Core-based portal image registration for automatic radiotherapy treatment verification

Fritsch, Daniel S.; Chaney, Edward L.; Boxwala, Aziz A.; McAuliffe, Matthew J.; Raghavan, Suraj; Thall, Andrew; Earnhart, John R.D.

doi:10.1016/0360-3016(95)02092-6

Cited by 49 publications

(39 citation statements)

References 40 publications

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“…A number of semiautomated 1-4 and fully automated [5][6][7][8][9][10][11][12][13][14] 2D-2D registration methods have been developed for the determination of setup errors in prostate radiotherapy. A limitation of 2D-2D registration methods is that they are typically unable to accurately estimate all six transformation parameters, especially in the presence of out-of-plane displacements.…”

Section: Introductionmentioning

confidence: 99%

Automated 2D–3D registration of portal images and CT data using line‐segment enhancement

et al. 2008

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In prostate radiotherapy, setup errors with respect to the patient's bony anatomy can be reduced by aligning 2D megavoltage ͑MV͒ portal images acquired during treatment to a reference 3D kilovoltage ͑kV͒ CT acquired for treatment planning purposes. The purpose of this study was to evaluate a fully automated 2D-3D registration algorithm to quantify setup errors in 3D through the alignment of line-enhanced portal images and digitally reconstructed radiographs computed from the CT. The line-enhanced images were obtained by correlating the images with a filter bank of short line segments, or "sticks" at different orientations. The proposed methods were validated on ͑1͒ accurately collected gold-standard data consisting of a 3D kV cone-beam CT scan of an anthropomorphic phantom of the pelvis and 2D MV portal images in the anterior-posterior ͑AP͒ view acquired at 15 different poses and ͑2͒ a conventional 3D kV CT scan and weekly 2D MV AP portal images of a patient over 8 weeks. The mean ͑and standard deviation͒ of the absolute registration error for rotations around the right-lateral ͑RL͒, inferior-superior ͑IS͒, and posterior-anterior ͑PA͒ axes were 0.212°͑0.214°͒, 0.055°͑0.033°͒ and 0.041°͑0.039°͒, respectively. The corresponding registration errors for translations along the RL, IS, and PA axes were 0.161͑0.131͒ mm, 0.096͑0.033͒ mm, and 0.612͑0.485͒ mm. The mean ͑and standard deviation͒ of the total registration error was 0.778͑0.543͒ mm. Registration on the patient images was successful in all eight cases as determined visually. The results indicate that it is feasible to automatically enhance features in MV portal images of the pelvis for use within a completely automated 2D-3D registration framework for the accurate determination of patient setup errors. They also indicate that it is feasible to estimate all six transformation parameters from a 3D CT of the pelvis and a single portal image in the AP view.

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Section: Introductionmentioning

confidence: 99%

Automated 2D–3D registration of portal images and CT data using line‐segment enhancement

et al. 2008

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“…A more detailed description of the segmentation programs and a presentation of the segmentation results of these methods can be found in ]. For some of the clinical uses described in Section 4 the method has been validated by clinical usefulness or accuracy [Fritsch 1995. In others, this validation is yet to be done.…”

Section: Summary and Discussionmentioning

confidence: 99%

“…In fact, Fritsch [1995] shows this registration can be done automatically and produces an accuracy superior to manual registration. For the example in Fig. 14 the registration requires around 10 seconds at present on a highend PC.…”

Section: Registrationmentioning

confidence: 99%

“…Sections follow indicating how these ideas can be used for image analysis, in particular, segmentation, registration, and measurement of shape anomaly or differences. However, since we wish to focus here on the shape representation, we leave details of the algorithms for these objectives to other papers on the individual algorithms [Wilson 1995, Wilson 1996, Fritsch 1995, Liu 1998]. In the discussion of image analysis we give examples from radiotherapy planning, surgery planning, and psychiatric diagnosis, but we leave fuller illustration of these applications to other papers on the individual clinical topics , Clary 1997].…”

Section: Introductionmentioning

confidence: 99%

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Segmentation, registration, and measurement of shape variation via image object shape

Pizer

Fritsch

Yushkevich

et al. 1999

IEEE Trans. Med. Imaging

279

179

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A model of object shape by nets of medial and boundary primitives is justified as richly capturing multiple aspects of shape and yet requiring representation space and image analysis work proportional to the number of primitives. Metrics are described that compute an object representation's prior probability of local geometry by reflecting variabilities in the net's node and link parameter values and that compute a likelihood function measuring the degree of match of an image to that object representation. A paradigm for image analysis of deforming such a model to optimize a posterior probability is described, and this paradigm is shown to be usable as a uniform approach for object definition, object-based registration between images of the same or different imaging modalities, and measurement of shape variation of an abnormal anatomical object compared with a normal. Examples of applications of these methods in radiotherapy, surgery, and psychiatry are given.

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“…This process is exactly the same as the registration method proposed and implemented by Fritsch [1995] and, due to the invariance properties built into the prior, does not change the value of the prior (internal energy) from its mean. Due to the use of relatively large scale medial information, this stage is remarkably robust in the presence of small-scale image disturbances (e.g., boundary and/or intensity noise) and exhibits a region of convergence for translation on the order of the average scale (half-width) of the figures comprising the object.…”

Section: Initialization Of the Shape Template For Object Localizationmentioning

confidence: 99%

Segmentation of medical image objects using deformable shape loci

Fritsch

Pizer

et al. 1997

Lecture Notes in Computer Science

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Robust localization and segmentation of normal anatomical objects in medical images require (1) methods for creating descriptive object models that adequately capture object shape and expected shape variation across a population, (2) methods for combining such shape models with unclassified image data, and (3) means for localizing and extracting corresponding objects from the image data using the model. A Bayesian approach is well suited as a general analytic framework for such a process; object shape models are associated with the prior, image information is associated with the likelihood function, and the posterior provides a means for combining models with images in a way that makes it possible to localize and segment normal figural shapes.Prior models that include multiscale medial and boundary analysis are well matched with a Bayesian approach since such models directly capture i) figural shape, ii) inter-figural shape relationships, and iii) boundary shape and location relative to figural shape. Described in this paper is such an approach to model-based segmentation, called deformable shape loci (DSLs), that has been successfully applied to 2D MR slices of the brain ventricle and CT slices of abdominal organs. The method combines the model and image data by warping the model to optimize an objective function measuring both the conformation of the warped model to the image data and the preservation of local neighbor relationships in the model. Large scale medial information stabilizes the localization of the object boundary region in the presence of image disturbances such as noise, blurring, and poor contrast resolution. Methods for forming the model on which the prior is based and for optimizing the posterior are described.

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Core-based portal image registration for automatic radiotherapy treatment verification

Cited by 49 publications

References 40 publications

Automated 2D–3D registration of portal images and CT data using line‐segment enhancement

Automated 2D–3D registration of portal images and CT data using line‐segment enhancement

Segmentation, registration, and measurement of shape variation via image object shape

Segmentation of medical image objects using deformable shape loci

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