Improved Signal-to-Noise Ratio in Parametric Images by Cluster Analysis

Kimura, Yuichi; Hsu, Hongbing; Toyama, Hinako; Senda, Michio; Alpert, Nathaniel M.

doi:10.1006/nimg.1999.0430

Cited by 62 publications

(47 citation statements)

References 8 publications

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“…A spatial map is simpler to interpret when compared to the original multidimensional data. However, similar to O'Sullivan's approach [15], our method is data driven and is independent of the properties of tracer that may be required by other methods [17]. Thus, our approach can be applied to a wide range of tracer studies.…”

Section: Discussionmentioning

confidence: 98%

“…One of the major aims of cluster analysis is to partition a large number of objects according to certain criteria into a smaller number of clusters that are mutually exclusive and exhaustive such that the objects within a cluster are similar to each other while objects drawn from different clusters are dissimilar [12]. Cluster analysis has also been applied to neuroimaging to help analyze data obtained from functional MRI (FMRI) [13], [14] and PET [15]- [17]. An example was given by O'Sullivan [15] who demonstrated the use of cluster analysis to partition a set of tissue TACs into a set of homogeneous TACs and it was found that a convex linear combination of those homogeneous TACs using a mixture model can accurately represent the original data while providing quantitative parametric images.…”

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confidence: 99%

“…An example was given by O'Sullivan [15] who demonstrated the use of cluster analysis to partition a set of tissue TACs into a set of homogeneous TACs and it was found that a convex linear combination of those homogeneous TACs using a mixture model can accurately represent the original data while providing quantitative parametric images. It was also demonstrated that an improved SNR can be achieved in applying cluster analysis to dynamic PET data [16], [17]. This study is motivated by our on-going work on a noninvasive modeling approach for quantification of [ F]fluorodeoxyglucose (FDG) PET studies where several ROIs of distinct kinetics are required [18].…”

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confidence: 99%

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Segmentation of dynamic PET images using cluster analysis

Wong

Feng

Meikle

et al. 2002

IEEE Trans. Nucl. Sci.

129

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Abstract-Quantitative positron emission tomography (PET) studies provide in vivo measurements of dynamic physiological and biochemical processes in humans. A limitation of PET is an inability to provide precise anatomic localization due to relatively poor spatial resolution when compared to magnetic resonance (MR) imaging. Manual placement of region-of-interest (ROI) is commonly used in clinical and research settings in analysis of PET datasets. However, this approach is operator dependent and time-consuming. A semi-or fully-automated ROI delineation (or segmentation) method offers advantages by reducing operator error/subjectivity and thereby improving reproducibility. In this work, we describe an approach to automatically segment dynamic PET images using cluster analysis and we validate our approach with a simulated phantom study and assess its performance with real dynamic PET data. Our preliminary results suggest that cluster analysis can automatically segment tissues in dynamic PET studies and has the potential to replace manual ROI delineation for some applications.Index Terms-Cluster analysis, functional imaging, positron emission tomography (PET), segmentation.

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Section: Discussionmentioning

confidence: 98%

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confidence: 99%

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confidence: 99%

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Segmentation of dynamic PET images using cluster analysis

Wong

Feng

Meikle

et al. 2002

IEEE Trans. Nucl. Sci.

129

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“…Despite the more general and realistic assumptions the new method is not significantly more complicated than more conventional analyses. Future work will investigate the feasibility of forming high quality parametric images of MBF with the MGH method using principal component clustering 33,34 and our previously developed 3D orthogonal grouping of voxels with similar time course. 16 Finally, double gating/dynamic imaging is an interesting research direction that is not presently performed routinely.…”

Section: Discussionmentioning

confidence: 99%

Single‐scan rest/stress imaging ¹⁸F‐labeled flow tracers

2012

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Purpose:The authors report a novel measurement strategy to obtain both rest and stress blood flow during a single, relatively short, scan session. Methods: Measurement of rest-stress myocardial blood flow with long-lived tracers usually requires separate scan sessions to remove the confounding effects of residual radioactivity concentration in the blood and tissue. The innovation of this method is to treat the rest-stress scan as a single entity in which the flow parameters change due to pharmacological challenge. With this approach the fate of a tracer molecule is naturally accounted for, no matter if it was introduced during the rest or stress phase of the study. Two new dual-injection kinetic models are considered that represent the response to pharmacological stress as a transitional or transient increase of myocardial blood flow. The authors present the theory of the method followed by the specific application of the theory to 18 F-Flurpiridaz, a new myocardial flow-imaging agent. Results: Myocardial blood flow was accurately and precisely estimated from a single-scan rest/stress study for the long half-lived tracer 18 F-Flurpiridaz. By accounting for the time-dependence of the kinetic parameters, the proposed models achieved good accuracy and precision (5%) under different vasodilators and different ischemic states. Conclusions: Detailed simulations predict that accurate and precise rest-stress blood flow measurements can be obtained in 20-30 min.

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“…In medical imaging, clustering has been used to segment MR and PET images [6][7][8], and to generate parametric images [9][10][11][12], where parameter estimation is integrated with cluster analysis and estimates associated with the cluster centroid curve proportionally represent all voxels belonging to the cluster. The "hard" clustering techniques, such as K-mean [11] and hierarchical clustering [12], assign instances to one specific cluster during iterative analysis.…”

Section: Introductionmentioning

confidence: 99%

Adaptive fuzzy clustering in constructing parametric images for low SNR functional imaging

Wen

Eberl

Fulham

et al. 2008

2008 IEEE 10th Workshop on Multimedia Signal Processing

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Abstract-Functional imaging can provide quantitative functional parameters to aid early diagnosis. Low signal to noise ratio (SNR) in functional imaging, especially for single photon emission computed tomography, poses a challenge in generating voxel-wise parametric images due to unreliable or physiologically meaningless parameter estimates. Our aim was to systematically investigate the performance of our recently proposed adaptive fuzzy clustering (AFC) technique, which applies standard fuzzy clustering to sub-divided data. Monte Carlo simulations were performed to generate noisy dynamic SPECT data with quantitative analysis for the fitting using the general linear least square method (GLLS) and enhanced model-aided GLLS methods. The results show that AFC substantially improves computational efficiency and obtains improved reliability as standard fuzzy clustering in estimating parametric images but is prone to slight underestimation. Normalization of tissue time activity curves may lead to severe overestimation for small structures when AFC is applied.

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Improved Signal-to-Noise Ratio in Parametric Images by Cluster Analysis

Cited by 62 publications

References 8 publications

Segmentation of dynamic PET images using cluster analysis

Segmentation of dynamic PET images using cluster analysis

Single‐scan rest/stress imaging ¹⁸F‐labeled flow tracers

Adaptive fuzzy clustering in constructing parametric images for low SNR functional imaging

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Improved Signal-to-Noise Ratio in Parametric Images by Cluster Analysis

Cited by 62 publications

References 8 publications

Segmentation of dynamic PET images using cluster analysis

Segmentation of dynamic PET images using cluster analysis

Single‐scan rest/stress imaging 18F‐labeled flow tracers

Adaptive fuzzy clustering in constructing parametric images for low SNR functional imaging

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Single‐scan rest/stress imaging ¹⁸F‐labeled flow tracers