
- T1c, contrast-enhanced T1
- UZ Gent, University hospital of Ghent
- UZ Leuven, University hospitals of Leuven
- Unsupervised classification
- cMRI, conventional magnetic resonance imaging
- hNMF, hierarchical non-negative matrix factorization
- mI, myo-inositol
- rCBV, relative cerebral blood volume
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Affiliation(s)
- N Sauwen
- KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Centre for Dynamical Systems, Signal Processing and Data Analytics, Leuven, Belgium; iMinds, Department of Medical Information Technologies, Belgium
| | - M Acou
- Ghent University Hospital, Department of Radiology, Ghent, Belgium
| | - S Van Cauter
- University Hospitals of Leuven, Department of Radiology, Leuven, Belgium; Ziekenhuizen Oost-Limburg, Department of Radiology, Leuven, Belgium
| | - D M Sima
- KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Centre for Dynamical Systems, Signal Processing and Data Analytics, Leuven, Belgium; iMinds, Department of Medical Information Technologies, Belgium
| | - J Veraart
- University of Antwerp, iMinds Vision Lab, Department of Physics, Antwerp, Belgium
| | - F Maes
- KU Leuven, Department of Electrical Engineering (ESAT), PSI Centre for Processing Speech and Images, Leuven, Belgium
| | - U Himmelreich
- KU Leuven, Biomedical MRI/MoSAIC, Department of Imaging and Pathology, Leuven, Belgium
| | - E Achten
- Ghent University Hospital, Department of Radiology, Ghent, Belgium
| | - S Van Huffel
- KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Centre for Dynamical Systems, Signal Processing and Data Analytics, Leuven, Belgium; iMinds, Department of Medical Information Technologies, Belgium
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Bharath HN, Sima DM, Sauwen N, Himmelreich U, De Lathauwer L, Van Huffel S. Nonnegative Canonical Polyadic Decomposition for Tissue-Type Differentiation in Gliomas. IEEE J Biomed Health Inform 2016; 21:1124-1132. [PMID: 27429452 DOI: 10.1109/jbhi.2016.2583539] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Magnetic resonance spectroscopic imaging (MRSI) reveals chemical information that characterizes different tissue types in brain tumors. Blind source separation techniques are used to extract the tissue-specific profiles and their corresponding distribution from the MRSI data. We focus on automatic detection of the tumor, necrotic and normal brain tissue types by constructing a 3D MRSI tensor from in vivo 2D-MRSI data of individual glioma patients. Nonnegative canonical polyadic decomposition (NCPD) is applied to the MRSI tensor to differentiate various tissue types. An in vivo study shows that NCPD has better performance in identifying tumor and necrotic tissue type in glioma patients compared to previous matrix-based decompositions, such as nonnegative matrix factorization and hierarchical nonnegative matrix factorization.
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Affiliation(s)
- H N Bharath
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
| | - D M Sima
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
| | - N Sauwen
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
| | - U Himmelreich
- Biomedical MRI Unit/Molecular Small Animal Imaging Center, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - L De Lathauwer
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
| | - S Van Huffel
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
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Bharath HN, Sima DM, Sauwen N, Himmelreich U, De Lathauwer L, Van Huffel S. Tensor based tumor tissue type differentiation using magnetic resonance spectroscopic imaging. Annu Int Conf IEEE Eng Med Biol Soc 2016; 2015:7003-6. [PMID: 26737904 DOI: 10.1109/embc.2015.7320004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Magnetic resonance spectroscopic imaging (MRSI) has the potential to characterise different tissue types in brain tumors. Blind source separation techniques are used to extract the specific tissue profiles and their corresponding distribution from the MRSI data. A 3-dimensional MRSI tensor is constructed from in vivo 2D-MRSI data of individual tumor patients. Non-negative canonical polyadic decomposition (NCPD) with common factor in mode-1 and mode-2 and l(1) regularization on mode-3 is applied on the MRSI tensor to differentiate various tissue types. Initial in vivo study shows that NCPD has better performance in identifying tumor and necrotic tissue type in high grade glioma patients compared to previous matrix-based decompositions, such as non-negative matrix factorization and hierarchical non-negative matrix factorization.
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Sauwen N, Sima DM, Van Cauter S, Veraart J, Leemans A, Maes F, Himmelreich U, Van Huffel S. Hierarchical non-negative matrix factorization to characterize brain tumor heterogeneity using multi-parametric MRI. NMR Biomed 2015; 28:1599-1624. [PMID: 26458729 DOI: 10.1002/nbm.3413] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2015] [Revised: 08/17/2015] [Accepted: 08/24/2015] [Indexed: 06/05/2023]
Abstract
Tissue characterization in brain tumors and, in particular, in high-grade gliomas is challenging as a result of the co-existence of several intra-tumoral tissue types within the same region and the high spatial heterogeneity. This study presents a method for the detection of the relevant tumor substructures (i.e. viable tumor, necrosis and edema), which could be of added value for the diagnosis, treatment planning and follow-up of individual patients. Twenty-four patients with glioma [10 low-grade gliomas (LGGs), 14 high-grade gliomas (HGGs)] underwent a multi-parametric MRI (MP-MRI) scheme, including conventional MRI (cMRI), perfusion-weighted imaging (PWI), diffusion kurtosis imaging (DKI) and short-TE (1)H MRSI. MP-MRI parameters were derived: T2, T1 + contrast, fluid-attenuated inversion recovery (FLAIR), relative cerebral blood volume (rCBV), mean diffusivity (MD), fractional anisotropy (FA), mean kurtosis (MK) and the principal metabolites lipids (Lip), lactate (Lac), N-acetyl-aspartate (NAA), total choline (Cho), etc. Hierarchical non-negative matrix factorization (hNMF) was applied to the MP-MRI parameters, providing tissue characterization on a patient-by-patient and voxel-by-voxel basis. Tissue-specific patterns were obtained and the spatial distribution of each tissue type was visualized by means of abundance maps. Dice scores were calculated by comparing tissue segmentation derived from hNMF with the manual segmentation by a radiologist. Correlation coefficients were calculated between each pathologic tissue source and the average feature vector within the corresponding tissue region. For the patients with HGG, mean Dice scores of 78%, 85% and 83% were obtained for viable tumor, the tumor core and the complete tumor region. The mean correlation coefficients were 0.91 for tumor, 0.97 for necrosis and 0.96 for edema. For the patients with LGG, a mean Dice score of 85% and mean correlation coefficient of 0.95 were found for the tumor region. hNMF was also applied to reduced MRI datasets, showing the added value of individual MRI modalities.
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Affiliation(s)
- Nicolas Sauwen
- KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Centre for Dynamical Systems, Signal Processing and Data Analytics, Leuven, Belgium
- iMinds Medical IT, Leuven, Belgium
| | - Diana M Sima
- KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Centre for Dynamical Systems, Signal Processing and Data Analytics, Leuven, Belgium
- iMinds Medical IT, Leuven, Belgium
| | - Sofie Van Cauter
- Department of Radiology, University Hospitals of Leuven, Leuven, Belgium
| | - Jelle Veraart
- iMinds Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium
- Center for Biomedical Imaging, Department of Radiology, New York University Langone Medical Center, New York, NY, USA
| | - Alexander Leemans
- Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Frederik Maes
- iMinds Medical IT, Leuven, Belgium
- KU Leuven, Department of Electrical Engineering (ESAT), PSI Centre for Processing Speech and Images, Leuven, Belgium
| | - Uwe Himmelreich
- Biomedical MRI/MoSAIC, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Sabine Van Huffel
- KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Centre for Dynamical Systems, Signal Processing and Data Analytics, Leuven, Belgium
- iMinds Medical IT, Leuven, Belgium
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Zeman ME, Sauwen N, Labey L, Mulier M, Van der Perre G, Jaecques SVN. Assessment of the primary rotational stability of uncemented hip stems using an analytical model: comparison with finite element analyses. J Orthop Surg Res 2008; 3:44. [PMID: 18817544 PMCID: PMC2570665 DOI: 10.1186/1749-799x-3-44] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2008] [Accepted: 09/25/2008] [Indexed: 11/26/2022] Open
Abstract
Background Sufficient primary stability is a prerequisite for the clinical success of cementless implants. Therefore, it is important to have an estimation of the primary stability that can be achieved with new stem designs in a pre-clinical trial. Fast assessment of the primary stability is also useful in the preoperative planning of total hip replacements, and to an even larger extent in intraoperatively custom-made prosthesis systems, which result in a wide variety of stem geometries. Methods An analytical model is proposed to numerically predict the relative primary stability of cementless hip stems. This analytical approach is based upon the principle of virtual work and a straightforward mechanical model. For five custom-made implant designs, the resistance against axial rotation was assessed through the analytical model as well as through finite element modelling (FEM). Results The analytical approach can be considered as a first attempt to theoretically evaluate the primary stability of hip stems without using FEM, which makes it fast and inexpensive compared to other methods. A reasonable agreement was found in the stability ranking of the stems obtained with both methods. However, due to the simplifying assumptions underlying the analytical model it predicts very rigid stability behaviour: estimated stem rotation was two to three orders of magnitude smaller, compared with the FEM results. Conclusion Based on the results of this study, the analytical model might be useful as a comparative tool for the assessment of the primary stability of cementless hip stems.
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Affiliation(s)
- Maria E Zeman
- Katholieke Universiteit Leuven (K,U,Leuven), Division of Biomechanics and Engineering Design (BMGO), Celestijnenlaan 300C, 3001 Heverlee, Belgium
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Sauwen N, Lenaerts G, Jaecques S, Mulier M, Van der Perre G, Jonkers I. SUBJECT-SPECIFIC GEOMETRY AND LOADING AFFECT STRESS DISTRIBUTION AFTER THR. J Biomech 2008. [DOI: 10.1016/s0021-9290(08)70036-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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