Automatic registration of portal images and volumetric CT for patient positioning in radiation therapy

Khamene, Ali; Bloch, Peter; Wein, Wolfgang; Svatos, M; Sauer, Frank

doi:10.1016/j.media.2005.06.002

Cited by 109 publications

(70 citation statements)

References 35 publications

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“…Clippe et al [6] and Kim et al [7] test the registration method on phantoms, but introducing markers inside the phantom to help the registration. Künzler's method [8] requires only a simple equalization of the portal image to register it, while Khamene et al [9] work with portal images without accelerator print.…”

Section: Motivation and Backgroundmentioning

confidence: 99%

DRR and Portal Image Registration for Automatic Patient Positioning in Radiotherapy Treatment

et al. 2011

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Image processing turns out to be essential in the planning and verification of radiotherapy treatments. Before applying a radiotherapy treatment, a dosimetry planning must be performed. Usually, the planning is done by means of an X-ray volumetric analysis using computerized tomography, where the area to be radiated is marked out. During the treatment phase, it is necessary to place the patient under the particle accelerator exactly as considered in the dosimetry stage. Coarse alignment is achieved using fiduciary markers placed over the patient's skin as external references. Later, fine alignment is provided by comparing a digitally reconstructed radiography (DRR) from the planning stage and a portal image captured by the accelerator in the treatment stage. The preprocessing of DRR and portal images, as well as the minimization of the non-shared information between both kinds of images, is mandatory for the correct operation of the image registration algorithm. With this purpose, mathematical morphology and image processing techniques have been used. The present work describes a fully automatic method to calculate more accurately the necessary displacement of the couch to place the patient exactly at the planned position. The proposed method to achieve the correct positioning of the patient is based on advanced image registration techniques. Preliminary results show a perfect match with the displacement estimated by the physician.

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Section: Motivation and Backgroundmentioning

confidence: 99%

DRR and Portal Image Registration for Automatic Patient Positioning in Radiotherapy Treatment

et al. 2011

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“…Ray casting is the most popular algorithm for volume rendering in general and DRR generation in particular [26,29,33]. One widespread used implementation of ray casting for the GPU is to utilize a bounding structure -a simple geometric body that encloses the voxel volume -to generate rays that are used to traverse and sample a volume in form of a texture.…”

Section: Ray Castingmentioning

confidence: 99%

“…2D/3D registration [23][24][25][26][27][28][29], the derivation of six degrees of rigid body motion from an iterative comparison of perspective volume renderings simulating x-ray images (also called digitally rendered radiographs or DRRs) and real x-ray data taken during irradiation might provide a non-invasive method to track tumor motion. However, the massive computational effort connected to DRR rendering does still result in typical runtimes of 30 to 100 seconds until the registration process is completed [28].…”

Section: Introductionmentioning

confidence: 99%

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

Spoerk

Gendrin

Weber

et al. 2012

Zeitschrift für Medizinische Physik

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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.

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“…One particular example is local cross-correlation, where brightness and contrast may vary, since they are implicitly computed, within the neighborhood of every image pixel [4]. It is also possible to implicitly recover unknown parameters of a linear combination of two image components within a similarity formulation [7].…”

Section: Projection Similaritymentioning

confidence: 99%

Detecting Patient Motion in Projection Space for Cone-beam Computed Tomography

Wein¹,

Ladikos²

2011

Lecture Notes in Computer Science

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Abstract. Cone-beam X-Ray systems strictly depend on the imaged object being stationary over the entire acquisition process. Even slight patient motion can affect the quality of the final 3D reconstruction. It would be desirable to be able to discover and model patient motion right from the actual projection images, in order to take it into account during reconstruction. However, while the source-detector arrangement is rotating around the patient, it is difficult to separate this motion from the additional patient motion. We present a novel similarity metric for successive X-Ray projections, which is able to distinguish between the expected rotational and additional sources of motion. We quantitatively evaluate it on simulated dental cone-beam X-Rays and qualitatively demonstrate its performance on real patient data.

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Automatic registration of portal images and volumetric CT for patient positioning in radiation therapy

Cited by 109 publications

References 35 publications

DRR and Portal Image Registration for Automatic Patient Positioning in Radiotherapy Treatment

DRR and Portal Image Registration for Automatic Patient Positioning in Radiotherapy Treatment

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

Detecting Patient Motion in Projection Space for Cone-beam Computed Tomography

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