Multiorgan motion tracking in dynamic magnetic resonance imaging for evaluation of pelvic system mobility and shear strain

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Numerical simulation of pelvic system could lead to a better understanding of common pathology through objective and reliable analyses of pelvic mobility according to mechanical principles. In clinical context, patient‐specific simulation has the potential for a proper patient‐personalised cure. For this purpose, a simulable 3D geometrical model, well suited to patient anatomy, is required. However, the geometric modelling of pelvic system from medical images (MRI) is a complex operator‐dependent and time‐consuming process, not adapted to patient‐specific applications. This paper is addressing this challenging computational problem. The objective is to develop a technique, providing a smooth, consistent, and readily usable 3D geometrical model, seamlessly from image to simulation. In this paper, we use a generic topologically‐simplified B‐Spline model to represent pelvic organs. The presented paper develops a Virtual Image Correlation method to find the best correlation between the geometry and the image. The final reconstructed geometrical model is to be compatible with meshing and finite element simulation. Then, a variety of tests are performed to prove the concept, through both prototypical and pelvic models. Finally, since the pelvic system is complex, including structures hardly identifiable in MRI, some feasible solutions to introduce more complex pelvic models are also discussed.

“…The extension of the method from 2D to 3D does not change the definition of virtual image and cost function. As given in previous studies, a level set function

scriptL : d \in double-struckR \to double-struckR

is defined by the following equation:

scriptL false(d false) = ({, \begin{matrix} array A_{m a x} i f d < 0, \\ array A_{m i n} + \frac{A_{m a x} - A_{m i n}}{2} \times (1 + c o s (\frac{π d}{τ})) \\ array i f 0 \leq d \leq τ, \\ array A_{m i n} i f d > τ . \end{matrix}),

where d is the distance from a position x to the target surface. A m a x , A m i n represents the maximum and minimum of gray level, near the boundary of the surface.…”

Section: D Virtual Image Correlationmentioning

confidence: 99%

“…In previous studies, we adapted the VIC technique to create the flexible 2D geometrical model of pelvic organs on 2D static MRIs, and to track these organs on the temporal sequence of 2D dynamic MRIs. In this paper, we are presenting this technique in the case of the 3D parametric reconstruction of geometrical models.…”

Section: Introductionmentioning

confidence: 99%

Virtual image correlation of magnetic resonance images for 3D geometric modelling of pelvic organs

Jiang

Mayeur

Witz

et al. 2019

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“…The purpose of this communication is to use an accurate boundary measurement to constrain the solution of the DIC, within an existing DIC code, in order to obtain the same precision close to the boundary and far from it. The method for detecting the position of the boundary is the Virtual Image Correlation (VIC), which is able to detect the position of a boundary in a digital image with better accuracy than computer graphics issued methods . The principle of the VIC is to find the best correlation between a virtual image and the contour.…”

Section: Introductionmentioning

confidence: 99%

Improvement of the digital image correlation close to the borders of an object

Baconnais

Réthoré

François

2020

The measurement error of the finite-element-based Digital Image Correlation is reduced along the borders of an object by using the precise measurement, obtained by Virtual Image Correlation (VIC). The proposed method is called Virtual and Digital Image Correlation (VDIC). The boundary, identified by the VIC with a subpixel precision, is used firstly to create an adapted mesh and secondly to create a pixel mask. Futhermore, the VDIC also uses the VIC measurement of the boundary in the deformed state as a constraint on the radial displacement field along the border. The optimal values of the parameters of the VDIC are discussed throughout a sensitivity analysis. The compared performances of the constrained and unconstrained VDIC are obtained thanks to a synthetic test of a plate with a hole in tension. Finally, the method is checked on a sample geometry which includes holes and a U-shaped notch.

“…For each image, it is important to follow material points of the contour over time with maximum precautions, especially to avoid drifting along the contour boundaries. Additional feature‐sensitive constraints are added in order to better track the material points and to ensure the plausibility of the computed motion . These constraints consist in the detection of corner points of high curvature in order to impose normal movements of the tracked points.…”

Section: Tracking In Dynamic Mrismentioning

confidence: 99%

“…As shown in Figure , [ pf 0 , pf 1 ,…, pf l ] are the selected points. Then, the contour is deformed to fit the other images in the DYN sequence, by minimizing iteratively an energy term (more details can be found in Jiang et al).…”

Section: Tracking In Dynamic Mrismentioning

confidence: 99%

Three‐dimensional physics‐based registration of pelvic system using 2D dynamic magnetic resonance imaging slices

Courtecuisse

Jiang

Mayeur

et al. 2020

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This paper introduces a method for dynamic 3D registration of female pelvic organs using 2D dynamic magnetic-resonance images (MRIs). The aim is to provide a better knowledge and understanding of pathologies such as prolapsus or abnormal mobility of tissues. 2D dynamic MRI sequences are commonly used in nowadays clinical routines in order to evaluate the dynamic of organs, but due to the limited view, subjectivity related to human perception cannot be avoided in the diagnoses. A novel method for 2D/3D registration is proposed combining 3D finite element models with a priori knowledge of boundary conditions, in order to provide a 3D extrapolation of the dynamic of the organs observed in a single 2D MRI slice. The method is applied to the four main structures of the female pelvic floor (bladder, vagina, uterus and rectum), providing a full 3D visualisation of the organs' displacements. The methodology is evaluated with two patient-specific data sets of volunteers presenting no pelvic pathology, and a sensitivity study is performed using synthetic data. The resulting simulations provide an estimation of the dynamic 3D shape of the organs facilitating diagnosis compared with 2D sequences. Moreover, the method follows a protocol compatible with current clinical constraints presenting this way potential short term medical applications.