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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

PET transaxial images (a, b, the colour scales are the same), histograms of the heterogeneity contributions (the mean intensity deviation per distance calculated according to Eq. 2) (c, d) and surface plots (e, f) of the uptake of [18F]FDG and 11C-labelled AnxA5 in a FaDu xenograft. The imaging was performed in the same animal >2 h apart on the same day. In e and f, the X- and Y-axes represent spatial dimensions and the Z-axis is the tracer uptake in SUVmean
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Fig5: PET transaxial images (a, b, the colour scales are the same), histograms of the heterogeneity contributions (the mean intensity deviation per distance calculated according to Eq. 2) (c, d) and surface plots (e, f) of the uptake of [18F]FDG and 11C-labelled AnxA5 in a FaDu xenograft. The imaging was performed in the same animal >2 h apart on the same day. In e and f, the X- and Y-axes represent spatial dimensions and the Z-axis is the tracer uptake in SUVmean

Mentions: The tumour images in Fig. 5a, b indicate that the uptakes of the two tracers have similar patterns but with some textural differences. The HF for [18F]FDG is about 25 % less than the HF for AnxA5. The histograms show more and a broader spectrum of deviations for AnxA5 (Fig. 5d) than for [18F]FDG (Fig. 5c). Differences in the uptakes of the two tracers are probably more pronounced in Fig. 5c, d since the data from the entire tumour volume is used instead of the two dimensions only in Fig. 5a, b, e, f.Fig. 5


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)

PET transaxial images (a, b, the colour scales are the same), histograms of the heterogeneity contributions (the mean intensity deviation per distance calculated according to Eq. 2) (c, d) and surface plots (e, f) of the uptake of [18F]FDG and 11C-labelled AnxA5 in a FaDu xenograft. The imaging was performed in the same animal >2 h apart on the same day. In e and f, the X- and Y-axes represent spatial dimensions and the Z-axis is the tracer uptake in SUVmean
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig5: PET transaxial images (a, b, the colour scales are the same), histograms of the heterogeneity contributions (the mean intensity deviation per distance calculated according to Eq. 2) (c, d) and surface plots (e, f) of the uptake of [18F]FDG and 11C-labelled AnxA5 in a FaDu xenograft. The imaging was performed in the same animal >2 h apart on the same day. In e and f, the X- and Y-axes represent spatial dimensions and the Z-axis is the tracer uptake in SUVmean
Mentions: The tumour images in Fig. 5a, b indicate that the uptakes of the two tracers have similar patterns but with some textural differences. The HF for [18F]FDG is about 25 % less than the HF for AnxA5. The histograms show more and a broader spectrum of deviations for AnxA5 (Fig. 5d) than for [18F]FDG (Fig. 5c). Differences in the uptakes of the two tracers are probably more pronounced in Fig. 5c, d since the data from the entire tumour volume is used instead of the two dimensions only in Fig. 5a, b, e, f.Fig. 5

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