High-performance GPU-based rendering for real-time, rigid 2D/3D-image registration and motion prediction in radiation oncology

Abstract

A common problem in image-guided radiation therapy (IGRT) of lung cancer as well as other malignant diseases is the compensation of periodic and aperiodic motion during dose delivery. Modern systems for image-guided radiation oncology allow for the acquisition of cone-beam computed tomography data in the treatment room as well as the acquisition of planar radiographs during the treatment. A mid-term research goal is the compensation of tumor target volume motion by 2D/3D registration. In 2D/3D registration, spatial information on organ location is derived by an iterative comparison of perspective volume renderings, so-called digitally rendered radiographs (DRR) from computed tomography volume data, and planar reference x-rays. Currently, this rendering process is very time consuming, and real-time registration, which should at least provide data on organ position in less than a second, has not come into existence. We present two GPU-based rendering algorithms which generate a DRR of 512 × 512 pixels size from a CT dataset of 53 MB size at a pace of almost 100 Hz. This rendering rate is feasible by applying a number of algorithmic simplifications which range from alternative volume-driven rendering approaches -namely so-called wobbled splatting -to sub-sampling of the DRR-image by means of specialized raycasting techniques. Furthermore, general purpose graphics processing unit (GPGPU) programming paradigms were consequently utilized. Rendering quality and performance as well as the influence on the quality and performance of the overall registration process were measured and analyzed in detail. The results show that both methods are competitive and pave the way for fast motion compensation by rigid and possibly even non-rigid 2D/3D registration and, beyond that, adaptive filtering of motion models in IGRT.

A support vector machine tool for adaptive tomotherapy treatments: Prediction of head and neck patients criticalities

“…Further, GPU computing allowed to significantly reducing the time for ART [30,31]. In 2014, the implementation of the Titan NVIDIA ® GPU (GeForce GTX Titan Black, graphic driver 344.11) and software code optimization during HDIR, have reduced drastically the time of registration: 242% of time gain with respect to the M approach.…”

Using Graphics Processing Units for Accelerated Information Retrieval

“…We intend to study new techniques of reducing patient exposure to treatment by faster processing of live images in order to improve precision of radiation therapy or allow fast and precise surgical interventions. Noticing the highly competitive and powerful general purpose graphic processors, researches have already begun conducting studies for including them in medical computations [23], [24], [25], [26] their results showing relevant performance improvements at low investment costs.…”

Multi-Modality Imaging: A Software Fusion and Image-Guided Therapy Perspective

“…This is widely used in image-guided surgery and image-guided radiotherapy. One or more DRRs are iteratively compared to radiographs which are usually taken prior to or during an intervention; while early efforts suffered from the high computational load connected to the iterative DRRgeneration, the advent of general purpose computing on graphics processor units (GPGPU) [47,[83][84][85] boosted this application as a valuable tool, especially for patient positioning in radiotherapy [84,[86][87][88][89]. Figure 6 shows a screenshot from such a program for 2D/3D registration of projective images-the software and its underlying mechanism is described in more detail in Spoerk et al [83], Gendrin et al [84], Furtado et al [87], Li et al [88].…”