Multimodal Image Registration for Preoperative Planning and Image-Guided Neurosurgical Procedures

Image registration is the process of transforming images acquired at different time points, or with different imaging modalities, into the same coordinate system. It is an essential part of any neurosurgical planning and navigation system because it facilitates combining images with important complementary structural and functional information to improve the information basis on which a surgeon makes critical decisions. Magnetic Resonance Imaging (MRI) can be used to generate both structural and functional neurological images and thus plays an important role in planning neurosurgical procedures (Figure 1). High-resolution anatomical MR images can be used to discriminate between healthy and pathological tissue, functional MRI (fMRI) can be used to identify the location, and extent, of important cognitive areas of the brain while Diffusion Tensor Imaging (DTI) can be used to identify the white matter connectivity of the brain. If important cognitive areas are injured or removed during surgery, it can have an adverse effect on the patient's quality of life post surgery. Figure 1 Anatomical and functional MR information acquired pre-operatively48. (A) An anatomical T1 scan of a patient with a lesion in the left frontal region. (B) Extracted white matter tracts using tractography from DTI. (C) Speech areas found with fMRI overlaid ... It is especially important to put these complementary images into the same coordinate system when functional areas are located adjacent to a tumor. A map of important anatomical structures and surrounding functional areas can then be built and used by the surgeon to plan the surgical procedure1. Because the brain is enclosed by the skull, a rigid transformation which consists of a translation and rotation is adequate to align these images. The importance of fMRI in planning of tumor resections is underlined by the findings of Petrella et al.2 where the effect of therapeutic decision making in patients with brain tumors was studied. Treatment plans before and after seeing the fMRI differed in 19 out of 49 patients, with a more aggressive approach recommended after imaging in 18 patients. The availability of fMRI resulted in reduced surgical time in 22 patients who underwent surgery, a more aggressive resection in 6 patients and a smaller resection in 2 patients. Other modalities than MRI have been and are also currently being used in neurosurgical navigation systems. Positron Emission Tomography (PET) allows analyzing function and metabolism of the brain. It is mainly used to localize the most malignant parts of brain tumors, but has also been used to locate eloquent areas of the cortex. However, it does not provide useful structural information and is therefore combined with high resolution structural images, e.g. MRI and Computed Tomography (CT), to accurately locate the functional areas in relation to anatomical structures3. For planning of neurosurgical procedures, PET images are usually combined with MRI because they provide the best tissue contrast. fMRI has now superseded PET as a functional imaging modality for locating eloquent cortex because it eliminates any radiation exposure. Registration also plays an important role in other neurosurgical guidance applications besides tumor removal, for instance in resection of suspected epileptogenic centers. The epileptogenic centers are located using information from subdural electrodes and the location of these electrodes extracted from CT. By registering the CT with structural MRI, the centers can be precisely located within the brain anatomy and used to plan the resection4. Because of brain-shift, tissue resection and retraction, the pre-operative image information does not necessarily reflect the intra-operative anatomy the surgeon sees during surgery, even after rigid registration. Brain-shift, a deformation of the brain tissue that occurs after the craniotomy and opening of the dura mater, is caused by various combined factors: cerebrospinal fluid (CSF) leakage, gravity, edema, tumor mass effect and administration of osmotic diuretics5,6. A clinical example of brain-shift is shown in Figure 2. Reported deformations of the brain tissue caused by brain-shift are up to 24mm7–9, and further deformations may be induced because of tissue manipulation during the resection5,10. Figure 2 Pre- and intra-operative MR images that shows brain-shift and resection. (A) Pre- operative image. (B) Intra-operative image. Notice the cavity which is a result of the tumor resection. (C) Absolute difference image of the images in (A) and (B). Notice ... As a way of acquiring up to date anatomical information during surgery, intra-operative MRI has been introduced into the surgical workflow11,12. Intra-operative imaging during neuro-surgical procedures helps identify any residual tumor tissue and leads to a significant increase in the extent of tumor removal and survival times13. However, intra-operative images are often of a degraded quality compared to the pre-operative images. Many factors lead to reduced quality in intra-operative MRI: fast acquisition protocols which are used to reduce the surgical time, intra-operative MRI scanners often operate on lower magnetic field strengths than diagnostic MRI scanners, surface coils are used instead of head coils, and disturbances to the main magnetic field caused by the surgery. Ultrasound (US) is another imaging modality used to acquire up-to-date images during neurosurgery 14. Compared to MRI, acquisition is faster and does not require the patient to move, but image quality and soft tissue discrimination is reduced. US can identify functional information such as blood flow, but not cognitive functions such as motor, visual and language cortices. Reliable and accurate acquisition of intra-operative fMRI is in general not feasible, either because of factors such as time constraint, the requirement that the patient be conscious during acquisition of fMRI, and disturbances of the magnetic field from the surgical procedure1. How then can we accurately and robustly provide the surgeon with the location of these important eloquent areas in relation to the intra-operative anatomy? Non-rigid transformations have a high degree of freedom and are capable of accommodating the local brain deformations that occur during surgery. Non-rigid intra-operative registration has therefore been introduced as a way of mapping the pre-operative functional information into the intra-operative space. In practice, a transformation cascade is constructed to facilitate intra-operative navigation of the pre-operative image data (see Figure 3). First, the functional images, typically fMRI and DTI, are rigidly registered to a high-resolution pre-operative anatomical image. Next, the pre-operative anatomical image is non-rigidly registered with the intra-operative image. Finally, to facilitate navigation of these images, the intra-operative image is registered with the physical patient space by estimating a rigid transformation from corresponding (fiducial) landmarks identified in the image and on the patient. Most commercial neuro-surgical navigation systems register the pre-operative images to the intra-operative space using fiducial markers and rigid registration and do not take into account the non-rigid deformations caused by brain-shift15. Figure 3 Transformation cascade. Three steps are needed to map the functional images, fMRI and DTI, into the intra-operative physical space. First, rigid, or affine, transforms, RfMRI and RDTI are estimated to align the functional images with the pre-operative ... Brigham and Women’s Hospital (BWH), an affiliate of Harvard Medical School, has been one of the pioneers in developing intra-operative registration methods for aligning pre-and intra-operative images of the brain12. In this paper we present a general overview of intra-operative registration and highlight some of the recent developments at BWH. The rest of the paper is organized as follows. First we introduce patient-to-image registration followed by a discussion of the error involved in such estimations. Next, we provide an introduction of image-to-image registration and the specific challenges of intra-operative registration and, finally, present an overview of the research that has been carried out to handle these challenges.