Accelerated gradient-based free form deformable registration for online adaptive radiotherapy

Abstract

The registration of planning fan-beam computed tomography (FBCT) and daily cone-beam CT (CBCT) is a crucial step in adaptive radiation therapy. The current intensity-based registration algorithms, such as Demons, may fail when they are used to register FBCT and CBCT, because the CT numbers in CBCT cannot exactly correspond to the electron densities. In this paper, we investigated the effects of CBCT intensity inaccuracy on the registration accuracy and developed an accurate gradient-based free form deformation algorithm (GFFD). GFFD distinguishes itself from other free form deformable registration algorithms by (a) measuring the similarity using the 3D gradient vector fields to avoid the effect of inconsistent intensities between the two modalities; (b) accommodating image sampling anisotropy using the local polynomial approximation-intersection of confidence intervals (LPA-ICI) algorithm to ensure a smooth and continuous displacement field; and (c) introducing a 'bi-directional' force along with an adaptive force strength adjustment to accelerate the convergence process. It is expected that such a strategy can decrease the effect of the inconsistent intensities between the two modalities, thus improving the registration accuracy and robustness. Moreover, for clinical application, the algorithm was implemented by graphics processing units (GPU) through OpenCL framework. The registration time of the GFFD algorithm for each set of CT data ranges from 8 to 13 s. The applications of on-line adaptive image-guided radiation therapy, including auto-propagation of contours, aperture-optimization and dose volume histogram (DVH) in the course of radiation therapy were also studied by in-house-developed software.

Parotid gland radiation dose‐xerostomia relationships based on actual delivered dose for nasopharyngeal carcinoma

“…We found that intensity‐based deformable registration algorithms are susceptible to distorting tissues because the presence of inconsistent intensities between CT and CBCT could cause significant registration errors. The DIR algorithm performed in this study was presented in our previous work, and was tested using phantom and clinical data, which demonstrated the significance of the registration method 23. We assessed the volume overlapping using the Dice Similarly coefficient (DSC) in that study.…”

An anthropomorphic abdominal phantom for deformable image registration accuracy validation in adaptive radiation therapy

“…The abdominal phantom (1), bolstered by a vacuum bag (5), was equipped with four connected blood pressure cuffs (2) with an inflation bulb (3), and an aneroid gauge (4), which were used to generate compression force to induce deformation. The frame (6) was used to support the ionization chamber cables (7) During deformable gel preparation, continuously stirring of the heated mixture of PVC and softener was important to avoid air bubbles trapped in the molten gel. It would be beneficial to place the polymerized gel inside a vacuum chamber for 1~2 min to induce a boiling effect by pressure change and the air bubbles will disappear upon pressure release.…”

“…Over the years, many efforts have been made in developing novel DIR approaches with applications in ART . Yet, a more urgent question we need to answer is how to assess the uncertainty or validate the accuracy of these existing DIR approaches in the context of ART.…”

“…Over the years, many efforts have been made in developing novel DIR approaches with applications in ART. 4,[6][7][8][9][10][11][12][13][14] Yet, a more urgent question we need to answer is how to assess the uncertainty or validate the accuracy of these existing DIR approaches in the context of ART. Traditionally, DIR accuracy is quantified by comparing the agreement between the reference and deformed image, or the congruence between the reference landmark/contour/shape and that derived from the calculated DVFs, using similarity metrics such as intensity difference metrics (e.g., mean squared difference), correlation-based metrics (e.g., cross correlation), information theory metrics (e.g., normalized mutual information), landmark-based metrics, and contour/surface-based metrics, etc.…”

Deformable image registration and interobserver variation in contour propagation for radiation therapy planning

“…Applications include multimodality image registration, (1) atlas‐based automatic segmentation, (2) dose summation, (3) and contour propagation for online, 4 , 5 and offline 4 , 6 adaptive radiation therapy. Unlike conventional “rigid” registration, DIR does not assume spatial invariance between all voxels of both image sets.…”