3D femur model reconstruction from biplane X-ray images: a novel method based on Laplacian surface deformation

Khullar, Vikas; Ravi, B.

doi:10.1007/s11548-014-1097-6

Cited by 32 publications

(27 citation statements)

References 26 publications

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“…For template-based 3D shape instantiation methods, statistical shape models (SSM) (Frangi et al, 2002), free form deformation (FFD) (Koh et al, 2011), and Laplacian surface deformation (Karade & Ravi, 2015) can be used for the representation of templates. SSM (Cootes et al, 1995) is a popular technique which represents a set of 3D meshes or 2D contours with the same number of vertices and connectivities.…”

Section: Introductionmentioning

confidence: 99%

A real-time and registration-free framework for dynamic shape instantiation

Zhou

Yang

Su-Lin

2018

Medical Image Analysis

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Real-time 3D navigation during minimally invasive procedures is an essential yet challenging task, especially when considerable tissue motion is involved. To balance image acquisition speed and resolution, only 2D images or low-resolution 3D volumes can be used clinically. In this paper, a real-time and registration-free framework for dynamic shape instantiation, generalizable to multiple anatomical applications, is proposed to instantiate high-resolution 3D shapes of an organ from a single 2D image intra-operatively. Firstly, an approximate optimal scan plane was determined by analyzing the pre-operative 3D statistical shape model (SSM) of the anatomy with sparse principal component analysis (SPCA) and considering practical constraints . Secondly, kernel partial least squares regression (KPLSR) was used to learn the relationship between the pre-operative 3D SSM and a synchronized 2D SSM constructed from 2D images obtained at the approximate optimal scan plane. Finally, the derived relationship was applied to the new intra-operative 2D image obtained at the same scan plane to predict the high-resolution 3D shape intra-operatively. A major feature of the proposed framework is that no extra registration between the pre-operative 3D SSM and the synchronized 2D SSM is required. Detailed validation was performed on studies including the liver and right ventricle (RV) of the heart. The derived results (mean accuracy of 2.19mm on patients and computation speed of 1ms) * Corresponding author

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Section: Introductionmentioning

confidence: 99%

A real-time and registration-free framework for dynamic shape instantiation

Zhou

Yang

Su-Lin

2018

Medical Image Analysis

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“…3E). A similar process have been validated by Karade and Ravi(2015) on bi-planar x-ray and found a mean error of 1.2 mm.…”

Section: Post-processingmentioning

confidence: 83%

A novel non-invasive method for measuring knee joint laxity in four dof: In vitro proof-of-concept and validation

Pedersen

Vanheule

Wirix-Speetjens

et al. 2019

Journal of Biomechanics

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Knee joint laxity or instability is a common problem that may have detrimental consequences for patients. Unfortunately, assessment of knee joint laxity is limited by current methodologies resulting in suboptimal diagnostics and treatment. This paper presents a novel method for accurately measuring non-invasive knee joint laxity in four degrees-of-freedom (DOF). An arthrometer, combining a parallel manipulator and a six-axis force/moment sensor, average mean difference for translations of 0.08 mm and an average limit of agreement between-1.64 mm and 1.80 mm. The average mean difference for rotations was 0.10°and the limit of agreement were between-0.85° and 1.05°. The presented method eliminates several limitations present in current methods and may prove a valuable tool for assessing knee joint laxity. Current stress radiography is also only assessing single plane laxity, but is not limited by low reliability (Schulz et al., 2005) or STA (Garavaglia et al., 2007). The radiation exposure from such measurements may, however, induce serious health risks for both the patient and operator making the method unsuitable for many applications (Balonov and Shrimpton, 2012). Joint instability must be assessed in multiple DOF in order to fully understand the complexity of the joint structures and the interplay between ligaments (Hirschmann and Müller, 2015). Neither clinical tests, arthrometry nor 1D stress radiography possesses the potential to obtain this information. Furthermore, these methods potentially over-constrain the joint during measurements, by restricting out-of-plane motion, making it appear more stable and potentially shift the load distribution unnaturally in the joint (Woo et al., 1999). Methods capable of measuring unconstrained knee joint laxity in multiple DOF not restricted by the above limitations are a prerequisite to progress the field of research in knee instability and advance current clinical assessment of joint instability. Novel methods utilizing robotics for assessing knee joint laxity has, therefore, recently emerged (Branch et al., 2015; Lorenz et al., 2015). These methods exercise high repeatability and accuracy in the application of forces, however, they are still limited by their inability to measure true bone motion due to STA. By combining robotic technology with new medical image modalities, the aforementioned limitation can be overcome which may make it possible to conceive a laxity measurement method with high accuracy, multiplanar assessment and unaffected by STA. Therefore, this paper proposes such a novel method for measuring knee joint laxity in multiple DOF, combining parallel manipulator technology and low dose biplanar x-ray acquisition. Methods Development An in vivo arthrometer was custom developed, combining a parallel manipulator (H-820, Physik Instrumente, Germany) and a six axis force/moment (F/M) sensor (Omega85 SI-1900-80, ATI Industrial Automation, USA) (see Fig. 1). The parallel manipulator has a manufacturer reported repeatability of ±1 µm and the F/M sensor has a re...

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“…Most of these studies require prior knowledge of the 3D anatomy model to compensate for the lack of information obtainable from the 2D images [1]. This information can be provided by incorporating a generic geometrical shape of the bone structure [3], [4], [27], by Statistical Shape Models (SSM) [5], [6], [7], [28], by Parametric Models [8], [9], [10], or by using independent shape and appearance models that are learned from a set of training data to encode the information of the bone [29]. To reduce radiation exposure, some authors have proposed 3D reconstruction methods from bi-planar Xray images based on stereovision techniques [4], [11], [12].…”

Section: Studies On the Reconstruction Of Bone Models Based On Informmentioning

confidence: 99%

3D Reconstruction of the proximal femur shape from few pairs of x-ray radiographs

Akkoul

Hafiane

Rozenbaum

et al. 2017

Signal Processing: Image Communication

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This paper presents a 3D reconstruction method of the proximal femur shape based on contour identification from pairs of 2D X-ray radiographs. The aim is to reconstruct the 3D surface of the proximal femur from a limited number of projections. Our approach is based on the reconstruction of several 3D contours, which are meshed to obtain a 3D shape. Our proposed algorithm is based on different processing steps to obtain the 3D personalized model. Three approaches are proposed. The first technique consists of contour extraction, matching the points of these contours and calculation of a 3D contour using an original algorithm. The second and third techniques use the results of the previous method as well as new points chosen by an operator from a reference model of femur to improve accuracy either globally or only in sensitive areas. The proposed method was evaluated on 10 cadaveric proximal femurs using the 3D CT-Scan models. Obtained results show good performance and promising perspectives for 3D shape reconstruction of the femur from only a few pairs of radiographs.

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3D femur model reconstruction from biplane X-ray images: a novel method based on Laplacian surface deformation

Cited by 32 publications

References 26 publications

A real-time and registration-free framework for dynamic shape instantiation

A real-time and registration-free framework for dynamic shape instantiation

A novel non-invasive method for measuring knee joint laxity in four dof: In vitro proof-of-concept and validation

3D Reconstruction of the proximal femur shape from few pairs of x-ray radiographs

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