Pub Date : 2008-05-14DOI: 10.1109/ISBI.2008.4541197
D. Mahapatra, Ying Sun
In this paper we propose the use of a neurobiology-based saliency measure to improve the performance of a quantitative- qualitative measure of mutual information for rigid registration of 4D renal perfusion MR images. Our registration method assigns greater importance to more salient voxels by applying a soft thresholding function to normalized saliency values. The resulting saliency map is a better representation of what is truly visually salient than an entropy-based saliency map. Our tests on real patient datasets show that incorporating this saliency measure produces better registration results than traditional entropy-based approaches.
{"title":"Registration of dynamic renal MR images using neurobiological model of saliency","authors":"D. Mahapatra, Ying Sun","doi":"10.1109/ISBI.2008.4541197","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541197","url":null,"abstract":"In this paper we propose the use of a neurobiology-based saliency measure to improve the performance of a quantitative- qualitative measure of mutual information for rigid registration of 4D renal perfusion MR images. Our registration method assigns greater importance to more salient voxels by applying a soft thresholding function to normalized saliency values. The resulting saliency map is a better representation of what is truly visually salient than an entropy-based saliency map. Our tests on real patient datasets show that incorporating this saliency measure produces better registration results than traditional entropy-based approaches.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"199 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130826383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-05-14DOI: 10.1109/ISBI.2008.4541044
Fritz Jetzek, E. Mylona, D. Manoussaki
Focal adhesions (FA) play a dominant role in determining adherent cell morphology. This is particularly true for FA close to the cell contour on a two-dimensional substrate, since they are required for stabilization of the elastic cell membrane protrusions. The identification of these FA yields valuable information about the shape and the general state of the cell. In the current paper, we present a new method that does not require FA staining - the method predicts FA locations based on cell contour morphology. Our method, which we term comparative Hough transform (CHT), derives from the linear Hough transform and offers a novel approach in identifying significant loci on the contour, where the lateral cell contour edges meet at a cusp.
{"title":"Prediction of potential locations of focal adhesions on the contour of adherent cells","authors":"Fritz Jetzek, E. Mylona, D. Manoussaki","doi":"10.1109/ISBI.2008.4541044","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541044","url":null,"abstract":"Focal adhesions (FA) play a dominant role in determining adherent cell morphology. This is particularly true for FA close to the cell contour on a two-dimensional substrate, since they are required for stabilization of the elastic cell membrane protrusions. The identification of these FA yields valuable information about the shape and the general state of the cell. In the current paper, we present a new method that does not require FA staining - the method predicts FA locations based on cell contour morphology. Our method, which we term comparative Hough transform (CHT), derives from the linear Hough transform and offers a novel approach in identifying significant loci on the contour, where the lateral cell contour edges meet at a cusp.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131031589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-05-14DOI: 10.1109/ISBI.2008.4541274
L. Hsu, A. Aletras, A. Arai
Quantitative analysis of cardiac magnetic resonance (MR) images is important in bringing objectivity in diagnosis of myocardial abnormalities. Prior to quantitative analysis, it is necessary to correct signal intensity inhomogeneity due to the non-uniform surface coil sensitivity profile. We present a method using non-rigid body image warping and polynomial function fitting to correct this intensity bias on imperfectly registered cardiac MR images. The method was validated on normal human MR images and significantly reduced signal variation from 20.0% to 3.9% in regions of normal myocardium. In MR images of acute myocardial infarction in dogs, signal intensity analysis detected edematous myocardium as 35.9% brighter than normal myocardium on T2- weighted images (p=0.002) while control regions of interest on PD-weighted images were uniform within 2.2% (p=NS). The proposed approach effectively corrected surface coil related signal intensity inhomogeneity in imperfect datasets and allowed confident detection of subtle pathophysiological abnormalities.
{"title":"Correcting surface coil intensity inhomogeneity improves quantitative analysis of cardiac magnetic resonance images","authors":"L. Hsu, A. Aletras, A. Arai","doi":"10.1109/ISBI.2008.4541274","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541274","url":null,"abstract":"Quantitative analysis of cardiac magnetic resonance (MR) images is important in bringing objectivity in diagnosis of myocardial abnormalities. Prior to quantitative analysis, it is necessary to correct signal intensity inhomogeneity due to the non-uniform surface coil sensitivity profile. We present a method using non-rigid body image warping and polynomial function fitting to correct this intensity bias on imperfectly registered cardiac MR images. The method was validated on normal human MR images and significantly reduced signal variation from 20.0% to 3.9% in regions of normal myocardium. In MR images of acute myocardial infarction in dogs, signal intensity analysis detected edematous myocardium as 35.9% brighter than normal myocardium on T2- weighted images (p=0.002) while control regions of interest on PD-weighted images were uniform within 2.2% (p=NS). The proposed approach effectively corrected surface coil related signal intensity inhomogeneity in imperfect datasets and allowed confident detection of subtle pathophysiological abnormalities.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130927646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-05-14DOI: 10.1109/ISBI.2008.4541238
M. Styner, I. Oguz, T. Heimann, G. Gerig
Establishing optimal correspondence across object populations is essential to statistical shape analysis. Minimizing the description length (MDL) is a popular method for finding correspondence. In this work, we extend the MDL method by incorporating various local curvature metrics. Using local curvature can improve performance by ensuring that corresponding points exhibit similar local geometric characteristics that can't always be captured by mere point locations. We illustrate results on a variety of anatomical structures. The MDL method with a combination of point locations and curvature outperforms all the other methods we analyzed, including traditional MDL and spherical harmonics (SPHARM) correspondence, when the analyzed object population exhibits complex structure. When the objects are of simple nature, however, there's no added benefit to using the local curvature. In our experiments, we did not observe a significant difference in the correspondence quality when different curvature metrics (e.g. principal curvatures, mean curvature, Gaussian curvature) were used.
{"title":"Minimum description length with local geometry","authors":"M. Styner, I. Oguz, T. Heimann, G. Gerig","doi":"10.1109/ISBI.2008.4541238","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541238","url":null,"abstract":"Establishing optimal correspondence across object populations is essential to statistical shape analysis. Minimizing the description length (MDL) is a popular method for finding correspondence. In this work, we extend the MDL method by incorporating various local curvature metrics. Using local curvature can improve performance by ensuring that corresponding points exhibit similar local geometric characteristics that can't always be captured by mere point locations. We illustrate results on a variety of anatomical structures. The MDL method with a combination of point locations and curvature outperforms all the other methods we analyzed, including traditional MDL and spherical harmonics (SPHARM) correspondence, when the analyzed object population exhibits complex structure. When the objects are of simple nature, however, there's no added benefit to using the local curvature. In our experiments, we did not observe a significant difference in the correspondence quality when different curvature metrics (e.g. principal curvatures, mean curvature, Gaussian curvature) were used.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133430000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-05-14DOI: 10.1109/ISBI.2008.4541291
D. Pham, S. Bonnet, R. Guillemaud, E. Castelli, Pham Thi Ngoc Yen
The cardiorespiratory signal is a fundamental vital sign to assess a person's health. Additionally, the cardio-respiratory signal gives a great deal of information to healthcare providers wishing to monitor healthy individuals. This paper proposes a method to detect the respiratory waveform from an accelerometer strapped onto the chest. A system was designed and several experiments were conducted on volunteers. The acquisition is performed in different status: normal, apnea, deep breathing and also in different postures: vertical (sitting, standing) or horizontal (lying down). This method could therefore be suitable for automatic identification of some respiratory malfunction, for example during the obstructive apnea.
{"title":"Estimation of respiratory waveform using an accelerometer","authors":"D. Pham, S. Bonnet, R. Guillemaud, E. Castelli, Pham Thi Ngoc Yen","doi":"10.1109/ISBI.2008.4541291","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541291","url":null,"abstract":"The cardiorespiratory signal is a fundamental vital sign to assess a person's health. Additionally, the cardio-respiratory signal gives a great deal of information to healthcare providers wishing to monitor healthy individuals. This paper proposes a method to detect the respiratory waveform from an accelerometer strapped onto the chest. A system was designed and several experiments were conducted on volunteers. The acquisition is performed in different status: normal, apnea, deep breathing and also in different postures: vertical (sitting, standing) or horizontal (lying down). This method could therefore be suitable for automatic identification of some respiratory malfunction, for example during the obstructive apnea.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132450510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-05-14DOI: 10.1109/ISBI.2008.4541181
R. Socher, Adrian Barbu, D. Comaniciu
In this paper we present a learning based method for vessel segmentation in angiographic videos. Vessel segmentation is an important task in medical imaging and has been investigated extensively in the past. Traditional approaches often require pre-processing steps, standard conditions or manually set seed points. Our method is automatic, fast and robust towards noise often seen in low radiation X-ray images. Furthermore, it can be easily trained and used for any kind of tubular structure. We formulate the segmentation task as a hierarchical learning problem over 3 levels: border points, cross-segments and vessel pieces, corresponding to the vessel's position, width and length. Following the marginal space learning paradigm the detection on each level is performed by a learned classifier. We use probabilistic boosting trees with Haar and steerable features. First results of segmenting the vessel which surrounds a guide wire in 200 frames are presented and future additions are discussed.
{"title":"A learning based hierarchical model for vessel segmentation","authors":"R. Socher, Adrian Barbu, D. Comaniciu","doi":"10.1109/ISBI.2008.4541181","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541181","url":null,"abstract":"In this paper we present a learning based method for vessel segmentation in angiographic videos. Vessel segmentation is an important task in medical imaging and has been investigated extensively in the past. Traditional approaches often require pre-processing steps, standard conditions or manually set seed points. Our method is automatic, fast and robust towards noise often seen in low radiation X-ray images. Furthermore, it can be easily trained and used for any kind of tubular structure. We formulate the segmentation task as a hierarchical learning problem over 3 levels: border points, cross-segments and vessel pieces, corresponding to the vessel's position, width and length. Following the marginal space learning paradigm the detection on each level is performed by a learned classifier. We use probabilistic boosting trees with Haar and steerable features. First results of segmenting the vessel which surrounds a guide wire in 200 frames are presented and future additions are discussed.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128808615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-05-14DOI: 10.1109/ISBI.2008.4541234
M. Baiker, J. Dijkstra, I. Que, C. Löwik, J. Reiber, B. Lelieveldt
In this article, we present an approach for organ approximation in low contrast muCT data of mice using a whole-body mouse atlas (Segars et al. [1]). Starting from a set of landmarks on bone and joint locations, further correspondences are derived on surface representations of the lung by atlas-based registration and on the skin by employing a local geodesic shape context. Subsequently, landmarks on the skeleton, the lung and the skin are used to constrain a Thin-Plate-Spline (TPS) based mapping of major organs from the atlas to the subject domain. The feasibility of the method has been tested by means of 26 CT mouse datasets and a different whole-body mouse atlas (Digimouse [2]). Proper mapping of the lung and the skin as well as major organs could be achieved in all cases yielding a mean Euclidean distance between surface nodes of 0.42 plusmn 0.068 mm for the lung and 0.34 plusmn 0.036 mm for the skin. The performance of the organ interpolation has been assessed on basis of manual segmentations of two CT datasets of mice with injected contrast agent and the Digimouse. The calculated dice indices of volume overlap show significant improvement compared to earlier studies.
{"title":"Organ approximation in μCT data with low soft tissue contrast using an articulated whole-body atlas","authors":"M. Baiker, J. Dijkstra, I. Que, C. Löwik, J. Reiber, B. Lelieveldt","doi":"10.1109/ISBI.2008.4541234","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541234","url":null,"abstract":"In this article, we present an approach for organ approximation in low contrast muCT data of mice using a whole-body mouse atlas (Segars et al. [1]). Starting from a set of landmarks on bone and joint locations, further correspondences are derived on surface representations of the lung by atlas-based registration and on the skin by employing a local geodesic shape context. Subsequently, landmarks on the skeleton, the lung and the skin are used to constrain a Thin-Plate-Spline (TPS) based mapping of major organs from the atlas to the subject domain. The feasibility of the method has been tested by means of 26 CT mouse datasets and a different whole-body mouse atlas (Digimouse [2]). Proper mapping of the lung and the skin as well as major organs could be achieved in all cases yielding a mean Euclidean distance between surface nodes of 0.42 plusmn 0.068 mm for the lung and 0.34 plusmn 0.036 mm for the skin. The performance of the organ interpolation has been assessed on basis of manual segmentations of two CT datasets of mice with injected contrast agent and the Digimouse. The calculated dice indices of volume overlap show significant improvement compared to earlier studies.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131303377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-05-14DOI: 10.1109/ISBI.2008.4541294
Bo Liu, E. Abdelsalam, J. Sheng, L. Ying
SENSE has been widely accepted and extensively studied in the community of parallel MRI. Although many regularization approaches have been developed to address the ill-conditioning problem for Cartesian SENSE, fewer efforts have been made to address this problem when the sampling trajectory is non-Cartesian. For non-Cartesian SENSE using the iterative conjugate gradient method, ill- conditioning can degrade not only the signal-to-noise ratio, but also the convergence behavior. This paper proposes a regularization technique for non-Cartesian SENSE using a multiscale wavelet model. The technique models the desired image as a random field whose wavelet transform coefficients obey a generalized Gaussian distribution. The effectiveness of the proposed method has been validated by in vivo experiments.
{"title":"Improved spiral sense reconstruction using a multiscale wavelet model","authors":"Bo Liu, E. Abdelsalam, J. Sheng, L. Ying","doi":"10.1109/ISBI.2008.4541294","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541294","url":null,"abstract":"SENSE has been widely accepted and extensively studied in the community of parallel MRI. Although many regularization approaches have been developed to address the ill-conditioning problem for Cartesian SENSE, fewer efforts have been made to address this problem when the sampling trajectory is non-Cartesian. For non-Cartesian SENSE using the iterative conjugate gradient method, ill- conditioning can degrade not only the signal-to-noise ratio, but also the convergence behavior. This paper proposes a regularization technique for non-Cartesian SENSE using a multiscale wavelet model. The technique models the desired image as a random field whose wavelet transform coefficients obey a generalized Gaussian distribution. The effectiveness of the proposed method has been validated by in vivo experiments.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115752406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-05-14DOI: 10.1109/ISBI.2008.4541236
W. B. H. Khelifa, A. Abdallah, F. Ghorbel
Myocardial scintigraphy SPECT (single photon emission computed tomography) is a functional imaging modality which is performed at stress and rest. The diagnosis is obtained by comparing myocardium blood flow at these two different patient states. We propose to add at this technique completely non invasive anatomical data to avoid the use of invasive modalities like coronarography for example. For this purpose, we intend to extend the powerful technique of 2D Fourier descriptor to 3D objects by modeling the left ventricle at stress and at rest using the spherical harmonic descriptors so as to provide quantitative information to the physician to evaluate the extent of an eventual ischemia.
{"title":"Three dimensional modeling of the left ventricle of the heart using spherical harmonic analysis","authors":"W. B. H. Khelifa, A. Abdallah, F. Ghorbel","doi":"10.1109/ISBI.2008.4541236","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541236","url":null,"abstract":"Myocardial scintigraphy SPECT (single photon emission computed tomography) is a functional imaging modality which is performed at stress and rest. The diagnosis is obtained by comparing myocardium blood flow at these two different patient states. We propose to add at this technique completely non invasive anatomical data to avoid the use of invasive modalities like coronarography for example. For this purpose, we intend to extend the powerful technique of 2D Fourier descriptor to 3D objects by modeling the left ventricle at stress and at rest using the spherical harmonic descriptors so as to provide quantitative information to the physician to evaluate the extent of an eventual ischemia.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"157 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115789398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-05-14DOI: 10.1109/ISBI.2008.4541013
Jean-Charles Baritaux, C. Seelamantula, M. Unser
Reconstruction algorithms for Optical Diffuse Tomography (ODT) rely heavily on fast and accurate forward models. Arbitrary geometries and boundary conditions need to be handled rigorously since they are the only input to the inverse problem. From this perspective, Finite Element Methods (FEM) are good candidates to implement a forward model. However, these methods require to mesh the domain of interest, which is impractical on a routine basis. The other downside of the FEM is that the basis functions are often not compatible with the ones used for solving the inverse problem, which typically have less degrees of freedom. In this work, we tackle the 2D problem, and propose a forward model that uses a mesh-free discretization based on linear B-Splines. It combines the advantages of the FEM, while offering a fast and much simpler way of handling complex geometries. Another motivation for this work is that the underlying B-spline model is equally suitable for the subsequent reconstruction part of the process (solving the inverse problem). In particular, it is compatible with wavelets and multiresolution-type signal representations.
{"title":"A spline-based forward model for Optical Diffuse Tomography","authors":"Jean-Charles Baritaux, C. Seelamantula, M. Unser","doi":"10.1109/ISBI.2008.4541013","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541013","url":null,"abstract":"Reconstruction algorithms for Optical Diffuse Tomography (ODT) rely heavily on fast and accurate forward models. Arbitrary geometries and boundary conditions need to be handled rigorously since they are the only input to the inverse problem. From this perspective, Finite Element Methods (FEM) are good candidates to implement a forward model. However, these methods require to mesh the domain of interest, which is impractical on a routine basis. The other downside of the FEM is that the basis functions are often not compatible with the ones used for solving the inverse problem, which typically have less degrees of freedom. In this work, we tackle the 2D problem, and propose a forward model that uses a mesh-free discretization based on linear B-Splines. It combines the advantages of the FEM, while offering a fast and much simpler way of handling complex geometries. Another motivation for this work is that the underlying B-spline model is equally suitable for the subsequent reconstruction part of the process (solving the inverse problem). In particular, it is compatible with wavelets and multiresolution-type signal representations.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"178 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116009765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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