Patient Set-Up Using Portal Images: 2D/2D Image Registration Using Mutual Information

Plattard, D.; Soret, Marine; Troccaz, Jocelyne; Vassal, P; Giraud, Jean-Yves; Champleboux, Guillaume; Artignan, Xavier; Bolla, M.

doi:10.3109/10929080009148893

Cited by 24 publications

(7 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to make this cost function invariant to rotations around the image centre, a rotated DRR I DRR is formed from I DRR by introducing a 2D/2D mutual information matching algorithm (Studholme et al 1997) prior to computing Z. Mutual information matching is a widely used intensity-based registration method for 3D/3D matching; despite the fact that the method suffers from sparsely populated joint histograms in lower dimensional cases, mutual information was also applied to 3D/2D (Kim et al 2001, Penney et al 1998, Zöllei 2001) and 2D/2D (Plattard et al 2000) registration problems. In our case, we have witnessed good performance for intramodal matching of two-dimensional images.…”

Section: An In-plane Rotation Invariant Cost Functionmentioning

confidence: 99%

A faster method for 3D/2D medical image registration—a simulation study

Birkfellner

Wirth²,

Burgstaller³

et al. 2003

Phys. Med. Biol.

View full text Add to dashboard Cite

3D/2D patient-to-computed-tomography (CT) registration is a method to determine a transformation that maps two coordinate systems by comparing a projection image rendered from CT to a real projection image. Iterative variation of the CT's position between rendering steps finally leads to exact registration. Applications include exact patient positioning in radiation therapy, calibration of surgical robots, and pose estimation in computer-aided surgery. One of the problems associated with 3D/2D registration is the fact that finding a registration includes solving a minimization problem in six degrees of freedom (dof) in motion. This results in considerable time requirements since for each iteration step at least one volume rendering has to be computed. We show that by choosing an appropriate world coordinate system and by applying a 2D/2D registration method in each iteration step, the number of iterations can be grossly reduced from n6 to n5. Here, n is the number of discrete variations around a given coordinate. Depending on the configuration of the optimization algorithm, this reduces the total number of iterations necessary to at least 1/3 of it's original value. The method was implemented and extensively tested on simulated x-ray images of a tibia, a pelvis and a skull base. When using one projective image and a discrete full parameter space search for solving the optimization problem, average accuracy was found to be 1.0 +/- 0.6(degrees) and 4.1 +/- 1.9 (mm) for a registration in six parameters, and 1.0 +/- 0.7(degrees) and 4.2 +/- 1.6 (mm) when using the 5 + 1 dof method described in this paper. Time requirements were reduced by a factor 3.1. We conclude that this hardware-independent optimization of 3D/2D registration is a step towards increasing the acceptance of this promising method for a wide number of clinical applications.

show abstract

Section: An In-plane Rotation Invariant Cost Functionmentioning

confidence: 99%

A faster method for 3D/2D medical image registration—a simulation study

Birkfellner

Wirth²,

Burgstaller³

et al. 2003

Phys. Med. Biol.

View full text Add to dashboard Cite

show abstract

“…These methods include grey-scale histogram analysis, [14][15][16][17][18][19] texture analysis, 20,21 edge enhancement and linking, 16,[20][21][22][23][24][25] region growing, 20,26,27 contour following, 16,21,23,25,28 and the maximisation of spatial correlation or mutual information between images. 18,[29][30][31][32][33][34][35][36][37] Landmark-based methods rely on marking and aligning easily identifiable point features, 15,18,38,39 and manual landmarking is one of the main methods used in the commercial proteome software. Segmentation methods divide the picture up into areas or 'segments', often by contour following, or by region growing and the related watershed methods, [40][41][42][43][44] which can then be moved for image alignment.…”

Section: Existing Methods For Automating Image Registrationmentioning

confidence: 99%

Fast automatic registration of images using the phase of a complex wavelet transform: application to proteome gels

Woodward

Rowland

Kell

2004

Analyst

View full text Add to dashboard Cite

Image registration describes the process of manipulating a distorted version of an image such that its pixels overlay the equivalent pixels in a clean, master or reference image. The need for it has assumed particular prominence in the analysis of images of electrophoretic gels used in the analysis of protein expression levels in living cells, but also has fundamental applications in most other areas of image analysis. Much of the positional information of a data feature is carried in the phase of a complex transform, so a complex transform allows explicit specification of the phase, and hence of the position of features in the image. Registration of a test gel to a reference gel is achieved by using a multiresolution movement map derived from the phase of a complex wavelet transform (the Q-shift wavelet transform) to dictate the warping directly via movement of the nodes of a Delaunay-triangulated mesh of points. This warping map is then applied to the original untransformed image such that the absolute magnitude of the spots remains unchanged. The technique is general to any type of image. Results are presented for a simple computer simulated gel, a simple real gel registration between similar "clean" gels with local warping vectors distributed about one main direction, a hard problem between a reference gel and a "dirty" test gel with multi-directional warping vectors and many artifacts, and some typical gels of present interest in post-genomic biology. The method compares favourably with others, since it is computationally rapid, effective and entirely automatic.

show abstract

“…Mutual information has been widely studied and used as a measure of similarity between images for image registration. [40][41][42][43][44][45][46][47][48][49][50] There are three ways to define mutual information, but all are identical and can be rewritten into each of the other forms. 51 One of the definitions is as follows:…”

Section: Iid1 Image Evaluationmentioning

confidence: 99%

Slow gantry rotation acquisition technique for on‐board four‐dimensional digital tomosynthesis

2010

View full text Add to dashboard Cite

A slow gantry rotation acquisition technique for 4D DTS was developed and investigated. Study results indicated that 4D DTS is a feasible technique for imaging lung tumor motion in the treatment room and requires shorter acquisition times and less imaging dose than 4D CBCT for larger tumors that do not require large scan angles for sagittal views and for situations where only coronal views are needed to meet clinical needs.

show abstract

Patient Set-Up Using Portal Images: 2D/2D Image Registration Using Mutual Information

Abstract: Mutual information is a feasible method for 2D/2D portal/portal and portal/simulator image registration in radiotherapy.

Cited by 24 publications

References 15 publications

A faster method for 3D/2D medical image registration—a simulation study

A faster method for 3D/2D medical image registration—a simulation study

Fast automatic registration of images using the phase of a complex wavelet transform: application to proteome gels

Slow gantry rotation acquisition technique for on‐board four‐dimensional digital tomosynthesis

Contact Info

Product

Resources

About