Quantitative assessment of effects of motion compensation for liver and lung tumors in CT perfusion.

RATIONALE AND OBJECTIVES To study the effects of four different rigid alignment approaches on both time-concentration curves (TCCs) and perfusion maps in computed tomography perfusion (CTp) studies of liver and lung tumors. MATERIALS AND METHODS Eleven data sets in patients who were subjected to axial CTp after contrast agent administration were assessed. Each data set consists of four different sequences, according to the different rigid alignment configurations considered to compute blood flow perfusion maps: no alignment, translational, craniocaudal, and three dimensional (3D). The color maps were built on TCCs according to the maximum slope method. The effects of motion correction procedures on the reliability of TCCs and perfusion maps were assessed both quantitatively and visually. RESULTS TCCs built after 3D alignments show the best indices as well as producing the most reliable maps. We show examinations in which the translational alignment only yields more accurate TCCs, but less reliable perfusion maps, than those achieved with no alignment. Furthermore, we show color maps with two different perfusion patterns, both considered reliable by radiologists, achieved with different motion correction approaches. CONCLUSIONS The quantitative index we conceived allows relating quality of 3D alignment and reliability of perfusion maps. A better alignment does not necessarily yield more reliable perfusion values: color maps resulting from either alignment procedure must be critically assessed by radiologists. This achievement will hopefully represent a step forward for the clinical use of CTp studies for staging, prognosis, and monitoring values of therapeutic regimens.

[1]  Steve Halligan,et al.  Quantitative assessment of colorectal cancer tumor vascular parameters by using perfusion CT: influence of tumor region of interest. , 2008, Radiology.

[2]  H. Fichte,et al.  Quantitative assessment of lung cancer perfusion using MDCT: does measurement reproducibility improve with greater tumor volume coverage? , 2006, AJR. American journal of roentgenology.

[3]  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.

[4]  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.

[5]  R. Prokesch,et al.  Measurement of Hepatic Perfusion with Dynamic Computed Tomography: Assessment of Normal Values and Comparison of Two Methods to Compensate for Motion Artifacts , 2000, Investigative radiology.

[6]  A Chandler,et al.  Validation of motion correction techniques for liver CT perfusion studies. , 2012, The British journal of radiology.

[7]  B. Fisher,et al.  Prediction and reduction of motion artifacts in free-breathing dynamic contrast enhanced CT perfusion imaging of primary and metastatic intrahepatic tumors. , 2013, Academic radiology.

[8]  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.

[9]  C. V. van Kuijk,et al.  Dynamic contrast-enhanced CT in patients treated with sorafenib and erlotinib for non-small cell lung cancer: a new method of monitoring treatment? , 2010, European Radiology.

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

[11]  M. Bellomi,et al.  CT perfusion in oncology: how to do it , 2010, Cancer imaging : the official publication of the International Cancer Imaging Society.

[12]  Wei Wei,et al.  Semiautomated motion correction of tumors in lung CT-perfusion studies. , 2011, Academic radiology.

[13]  Ernst Klotz,et al.  Lung cancer perfusion at multi-detector row CT: reproducibility of whole tumor quantitative measurements. , 2006, Radiology.

[14]  Carlo Catalano,et al.  Whole-tumor perfusion CT in patients with advanced lung adenocarcinoma treated with conventional and antiangiogenetic chemotherapy: initial experience. , 2011, Radiology.

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

[16]  P. Rogalla,et al.  Comparison of free breathing versus breath-hold in perfusion imaging using dynamic volume CT , 2012, Insights into Imaging.

[17]  Massimo Bellomi,et al.  Quantification of variability in breath-hold perfusion CT of hepatocellular carcinoma: a step toward clinical use. , 2012, Radiology.

[18]  Wei Wei,et al.  Reproducibility of perfusion parameters obtained from perfusion CT in lung tumors. , 2011, AJR. American journal of roentgenology.

[19]  S. Meijer,et al.  Total-liver-volume perfusion CT using 3-D image fusion to improve detection and characterization of liver metastases , 2008, European Radiology.

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

[21]  J. Horiguchi,et al.  Quantitative measurement of hepatic portal perfusion by multidetector row CT with compensation for respiratory misregistration. , 2004, The British journal of radiology.

[22]  V. Goh,et al.  Radiation dose from volumetric helical perfusion CT of the thorax, abdomen or pelvis , 2011, European Radiology.