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A method for comparing intra-tumoural radioactivity uptake heterogeneity in preclinical positron emission tomography studies.

Grafström J, Ahlzén HS, Stone-Elander S - EJNMMI Phys (2015)

Bottom Line: Histograms visualising the spread and frequency of contributions to the heterogeneity were also generated.Analyses of the distribution of different tracers gave results that were generally in accordance with single plane preclinical images, indicating that it could appropriately handle comparisons of targeting vs. non-targeting tracers and also for different target levels.Altering the reconstruction algorithm, pixel size, tumour ROI volumes and lower cut-off limits affected the calculated heterogeneity factors in expected directions but did not reverse conclusions about which tumour was more or less heterogeneous.

View Article: PubMed Central - PubMed

Affiliation: Division of Biochemistry, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, SE-17177, Stockholm, Sweden. hannastina@gmail.com.

ABSTRACT

Background: Non-uniformity influences the interpretation of nuclear medicine based images and consequently their use in treatment planning and monitoring. However, no standardised method for evaluating and ranking heterogeneity exists. Here, we have developed a general algorithm that provides a ranking and a visualisation of the heterogeneity in small animal positron emission tomography (PET) images.

Methods: The code of the algorithm was written using the Matrix Laboratory software (MATLAB). Parameters known to influence the heterogeneity (distances between deviating peaks, gradients and size compensations) were incorporated into the algorithm. All data matrices were mathematically constructed in the same format with the aim of maintaining overview and control. Histograms visualising the spread and frequency of contributions to the heterogeneity were also generated. The construction of the algorithm was tested using mathematically generated matrices and by varying post-processing parameters. It was subsequently applied in comparisons of radiotracer uptake in preclinical images in human head and neck carcinoma and endothelial and ovarian carcinoma xenografts.

Results: Using the developed algorithm, entire tissue volumes could be assessed and gradients could be handled in an indirect manner. Similar-sized volumes could be compared without modifying the algorithm. Analyses of the distribution of different tracers gave results that were generally in accordance with single plane preclinical images, indicating that it could appropriately handle comparisons of targeting vs. non-targeting tracers and also for different target levels. Altering the reconstruction algorithm, pixel size, tumour ROI volumes and lower cut-off limits affected the calculated heterogeneity factors in expected directions but did not reverse conclusions about which tumour was more or less heterogeneous.

Conclusions: The algorithm constructed is an objective and potentially user-friendly tool for one-to-one comparisons of heterogeneity in whole similar-sized tumour volumes in PET imaging.

No MeSH data available.


Related in: MedlinePlus

Visualising the results of the application of the algorithm. The heterogeneity contribution for one plane (k) would be calculated as
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Fig2: Visualising the results of the application of the algorithm. The heterogeneity contribution for one plane (k) would be calculated as

Mentions: Each pair of intensity deviations is now recalculated as a mean (Eq. 2) and the 2-D Euclidean distance between every intensity deviation in the matrix created in the previous step is calculated (Fig. 2). When the distances (dpq) are less than the resolution of the camera at CFOV (1.2 mm) or larger than the minimum tumour dimension (here 4 mm), those paired intensity deviations are omitted. Omitting distances that are too small also avoids including voxels that might be of the same peak. Thus, a two-column matrix is formed in which the first column contains the distances and the second column contains the means of the two actual intensity deviations from the associated mean.Fig. 2


A method for comparing intra-tumoural radioactivity uptake heterogeneity in preclinical positron emission tomography studies.

Grafström J, Ahlzén HS, Stone-Elander S - EJNMMI Phys (2015)

Visualising the results of the application of the algorithm. The heterogeneity contribution for one plane (k) would be calculated as
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4562910&req=5

Fig2: Visualising the results of the application of the algorithm. The heterogeneity contribution for one plane (k) would be calculated as
Mentions: Each pair of intensity deviations is now recalculated as a mean (Eq. 2) and the 2-D Euclidean distance between every intensity deviation in the matrix created in the previous step is calculated (Fig. 2). When the distances (dpq) are less than the resolution of the camera at CFOV (1.2 mm) or larger than the minimum tumour dimension (here 4 mm), those paired intensity deviations are omitted. Omitting distances that are too small also avoids including voxels that might be of the same peak. Thus, a two-column matrix is formed in which the first column contains the distances and the second column contains the means of the two actual intensity deviations from the associated mean.Fig. 2

Bottom Line: Histograms visualising the spread and frequency of contributions to the heterogeneity were also generated.Analyses of the distribution of different tracers gave results that were generally in accordance with single plane preclinical images, indicating that it could appropriately handle comparisons of targeting vs. non-targeting tracers and also for different target levels.Altering the reconstruction algorithm, pixel size, tumour ROI volumes and lower cut-off limits affected the calculated heterogeneity factors in expected directions but did not reverse conclusions about which tumour was more or less heterogeneous.

View Article: PubMed Central - PubMed

Affiliation: Division of Biochemistry, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, SE-17177, Stockholm, Sweden. hannastina@gmail.com.

ABSTRACT

Background: Non-uniformity influences the interpretation of nuclear medicine based images and consequently their use in treatment planning and monitoring. However, no standardised method for evaluating and ranking heterogeneity exists. Here, we have developed a general algorithm that provides a ranking and a visualisation of the heterogeneity in small animal positron emission tomography (PET) images.

Methods: The code of the algorithm was written using the Matrix Laboratory software (MATLAB). Parameters known to influence the heterogeneity (distances between deviating peaks, gradients and size compensations) were incorporated into the algorithm. All data matrices were mathematically constructed in the same format with the aim of maintaining overview and control. Histograms visualising the spread and frequency of contributions to the heterogeneity were also generated. The construction of the algorithm was tested using mathematically generated matrices and by varying post-processing parameters. It was subsequently applied in comparisons of radiotracer uptake in preclinical images in human head and neck carcinoma and endothelial and ovarian carcinoma xenografts.

Results: Using the developed algorithm, entire tissue volumes could be assessed and gradients could be handled in an indirect manner. Similar-sized volumes could be compared without modifying the algorithm. Analyses of the distribution of different tracers gave results that were generally in accordance with single plane preclinical images, indicating that it could appropriately handle comparisons of targeting vs. non-targeting tracers and also for different target levels. Altering the reconstruction algorithm, pixel size, tumour ROI volumes and lower cut-off limits affected the calculated heterogeneity factors in expected directions but did not reverse conclusions about which tumour was more or less heterogeneous.

Conclusions: The algorithm constructed is an objective and potentially user-friendly tool for one-to-one comparisons of heterogeneity in whole similar-sized tumour volumes in PET imaging.

No MeSH data available.


Related in: MedlinePlus