Improved Quantification of Dynamic Contrast Enhanced MR Imaging (DCE-MRI) in Adnexal Masses by Elastic Registration Method


avatar Elaheh Kia 1 , avatar Anahita Fathi Kazerooni 1 , avatar Alireza Ahmadian 1 , avatar Hamidreza Saligheh Rad 1 , *

Tehran University of Medical Sciences, Tehran, IR Iran

how to cite: Kia E, Fathi Kazerooni A, Ahmadian A, Saligheh Rad H. Improved Quantification of Dynamic Contrast Enhanced MR Imaging (DCE-MRI) in Adnexal Masses by Elastic Registration Method. Innov J Radiol. 2014;11(30th Iranian Congress of Radiology):e21290.



Ovarian cancer is the fifth commonest cancer affecting women in the developed world and is the primary indication for gynecological surgery. Quantitative dynamic contrast enhanced MRI (DCE-MRI) has been shown beneficial to differentiate malignant and benign tumors and is increasingly used as the investigational biomarker of response in clinical studies based on the measurement of enhancement characteristics. One of the major assumptions in quantification of DCE-MRI in abdominal organs is spatially-fixed region of interest over the time course of contrast agent passage. However, there are two types of motion occurring in the image series, which could invalidate this assumption and thus the quantification outcome: one of them from complex motion resulting from breathing, pulsation and the natural movement of the organ of interest, and the other one from the motion of the contrast agent. Thus, the accurate quantification of DCE-MR image series highly depends on minimization of motion artifacts.


To this end, proper registration of images that have been acquired in different time points is essential for deriving diagnostic information to produce a dataset without motion artifact. Here, we have proposed a registration approach for accurate quantification of DCE-MRI in ovary, employing elastic registration to account for spatially-varying intensity changes within the registration framework.

Patients and Methods:

Data Acquisition: DCE-MR images of eighteen patients (10 benign and 8 malignant tumors based on histopathology) diagnosed with solid or solid/cystic ovarian masses were acquired on a 3T MR scanner (Siemens MAGNETOM Tim TRIO) using a surface phased-array coil, TE/TR = 1.74/5 ms, flip angle = 60, image matrix = 156192, FOV=2323 cm2, slice thickness = 5 mm, number of measurements = 52 at 6 s/volume, number of slices = 16. The acquisition was performed before and immediately after injection of 0.2 mL/kg of Gadolinium (DOTAREM; Guerbet, Aulnay, France), followed by injection of 20 cc normal saline solution with 3 mL/min injection rate.
Image registration: The pre-contrast image is taken as the reference and the consequent images are aligned with the reference image. We have employed elastic registration algorithm developed by Periaswamy and Farid. The geometric transformation is a local affine model with a global smoothness constraint. Intensity variations are modeled with local changes in brightness and contrast. The mean squared error metric was applied to the intensity values to correct the nonlinear distortion. A least-squares technique was used to minimize the error function which is linear in model parameters. An iterative nonlinear minimization scheme is used to minimize the nonlinear error function.
Quantification: As proposed, time-to-peak (TTP) and wash-in-rate (WIR), defined as (SImax-SI0)/TTP, can be used to distinguish between benign and malignant ovarian masses.


The impact of the registration methods was shown on the selected regions-of-interest (ROIs) located in solid part of the tumor in malignant forms. As a result, the elastic registration algorithm significantly improves the signal intensity curves, especially in psoas that inhibited much distortion. In addition, the mean and the median of the standard deviation within the ROIs selected on tumor and psoas over the time courses of contrast agent passage were computed, which suggests that the elastic registration (E-Reg) method significantly improves the signal intensity-time courses in contrast to the unregistered (UnReg) images. Similar results are obtained for the other data sets. Also, quantitative parameters were calculated for unregistered and elastic registered images. It can be inferred that the value of mean to standard deviation ratio of the parameters increased after registration, which would improve the characterization of benign from malignant ovarian masses.


From the results attained in this work, it can be concluded that the outcome of ovarian cancer characterization could benefit from elastic registration approach and hence this method can be reliably used for quantification of DCE-MR images of adnexal tumors.

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