Application of correlated component analysis to dynamic PET time-activity curves denoising

2021 ◽  
Author(s):  
Paulus Kapundja Shigwedha ◽  
Takahiro Yamada ◽  
Kohei Hanaoka ◽  
Kazunari Ishii ◽  
Yuichi Kimura ◽  
...  
Keyword(s):  
Component Analysis ◽  
Dynamic Pet ◽  
Time Activity

scholarly journals Voxelwise Principal Component Analysis of Dynamic [S-Methyl-11C]Methionine PET Data in Glioma Patients

Cancers ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 2342
Author(s):  
Corentin Martens ◽  
Olivier Debeir ◽  
Christine Decaestecker ◽  
Thierry Metens ◽  
Laetitia Lebrun ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Added Value ◽  
Time Activity ◽  
Positron Emission ◽  
Activity Curve ◽  
The Mean ◽  
Mean Time ◽  
Met Pet

Recent works have demonstrated the added value of dynamic amino acid positron emission tomography (PET) for glioma grading and genotyping, biopsy targeting, and recurrence diagnosis. However, most of these studies are based on hand-crafted qualitative or semi-quantitative features extracted from the mean time activity curve within predefined volumes. Voxelwise dynamic PET data analysis could instead provide a better insight into intra-tumor heterogeneity of gliomas. In this work, we investigate the ability of principal component analysis (PCA) to extract relevant quantitative features from a large number of motion-corrected [S-methyl-11C]methionine ([11C]MET) PET frames. We first demonstrate the robustness of our methodology to noise by means of numerical simulations. We then build a PCA model from dynamic [11C]MET acquisitions of 20 glioma patients. In a distinct cohort of 13 glioma patients, we compare the parametric maps derived from our PCA model to these provided by the classical one-compartment pharmacokinetic model (1TCM). We show that our PCA model outperforms the 1TCM to distinguish characteristic dynamic uptake behaviors within the tumor while being less computationally expensive and not requiring arterial sampling. Such methodology could be valuable to assess the tumor aggressiveness locally with applications for treatment planning and response evaluation. This work further supports the added value of dynamic over static [11C]MET PET in gliomas.

scholarly journals EP-2029 Principal component analysis for quantitative and robust analysis of dynamic PET/MR imaging data

2019 ◽  
Vol 133 ◽  
pp. S1113-S1114
Author(s):  
R. Winter ◽  
S. Leibfarth ◽  
S. Boeke ◽  
P. Mena-Romano ◽  
M. Krueger ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Mr Imaging ◽  
Principal Component ◽  
Component Analysis ◽  
Dynamic Pet ◽  
Imaging Data ◽  
Robust Analysis

Spatiotemporal Clustering Method for Automatic Estimation of the FDG Input Function for Small-Animal PET

2010 ◽  
Vol 143-144 ◽  
pp. 358-363
Author(s):  
Li Sun ◽  
Yan Ning Zhang ◽  
Miao Ma ◽  
Guang Jian Tian
Keyword(s):  
Small Animal ◽  
Input Function ◽  
Small Animal Pet ◽  
Image Clustering ◽  
Dynamic Pet ◽  
Clustering Methods ◽  
Spatial And Temporal Patterns ◽  
Time Activity ◽  
Arterial Blood ◽  
Activity Curve

The plasma time-activity curve is often required as the input function for dynamic quantitative FDG PET studies to estimate the metabolic rate of glucose. The invasive gold standard arterial blood sampling has been suggested, however, it has many inconveniences and challenges in clinical and pre-clinical settings. Thus, the image-derived input function has been proposed to obtain the input function from dynamic images non-invasively. This method often needs a manual drawing of one or two regions of interest (ROIs), which is an operator-dependent and time-consuming task. The aim of the presented study was to capture the spatial and temporal patterns of dynamic PET images for automatic ROI extraction. Our proposed approach tries to overcome the main limitation of image clustering methods: the loss of temporal information for dynamic PET ROI definition. The experiments showed that the proposed automatic ROI method can be used for dynamic PET parameter estimation.

scholarly journals Quantification of 5-HT2A Receptors in the Human Brain Using [18F]Altanserin-PET and the Bolus/Infusion Approach

2003 ◽  
Vol 23 (8) ◽  
pp. 985-996 ◽  
Author(s):  
Lars H Pinborg ◽  
Karen H Adams ◽  
Claus Svarer ◽  
Søren Holm ◽  
Steen G Hasselbalch ◽  
...  
Keyword(s):  
Steady State ◽  
Specific Binding ◽  
Venous Blood ◽  
Normal Subjects ◽  
Rate Of Change ◽  
Dynamic Pet ◽  
Time Activity ◽  
Bolus Infusion ◽  
Positron Emission ◽  
Activity Curve

The aim of the present study is to describe and validate a method for accurate quantification of 5-hydroxytryptamine (5-HT)2A receptors using [18F]altanserin-positron emission tomography (PET) and the bolus/infusion approach. A bolus/infusion ratio of 1.75 h aimed at attaining rapid steady state in blood and brain was predicted from previous bolus studies performed in our laboratory. The infusion schedule was tested in normal subjects (n = 10) using dynamic PET and frequent plasma sampling for 6 h. Steady state was attained in brain and plasma within 2 h, and time–activity curves remained constant for another 3 h. To represent free and nonspecifically bound [18F]altanserin and its radiolabeled metabolites only, cerebellum must show no displacement in 5-HT2A displacement studies. To validate this, saturating doses of cold ketanserin were administered and it was found that specific binding of [18F]altanserin decreased uniformly to the level of the cerebellum and no change in the cerebellar time–activity curve was found after ketanserin administration. A shorter experimental setup was tested in a second group (n = 20) including patients with neuropsychiatric disorders. Dynamic PET (five frames of 8 minutes each) and venous blood sampling at midscan time started 2 h after [18F]altanserin administration. The mean percentage rate of change per hour in the outcome parameter, DV3′, was low (mean −0.3% h−1; range −7.3–7.2% h−1) and no correlation of DV3′ versus time was demonstrated. It is concluded that 5-HT2A receptor studies can be conducted within 2 h of [18F]altanserin infusion, yielding reliable results.

scholarly journals Characterization and Reduction of Noise in Dynamic PET Data Using Masked Volumewise Principal Component Analysis

2011 ◽  
Vol 39 (1) ◽  
pp. 27-34 ◽  
Author(s):  
P.-E. Svensson ◽  
J. Olsson ◽  
F. Engbrant ◽  
E. Bengtsson ◽  
P. Razifar
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Dynamic Pet

scholarly journals Propagation of Blood Function Errors to the Estimates of Kinetic Parameters with Dynamic PET

2011 ◽  
Vol 2011 ◽  
pp. 1-12
Author(s):  
Yafang Cheng ◽  
İmam Şamil Yetik
Keyword(s):  
Kinetic Parameter ◽  
Kinetic Parameters ◽  
Input Function ◽  
Temporal Variations ◽  
Parameter Estimates ◽  
Dynamic Pet ◽  
Tracer Concentration ◽  
Time Activity ◽  
Activity Curve ◽  
Specific Tissue

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 tracer concentration is invasive, blind methods to estimate both blood input function and kinetic parameters have recently drawn attention. Several methods have been developed, but the effect of accuracy of the estimated blood function on the estimation of the kinetic parameters is not studied. In this paper, we present a method to compute the error in the kinetic parameter estimates caused by the error in the blood input function. Computer simulations show that analytical expressions we derive are sufficiently close to results obtained from numerical methods. Our findings are important to observe the effect of the blood function on kinetic parameter estimation, but also useful to evaluate various blind methods and observe the dependence of kinetic parameter estimates to certain parts of the blood function.

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