摘要
Hepatocellular carcinoma (HCC) is one of the world’s most common malignant tumours. As known, liver tumour tissue is characterised by an increased blood supply related to neoangionesis which causes an increased arterial vascularisation. CT Perfusion Imaging is an important, non invasive, technique for qualitative assessment of tissue perfusion after contrast agent administration. Nevertheless, being able to reliably quantifying angiogenesis is increasingly important to both the evaluation of the disease progression and monitoring of the therapeutic response of HCC. With this in mind, we believe that could be helpful to employ Standardised Perfusion Value (SPV), which has the potential to be a useful non-invasive marker of HCC angiogenesis. However, before using SPV in clinical practice, we need to verify its reliability. There are different causes of variability in applying the SPV index, e.g., the technical specifications of the CT system employed and the image processing system. In this paper the authors will analyse the variability of the BFa estimates and the variability due to the calibration procedure of the CT system, this with the objective of verifying how these factors affects SPV values. In our case, perfusion MDCT images of seventeen HCC patients were analysed. A software application, based on maximum slope method, was developed to compute BFa and SPV values. Four radiologists were involved in images processing evaluating variability related to ROI selection;each radiologist repeated the ROI drawing four times on the same image set. We computed the k calibration factor in order to evaluate SPV variability due to calibration protocol of CT systems. Results show that calibration factor variance, due to the position in the gantry, is less than BFa variability. So, we conclude that, when daily calibration is preferred, a simplified protocol, which neglects the dependence of K factor from the position, may be utilised;at least until the intrinsic variability of perfusion parameter computation operator-dependent will be reduced.
Hepatocellular carcinoma (HCC) is one of the world’s most common malignant tumours. As known, liver tumour tissue is characterised by an increased blood supply related to neoangionesis which causes an increased arterial vascularisation. CT Perfusion Imaging is an important, non invasive, technique for qualitative assessment of tissue perfusion after contrast agent administration. Nevertheless, being able to reliably quantifying angiogenesis is increasingly important to both the evaluation of the disease progression and monitoring of the therapeutic response of HCC. With this in mind, we believe that could be helpful to employ Standardised Perfusion Value (SPV), which has the potential to be a useful non-invasive marker of HCC angiogenesis. However, before using SPV in clinical practice, we need to verify its reliability. There are different causes of variability in applying the SPV index, e.g., the technical specifications of the CT system employed and the image processing system. In this paper the authors will analyse the variability of the BFa estimates and the variability due to the calibration procedure of the CT system, this with the objective of verifying how these factors affects SPV values. In our case, perfusion MDCT images of seventeen HCC patients were analysed. A software application, based on maximum slope method, was developed to compute BFa and SPV values. Four radiologists were involved in images processing evaluating variability related to ROI selection;each radiologist repeated the ROI drawing four times on the same image set. We computed the k calibration factor in order to evaluate SPV variability due to calibration protocol of CT systems. Results show that calibration factor variance, due to the position in the gantry, is less than BFa variability. So, we conclude that, when daily calibration is preferred, a simplified protocol, which neglects the dependence of K factor from the position, may be utilised;at least until the intrinsic variability of perfusion parameter computation operator-dependent will be reduced.
