Fast and accurate registration of cranial CT images with A-mode ultrasound

Fieten, Lorenz; Schmieder, Kirsten; Engelhardt, Martin; Pasalic, Lamija; Radermacher, Klaus; Heger, Stefan

doi:10.1007/s11548-009-0288-z

Cited by 19 publications

(26 citation statements)

References 29 publications

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“…Keywords ICP Á kd-tree Á Anisotropy Á Weighting Purpose Intra-operative rigid registration is used to establish the spatial relationship between pre-operative images and the surgical site and usually relies on reference structures localized in both reference frames. In automated surface-based registration using algorithms like the iterative closest point (ICP) algorithm or ICP variants such as the Random-ICP (R-ICP) [1], the computer iteratively establishes correspondences between the intra-operatively measured points (data points) and bone surface points (model points) by switching between two steps, namely closest point (CP) computation and point-to-point (PTP) registration. The ICP and most ICP variants aim at the minimization of a cost function representing the Euclidean distances between the transformed data points and the corresponding model points and assuming that the points are corrupted by isotropic and homogeneous noise.…”

Section: Resultsmentioning

confidence: 99%

“…A system of this accuracy outperforms current clinical standards, and has the potential to improve intervention on small thoracic lesions, leading to better patient care. Purpose Time-of-Flight (ToF) cameras are an emerging modality for quickly acquiring distance information [1] and are becoming increasingly popular for intraoperative surface acquisition. Using the ToF technique as intraoperative imaging modality requires a seamless integration in the clinical workflow.…”

Section: Discussionmentioning

confidence: 99%

“…We focused on localization errors due to optical tracking as this can be seen as an important error source in the application area of skull surgery. In our experimental setup, only relatively small initial misalignments (0°, 1°, 2°, 3°) were considered for two reasons: Firstly, the conventional R-ICP without anisotropic weighting, which is cheaper in terms of computational costs, is already supposed to lead to small misalignments [1]; secondly, gross misalignments imply that the establishment of point correspondences by CP computation is another error source and, hence, anisotropic weighting according to measurement error distributions is less appropriate [5]. Results CP computation with the new method turned out to be several hundred times faster than CP computation using a brute-force approach.…”

Section: Methodsmentioning

confidence: 99%

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Image processing and visualization

2011

Int J CARS

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Keywords Image-guided surgery Á High accuracy Á Noninvasiveness Á Temporal bone PurposeThe temporal bone that is drilled in otologic surgery conceals important organs inside small area, e.g. carotid artery, jugular vein, facial nerve, inner ear etc. Surgeons will reach some of these organs as surgical targets while not injuring others. Three dimensionally located complex anatomical structures in the bone imply a necessity for image-guidance with high accuracy. However, because most otologic surgery is basically less invasive, surgical navigation for otologic surgeries require demanding accuracy as well as less invasiveness. We recently introduced a new strategy (STAMP method) for less invasive, but accurate registration method for image-guided otologic surgery. In the original STAMP method we edited each patient's CT data to virtually place markers on the patient's bone. The template of the bone surface with small holes is then produced to transfer the virtual markers to the patient's bone during the surgery. In the operating room, the surgeon attaches the STAMP template on the patient's bone surface and transfer virtual markers to the patient's temporal bone. Then the surgeon finishes the paired-point registration using the transferred markers. In more than 20 cases of the imageguided cochlear implant surgery, we used this method and the registration errors were about 1 mm. This noninvasive and quick method was particularly useful in cases of children. However, this method is rather complex for surgeons who are unfamiliar with the image guided surgery. In phantom study, there is an apparent difference in the registration time and errors between surgeons.In this study, we modified this STAMP method to make the procedure as simple as possible in the operating room. We attached optical markers on the STAMP template and registered the relative points of virtual markers pre-operatively. The only procedure in the operating room is to attach the STAMP template on the patient's bone surface for \10 s. The engineers did the rest of the process either in the lab or in the operating room. We report the comparison of registration time and registration errors of original STAMP and preregistered STAMP methods. MethodsThe registration methods were evaluated in phantom study. We used a synthetic model of a temporal bone (KEZLEX, Ono & Co., Japan). We edited the CT data of this model to virtually place 8 markers on the surface. The STAMP template that fits on the surface of the bone, and also shows the location of the 8 virtual markers, is then produced using laser-sintering process. The above process is the same with the original STAMP method except that the template now has a pole to fix the optical trackers for pre-registering purpose (Fig. 1a). The virtual markers were pre-registered to the attached optical trackers (Fig. 1b). The above process can be finished before the surgery by engineers. Then the surgeon performed the only required process in the operating room, which is to attach the pre-registered STAMP template on th...

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Image processing and visualization

2011

Int J CARS

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“…The surface data in 3-D point-cloud form is usually acquired by utilizing non-invasive devices such as laser range scanner (LRS) or time-of-flight camera [12]. By applying a surface registration algorithm, such as the wellknown Iterative Closet Point (ICP) [13][14][15][16], the surface data can be used to register with the same surface extracted from the patient's medical images. However, the ICP algorithm has two drawbacks: the registration result is sensitive to the initial position of these two data sets; in other words, the algorithm might get trapped into a local minimum.…”

Section: Introductionmentioning

confidence: 99%

A Kinect-based Medical Augmented Reality System for Craniofacial Applications Using Image-to-Patient Registration

Hsieh¹,

Lee²,

Wu³

2017

Neuropsychiatry

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In this study, we proposed a kinect-based medical augmented reality (AR) system for craniofacial applications.By using a Kinect sensor to acquire the surface structure of the patient, image-to-patient registration is accomplished by an Enhanced Iterative Closest Point (EICP) algorithm automatically. Moreover, a pattern-free AR scheme is designed by integrating the Kanade-Lucas-Tomasi (KLT) feature tracking and RANdom Sample Consensus (RANSAC) correction, which is better than traditional pattern-based and sensor-based AR environment. The demonstrated system was evaluated with a plastic dummy head and a human subject. Result shows that the image-to-patient registration error is around 3~4 mm, and the pattern-free AR scheme can provide smooth and accurate AR camera localization as the commercial tracking device does.

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“…A-Mode US is attractive for surface-based registration of the bone as it is cost-efficient, intuitive, and completely non- invasive [1], [4]- [6]. Usually, a freehand-guided and optically tracked single element US transducer serves as a range sensor by means of a virtual transcutaneous pointer [1].…”

Section: Introductionmentioning

confidence: 99%

Integration of model-based weighting into an ICP variant to account for measurement errors in intra-operative A-Mode ultrasound-based registration

Fieten

Radermacher

Kernenbach

et al. 2010

2010 Annual International Conference of the IEEE Engineering in Medicine and Biology

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This paper addresses error modeling in A-Mode ultrasound- (US-) based registration and integration of model-based weighting into the Random-ICP (R-ICP) algorithm. The R-ICP is a variant of the Iterative Closest Point (ICP) algorithm, and it was suggested for surface-based registration using A-Mode US in the context of skull surgery. In that application area the R-ICP could yield high accuracy even in case of a small number of data points and a very inaccurate user-interactive pre-registration. However, it cannot cope with unequal point uncertainty, which is an important drawback in the context of hip surgery: Uncertainty about the average speed of sound is an error source, whose impact on the registration accuracy increases with the thickness of the scanned soft tissue. It can, therefore, lead to considerable localization errors if a thick soft tissue layer is scanned, and it might vary a lot from data point to data point as the soft tissue thickness is inhomogeneous. The present work investigates how to account for this error source considering also other error sources such as the establishment of point correspondences. Simulation results show that registration accuracy can be substantially improved when model-based weighting is integrated into the R-ICP.

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Fast and accurate registration of cranial CT images with A-mode ultrasound

Cited by 19 publications

References 29 publications

Image processing and visualization

Image processing and visualization

A Kinect-based Medical Augmented Reality System for Craniofacial Applications Using Image-to-Patient Registration

Integration of model-based weighting into an ICP variant to account for measurement errors in intra-operative A-Mode ultrasound-based registration

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