Pub Date : 2014-07-31DOI: 10.1109/ISBI.2014.6868105
G. Michelin, L. Guignard, Ulla-Maj Fiúza, G. Malandain
Image-based studies of developing organs or embryos produce a huge quantity of data. To handle such high-throughput experimental protocols, automated computer-assisted methods are highly desirable. This article aims at designing an efficient cell segmentation method from microscopic images. The proposed approach is twofold: first, cell membranes are enhanced or extracted by the means of structure-based filters, and then perceptual grouping (i.e. tensor voting) allows to correct for segmentation gaps. To decrease the computational cost of this last step, we propose different methodologies to reduce the number of voters. Assessment on real data allows us to deduce the most efficient approach.
{"title":"Embryo cell membranes reconstruction by tensor voting","authors":"G. Michelin, L. Guignard, Ulla-Maj Fiúza, G. Malandain","doi":"10.1109/ISBI.2014.6868105","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6868105","url":null,"abstract":"Image-based studies of developing organs or embryos produce a huge quantity of data. To handle such high-throughput experimental protocols, automated computer-assisted methods are highly desirable. This article aims at designing an efficient cell segmentation method from microscopic images. The proposed approach is twofold: first, cell membranes are enhanced or extracted by the means of structure-based filters, and then perceptual grouping (i.e. tensor voting) allows to correct for segmentation gaps. To decrease the computational cost of this last step, we propose different methodologies to reduce the number of voters. Assessment on real data allows us to deduce the most efficient approach.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"233 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122390464","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 : 2014-07-31DOI: 10.1109/ISBI.2014.6867956
O. Michailovich, Y. Rathi
Among the existing methods of diffusion MRI, high angular resolution diffusion imaging (HARDI) excels in its ability to resolve the complex orientations of crossing and branching neural fibre tracts in the brain. Unfortunately, a widespread integration of HARDI into clinical workflows is still hindered by a few practical obstacles, chief among which relates to prohibitively long scan times required by current implementations of this protocol. In addition, the dependency of HARDI on rapid acquisition schemes, such as single-shot echo planar imaging, imposes limitations on the maximal spatial resolution that one can attain at an acceptable level of signal-to-noise ratio. A possible solution to the problem of limited spatial resolution of HARDI could be to modify the pattern of k-space encoding so as to maximally utilize the bandwidth efficiency of frequency encoding at the expense of using a smaller number of phase encoding steps. At the same time, a substantial reduction in the total acquisition time could be achieved through a subcritical sampling in the q-space. Although both the above mechanisms are bound to yield highly incomplete data, a stable and reliable reconstruction of the associated HARDI signals is still possible to achieve within the framework of compressed sensing. To solve this problem, we introduce an efficient reconstruction procedure, whose effectiveness is demonstrated through both in silico and in vivo experiments.
{"title":"A generalized compressed sensing approach to high angular resolution diffusion imaging","authors":"O. Michailovich, Y. Rathi","doi":"10.1109/ISBI.2014.6867956","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867956","url":null,"abstract":"Among the existing methods of diffusion MRI, high angular resolution diffusion imaging (HARDI) excels in its ability to resolve the complex orientations of crossing and branching neural fibre tracts in the brain. Unfortunately, a widespread integration of HARDI into clinical workflows is still hindered by a few practical obstacles, chief among which relates to prohibitively long scan times required by current implementations of this protocol. In addition, the dependency of HARDI on rapid acquisition schemes, such as single-shot echo planar imaging, imposes limitations on the maximal spatial resolution that one can attain at an acceptable level of signal-to-noise ratio. A possible solution to the problem of limited spatial resolution of HARDI could be to modify the pattern of k-space encoding so as to maximally utilize the bandwidth efficiency of frequency encoding at the expense of using a smaller number of phase encoding steps. At the same time, a substantial reduction in the total acquisition time could be achieved through a subcritical sampling in the q-space. Although both the above mechanisms are bound to yield highly incomplete data, a stable and reliable reconstruction of the associated HARDI signals is still possible to achieve within the framework of compressed sensing. To solve this problem, we introduce an efficient reconstruction procedure, whose effectiveness is demonstrated through both in silico and in vivo experiments.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115168944","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 : 2014-07-31DOI: 10.1109/ISBI.2014.6868130
A. Giusti, Claudio Caccia, D. Ciresan, J. Schmidhuber, L. Gambardella
We consider the problem of detecting mitotic figures in breast cancer histology slides. We investigate whether the performance of state-of-the-art detection algorithms is comparable to the performance of humans, when they are compared under fair conditions: our test subjects were not previously exposed to the task, and were required to learn their own classification criteria solely by studying the same training set available to algorithms. We designed and implemented a standardized web-based test based on the publicly-available MITOS dataset, and compared results with the performance of the 6 top-scoring algorithms in the ICPR 2012 Mitosis Detection Contest. The problem is presented as a classification task on a balanced dataset. 45 different test subjects produced a total of 3009 classifications. The best individual (accuracy = 0.859 ± 0.012), is outperformed by the most accurate algorithm (accuracy = 0.873 ± 0.004). This suggests that state-of-the-art detection algorithms are likely limited by the size of the training set, rather than by lack of generalization ability.
{"title":"A comparison of algorithms and humans for mitosis detection","authors":"A. Giusti, Claudio Caccia, D. Ciresan, J. Schmidhuber, L. Gambardella","doi":"10.1109/ISBI.2014.6868130","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6868130","url":null,"abstract":"We consider the problem of detecting mitotic figures in breast cancer histology slides. We investigate whether the performance of state-of-the-art detection algorithms is comparable to the performance of humans, when they are compared under fair conditions: our test subjects were not previously exposed to the task, and were required to learn their own classification criteria solely by studying the same training set available to algorithms. We designed and implemented a standardized web-based test based on the publicly-available MITOS dataset, and compared results with the performance of the 6 top-scoring algorithms in the ICPR 2012 Mitosis Detection Contest. The problem is presented as a classification task on a balanced dataset. 45 different test subjects produced a total of 3009 classifications. The best individual (accuracy = 0.859 ± 0.012), is outperformed by the most accurate algorithm (accuracy = 0.873 ± 0.004). This suggests that state-of-the-art detection algorithms are likely limited by the size of the training set, rather than by lack of generalization ability.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131814664","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 : 2014-07-31DOI: 10.1109/ISBI.2014.6868133
F. S. Nezhad, H. S. Rad, H. Soltanian-Zadeh
Lack of anatomical details in diffusion weighted magnetic resonance images limits their utilization and treatment response monitoring, shadowing the useful information they contain. Contemporary methods of utilizing these images are based on manual selection of region of interest, raising concerns about susceptibility of manual ROI placement to human errors, and limiting the investigation in specific spatial regions. In contrary to the whole body bone marrow segmentation with the luxury to include all the diseased bone marrow, high profile analysis could be applied. In this paper, we propose an automatic method for segmentation of pelvic bone with possible bone metastasis in apparent diffusion coefficient (ADC) maps. This method is a multi-parametric registration-segmentation method, taking advantage of prior information of the pelvic anatomy. Intensity inhomogeneity in the bone structure caused by bone marrow metastasis challenges the segmentation process on anatomical MR images. Specifically, we first build a probability map which provides shape and volume constraints for the segmentation. Then, T1-weighted MR images are rigidly registered to the probability map, and then the registered T1-weighted image is non-rigidly registered to its' corresponding ADC maps. Finally, the probability map is coupled with a local level set framework for automatic pelvic bone segmentation of the T1-weighted images. The segmented bone is used as a mask on the ADC map. The method is validated on 10 pairs of ADC/T1 images of breast cancer with bone marrow metastases patients. Both quantitative and qualitative evaluation results demonstrate the validity of the proposed method.
{"title":"Segmentation of bone from ADC maps in pelvis area using local level-set and prior information","authors":"F. S. Nezhad, H. S. Rad, H. Soltanian-Zadeh","doi":"10.1109/ISBI.2014.6868133","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6868133","url":null,"abstract":"Lack of anatomical details in diffusion weighted magnetic resonance images limits their utilization and treatment response monitoring, shadowing the useful information they contain. Contemporary methods of utilizing these images are based on manual selection of region of interest, raising concerns about susceptibility of manual ROI placement to human errors, and limiting the investigation in specific spatial regions. In contrary to the whole body bone marrow segmentation with the luxury to include all the diseased bone marrow, high profile analysis could be applied. In this paper, we propose an automatic method for segmentation of pelvic bone with possible bone metastasis in apparent diffusion coefficient (ADC) maps. This method is a multi-parametric registration-segmentation method, taking advantage of prior information of the pelvic anatomy. Intensity inhomogeneity in the bone structure caused by bone marrow metastasis challenges the segmentation process on anatomical MR images. Specifically, we first build a probability map which provides shape and volume constraints for the segmentation. Then, T1-weighted MR images are rigidly registered to the probability map, and then the registered T1-weighted image is non-rigidly registered to its' corresponding ADC maps. Finally, the probability map is coupled with a local level set framework for automatic pelvic bone segmentation of the T1-weighted images. The segmented bone is used as a mask on the ADC map. The method is validated on 10 pairs of ADC/T1 images of breast cancer with bone marrow metastases patients. Both quantitative and qualitative evaluation results demonstrate the validity of the proposed method.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"188 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134253339","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 : 2014-07-31DOI: 10.1109/ISBI.2014.6867852
L. Zhan, N. Jahanshad, Yan Jin, T. Nir, Cassandra D. Leonardo, M. Bernstein, B. Borowski, C. Jack, P. Thompson
Large multi-site studies, such as the Alzheimer's disease Neuroimaging Initiative (ADNI) are designed to harmonize imaging protocols as far as possible across scanning sites. ADNI-2 collects diffusion-weighted images (DWI) at 14 sites, with a consistent scanner manufacturer (General Electric), magnetic field strength (3T) and consistent acquisition parameters - including voxel size and the number of gradient directions. Here we studied how the SNR, voxel-wise and ROI-based diffusion measures, and derived connectivity matrices and network properties depended on the scanner platform (with "HD" denoting version 16.x software and lower and DV being 20.x and higher). We found scanner platform effects on voxel-based FA, in several ROIs, but not on SNR or network properties. These results indicate the importance of accounting for any differences in scanner platform in multi-site DTI studies, even when the protocols are harmonized in all other respects.
{"title":"Understanding scanner upgrade effects on brain integrity & connectivity measures","authors":"L. Zhan, N. Jahanshad, Yan Jin, T. Nir, Cassandra D. Leonardo, M. Bernstein, B. Borowski, C. Jack, P. Thompson","doi":"10.1109/ISBI.2014.6867852","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867852","url":null,"abstract":"Large multi-site studies, such as the Alzheimer's disease Neuroimaging Initiative (ADNI) are designed to harmonize imaging protocols as far as possible across scanning sites. ADNI-2 collects diffusion-weighted images (DWI) at 14 sites, with a consistent scanner manufacturer (General Electric), magnetic field strength (3T) and consistent acquisition parameters - including voxel size and the number of gradient directions. Here we studied how the SNR, voxel-wise and ROI-based diffusion measures, and derived connectivity matrices and network properties depended on the scanner platform (with \"HD\" denoting version 16.x software and lower and DV being 20.x and higher). We found scanner platform effects on voxel-based FA, in several ROIs, but not on SNR or network properties. These results indicate the importance of accounting for any differences in scanner platform in multi-site DTI studies, even when the protocols are harmonized in all other respects.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134519538","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 : 2014-07-31DOI: 10.1109/ISBI.2014.6867930
Loizos Markides, D. Gillies
Decoding mental states from task-related fMRI data has recently been the focus of much research. Nevertheless, high levels of acquisition and physiological noise still makes inter-subject decoding a difficult and quite unstable process. Since all of the existing decoding approaches are applied on a volume-by-volume basis, it would be sensible to ensure that sudden signal changes reflect a true change of cognitive state rather than noise artefacts. Correction of the temporal signal can be achieved through temporal smoothing, which over the years has always been a debatable fMRI preprocessing step among the neuroscience community. In this paper, we present two methods for improving decoding accuracy by correcting the temporal dynamics of a number of functional regions, using parametrized temporal smoothing. We test our methods on a real fMRI dataset and we show that when temporal smoothing is applied separately in multiple scales and is both properly constrained and conditioned, it can remove sudden artefact-driven peaks and drops from the fMRI signal and thus improve the prediction accuracy of different tasks. Moreover, since our methods are performed independently from the decoding operations, they can be used in conjunction with any feature selection and classification algorithm.
{"title":"Improving brain decoding through constrained and parametrized temporal smoothing","authors":"Loizos Markides, D. Gillies","doi":"10.1109/ISBI.2014.6867930","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867930","url":null,"abstract":"Decoding mental states from task-related fMRI data has recently been the focus of much research. Nevertheless, high levels of acquisition and physiological noise still makes inter-subject decoding a difficult and quite unstable process. Since all of the existing decoding approaches are applied on a volume-by-volume basis, it would be sensible to ensure that sudden signal changes reflect a true change of cognitive state rather than noise artefacts. Correction of the temporal signal can be achieved through temporal smoothing, which over the years has always been a debatable fMRI preprocessing step among the neuroscience community. In this paper, we present two methods for improving decoding accuracy by correcting the temporal dynamics of a number of functional regions, using parametrized temporal smoothing. We test our methods on a real fMRI dataset and we show that when temporal smoothing is applied separately in multiple scales and is both properly constrained and conditioned, it can remove sudden artefact-driven peaks and drops from the fMRI signal and thus improve the prediction accuracy of different tasks. Moreover, since our methods are performed independently from the decoding operations, they can be used in conjunction with any feature selection and classification algorithm.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"2012 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114544305","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 : 2014-07-31DOI: 10.1109/ISBI.2014.6867832
R. R. Ribeiro, A. R. S. Feitosa, R. E. D. Souza, W. Santos
The development and improvement of non-invasive imaging techniques have been increasing in the last decades, due to interests from both academy and industry. Electrical Impedance Tomography (EIT) is a noninvasive imaging technique that offers a vast field of possibilities due to its low cost, portability, and safety of handling. However, EIT image reconstruction is an ill-posed problem. Herein this work we present an EIT reconstruction method based on the optimization of the relative error of reconstruction using genetic algorithms employing elitist strategies. The initial set of solutions used by the elitist genetic algorithm includes a noisy version of the solution obtained from the backprojection algorithm, according to Saha and Bandyopadhyay's criterion for non-blind initial search in optimization algorithms, in order to accelerate convergence and improve performance.
{"title":"Reconstruction of electrical impedance tomography images using genetic algorithms and non-blind search","authors":"R. R. Ribeiro, A. R. S. Feitosa, R. E. D. Souza, W. Santos","doi":"10.1109/ISBI.2014.6867832","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867832","url":null,"abstract":"The development and improvement of non-invasive imaging techniques have been increasing in the last decades, due to interests from both academy and industry. Electrical Impedance Tomography (EIT) is a noninvasive imaging technique that offers a vast field of possibilities due to its low cost, portability, and safety of handling. However, EIT image reconstruction is an ill-posed problem. Herein this work we present an EIT reconstruction method based on the optimization of the relative error of reconstruction using genetic algorithms employing elitist strategies. The initial set of solutions used by the elitist genetic algorithm includes a noisy version of the solution obtained from the backprojection algorithm, according to Saha and Bandyopadhyay's criterion for non-blind initial search in optimization algorithms, in order to accelerate convergence and improve performance.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124990661","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 : 2014-07-31DOI: 10.1109/ISBI.2014.6867906
T. Pécot, J. Boulanger, C. Kervrann, P. Bouthemy, J. Salamero
Automatic analysis of the dynamic content in fluorescence video-microscopy is crucial for understanding molecular mechanisms involved in cell functions. In this paper, we propose an original approach for analyzing particle trafficking in these sequences. Instead of individually tracking every particle, we estimate the particle flows between predefined regions. This approach allows us to process image sequences with a high number of particles and a low frame rate. We investigate several ways to estimate the particle flow at the cellular level and evaluate their performance in synthetic and real image sequences.
{"title":"Estimation of the flow of particles within a partition of the image domain in fluorescence video-microscopy","authors":"T. Pécot, J. Boulanger, C. Kervrann, P. Bouthemy, J. Salamero","doi":"10.1109/ISBI.2014.6867906","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867906","url":null,"abstract":"Automatic analysis of the dynamic content in fluorescence video-microscopy is crucial for understanding molecular mechanisms involved in cell functions. In this paper, we propose an original approach for analyzing particle trafficking in these sequences. Instead of individually tracking every particle, we estimate the particle flows between predefined regions. This approach allows us to process image sequences with a high number of particles and a low frame rate. We investigate several ways to estimate the particle flow at the cellular level and evaluate their performance in synthetic and real image sequences.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125111825","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 : 2014-07-31DOI: 10.1109/ISBI.2014.6868051
Guang Yang, F. Raschke, T. Barrick, F. Howe
Proton magnetic resonance spectroscopy (1H MRS) provides non-invasive information on brain tumour biochemistry. Many studies have shown that 1H MRS can be used in an objective decision support system, which gives additional diagnosis and prognostic information to the data obtained using conventional radiological modalities. Fully automatic analyses of 1H MRS have been previously applied and can be separated into two types: (i) model dependent signal quantification followed by pattern recognition (PR), or (ii) model independent PR methods. However, there is not yet a consensus as to the best techniques of MRS post-processing or feature extraction to be used for optimum classification. In this study, we analysed the single-voxel MRS acquisitions of 74 patients with histologically diagnosed brain tumours. Our classification results show that the model independent nonlinear manifold learning method can produce superior results to those of using model dependent metabolite quantification.
{"title":"Classification of brain tumour 1H MR spectra: Extracting features by metabolite quantification or nonlinear manifold learning?","authors":"Guang Yang, F. Raschke, T. Barrick, F. Howe","doi":"10.1109/ISBI.2014.6868051","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6868051","url":null,"abstract":"Proton magnetic resonance spectroscopy (1H MRS) provides non-invasive information on brain tumour biochemistry. Many studies have shown that 1H MRS can be used in an objective decision support system, which gives additional diagnosis and prognostic information to the data obtained using conventional radiological modalities. Fully automatic analyses of 1H MRS have been previously applied and can be separated into two types: (i) model dependent signal quantification followed by pattern recognition (PR), or (ii) model independent PR methods. However, there is not yet a consensus as to the best techniques of MRS post-processing or feature extraction to be used for optimum classification. In this study, we analysed the single-voxel MRS acquisitions of 74 patients with histologically diagnosed brain tumours. Our classification results show that the model independent nonlinear manifold learning method can produce superior results to those of using model dependent metabolite quantification.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134359800","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 : 2014-07-31DOI: 10.1109/ISBI.2014.6867838
S. Johnston, G. Johnson, C. Badea
While helical scanning is well established in the clinical arena, most micro-CT scanners use circular cone beam trajectories and approximate reconstructions based on a filtered backprojection (FBP) algorithm. This may be sufficient for some applications, but in studies of larger animals, such as rats, the size of the detector can constrain the field of view and extend scan time. To address this problem, we have designed and implemented helical scanning and reconstruction procedures for an in-house-developed dual source cone-beam micro-CT system. The reconstruction uses a simultaneous algebraic reconstruction technique combined with total variation regularization (SART-TV). We implemented this algorithm on a graphics processing unit (GPU) to reduce run time. The results demonstrate the speed and accuracy of the GPU-based SART-TV algorithm. The helical scan enables the reconstruction of volumes with extended field of view for whole body micro-CT imaging of large rodents.
{"title":"Helical dual source cone-beam micro-CT","authors":"S. Johnston, G. Johnson, C. Badea","doi":"10.1109/ISBI.2014.6867838","DOIUrl":"https://doi.org/10.1109/ISBI.2014.6867838","url":null,"abstract":"While helical scanning is well established in the clinical arena, most micro-CT scanners use circular cone beam trajectories and approximate reconstructions based on a filtered backprojection (FBP) algorithm. This may be sufficient for some applications, but in studies of larger animals, such as rats, the size of the detector can constrain the field of view and extend scan time. To address this problem, we have designed and implemented helical scanning and reconstruction procedures for an in-house-developed dual source cone-beam micro-CT system. The reconstruction uses a simultaneous algebraic reconstruction technique combined with total variation regularization (SART-TV). We implemented this algorithm on a graphics processing unit (GPU) to reduce run time. The results demonstrate the speed and accuracy of the GPU-based SART-TV algorithm. The helical scan enables the reconstruction of volumes with extended field of view for whole body micro-CT imaging of large rodents.","PeriodicalId":440405,"journal":{"name":"2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)","volume":"364 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132524273","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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