Automatic CT-ultrasound registration for diagnostic imaging and image-guided intervention

Wein, Wolfgang; Brunke, Shelby; Khamene, Ali; Callstrom, Matthew R.; Navab, Nassir

doi:10.1016/j.media.2008.06.006

Cited by 300 publications

(238 citation statements)

References 21 publications

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“…Last but not least, we have investigated the effect of using the dot product of the MRI gradient g with the US beam direction, instead of g directly. This reduces the influence of vertical gradients, similar to the US simulation in [13]. Interestingly, this results in 10 − 25% more outliers (the cost function becomes more non-linear due to the added directional dependance).…”

Section: Parameter Sensitivitymentioning

confidence: 63%

“…Similarity Measure: Instead of correlating US intensities with two channels of simulated information from CT as in [13], we use LC 2 to correlate US with both the MRI intensity values p and its spatial gradient magnitude g = |∇p|.…”

Section: Methodsmentioning

confidence: 99%

“…local brightness and contrast). In [13] the similarity measure Linear Correlation of Linear Combination (LC 2 ) is presented, which exhibits local invariance to how much two channels of information contribute to an ultrasound image. The entire method has been specially designed for US-CT registration, where a strong correlation between X-ray attenuation coefficients and acoustic impedance is known, which allows a simulation of ultrasound effects from CT.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Global Registration of Ultrasound to MRI Using the LC2 Metric for Enabling Neurosurgical Guidance

Wein¹,

Ladikos²,

Fuerst

et al. 2013

Lecture Notes in Computer Science

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Abstract. Automatic and robust registration of pre-operative magnetic resonance imaging (MRI) and intra-operative ultrasound (US) is essential to neurosurgery. We reformulate and extend an approach which uses a Linear Correlation of Linear Combination (LC 2 )-based similarity metric, yielding a novel algorithm which allows for fully automatic US-MRI registration in the matter of seconds. It is invariant with respect to the unknown and locally varying relationship between US image intensities and both MRI intensity and its gradient. The overall method based on this both recovers global rigid alignment, as well as the parameters of a free-form-deformation (FFD) model. The algorithm is evaluated on 14 clinical neurosurgical cases with tumors, with an average landmark-based error of 2.52 mm for the rigid transformation. In addition, we systematically study the accuracy, precision, and capture range of the algorithm, as well as its sensitivity to different choices of parameters.

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Section: Parameter Sensitivitymentioning

confidence: 63%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Global Registration of Ultrasound to MRI Using the LC2 Metric for Enabling Neurosurgical Guidance

Wein¹,

Ladikos²,

Fuerst

et al. 2013

Lecture Notes in Computer Science

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“…For CT-ultrasound registration in the abdomen, the stateof-the-art uses a simulation of ultrasonic echogenicity and reflection from CT, and a specific similarity metric to overcome their unknown combination [5]. In the case of echocardiography, only the reflection term is significant, showing the myocardial walls of the cardiac cavities.…”

Section: Registration Of Single Volumementioning

confidence: 99%

“…imaging the left atrium from the right atrium). We therefore drop the simulated echogenicity and orientation-dependency from the approach in [5], and precompute a global simulated ultrasound reflection volume, henceforth denoted R, from the baseline C-arm CT volume. The registration of a single end-diastole ICE volume U to the baseline volume is then described as…”

Section: Registration Of Single Volumementioning

confidence: 99%

Towards Guidance of Electrophysiological Procedures with Real-Time 3D Intracardiac Echocardiography Fusion to C-arm CT

Wein

Camus

John

et al. 2009

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009

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Abstract. This paper describes a novel method for improving the navigation and guidance of devices and catheters in electrophysiology and interventional cardiology procedures using volumetric data fusion. The clinical workflow includes the acquisition and reconstruction of CT data from a C-arm X-ray angiographic system and the real-time acquisition of volumetric ultrasound datasets with a new intracardiac real-time 3D ultrasound catheter. Mono-and multi-modal volumetric registration methods, as well as visualization modes, that are suitable for real-time fusion are described, which are the key components of this work. Evaluation on phantom and in-vivo animal data shows that it is feasible to register and track the motion of real-time 3D intracardiac ultrasound in C-arm CT.

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Thoracic CT‐MRI coregistration for regional pulmonary structure–function measurements of obstructive lung disease

et al. 2017

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Automatic CT-ultrasound registration for diagnostic imaging and image-guided intervention

Cited by 300 publications

References 21 publications

Global Registration of Ultrasound to MRI Using the LC2 Metric for Enabling Neurosurgical Guidance

Global Registration of Ultrasound to MRI Using the LC2 Metric for Enabling Neurosurgical Guidance

Towards Guidance of Electrophysiological Procedures with Real-Time 3D Intracardiac Echocardiography Fusion to C-arm CT

Thoracic CT‐MRI coregistration for regional pulmonary structure–function measurements of obstructive lung disease

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