A statistical model-based technique for accounting for prostate gland deformation in endorectal coil-based MR imaging

Abstract

In prostate brachytherapy procedures, combining high-resolution endorectal coil (ERC)-MRI with Computed Tomography (CT) images has shown to improve the diagnostic specificity for malignant tumors. Despite such advantage, there exists a major complication in fusion of the two imaging modalities due to the deformation of the prostate shape in ERC-MRI. Conventionally, nonlinear deformable registration techniques have been utilized to account for such deformation. In this work, we present a model-based technique for accounting for the deformation of the prostate gland in ERC-MR imaging, in which a unique deformation vector is estimated for every point within the prostate gland. Modes of deformation for every point in the prostate are statistically identified using a set of MR-based training set (with and without ERC-MRI). Deformation of the prostate from a deformed (ERC-MRI) to a non-deformed state in a different modality (CT) is then realized by first calculating partial deformation information for a limited number of points (such as surface points or anatomical landmarks) and then utilizing the calculated deformation from a subset of the points to determine the coefficient values for the modes of deformations provided by the statistical deformation model. Using a leave-one-out cross-validation, our results demonstrated a mean estimation error of 1mm for a MR-to-MR registration.

Fig. 1

MRI with and without ERC.

Fig. 2

Flowchart of the proposed method. See Section II for details.

Fig. 3

Mean±std of the normalized eigen coefficients from 72 cases calculated in a leave-one-out framework.

Fig. 4

(a) Distribution of error between the estimated and the actual deformation field combining all 72 cases from the leave-one-out cross-validation. Mean error is depicted by a red vertical line; (b) Localization of the distribution of error between the estimated and the actual deformation field combining all 72 cases from the leave-one-out cross-validation. The distribution is thresholded at 0.25th and 0.75th quantiles.

Fig. 5

Error of estimation from the leave-one-out cross-validation for different number of deformation modes.