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The estimated TREs for a FLE of 5 mm (obtained experimentally and based on the analytical expression) is in the millimetre range, and compares favourably to the accuracy achieved by a neurosurgeon performing the targeted neurosurgical procedures using a freehand technique, which is sometimes in the centimetre range. The discrepancies between the TREs obtained experimentally and those based on the analytical expression are because of the different assumptions regarding FLEs. TREs determined using the analytical expression were based on the assumption that the FLEs are independent, normally distributed random variables with zero means and equal variances while the FLEs used to determine TREs experimentally (based on

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 2 5 6 8 9 10 11 12 13 14 15 16 TRE (m m ) Fiducial

TRE

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measurements using the anthropomorphic skull) were based on random 5mm errors with a non-zero mean and unequal variance. The TREs obtained in the phantom study are more indicative of the expected errors for a given landmark configuration and localisation error.

It should be noted that the TREs obtained would underestimate the actual errors achieved in practice. This is because several factors are not accounted for, such as patient movement, the accuracy of a robot to find a position in space, etc. However, errors due to these may be reduced by proper calibration of the robot as well as ensuring that the patient‟s movement is minimised. The patient‟s head for example, may be held relatively stationary by immobilising it with a clamp, as is common in neurosurgery, thereby reducing potential errors due to patient movement. A landmark-based approach can therefore yield a sufficiently accurate registration for the required clinical accuracy of the targeted procedures if the landmarks can be localised within 5 mm of their true position and others sources of errors can be reduced.

The limitation of the proposed registration framework is that the required anatomical landmarks i.e. medial canthus and tragus may be unreliable or absent, as would be the case in the occurrence of facial/head trauma that distorts a patient‟s normal anatomy. In this circumstance, the registration would have to be performed using skin-affixed markers, although this would mean that the patient would be required to undergo a secondary scan.

Another drawback of the proposed registration framework is that because the surgical targets are predefined at the preoperative stage, the system cannot compensate for any errors due to imaging, surgical tool deflection and tissue deformation. It may also be prone to errors if the brain shifts after acquisition of preoperative images due to an expanding haematoma, patient positioning or because of the surgery itself. However, this problem is not unique to the proposed registration framework but is common to all registration methods that use preoperative images for registration. Intraoperative brain shift can only be accounted for by intraoperative imaging e.g. intraoperative CT/MRI. Potential errors due to brain shift may be reduced by careful patient positioning e.g. placing a patient in a position similar to their position during imaging. Reducing the time between imaging and surgery may reduce errors due to

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brain shift. Fortunately, brain shift is less of an issue with burr hole procedure than with an open craniotomy.

A framework for registering CT images to patient for MISEN has been described. Specifically, the proposed registration has been developed to support three emergency neurosurgical procedures. Because the three targeted neurosurgical procedures are intended to be performed robotically, image guidance is required. The registration system is a part of MISEN where guidance of the robot manipulator is based on registration between an image and the physical anatomy.

Simulation and experimental results of the registration framework based on a FLE of 5 mm showed an estimated TRE of within the millimetre range, which is the required accuracy for these procedures. In contrast, the accuracy of neurosurgeons performing the targeted procedures is sometimes in the centimetre range. Therefore, the proposed registration approach is sufficiently accurate and meets the required clinical accuracy of the targeted procedures. To reduce the subjectivity inherent when localising the landmarks, the automatic localisation of these landmarks is proposed. The next chapter describes the automatic localisation of these landmarks in CT images.

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Chapter 4

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4.1 Introduction

The use of anatomical landmarks as a basis for image to patient registration is appealing because the registration may be performed retrospectively. Two anatomical soft tissue landmarks of the head, the medial canthus and the tragus have been proposed as a registration basis for an automated CT image to patient registration system, as inputs for a rigid body registration algorithm.

In this chapter, algorithms for the automatic localisation of these landmarks in CT images are described. At present, anatomical soft tissue landmarks of the head are localised manually on CT images [124]. However, as CT images are digital in nature, a computational approach to the localisation of these landmarks was developed. The automatic localisation of these landmarks in CT images is an enabling step towards automating image to patient registration, with the aim of reducing the subjectivity inherent in the landmark selection process. A brief review of previous work on anatomical landmark localisation in CT images is presented, with the algorithm described in the subsequent section.