The influence of respiratory motion on the cumulative SUV-volume histogram and fractal analyses of intratumoral heterogeneity in PET/CT imaging

Objective The purpose of this study was to investigate the influence of respiratory motion on the evaluation of the intratumoral heterogeneity of FDG uptake using cumulative SUV-volume histogram (CSH) and fractal analyses.MethodsWe used an NEMA IEC body phantom with a homogeneous hot sphere phantom (HO) and two heterogeneous hot sphere phantoms (HE1 and HE2). The background radioactivity of 18F in the NEMA phantom was 5.3 kBq/mL. The ratio of radioactivity was 4:2:1 for the HO and the outer rims of the HE1 and HE2 phantoms, the inner cores of the HE1 and HE2 phantoms, and background, respectively. Respiratory motion was simulated using a motion table with an amplitude of 2 cm. PET/CT data were acquired using Biograph mCT in motionless and moving conditions. The PET images were analyzed by both CSH and fractal analyses. The area under the CSH (AUC-CSH) and the fractal dimension (FD) was used as quantitative metrics.ResultsIn motionless conditions, the AUC-CSHs of the HO (0.80), HE1 (0.75) and HE2 (0.65) phantoms were different. They did not differ in moving conditions (HO, 0.63; HE1, 0.65; HE2, 0.60). The FD of the HO phantom (0.77) was smaller than the FDs of the HE1 (1.71) and HE2 (1.98) phantoms in motionless conditions; however, the FDs of the HO (1.99) and HE1 (2.19) phantoms were not different from each other and were smaller than that of the HE2 (3.73) phantom in moving conditions.ConclusionRespiratory motion affected the results of the CSH and fractal analyses for the evaluation of the heterogeneity of the PET/CT images. The influence of respiratory motion was considered to vary depending on the object size.

[1]  C. Ling,et al.  Effect of respiratory gating on quantifying PET images of lung cancer. , 2002, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[2]  M. V. van Herk,et al.  Precise and real-time measurement of 3D tumor motion in lung due to breathing and heartbeat, measured during radiotherapy. , 2002, International journal of radiation oncology, biology, physics.

[3]  Kenya Murase,et al.  Measurement of heterogeneous distribution on Technegas SPECT images by three-dimensional fractal analysis , 2002, Annals of nuclear medicine.

[4]  M. Schwaiger,et al.  Positron emission tomography in non-small-cell lung cancer: prediction of response to chemotherapy by quantitative assessment of glucose use. , 2003, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[5]  Alfred A Bartolucci,et al.  The accuracy of integrated PET-CT compared with dedicated PET alone for the staging of patients with nonsmall cell lung cancer. , 2004, The Annals of thoracic surgery.

[6]  I. Buvat,et al.  Partial-Volume Effect in PET Tumor Imaging* , 2007, Journal of Nuclear Medicine.

[7]  Issam El-Naqa,et al.  Exploring feature-based approaches in PET images for predicting cancer treatment outcomes , 2009, Pattern Recognit..

[8]  B. Manaster Spatial Heterogeneity in Sarcoma 18F-FDG Uptake as a Predictor of Patient Outcome , 2010 .

[9]  Importance of gated CT acquisition for the quantitative improvement of the gated PET/CT in moving phantom , 2010, Annals of nuclear medicine.

[10]  M. Senda,et al.  Japanese guideline for the oncology FDG-PET/CT data acquisition protocol: synopsis of Version 1.0 , 2010, Annals of nuclear medicine.

[11]  Determination of the optimal acquisition protocol of breath-hold PET/CT for the diagnosis of thoracic lesions , 2011, Nuclear medicine communications.

[12]  Ronald Boellaard,et al.  Evaluation of a cumulative SUV-volume histogram method for parameterizing heterogeneous intratumoural FDG uptake in non-small cell lung cancer PET studies , 2011, European Journal of Nuclear Medicine and Molecular Imaging.

[13]  E. V. Beek,et al.  Integrated imaging of non-small cell lung cancer recurrence: CT and PET-CT findings, possible pitfalls and risk of recurrence criteria , 2012, European Radiology.

[14]  M. Hatt,et al.  Reproducibility of Tumor Uptake Heterogeneity Characterization Through Textural Feature Analysis in 18F-FDG PET , 2012, The Journal of Nuclear Medicine.

[15]  Koichiro Abe,et al.  Improvement in PET/CT Image Quality with a Combination of Point-Spread Function and Time-of-Flight in Relation to Reconstruction Parameters , 2012, The Journal of Nuclear Medicine.

[16]  Bal Sanghera,et al.  Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? , 2012, Insights into Imaging.

[17]  Vicky Goh,et al.  Are Pretreatment 18F-FDG PET Tumor Textural Features in Non–Small Cell Lung Cancer Associated with Response and Survival After Chemoradiotherapy? , 2013, The Journal of Nuclear Medicine.

[18]  Masayuki Sasaki,et al.  FDG uptake heterogeneity evaluated by fractal analysis improves the differential diagnosis of pulmonary nodules. , 2014, European journal of radiology.

[19]  Shingo Baba,et al.  Accuracy of amplitude-based respiratory gating for PET/CT in irregular respirations , 2014, Annals of Nuclear Medicine.

[20]  I. Apostolova,et al.  Asphericity of pretherapeutic tumour FDG uptake provides independent prognostic value in head-and-neck cancer , 2014, European Radiology.

[21]  M. Senda,et al.  Japanese guideline for the oncology FDG-PET/CT data acquisition protocol: synopsis of Version 2.0 , 2014, Annals of Nuclear Medicine.