Automatic detection of misleading blood flow values in CT perfusion studies of lung cancer

Abstract In the oncology field, the anti-angiogenetic therapies aim at inhibiting tumour vascularization, that is the development of new capillary blood vessels in tumours, that allows them to grow and spread and, potentially, to metastasi. Computed tomography perfusion (CTp) is a dynamic contrast-enhanced technique that has emerged in the last few years as a promising approach for earlier assessment of such therapies, and of tumour response, in general, since functional changes precede morphological changes, that take more time to become evident. However several issues, such as patient motion and several types of artefacts, jeopardize quantitative measurements, this preventing CTp to be used in standard clinics. This paper presents an original automatic approach, based on the voxel-based analysis of the time–concentration curves (TCCs), that allows emphasizing those physiological structures, such as vessels, bronchi or artefacts, that could affect the final computation of blood flow perfusion values in CTp studies of lung cancer. The automatic exclusion of these misleading values represents a step towards a quantitative CTp, hence its routine use in clinics.

[1]  Alessandro Bevilacqua,et al.  Quantitative assessment of effects of motion compensation for liver and lung tumors in CT perfusion. , 2014, Academic radiology.

[2]  J. Remy,et al.  Perfusion CT allows prediction of therapy response in non-small cell lung cancer treated with conventional and anti-angiogenic chemotherapy , 2013, European Radiology.

[3]  A. Chandler,et al.  Reproducibility of CT perfusion parameters in liver tumors and normal liver. , 2011, Radiology.

[4]  T. Frauenfelder,et al.  Assessment of Bronchial and Pulmonary Blood Supply in Non-Small Cell Lung Cancer Subtypes Using Computed Tomography Perfusion , 2015, Investigative radiology.

[5]  F. Zaccagna,et al.  Whole-tumour CT-perfusion of unresectable lung cancer for the monitoring of anti-angiogenetic chemotherapy effects. , 2013, The British journal of radiology.

[6]  L. Goldman Principles of CT and CT Technology* , 2007, Journal of Nuclear Medicine Technology.

[7]  M. Bellomi,et al.  CT perfusion in solid-body tumours. Part I: technical issues , 2010, La radiologia medica.

[8]  B. Seifert,et al.  Multiparametric PET/CT-perfusion does not add significant additional information for initial staging in lung cancer compared with standard PET/CT , 2014, EJNMMI Research.

[9]  Thomas E Yankeelov,et al.  Methods and challenges in quantitative imaging biomarker development. , 2015, Academic radiology.

[10]  C. Thng,et al.  Dynamic contrast-enhanced CT imaging of hepatocellular carcinoma in cirrhosis: feasibility of a prolonged dual-phase imaging protocol with tracer kinetics modeling , 2009, European Radiology.

[11]  Yue Cao,et al.  Correction of arterial input function in dynamic contrast‐enhanced MRI of the liver , 2012, Journal of magnetic resonance imaging : JMRI.

[12]  Eric A Hoffman,et al.  Pulmonary perfused blood volume with dual-energy CT as surrogate for pulmonary perfusion assessed with dynamic multidetector CT. , 2013, Radiology.

[13]  M. P. Hayball,et al.  Current status and guidelines for the assessment of tumour vascular support with dynamic contrast-enhanced computed tomography , 2012, European Radiology.

[14]  G. Zack,et al.  Automatic measurement of sister chromatid exchange frequency. , 1977, The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society.

[15]  Alessandro Bevilacqua,et al.  Computer assisted detection of regions of interest in histopathology using a hybrid supervised and unsupervised approach , 2013, Medical Imaging.

[16]  S. Sironi,et al.  Quantitative assessment of tumour associated neovascularisation in patients with liver cirrhosis and hepatocellular carcinoma: role of dynamic-CT perfusion imaging , 2012, European Radiology.

[17]  G J M Parker,et al.  Comparison of dynamic contrast‐enhanced MRI and dynamic contrast‐enhanced CT biomarkers in bladder cancer , 2011, Magnetic resonance in medicine.

[18]  Agostino Gibaldi,et al.  Effects of guided random sampling of TCCs on blood flow values in CT perfusion studies of lung tumors. , 2015, Academic radiology.

[19]  M. Maurin,et al.  REVIEW ARTICLE doi: 10.1111/j.1472-8206.2008.00633.x The Hill equation: a review of its capabilities in pharmacological modelling , 2008 .

[20]  Thierry Bastogne,et al.  Data-driven modeling and characterization of anti-angiogenic molecule effects on tumoral vascular density , 2015, Biomed. Signal Process. Control..

[21]  R. Subramaniam A defining moment: cultural change in radiology. , 2015, Academic radiology.

[22]  C. H. Thng,et al.  Assessment of Tumor Blood Flow Distribution by Dynamic Contrast-Enhanced CT , 2013, IEEE Transactions on Medical Imaging.

[23]  Hongliang Sun,et al.  Assessment of tumor grade and angiogenesis in colorectal cancer: whole-volume perfusion CT. , 2014, Academic radiology.

[24]  D. Sahani,et al.  Computed tomography perfusion imaging as a potential imaging biomarker of colorectal cancer. , 2014, World journal of gastroenterology.

[25]  D. Sahani,et al.  CT perfusion as an imaging biomarker in monitoring response to neoadjuvant bevacizumab and radiation in soft-tissue sarcomas: comparison with tumor morphology, circulating and tumor biomarkers, and gene expression. , 2015, AJR. American journal of roentgenology.

[26]  Julia F. Barrett,et al.  Artifacts in CT: recognition and avoidance. , 2004, Radiographics : a review publication of the Radiological Society of North America, Inc.

[27]  V. Goh,et al.  CT perfusion in oncologic imaging: a useful tool? , 2013, AJR. American journal of roentgenology.

[28]  Ramin V. Parsey,et al.  Optimal Metabolite Curve Fitting for Kinetic Modeling of 11C-WAY-100635 , 2007, Journal of Nuclear Medicine.

[29]  N. Mullani,et al.  First-pass measurements of regional blood flow with external detectors. , 1983, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[30]  H. Tseng,et al.  Assessment of Blood Flow in Hepatocellular Carcinoma: Correlations of Computed Tomography Perfusion Imaging and Circulating Angiogenic Factors , 2013, International journal of molecular sciences.

[31]  Osamu Okazaki,et al.  Long fasting is effective in inhibiting physiological myocardial 18F-FDG uptake and for evaluating active lesions of cardiac sarcoidosis , 2014, EJNMMI Research.