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Journal of Medical Devices | Volume 10 | Issue 1 | research-article
Research Papers

Design Optimization of a Magnetic Field-Based Localization Device for Enhanced Ventriculostomy

[+] Author and Article Information
Luc Maréchal, Shuoyu Ding

Engineering Product Development,
Singapore University of Technology and Design,
8 Somapah Road,
Singapore 487372, Singapore

Shaohui Foong

Assistant Professor
Engineering Product Development,
Singapore University of Technology and Design,
8 Somapah Road,
Singapore 487372, Singapore
e-mail: foongshaohui@sutd.edu.sg

Kristin L. Wood

Professor and Head of Pillar Engineering Product
Development,
Singapore University of Technology and Design,
8 Somapah Road,
Singapore 487372, Singapore

Vaibhav Patil

Patient Informatics,
Boston, MA 02113

Rajiv Gupta

Associate Professor
Harvard Medical School,
Massachusetts General Hospital,
55 Fruit Street, GRB-273A,
Boston, MA 02114

1Corresponding author.

Manuscript received February 16, 2015; final manuscript received November 11, 2015; published online February 11, 2016. Assoc. Editor: Rosaire Mongrain.

J. Med. Devices 10(1), 011006 (Feb 11, 2016) (9 pages) Paper No: MED-15-1024; doi: 10.1115/1.4032614 History: Received February 16, 2015; Revised November 11, 2015
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The accuracy of many freehand medical procedures can be improved with assistance from real-time localization. Magnetic localization systems based on harnessing passive permanent magnets (PMs) are of great interest to track objects inside the body because they do not require a powered source and provide noncontact sensing without the need for line-of-sight. While the effect of the number of sensors on the localization accuracy in such systems has been reported, the spatial design of the sensing assembly is an open problem. This paper presents a systematic approach to determine an optimal spatial sensor configuration for localizing a PM during a medical procedure. Two alternative approaches were explored and compared through numerical simulations and experimental investigation: one based on traditional grid configuration and the other derived using genetic algorithms (GAs). Our results strongly suggest that optimizing the spatial arrangement has a larger influence on localization performance than increasing the number of sensors in the assembly. We found that among all the optimization schemes, the approach utilizing GA produced sensor designs with the smallest localization errors.
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References

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Figures

Grahic Jump Location
Fig. 1

Magnet dipole model. Pm and Ps,i are the position coordinates of, respectively, the magnet and ith sensor with regard to the origin O. U is the magnet's magnetization vector.

+Magnet dipole model. Pm and Ps,i are the position coordinates of, respectively, the magnet and ith sensor with regard to the origin O. U is the magnet's magnetization vector.
View Large  |  Save Figure  |  Download Slide (.ppt)
Magnet dipole model. Pm and Ps,i are the position coordinates of, respectively, the magnet and ith sensor with regard to the origin O. U is the magnet's magnetization vector.
Grahic Jump Location
Fig. 3

Flow chart of a GA optimization algorithm and genetic operators used to vary the programing of individuals from one generation to the next: (a) crossover and (b) mutation

+Flow chart of a GA optimization algorithm and genetic operators used to vary the programing of individuals from one generation to the next: (a) crossover and (b) mutation
View Large  |  Save Figure  |  Download Slide (.ppt)
Flow chart of a GA optimization algorithm and genetic operators used to vary the programing of individuals from one generation to the next: (a) crossover and (b) mutation
Grahic Jump Location
Fig. 2

Approaches in spatial sensor configuration in bounded 2D space. (a) Rectangular array: the parameters xspacing and yspacing are the spatial spacing between two sensors, and nbs in x and nbs in y are the number of sensors in x and y directions, respectively. (b) Circular pattern: n sensors are equally spaced along a circle defined by the parameters center and radius. (c) GA pattern: Each node in a predefined spatial grid is a possible sensor location.

+Approaches in spatial sensor configuration in bounded 2D space. (a) Rectangular array: the parameters xspacing and yspacing are the spatial spacing between two sensors, and nbs in x and nbs in y are the number of sensors in x and y directions, respectively. (b) Circular pattern: n sensors are equally spaced along a circle defined by the parameters center and radius. (c) GA pattern: Each node in a predefined spatial grid is a possible sensor location.
View Large  |  Save Figure  |  Download Slide (.ppt)
Approaches in spatial sensor configuration in bounded 2D space. (a) Rectangular array: the parameters xspacing and yspacing are the spatial spacing between two sensors, and nbs in x and nbs in y are the number of sensors in x and y directions, respectively. (b) Circular pattern: n sensors are equally spaced along a circle defined by the parameters center and radius. (c) GA pattern: Each node in a predefined spatial grid is a possible sensor location.
Grahic Jump Location
Fig. 4

Influence of the initial population diversity on the GA convergence and performance. Two different initial populations are tested here for a nine-sensor configuration. The initial populations in (a) has a lower diversity than in (b) and appears to stagnate to a local minimum and results in a higher objective function value.

+Influence of the initial population diversity on the GA convergence and performance. Two different initial populations are tested here for a nine-sensor configuration. The initial populations in (a) has a lower diversity than in (b) and appears to stagnate to a local minimum and results in a higher objective function value.
View Large  |  Save Figure  |  Download Slide (.ppt)
Influence of the initial population diversity on the GA convergence and performance. Two different initial populations are tested here for a nine-sensor configuration. The initial populations in (a) has a lower diversity than in (b) and appears to stagnate to a local minimum and results in a higher objective function value.
Grahic Jump Location
Fig. 8

Experimental error comparison at various z displacements along the trajectory points

+Experimental error comparison at various z displacements along the trajectory points
View Large  |  Save Figure  |  Download Slide (.ppt)
Experimental error comparison at various z displacements along the trajectory points
Grahic Jump Location
Fig. 9

(a) Experimental setup with the 3D measurement device. (b) Experimental localization. The reference trajectory performed by the robot arm aims at targeting the ventricles (in gray) in the patient's brain.

+(a) Experimental setup with the 3D measurement device. (b) Experimental localization. The reference trajectory performed by the robot arm aims at targeting the ventricles (in gray) in the patient's brain.
View Large  |  Save Figure  |  Download Slide (.ppt)
(a) Experimental setup with the 3D measurement device. (b) Experimental localization. The reference trajectory performed by the robot arm aims at targeting the ventricles (in gray) in the patient's brain.
Grahic Jump Location
Fig. 5

CT scan-rendered model of the head with fitted ellipsoid shape. The GA-optimized sensor position for a nine-sensor configuration is shown on the ellipsoid mesh. The sensors are oriented so that their Zs axis is normal to the ellipsoid surface and Ys axis aligned with the longitude of the mesh. The dots on the mesh represent the spatial position of individual sensors through GA optimization.

+CT scan-rendered model of the head with fitted ellipsoid shape. The GA-optimized sensor position for a nine-sensor configuration is shown on the ellipsoid mesh. The sensors are oriented so that their Zs axis is normal to the ellipsoid surface and Ys axis aligned with the longitude of the mesh. The dots on the mesh represent the spatial position of individual sensors through GA optimization.
View Large  |  Save Figure  |  Download Slide (.ppt)
CT scan-rendered model of the head with fitted ellipsoid shape. The GA-optimized sensor position for a nine-sensor configuration is shown on the ellipsoid mesh. The sensors are oriented so that their Zs axis is normal to the ellipsoid surface and Ys axis aligned with the longitude of the mesh. The dots on the mesh represent the spatial position of individual sensors through GA optimization.
Grahic Jump Location
Fig. 6

Absolute positional estimation error at various distances from the insertion point of the catheter across the trajectory path, in presence of 2.5 μT RMS Gaussian measurement noise

+Absolute positional estimation error at various distances from the insertion point of the catheter across the trajectory path, in presence of 2.5 μT RMS Gaussian measurement noise
View Large  |  Save Figure  |  Download Slide (.ppt)
Absolute positional estimation error at various distances from the insertion point of the catheter across the trajectory path, in presence of 2.5 μT RMS Gaussian measurement noise
Grahic Jump Location
Fig. 7

Experimental setup comprising of six-axis robot arm with N52 cylindrical magnet at end-effector and magnetic sensor board affixed onto the worktable

+Experimental setup comprising of six-axis robot arm with N52 cylindrical magnet at end-effector and magnetic sensor board affixed onto the worktable
View Large  |  Save Figure  |  Download Slide (.ppt)
Experimental setup comprising of six-axis robot arm with N52 cylindrical magnet at end-effector and magnetic sensor board affixed onto the worktable

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