Registration of freehand 3D ultrasound and magnetic resonance liver images

Penney, Graeme P.; Blackall, J; Hamady, Mohamad; Sabharwal, Tushar; Adam, Andreas; Hawkes, David J.

doi:10.1016/j.media.2003.07.003

Cited by 201 publications

(162 citation statements)

References 31 publications

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“…This compressed region is very distinct from the remaining anatomical structures, its size (3.6mm) being consistent on all data we obtained from patients ( figure 1). This ROI definition is similar to the ones used in [8] and [9].…”

Section: Occlusion Handlingmentioning

confidence: 99%

“…Roche et al [7] use an adapted correlation ratio similarity measure in order to register the ultrasonic data simultaneously to both the intensity and the gradient information of a MRI scan. A registration involving an automatic mapping of MR and Ultrasound data to Vessel probability values and successive registration of this information is proposed in [8]. Using a CT data of a kidney, where the intensity values are enhanced with strong edges from the gradient, a registration with freehand 3D ultrasound is performed in [9].…”

Section: Introductionmentioning

confidence: 99%

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Automatic Registration and Fusion of Ultrasound with CT for Radiotherapy

Wein

Röper

Navab

2005

Lecture Notes in Computer Science

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Abstract. We present a framework for rigid registration of a set of B-mode ultrasound images to a CT scan in the context of Radiotherapy planning. Our main focus is on deriving an appropriate similarity measure based on the physical properties and artifacts of ultrasound. A combination of a weighted Mutual Information term, edge correlation, clamping to the skin surface and occlusion detection is able to assess the alignment of structures in ultrasound images and simulated slices generated from the CT data. Hence a set of ultrasound images, whose relative transformations are given by a magnetic tracking device, can be registered automatically to the CT scan. We validated our methods on neck data of patients with head and neck tumors and cervical lymph node metastases.

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Section: Occlusion Handlingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic Registration and Fusion of Ultrasound with CT for Radiotherapy

Wein

Röper

Navab

2005

Lecture Notes in Computer Science

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“…Many of the best existing approaches transform MRI and/or US intensities under applicationand organ-specific considerations, in order to make them easily comparable. This is done, for example, for liver vasculature in [9], with significant effort due to learning-based pre-processing. Similarly, pseudo-US images may be generated using segmented structures from MRI [1,2,5,6].…”

Section: Introductionmentioning

confidence: 99%

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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“…The registration-steps described above are based on the idea of volume-toslice registration, which was introduced for rigid registration by Penney et al [2] and for non-linear registration by Heldmann and Papenberg [3]. In the following we describe the specialized non-linear registration problem that will be solved using the discretize-then-optimize approach in our proposed algorithm.…”

Section: Methodsmentioning

confidence: 99%

Focused Registration of Tracked 2D US to 3D CT Data of the Liver

Olesch

Fischer

2011

Bildverarbeitung Für Die Medizin 2011

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Abstract. The paper deals with the registration of pre-operative 3D-CT-data to tracked intra-operative 2D-US-slices in the context of liver surgery. To bring such a method to clinical practice, it has to be fast and robust. In order to meet these demanding criteria, we propose two strategies. Instead of applying a time-consuming compounding process to obtain a 3D-US image, we use the 2D-slices directly and thereby drastically reduce the complexity and enhance the robustness of the scheme. Naturally, the surgeon does not need the same high resolution for the whole liver. We make use of this fact by applying a focusing technique to regions of special interest. With this, we reduce the overall amount of data to register significantly without sacrificing the accuracy in the ROIs. In contrast to other attempts, the high resolution result in the ROI is combined in a natural way with a global deformation field to obtain a smooth registration of the whole liver. Overall we arrive at a method with a favorable timing. The proposed algorithm was applied to four different patient data-sets and evaluated with respect to the reached vessel-overlap on validation slices. The obtained results are very convincing and will help to bring non-linear registration techniques to the operation theater.

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Registration of freehand 3D ultrasound and magnetic resonance liver images

Cited by 201 publications

References 31 publications

Automatic Registration and Fusion of Ultrasound with CT for Radiotherapy

Automatic Registration and Fusion of Ultrasound with CT for Radiotherapy

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

Focused Registration of Tracked 2D US to 3D CT Data of the Liver

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