scholarly journals A Bayesian approach to tissue-fraction estimation for oncological PET segmentation

Author(s):  
Ziping Liu ◽  
Joyce C Mhlanga ◽  
Richard Laforest ◽  
Paul-Robert Derenoncourt ◽  
Barry A Siegel ◽  
...  
2001 ◽  
Vol 40 (05) ◽  
pp. 164-171 ◽  
Author(s):  
B. Nowak ◽  
H.-J. Kaiser ◽  
S. Block ◽  
K.-C. Koch ◽  
J. vom Dahl ◽  
...  

Summary Aim: In the present study a new approach has been developed for comparative quantification of absolute myocardial blood flow (MBF), myocardial perfusion, and myocardial metabolism in short-axis slices. Methods: 42 patients with severe CAD, referred for myocardial viability diagnostics, were studied consecutively with 0-15-H2O PET (H2O-PET) (twice), Tc-99m-Tetrofosmin 5PECT (TT-SPECT) and F-18-FDG PET (FDG-PET). All dato sets were reconstructed using attenuation correction and reoriented into short axis slices. Each heart was divided into three representative slices (base, rnidventricular, apex) and 18 ROIs were defined on the FDG PET images and transferred to the corresponding H2O-PET and TT-SPECT slices. TT-SPECT and FDG-PET data were normalized to the ROI showing maximum perfusion. MBF was calculated for all left-ventricular ROIs using a single-compartment-model fitting the dynamic H2O-PET studies. Microsphere equivalent MBF (MBF_micr) was calculated by multiplying MBF and tissue-fraction, a parameter which was obtained by fitting the dynamic H2O-PET studies. To reduce influence of viability only well perfused areas (>70% TT-SPECT) were used for comparative quantification. Results: First and second mean global MBF values were 0.85 ml × min-1 × g-1 and 0.84 ml × min-1 × g1, respectively, with a repeatability coefficient of 0.30 ml ÷ min-1 × gl. After sectorization mean MBF_micr was between 0.58 ml × min1 ÷ ml"1 and 0.68 ml × min-1 × ml"1 in well perfused areas. Corresponding TT-SPECT values ranged from 83 % to 91 %, and FDG-PET values from 91 % to 103%. All procedures yielded higher values for the lateral than the septal regions. Conclusion: Comparative quantification of MBF, MBF_micr, TT-SPECT perfusion and FDG-PET metabolism can be done with the introduced method in short axis slices. The obtained values agree well with experimentally validated values of MBF and MBF_micr.


2020 ◽  
Author(s):  
Laetitia Zmuda ◽  
Charlotte Baey ◽  
Paolo Mairano ◽  
Anahita Basirat

It is well-known that individuals can identify novel words in a stream of an artificial language using statistical dependencies. While underlying computations are thought to be similar from one stream to another (e.g. transitional probabilities between syllables), performance are not similar. According to the “linguistic entrenchment” hypothesis, this would be due to the fact that individuals have some prior knowledge regarding co-occurrences of elements in speech which intervene during verbal statistical learning. The focus of previous studies was on task performance. The goal of the current study is to examine the extent to which prior knowledge impacts metacognition (i.e. ability to evaluate one’s own cognitive processes). Participants were exposed to two different artificial languages. Using a fully Bayesian approach, we estimated an unbiased measure of metacognitive efficiency and compared the two languages in terms of task performance and metacognition. While task performance was higher in one of the languages, the metacognitive efficiency was similar in both languages. In addition, a model assuming no correlation between the two languages better accounted for our results compared to a model where correlations were introduced. We discuss the implications of our findings regarding the computations which underlie the interaction between input and prior knowledge during verbal statistical learning.


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