Pub Date : 2008-05-14DOI: 10.1109/ISBI.2008.4541088
Kenji Suzuki, Mark L. Epstein, Ivan Sheu, R. Kohlbrenner, D. Rockey, A. Dachman
A major challenge in computer-aided detection (CAD) of polyps in CT colonography (CTC) is the detection of "difficult" polyps which radiologists are likely to miss. Our purpose was to develop massive-training artificial neural networks (MTANNs) for improving the performance of a CAD scheme on false-negative cases in a large multicenter clinical trial. We developed 3D MTANNs designed to differentiate between polyps and several types of non- polyps and tested on 14 polyps/masses that were actually "missed" by radiologists in the trial. Our initial CAD scheme detected 71.4% of "missed" polyps with 18.9 false positives (FPs) per case. The MTANNs removed 75% of the FPs without loss of any true positives; thus, the performance of our CAD scheme was improved to 4.8 FPs per case at the sensitivity of 71.4% of the polyps "missed" by radiologists.
{"title":"Massive-training artificial neural networks for CAD for detection of polyps in CT colonography: False-negative cases in a large multicenter clinical trial","authors":"Kenji Suzuki, Mark L. Epstein, Ivan Sheu, R. Kohlbrenner, D. Rockey, A. Dachman","doi":"10.1109/ISBI.2008.4541088","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541088","url":null,"abstract":"A major challenge in computer-aided detection (CAD) of polyps in CT colonography (CTC) is the detection of \"difficult\" polyps which radiologists are likely to miss. Our purpose was to develop massive-training artificial neural networks (MTANNs) for improving the performance of a CAD scheme on false-negative cases in a large multicenter clinical trial. We developed 3D MTANNs designed to differentiate between polyps and several types of non- polyps and tested on 14 polyps/masses that were actually \"missed\" by radiologists in the trial. Our initial CAD scheme detected 71.4% of \"missed\" polyps with 18.9 false positives (FPs) per case. The MTANNs removed 75% of the FPs without loss of any true positives; thus, the performance of our CAD scheme was improved to 4.8 FPs per case at the sensitivity of 71.4% of the polyps \"missed\" by radiologists.","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":"122071143","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.2008.4540948
Szu-Hao Huang, S. Lai, C. Novak
Automatically extracting vertebra regions from a spinal magnetic resonance image is normally required as the first step to an intelligent spinal MR image diagnosis system. In this work, we develop a fully automatic vertebra detection and segmentation method. Our system consists of three stages; namely, AdaBoost-based vertebra detection, detection refinement via robust curve fitting, and vertebra segmentation by an iterative normalized cut algorithm. We proposed an efficient and effective vertebra detector, which is trained by the improved AdaBoost algorithm, to locate the initial vertebra positions. Then, a robust estimation procedure is applied to fit all the vertebrae as a polynomial spinal curve to refine the vertebra detection results. Finally, an iterative segmentation algorithm based on normalized-cut energy minimization is applied to extract the precise vertebra regions from the detected windows. The experimental results show our system can achieve high accuracy on a number of testing 3D spinal MRI data sets.
{"title":"A statistical learning appproach to vertebra detection and segmentation from spinal MRI","authors":"Szu-Hao Huang, S. Lai, C. Novak","doi":"10.1109/ISBI.2008.4540948","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4540948","url":null,"abstract":"Automatically extracting vertebra regions from a spinal magnetic resonance image is normally required as the first step to an intelligent spinal MR image diagnosis system. In this work, we develop a fully automatic vertebra detection and segmentation method. Our system consists of three stages; namely, AdaBoost-based vertebra detection, detection refinement via robust curve fitting, and vertebra segmentation by an iterative normalized cut algorithm. We proposed an efficient and effective vertebra detector, which is trained by the improved AdaBoost algorithm, to locate the initial vertebra positions. Then, a robust estimation procedure is applied to fit all the vertebrae as a polynomial spinal curve to refine the vertebra detection results. Finally, an iterative segmentation algorithm based on normalized-cut energy minimization is applied to extract the precise vertebra regions from the detected windows. The experimental results show our system can achieve high accuracy on a number of testing 3D spinal MRI data sets.","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":"124476692","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.2008.4541208
James V. Miller, Girish Gopalakrishnan, M. Datar, Paulo R. S. Mendonça, R. Mullick
Intra-subject deformable registration applications, such as longitudinal analysis and multi-modal imaging, use a high degree freedom deformation to accurately align soft tissue. However, smoothness constraints applied to the deformation and insufficient degrees of freedom in the deformation may distort the more rigid tissue types such as bone. In this paper, we present a technique that aligns rigid structures using rigid constraints while aligning soft tissue with a high degree of freedom deformation.
{"title":"Deformable registration with spatially varying degrees of freedom constraints","authors":"James V. Miller, Girish Gopalakrishnan, M. Datar, Paulo R. S. Mendonça, R. Mullick","doi":"10.1109/ISBI.2008.4541208","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541208","url":null,"abstract":"Intra-subject deformable registration applications, such as longitudinal analysis and multi-modal imaging, use a high degree freedom deformation to accurately align soft tissue. However, smoothness constraints applied to the deformation and insufficient degrees of freedom in the deformation may distort the more rigid tissue types such as bone. In this paper, we present a technique that aligns rigid structures using rigid constraints while aligning soft tissue with a high degree of freedom deformation.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"16 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":"125786820","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.2008.4541124
K. Mosaliganti, R. Machiraju, Kun Huang
There are natural geometric patterns in biology. Tissue layers, for example, differ mainly in the spatial distributions, size and packing of microstructure components such as the red blood cells, nuclei and cytoplasm etc. Expressive visualization by using the N-point correlation functions, involves the discovery of feature spaces that estimate and spatially delineate component distributions unique to a salient tissue. These functions provide feature spaces that are used to set useful transfer functions. We obtain insightful 3D visualizations of the epithelial cell lining in mouse mammary ducts and evolving structures in a zebrafish embryo. These are large datasets acquired from light and confocal microscopy scanners respectively.
{"title":"Geometry-driven visualization of microscopic structures in biology","authors":"K. Mosaliganti, R. Machiraju, Kun Huang","doi":"10.1109/ISBI.2008.4541124","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541124","url":null,"abstract":"There are natural geometric patterns in biology. Tissue layers, for example, differ mainly in the spatial distributions, size and packing of microstructure components such as the red blood cells, nuclei and cytoplasm etc. Expressive visualization by using the N-point correlation functions, involves the discovery of feature spaces that estimate and spatially delineate component distributions unique to a salient tissue. These functions provide feature spaces that are used to set useful transfer functions. We obtain insightful 3D visualizations of the epithelial cell lining in mouse mammary ducts and evolving structures in a zebrafish embryo. These are large datasets acquired from light and confocal microscopy scanners respectively.","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":"125889356","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.2008.4541290
A. George, M. Butala, R. Frazin, F. Kamalabadi, Y. Bresler
We propose an algorithm to solve the problem of Time-Resolved Cardiac Computed Tomography (CT). The algorithm reconstructs a snapshot of the moving heart at any time instant from CT projection data acquired over a single heart-cycle. The object is modeled by a spatio-temporal state-space model, and an ensemble Kalman Filter (a Monte-Carlo approximation to the Kalman filter) is used to assimilate the sequentially acquired projection data. Simulation results of the dynamic NCAT cardiac phantom, under the fan-beam geometry and a two-source CT system, show reconstructions that are free of the motion artifacts that mar conventional methods.
{"title":"Time-resolved cardiac CT reconstruction using the ensemble Kalman Filter","authors":"A. George, M. Butala, R. Frazin, F. Kamalabadi, Y. Bresler","doi":"10.1109/ISBI.2008.4541290","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541290","url":null,"abstract":"We propose an algorithm to solve the problem of Time-Resolved Cardiac Computed Tomography (CT). The algorithm reconstructs a snapshot of the moving heart at any time instant from CT projection data acquired over a single heart-cycle. The object is modeled by a spatio-temporal state-space model, and an ensemble Kalman Filter (a Monte-Carlo approximation to the Kalman filter) is used to assimilate the sequentially acquired projection data. Simulation results of the dynamic NCAT cardiac phantom, under the fan-beam geometry and a two-source CT system, show reconstructions that are free of the motion artifacts that mar conventional methods.","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":"129683400","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.2008.4541074
Thomas D. Capricelli
When studying problems such as tomography with bounded noise or IMRT, we need to solve systems with many linear inequality constraints. Projection-based algorithms are often used to solve this kind of problem. We see how previous work for accelerating the convergence of linear algorithms can be recast within the most recent generic framework, and show that it gives better results in specific cases. The proposed algorithm allows general convex constraints as well and the conditions for convergence are less restrictive than tradition- nal algorithms. We provide numerical results carried out in the context of tomography and IMRT.
{"title":"A fast parallel method for medical imaging problems including linear inequality constraints","authors":"Thomas D. Capricelli","doi":"10.1109/ISBI.2008.4541074","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541074","url":null,"abstract":"When studying problems such as tomography with bounded noise or IMRT, we need to solve systems with many linear inequality constraints. Projection-based algorithms are often used to solve this kind of problem. We see how previous work for accelerating the convergence of linear algorithms can be recast within the most recent generic framework, and show that it gives better results in specific cases. The proposed algorithm allows general convex constraints as well and the conditions for convergence are less restrictive than tradition- nal algorithms. We provide numerical results carried out in the context of tomography and IMRT.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"7 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":"128576887","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.2008.4541314
Yafang Cheng, I. Yetik
PET is an imaging modality widely used in areas such as oncology, neurology, cardiology and neuropsychology/cognitive neuro-science. Dynamic PET, in contrast to static PET, can identify temporal variations in the radiotracer concentration. Mathematical modeling of the tissue of interest in dynamic PET can be simplified using compartment models as a linear system where the time activity curve of a specific tissue is the convolution of the tracer concentration in the plasma and the impulse response of the tissue containing kinetic parameters. Since the arterial sampling of blood to acquire the value of the tracer concentration is invasive, blind identification to estimate both the blood input function and the kinetic parameters has recently drawn attention. Several methods have been developed for this purpose, but the effect of the estimated blood on the estimation of the kinetic parameters is not studied. In this paper, we present a mathematical model to compute the error in the kinetic parameter estimates caused by the error in estimation of the blood input function. Computer simulations show that analytical expressions we derive are sufficiently close to results obtained from optimization. Our findings are conceptually important to observe the effect of the blood function on kinetic parameter estimation, but also practically useful to evaluate various blind methods.
{"title":"Effect of the blood function error on the estimated kinetic parameters with dynamic pet","authors":"Yafang Cheng, I. Yetik","doi":"10.1109/ISBI.2008.4541314","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541314","url":null,"abstract":"PET is an imaging modality widely used in areas such as oncology, neurology, cardiology and neuropsychology/cognitive neuro-science. Dynamic PET, in contrast to static PET, can identify temporal variations in the radiotracer concentration. Mathematical modeling of the tissue of interest in dynamic PET can be simplified using compartment models as a linear system where the time activity curve of a specific tissue is the convolution of the tracer concentration in the plasma and the impulse response of the tissue containing kinetic parameters. Since the arterial sampling of blood to acquire the value of the tracer concentration is invasive, blind identification to estimate both the blood input function and the kinetic parameters has recently drawn attention. Several methods have been developed for this purpose, but the effect of the estimated blood on the estimation of the kinetic parameters is not studied. In this paper, we present a mathematical model to compute the error in the kinetic parameter estimates caused by the error in estimation of the blood input function. Computer simulations show that analytical expressions we derive are sufficiently close to results obtained from optimization. Our findings are conceptually important to observe the effect of the blood function on kinetic parameter estimation, but also practically useful to evaluate various blind methods.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"15 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":"128160341","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.2008.4541317
Damon E. Hyde, E. Miller, D. Brooks, V. Ntziachristos
We examine approaches to the incorporation of anatomic structural information into the inverse problem of fluorescence molecular tomography (FMT). Using an appropriate relationship between anatomic and reconstruction image resolution, we build an inverse problem parameterized along the anatomical segmentation. These values serve as the basis for two new regularization techniques. The first regularizes individual voxels in proportion to the importance of the underlying segments in reducing the residual error. The second is based on a well known statistical interpretation of Tikhonov-type regularization in which the statistical prior is defined implicitly as the solution to a PDE whose structure is based on the anatomical segmentation. Results are shown using both techniques for a simulated experiment within the chest cavity of a mouse.
{"title":"New techniques for data fusion in multimodal FMT-CT imaging","authors":"Damon E. Hyde, E. Miller, D. Brooks, V. Ntziachristos","doi":"10.1109/ISBI.2008.4541317","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541317","url":null,"abstract":"We examine approaches to the incorporation of anatomic structural information into the inverse problem of fluorescence molecular tomography (FMT). Using an appropriate relationship between anatomic and reconstruction image resolution, we build an inverse problem parameterized along the anatomical segmentation. These values serve as the basis for two new regularization techniques. The first regularizes individual voxels in proportion to the importance of the underlying segments in reducing the residual error. The second is based on a well known statistical interpretation of Tikhonov-type regularization in which the statistical prior is defined implicitly as the solution to a PDE whose structure is based on the anatomical segmentation. Results are shown using both techniques for a simulated experiment within the chest cavity of a mouse.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"2 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":"128276517","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.2008.4541205
A. El-Baz, G. Gimel'farb, R. Falk, M. El-Ghar, H. Refaie
Our long term research goal is to develop a fully automated, image- based diagnostic system for early diagnosis of pulmonary nodules that may lead to lung cancer. This paper focuses on monitoring the development of lung nodules detected in successive chest low dose (LD) CT scans of a patient. We propose a new methodology for 3D LDCT data registration which is non-rigid and involves two steps: (i) global alignment of one scan (target) to another scan (reference or prototype) using the learned prior appearance model followed by (ii) local alignment in order to correct for intricate deformations. After equalizing signals for two subsequent chest scans, visual appearance of these chest images is modeled with a Markov-Gibbs random field with pairwise interaction. We estimate the affine transformation that globally register the target to the prototype by gradient descent maximization of a special Gibbs energy function. To handle local deformations, we deform each voxel of the target over evolving closed equi-spaced surfaces (iso-surfaces) to closely match the prototype. The evolution of the iso-surfaces is guided by an exponential speed function in the directions that minimize distances between the corresponding voxel pairs on the iso-surfaces in both the data sets. Preliminary results on the 135 LDCT data sets from 27 patients show that our proper registration could lead to precise diagnosis and identification of the development of the detected pulmonary nodules.
{"title":"Promising results for early diagnosis of lung cancer","authors":"A. El-Baz, G. Gimel'farb, R. Falk, M. El-Ghar, H. Refaie","doi":"10.1109/ISBI.2008.4541205","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541205","url":null,"abstract":"Our long term research goal is to develop a fully automated, image- based diagnostic system for early diagnosis of pulmonary nodules that may lead to lung cancer. This paper focuses on monitoring the development of lung nodules detected in successive chest low dose (LD) CT scans of a patient. We propose a new methodology for 3D LDCT data registration which is non-rigid and involves two steps: (i) global alignment of one scan (target) to another scan (reference or prototype) using the learned prior appearance model followed by (ii) local alignment in order to correct for intricate deformations. After equalizing signals for two subsequent chest scans, visual appearance of these chest images is modeled with a Markov-Gibbs random field with pairwise interaction. We estimate the affine transformation that globally register the target to the prototype by gradient descent maximization of a special Gibbs energy function. To handle local deformations, we deform each voxel of the target over evolving closed equi-spaced surfaces (iso-surfaces) to closely match the prototype. The evolution of the iso-surfaces is guided by an exponential speed function in the directions that minimize distances between the corresponding voxel pairs on the iso-surfaces in both the data sets. Preliminary results on the 135 LDCT data sets from 27 patients show that our proper registration could lead to precise diagnosis and identification of the development of the detected pulmonary nodules.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"32 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":"129535189","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.2008.4541058
Q. Tieng, V. Vegh, G. Cowin, Zhengyi Yang
SMACKER is a method of calculating sensitivity maps from k-space reconstruction coefficients using only a few lines of inner k-space. In this method the problem of sensitivities ending at object boundaries is eliminated, unlike in other established methods. The method allows for the rapid calculation of sensitivity profiles from images, and it is proposed here that the approach can be used in functional MRI to obtain reconstructed images in little time. Functional MRI relying on fast parallel reconstruction techniques naturally lends itself to a method that can generate and use sensitivity maps directly from images.
{"title":"Fast parallel image reconstruction using smacker for functional magnetic resonance imaging","authors":"Q. Tieng, V. Vegh, G. Cowin, Zhengyi Yang","doi":"10.1109/ISBI.2008.4541058","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541058","url":null,"abstract":"SMACKER is a method of calculating sensitivity maps from k-space reconstruction coefficients using only a few lines of inner k-space. In this method the problem of sensitivities ending at object boundaries is eliminated, unlike in other established methods. The method allows for the rapid calculation of sensitivity profiles from images, and it is proposed here that the approach can be used in functional MRI to obtain reconstructed images in little time. Functional MRI relying on fast parallel reconstruction techniques naturally lends itself to a method that can generate and use sensitivity maps directly from images.","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":"129624947","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：