A feasibility study to investigate the use of thin-plate splines to account for prostate deformation

Abstract

Image registration is an important step in the radiotherapy treatment planning process. It provides a method of fusing different types of diagnostic imaging information. One such application is to combine magnetic resonance spectroscopic images (MRSI) of the prostate with anatomical MRI and/or computed tomography images that are routinely used in the radiation treatment planning of prostate cancer. MRSI provides in vivo information related to the underlying metabolic activity of tissues, and can be related to the presence of cancer. However, the inflated endorectal coil required during MRS imaging poses a potential problem by deforming the prostate when it is filled with approximately 100 cm3 of air during image acquisition. This pushes the prostate superiorly/anteriorly, deforming the prostate and consequently the spectroscopic imaging data in a nonlinear manner. In this application, the coil-deformed MRS images are warped back to a non-deformed state, using a single data set. A nonlinear warping algorithm is presented to achieve this. Results indicate that the algorithm attains an accuracy of 97% (4 cm3 difference) when reproducing the total prostate volume compared to a Radiation Oncologist defined prostate volume. This difference is slightly smaller than the measured intra-operator variance of +/-1.5 cm3 (deflated coil) and the measured algorithm variance of +/-1.0 cm3. Additionally, intraprostatic nodules were used to assess the accuracy of the warping algorithm in regions inside the prostate. While choosing anatomical tie points along the external prostate surface, analysis of the nodules revealed the algorithm accuracy reduced to 63-93%.

In silico comparison of photons versus carbon ions in single fraction therapy of lung cancer

“…Several different DIR algorithms are available, such as B-spline [Rueckert et al, 1999], Demons [Thirion, 1998], linear elastic finite element [Venugopal et al, 2005], optical flow [Zhong et al, 2007] or viscous fluid [Christensen et al, 1996].…”

Assessment of a commercially available automatic deformable registration system

“…The predominant application of deformable registration of prostate MRI to CT images in the literature is to account for the deformation of the prostate caused by the insertion of an endorectal coil during magnetic resonance spectroscopic imaging (MRSI). ( 21 , 30 ) Acquisition of anatomical MRI images with surface or other noninvasive coils significantly reduces problems related to prostate deformation but daily prostate motion ( 45 , 46 ) remains a major concern. As a result, any translational offset between prostate positions in the MRI and CT with respect to surrounding anatomy may lead to inaccurate overlap of the MRI and CT prostate volumes after rigid registration.…”

“…Various measures of volume overlap evaluated using radiation oncologist delineated prostate contours have been used for validation of deformable registration of ${\mathrm{normalT}}_2$‐weighted pelvic images with and without inflated endorectal coils. ( 29 , 30 , 31 ) Volumetric‐based methods have also been used when validating deformable registration of pelvic CT ( 1 , 2 ) and head and neck CT ( 16 ) images.…”

Learning statistical correlation for fast prostate registration in image‐guided radiotherapy

“…[10][11][12][13] So far, many prostate registration algorithms have been developed in the literature, which can be broadly classified into two categories. The first category of methods is mainly based on correspondence detection or interpolation, i.e., first detecting correspondences through the segmentation of corresponding organs and then interpolating the dense correspondences for the rest regions of the image using thin plate splines (TPSs) based interpolation methods, [14][15][16] finite element methods, [17][18][19][20] or other techniques. 20,21 For instance, Venugopal et al 15 used TPS to estimate the prostate motion given homologous landmark points in the two prostate images.…”

“…The first category of methods is mainly based on correspondence detection or interpolation, i.e., first detecting correspondences through the segmentation of corresponding organs and then interpolating the dense correspondences for the rest regions of the image using thin plate splines (TPSs) based interpolation methods, [14][15][16] finite element methods, [17][18][19][20] or other techniques. 20,21 For instance, Venugopal et al 15 used TPS to estimate the prostate motion given homologous landmark points in the two prostate images. Bharatha et al 19 used an elastic finite element model to align the preprocedural images with the intraprocedural images of the prostate and showed a significant increase in overlap between the registered preprocedural and intraprocedural prostate images, comparing to only using rigid transformation.…”