Implementation of k-means clustering algorithm in 177Lu-dotate dosimetry calculations

Eero Tapio Hippeläinen, Vappu Reijonen, Sauli Savolainen

Research output: Contribution to journalConference articleScientific

Abstract

Aim: Single photon emission computed tomography (SPECT) enables quantitative in vivo measurements of radiopharmaceutical distributions, which are used for radionuclide therapy (RT) dosimetry calculations. Acquiring time activity curves (TAC) manually from a set of SPECT images appears operator-dependent
and time-consuming. Using semi- or fully-automated volume of interest (VOI) delineation methods the measurements can be performed more objectively and the reproducibility and the coherence of the dosimetry calculation process can be improved. In this study, we investigate the use of an unsupervised learning clustering algorithm built for TAC analysis and dosimetry calculations.

Materials and methods: A male patient received 7.4 GBq of 177Lu-dotatate
with kidney protective amino acids infusion. Four separate SPECT/CT acquisitions were performed at 1 h, 26 h, 3 d and 7 d post injection. Separate SPECT images were coregistered, filtered and masked with a simple threshold method to restrict
the analysis within the patient volume. A clustered composition of the images was created using a k-means clustering algorithm. The clustering was run using several different number of clusters (K) and the results were evaluated by visual assessment and calculating average mean-squared-error (MSE) across the
clusters. Average TACs were extracted from the cluster centroids.

Results: The average MSE decreases as a function of K, as expected. At K > 10, the MSE reaches a plateau and decreases slowly towards zero. Visually, homogeneous regions fragmented when using more than eight (K = 8) clusters. Interestingly, most of the hot spots in the patient’s liver and both kidneys were
separated from background even with three clusters. The next step is to calculate the absorbed doses corresponding to the different number of K –values and the different protocols used in the clinic.

Discussion and Conclusion: A simple K-means clustering algorithm provides an easy access to TAC of 177Lu-dotate SPECT studies. A moderate number of clusters is recommended to provide coarse TACs for different tissues. The clustered image (VOI-image) might also be useful for data comparison, when the patient goes through several separate treatment cycles. In order to plan the treatment and to assess the response advantageously, it is essential to perform the dosimetry calculations on a patient specific basis and it appears to us that the clustering algorithms may provide an effective tool to improve this process in the near future.
Original languageEnglish
JournalEuropean Journal of Nuclear Medicine and Molecular Imaging
Volume39
Issue numberSuppl 2
Pages (from-to)S311
Number of pages1
ISSN1619-7070
DOIs
Publication statusPublished - 2012
MoE publication typeB3 Article in conference proceedings
EventAnnual Congress of the EANM 2012 - , Italy
Duration: 1 Jan 1800 → …

Fields of Science

  • 114 Physical sciences

Cite this

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title = "Implementation of k-means clustering algorithm in 177Lu-dotate dosimetry calculations",
abstract = "Aim: Single photon emission computed tomography (SPECT) enables quantitative in vivo measurements of radiopharmaceutical distributions, which are used for radionuclide therapy (RT) dosimetry calculations. Acquiring time activity curves (TAC) manually from a set of SPECT images appears operator-dependent and time-consuming. Using semi- or fully-automated volume of interest (VOI) delineation methods the measurements can be performed more objectively and the reproducibility and the coherence of the dosimetry calculation process can be improved. In this study, we investigate the use of an unsupervised learning clustering algorithm built for TAC analysis and dosimetry calculations. Materials and methods: A male patient received 7.4 GBq of 177Lu-dotatate with kidney protective amino acids infusion. Four separate SPECT/CT acquisitions were performed at 1 h, 26 h, 3 d and 7 d post injection. Separate SPECT images were coregistered, filtered and masked with a simple threshold method to restrict the analysis within the patient volume. A clustered composition of the images was created using a k-means clustering algorithm. The clustering was run using several different number of clusters (K) and the results were evaluated by visual assessment and calculating average mean-squared-error (MSE) across the clusters. Average TACs were extracted from the cluster centroids. Results: The average MSE decreases as a function of K, as expected. At K > 10, the MSE reaches a plateau and decreases slowly towards zero. Visually, homogeneous regions fragmented when using more than eight (K = 8) clusters. Interestingly, most of the hot spots in the patient’s liver and both kidneys were separated from background even with three clusters. The next step is to calculate the absorbed doses corresponding to the different number of K –values and the different protocols used in the clinic. Discussion and Conclusion: A simple K-means clustering algorithm provides an easy access to TAC of 177Lu-dotate SPECT studies. A moderate number of clusters is recommended to provide coarse TACs for different tissues. The clustered image (VOI-image) might also be useful for data comparison, when the patient goes through several separate treatment cycles. In order to plan the treatment and to assess the response advantageously, it is essential to perform the dosimetry calculations on a patient specific basis and it appears to us that the clustering algorithms may provide an effective tool to improve this process in the near future.",
keywords = "114 Physical sciences",
author = "Hippel{\"a}inen, {Eero Tapio} and Vappu Reijonen and Sauli Savolainen",
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year = "2012",
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Implementation of k-means clustering algorithm in 177Lu-dotate dosimetry calculations. / Hippeläinen, Eero Tapio; Reijonen, Vappu; Savolainen, Sauli.

In: European Journal of Nuclear Medicine and Molecular Imaging, Vol. 39, No. Suppl 2, 2012, p. S311.

Research output: Contribution to journalConference articleScientific

TY - JOUR

T1 - Implementation of k-means clustering algorithm in 177Lu-dotate dosimetry calculations

AU - Hippeläinen, Eero Tapio

AU - Reijonen, Vappu

AU - Savolainen, Sauli

N1 - Volume: 39 Proceeding volume:

PY - 2012

Y1 - 2012

N2 - Aim: Single photon emission computed tomography (SPECT) enables quantitative in vivo measurements of radiopharmaceutical distributions, which are used for radionuclide therapy (RT) dosimetry calculations. Acquiring time activity curves (TAC) manually from a set of SPECT images appears operator-dependent and time-consuming. Using semi- or fully-automated volume of interest (VOI) delineation methods the measurements can be performed more objectively and the reproducibility and the coherence of the dosimetry calculation process can be improved. In this study, we investigate the use of an unsupervised learning clustering algorithm built for TAC analysis and dosimetry calculations. Materials and methods: A male patient received 7.4 GBq of 177Lu-dotatate with kidney protective amino acids infusion. Four separate SPECT/CT acquisitions were performed at 1 h, 26 h, 3 d and 7 d post injection. Separate SPECT images were coregistered, filtered and masked with a simple threshold method to restrict the analysis within the patient volume. A clustered composition of the images was created using a k-means clustering algorithm. The clustering was run using several different number of clusters (K) and the results were evaluated by visual assessment and calculating average mean-squared-error (MSE) across the clusters. Average TACs were extracted from the cluster centroids. Results: The average MSE decreases as a function of K, as expected. At K > 10, the MSE reaches a plateau and decreases slowly towards zero. Visually, homogeneous regions fragmented when using more than eight (K = 8) clusters. Interestingly, most of the hot spots in the patient’s liver and both kidneys were separated from background even with three clusters. The next step is to calculate the absorbed doses corresponding to the different number of K –values and the different protocols used in the clinic. Discussion and Conclusion: A simple K-means clustering algorithm provides an easy access to TAC of 177Lu-dotate SPECT studies. A moderate number of clusters is recommended to provide coarse TACs for different tissues. The clustered image (VOI-image) might also be useful for data comparison, when the patient goes through several separate treatment cycles. In order to plan the treatment and to assess the response advantageously, it is essential to perform the dosimetry calculations on a patient specific basis and it appears to us that the clustering algorithms may provide an effective tool to improve this process in the near future.

AB - Aim: Single photon emission computed tomography (SPECT) enables quantitative in vivo measurements of radiopharmaceutical distributions, which are used for radionuclide therapy (RT) dosimetry calculations. Acquiring time activity curves (TAC) manually from a set of SPECT images appears operator-dependent and time-consuming. Using semi- or fully-automated volume of interest (VOI) delineation methods the measurements can be performed more objectively and the reproducibility and the coherence of the dosimetry calculation process can be improved. In this study, we investigate the use of an unsupervised learning clustering algorithm built for TAC analysis and dosimetry calculations. Materials and methods: A male patient received 7.4 GBq of 177Lu-dotatate with kidney protective amino acids infusion. Four separate SPECT/CT acquisitions were performed at 1 h, 26 h, 3 d and 7 d post injection. Separate SPECT images were coregistered, filtered and masked with a simple threshold method to restrict the analysis within the patient volume. A clustered composition of the images was created using a k-means clustering algorithm. The clustering was run using several different number of clusters (K) and the results were evaluated by visual assessment and calculating average mean-squared-error (MSE) across the clusters. Average TACs were extracted from the cluster centroids. Results: The average MSE decreases as a function of K, as expected. At K > 10, the MSE reaches a plateau and decreases slowly towards zero. Visually, homogeneous regions fragmented when using more than eight (K = 8) clusters. Interestingly, most of the hot spots in the patient’s liver and both kidneys were separated from background even with three clusters. The next step is to calculate the absorbed doses corresponding to the different number of K –values and the different protocols used in the clinic. Discussion and Conclusion: A simple K-means clustering algorithm provides an easy access to TAC of 177Lu-dotate SPECT studies. A moderate number of clusters is recommended to provide coarse TACs for different tissues. The clustered image (VOI-image) might also be useful for data comparison, when the patient goes through several separate treatment cycles. In order to plan the treatment and to assess the response advantageously, it is essential to perform the dosimetry calculations on a patient specific basis and it appears to us that the clustering algorithms may provide an effective tool to improve this process in the near future.

KW - 114 Physical sciences

U2 - 10.1007/s00259-012-2222-9

DO - 10.1007/s00259-012-2222-9

M3 - Conference article

VL - 39

SP - S311

JO - European Journal of Nuclear Medicine and Molecular Imaging

JF - European Journal of Nuclear Medicine and Molecular Imaging

SN - 1619-7070

IS - Suppl 2

ER -