Robust nonrigid registration to capture brain shift from intraoperative MRI

Origins of intraoperative MRI

Neurosurgical diagnosis and intervention has evolved through improved neuroimaging, allowing better visualization of anatomy and pathology. This article discusses the various systems that have been designed over the last decade to meet the requirements of neurosurgical patients and opines on the potential future developments in the technology and application of intraoperative MRI. Because the greatest amount of experience with intraoperative MRI comes from its use in brain tumor resection, this article focuses on the origins of intraoperative MRI in relation to this field.

Awake craniotomy and intraoperative magnetic resonance imaging: patient selection, preparation, and technique

OBJECTIVES

Intraoperative magnetic resonance imaging (iMRI) has been reported to augment radical brain tumor resection. "Awake craniotomy" is a technique to conserve function during brain tumor surgery. We report on the combination of these 2 techniques, with special emphasis on potential adverse effects, caveats, and patient preparation.

METHODS

Thirty-four patients had 38 awake craniotomies with cortical stimulation within an integrated MRI-operating room with a 1.5-T unit. Thirty-two lesions were left hemispheric, 6 on the right side.

RESULTS

Preparation for iMRI per patient amounted to 20 to 25 minutes, in addition to scan time. The procedure was well tolerated by all patients. Thirty-two stated that they would undergo this procedure again, if necessary. Four underwent a second "awake" surgery in the iMRI for recurrent disease. Intraoperative MRI had no adverse effect, such as seizures. Cortical stimulation could be performed without restrictions outside the 5-gauss line.

CONCLUSIONS

The combination of iMRI and awake craniotomy is demanding but well tolerated by patients. Careful preoperative evaluation is essential to ensure compliance. There is no adverse effect through iMRI on the awake patient or the results of cortical stimulation. Since the introduction of the iMRI in our department in 2005, all awake craniotomies were done in this setting. The implementation of these 2 techniques into our procedures is demanding, and necessitates thorough preparation but has broadened our basis for surgical decision making. However, to substantiate our positive perception, more clinical data are being compiled.

Semiautomatic registration of pre- and postbrain tumor resection laser range data: method and validation

This paper presents a semiautomatic method for the registration of images acquired during surgery with a tracked laser range scanner (LRS). This method, which relies on the registration of vessels that can be visualized in the pre- and the postresection images, is a component of a larger system designed to compute brain shift that occurs during tumor resection cases. Because very large differences between pre- and postresection images are typically observed, the development of fully automatic methods to register these images is difficult. The method presented herein is semiautomatic and requires only the identification of a number of points along the length of the vessels. Vessel segments joining these points are then automatically identified using an optimal path finding algorithm that relies on intensity features extracted from the images. Once vessels are identified, they are registered using a robust point-based nonrigid registration algorithm. The transformation computed with the vessels is then applied to the entire image. This permits establishment of a complete correspondence between the pre- and post-3-D LRS data. Experiments show that the method is robust to operator errors in localizing homologous points and a quantitative evaluation performed on ten surgical cases shows submillimetric registration accuracy.

On the unimportance of constitutive models in computing brain deformation for image-guided surgery

Imaging modalities that can be used intra-operatively do not provide sufficient details to confidently locate the abnormalities and critical healthy areas that have been identified from high-resolution pre-operative scans. However, as we have shown in our previous work, high quality pre-operative images can be warped to the intra-operative position of the brain. This can be achieved by computing deformations within the brain using a biomechanical model. In this paper, using a previously developed patient-specific model of brain undergoing craniotomy-induced shift, we conduct a parametric analysis to investigate in detail the influences of constitutive models of the brain tissue. We conclude that the choice of the brain tissue constitutive model, when used with an appropriate finite deformation solution, does not affect the accuracy of computed displacements, and therefore a simple linear elastic model for the brain tissue is sufficient.

Bayesian registration via local image regions: information, selection and marginalization

We propose a novel Bayesian registration formulation in which image location is represented as a latent random variable. Location is marginalized to determine the maximum a priori (MAP) transform between images, which results in registration that is more robust than the alternatives of omitting locality (i.e. global registration) or jointly maximizing locality and transform (i.e. iconic registration). A mathematical link is established between the Bayesian registration formulation and the mutual information (MI) similarity measure. This leads to a novel technique for selecting informative image regions for registration, based on the MI of image intensity and spatial location. Experimental results demonstrate the effectiveness of the marginalization formulation and the MI-based region selection technique for ultrasound (US) to magnetic resonance (MR) registration in an image-guided neurosurgical application.

Nonrigid Registration of Brain Tumor Resection MR Images Based on Joint Saliency Map and Keypoint Clustering

This paper proposes a novel global-to-local nonrigid brain MR image registration to compensate for the brain shift and the unmatchable outliers caused by the tumor resection. The mutual information between the corresponding salient structures, which are enhanced by the joint saliency map (JSM), is maximized to achieve a global rigid registration of the two images. Being detected and clustered at the paired contiguous matching areas in the globally registered images, the paired pools of DoG keypoints in combination with the JSM provide a useful cluster-to-cluster correspondence to guide the local control-point correspondence detection and the outlier keypoint rejection. Lastly, a quasi-inverse consistent deformation is smoothly approximated to locally register brain images through the mapping the clustered control points by compact support radial basis functions. The 2D implementation of the method can model the brain shift in brain tumor resection MR images, though the theory holds for the 3D case.

Combination of intraoperative 5-aminolevulinic acid-induced fluorescence and 3-D MR imaging for guidance of robotic laser ablation for precision neurosurgery

A combination of 5-aminolevulinic acid (5-ALA)-induced fluorescence and three-dimensional (3-D) magnetic resonance imaging (MRI) of a brain tumor has been incorporated into a robotic laser ablation neurosurgery system. 5-ALA is a non-fluorescent prodrug that leads to intracellular accumulation of fluorescent protoporphyrins IX (PpIX) in malignant glioma. The PpIX tends to accumulate in pathological lesions, and emits red fluorescence when excited by blue light. This fluorescence is illuminated with laser excitation, enables intra-operative identification of the position of a tumor and provides guidance for resection with laser photocoagulation. The information provided by the MRI is enhanced by the 5-ALA fluorescence data, and this enhanced information is integrated into a robotic laser ablation system. The accuracy of the fluorescent measurement of the tumor is improved using high-precision spectral analysis. The fluorescence assists in the detection of malignant brain tumors intraoperatively and improves their removal rate.

A nonrigid image registration framework for identification of tissue mechanical parameters

We present a modular framework for mechanically regularized nonrigid image registration of 3D ultrasound and for identification of tissue mechanical parameters. Mechanically regularized deformation fields are computed from sparsely estimated local displacements. We enforce image-based local motion estimates by applying concentrated forces at mesh nodes of a mechanical finite-element model. The concentrated forces are generated by the elongation of regularization springs connected to the mesh nodes as their free ends are displaced according to local motion estimates. The regularization energy corresponding to the potential energy stored in the springs is minimized when the mechanical response of the model matches the observed response of the organ. We demonstrate that this technique is suitable for identification of material parameters of a nonlinear viscoelastic liver model and demonstrate its benefits over traditional indentation methods in terms of improved volumetric agreement between the model response and the experiment.

A comparative analysis of coregistered ultrasound and magnetic resonance imaging in neurosurgery

OBJECTIVE

This work presents qualitative and quantitative side-by-side comparisons of oblique coregistered magnetic resonance imaging (MRI) scans and ultrasound images obtained during 35 neurosurgical procedures.

METHODS

Spatially registered series of ultrasound images were recorded for subsequent off-line evaluation and comparison with corresponding preoperative MRI studies. The degree of misalignment was reduced by reregistering the target volume directly with segmented features.

RESULTS

The initial apparent spatial misalignment of the target volume after craniotomy ranged from 0.11 to 8.73 mm (mean, 4.01 mm). After reregistration, the mutual information in overlapping segmented features was increased, presumably evidence of a better alignment locally. Additionally, the degree of feature congruence, which was assessed quantitatively through a convex hull approximation, demonstrated that the ultrasound volume was consistently smaller than its MRI counterpart.

CONCLUSION

Although intraoperative ultrasound tends to be difficult to interpret by itself, when accurately coregistered with preoperative MRI scans, its potential utility as a navigational guide is enhanced.

Compensation of geometric distortion effects on intraoperative magnetic resonance imaging for enhanced visualization in image-guided neurosurgery

OBJECTIVE

Preoperative magnetic resonance imaging (MRI), functional MRI, diffusion tensor MRI, magnetic resonance spectroscopy, and positron-emission tomographic scans may be aligned to intraoperative MRI to enhance visualization and navigation during image-guided neurosurgery. However, several effects (both machine- and patient-induced distortions) lead to significant geometric distortion of intraoperative MRI. Therefore, a precise alignment of these image modalities requires correction of the geometric distortion. We propose and evaluate a novel method to compensate for the geometric distortion of intraoperative 0.5-T MRI in image-guided neurosurgery.

METHODS

In this initial pilot study, 11 neurosurgical procedures were prospectively enrolled. The scheme used to correct the geometric distortion is based on a nonrigid registration algorithm introduced by our group. This registration scheme uses image features to establish correspondence between images. It estimates a smooth geometric distortion compensation field by regularizing the displacements estimated at the correspondences. A patient-specific linear elastic material model is used to achieve the regularization. The geometry of intraoperative images (0.5 T) is changed so that the images match the preoperative MRI scans (3 T).

RESULTS

We compared the alignment between preoperative and intraoperative imaging using 1) only rigid registration without correction of the geometric distortion, and 2) rigid registration and compensation for the geometric distortion. We evaluated the success of the geometric distortion correction algorithm by measuring the Hausdorff distance between boundaries in the 3-T and 0.5-T MRIs after rigid registration alone and with the addition of geometric distortion correction of the 0.5-T MRI. Overall, the mean magnitude of the geometric distortion measured on the intraoperative images is 10.3 mm with a minimum of 2.91 mm and a maximum of 21.5 mm. The measured accuracy of the geometric distortion compensation algorithm is 1.93 mm. There is a statistically significant difference between the accuracy of the alignment of preoperative and intraoperative images, both with and without the correction of geometric distortion (P < 0.001).

CONCLUSION

The major contributions of this study are 1) identification of geometric distortion of intraoperative images relative to preoperative images, 2) measurement of the geometric distortion, 3) application of nonrigid registration to compensate for geometric distortion during neurosurgery, 4) measurement of residual distortion after geometric distortion correction, and 5) phantom study to quantify geometric distortion.

Image based modeling of defibrillation in children

Volume imaging, defibrillation electrode models, and finite element modeling are employed in patient-specific procedural modeling in pediatric patients with cardiac arrhythmias. Due to variable size and anatomy, these patients may not be well-served by devices designed for adult defibrillation. A pipeline for rapid creation of image based models that can be interactively interrogated to determine optimal defibrillation scenarios and preliminary proof-of-concept work are presented. This approach has potential clinical applications for therapy planning and broad applications for finite element modeling in anatomical models. Clinical studies investigating the effects of body size, habitus, and anatomical variation on myocardial voltage gradients are planned.

Adaptive boundary conditions for physically based follow-up breast MR image registration

This paper presents an algorithm for non-rigid registration of breast MRI follow-up images that compensates for differences in patient positioning while maintaining real anatomical and pathological changes. The proposed method uses a biomechanical model to constrain the deformation of the internal breast tissue according to elastic continuum mechanics, which is driven by suitable boundary conditions that align the breast surfaces in the images to be registered. Typically, such boundary conditions impose one-to-one surface point correspondences that are established a priori. We investigate alternative, more flexible boundary conditions that do not depend on fixed point correspondences and do not assume completely accurate breast surface segmentation in both images. More specifically, we allow for sliding motion of one surface over the other during deformation as well as for restricted motion perpendicular to the initially segmented boundary surface, based on the internal elastic forces and local intensity information. We evaluate the impact of different boundary conditions on registration quality from the subtraction images obtained for repeated scans of healthy volunteers with intermediate repositioning, using rigid body and free form whole volume intensity based registration for comparison, and also present initial results for actual patient data. Our results demonstrate a drastic reduction in subtraction artifacts using our model, without compromising the biomechanical validity of the deformation field such as unrealistically large local volume changes as with traditional voxel intensity based registration.

Simulations of the consequences of tongue surgery on tongue mobility: implications for speech production in post-surgery conditions

BACKGROUND

We studied the ability of a three-dimensional (3D) biomechanical model of the oral cavity to predict the consequences of tongue surgery on tongue movements, according to the size and location of the tissue loss and the nature of the flap used by the surgeon.

METHOD

The core of our model consists of a 3D biomechanical model representing the tongue as a finite element structure with hexahedral elements and hyperelastic properties, in which muscles are represented by specific subsets of elements. This model is inserted in the oral cavity including jaw, palate and pharyngeal walls. Hemiglossectomy and large resection of the mouth floor are simulated by removing the elements corresponding to the tissue losses. Three kinds of reconstruction are modelled, assuming flaps with low, medium or high stiffness.

RESULTS

The consequences of these different surgical treatments during the activation of some of the main tongue muscles are shown. Differences in global 3D tongue shape and in velocity patterns are evaluated and interpreted in terms of their potential impact on speech articulation. These simulations have been shown to be efficient in accounting for some of the clinically observed consequences of tongue surgery.

CONCLUSION

Further improvements still need to be done before being able to generate patient-specific models easily and to decrease the computation time significantly. However, this approach should represent a significant improvement in planning tongue surgery systems and should be a very useful means of improving the understanding of muscle behaviour after partial resection.

Automated finite-element analysis for deformable registration of prostate images

Two major factors preventing the routine clinical use of finite-element analysis for image registration are: 1) the substantial labor required to construct a finite-element model for an individual patient's anatomy and 2) the difficulty of determining an appropriate set of finite-element boundary conditions. This paper addresses these issues by presenting algorithms that automatically generate a high quality hexahedral finite-element mesh and automatically calculate boundary conditions for an imaged patient. Medial shape models called m-reps are used to facilitate these tasks and reduce the effort required to apply finite-element analysis to image registration. Encouraging results are presented for the registration of CT image pairs which exhibit deformation caused by pressure from an endorectal imaging probe and deformation due to swelling.

Integrated image- and function-guided surgery in eloquent cortex: a technique report

The ability to effectively identify eloquent cortex in close proximity to brain tumours is a critical component of surgical planning prior to resection. The use of electrocortical stimulation testing (ECS) during awake neurosurgical procedures remains the gold standard for mapping functional areas, yet the preoperative use of non-invasive brain imaging techniques such as fMRI are gaining popularity as supplemental surgical planning tools. In addition, the intraoperative three-dimensional display of fMRI findings co-registered to structural imaging data maximizes the utility of the preoperative mapping for the surgeon. Advances in these techniques have the potential to limit the size and duration of craniotomies as well as the strain placed on the patient, but more research accurately demonstrating their efficacy is required. In this paper, we demonstrate the integration of preoperative fMRI within a neuronavigation system to aid in surgical planning, as well as the integration of these fMRI data with intraoperative ECS mapping results into a three-dimensional dataset for the purpose of cross-validation.

Three-dimensional reconstruction of stained histological slices and 3D non-linear registration with in-vivo MRI for whole baboon brain

The correlation between post-mortem data and in-vivo brain images is of high interest for studying neurodegenerative diseases. This paper describes a protocol that matches a series of stained histological slices of a baboon brain with an anatomical MRI scan of the same subject using an intermediate 3D-consistent volume of "blockface" photographs taken during the sectioning process. Each stained histological section of the baboon brain was first registered to its corresponding blockface photograph using a novel "hemi-rigid" transformation. This piecewise rigid 2D transformation was specifically adapted to the registration of slices which contained both hemispheres. Subsenquently, to correct the global 3D deformations of the brain caused by histological preparation and fixation, a 3D elastic transformation was estimated between the blockface volume and the MRI data. This 3D elastic transformation was then applied to the histological volume previously aligned using the hemi-rigid method to complete the registration of the series of stained histological slices with the MRI data. We assessed the efficacy of our method by evaluating the quality of matching of anatomical features as well as the difference of volume measurements between the MRI and the histological images. Two complete baboon brains (with the exception of cerebellum) were successfully processed using our protocol.

Functional brain mapping and its applications to neurosurgery

Functional brain mapping may be useful for both preoperative planning and intraoperative neurosurgical decision making. "Gold standard" functional studies such as direct electrical stimulation and recording are complemented by newer, less invasive techniques such as functional magnetic resonance imaging. Less invasive techniques allow more areas of the brain to be mapped in more subjects (including healthy subjects) more often (including pre- and postoperatively). Expansion of the armamentarium of tools allows convergent evidence from multiple brain mapping techniques to bear on pre- and intraoperative decision making. Functional imaging techniques are used to map motor, sensory, language, and memory areas in neurosurgical patients with conditions as diverse as brain tumors, vascular lesions, and epilepsy. In the future, coregistration of high resolution anatomic and physiological data from multiple complementary sources will be used to plan more neurosurgical procedures, including minimally invasive procedures. Along the way, new insights on fundamental processes such as the biology of tumors and brain plasticity are likely to be revealed.

Non-rigid alignment of pre-operative MRI, fMRI, and DT-MRI with intra-operative MRI for enhanced visualization and navigation in image-guided neurosurgery

OBJECTIVE

The usefulness of neurosurgical navigation with current visualizations is seriously compromised by brain shift, which inevitably occurs during the course of the operation, significantly degrading the precise alignment between the pre-operative MR data and the intra-operative shape of the brain. Our objectives were (i) to evaluate the feasibility of non-rigid registration that compensates for the brain deformations within the time constraints imposed by neurosurgery, and (ii) to create augmented reality visualizations of critical structural and functional brain regions during neurosurgery using pre-operatively acquired fMRI and DT-MRI.

MATERIALS AND METHODS

Eleven consecutive patients with supratentorial gliomas were included in our study. All underwent surgery at our intra-operative MR imaging-guided therapy facility and have tumors in eloquent brain areas (e.g. precentral gyrus and cortico-spinal tract). Functional MRI and DT-MRI, together with MPRAGE and T2w structural MRI were acquired at 3 T prior to surgery. SPGR and T2w images were acquired with a 0.5 T magnet during each procedure. Quantitative assessment of the alignment accuracy was carried out and compared with current state-of-the-art systems based only on rigid registration.

RESULTS

Alignment between pre-operative and intra-operative datasets was successfully carried out during surgery for all patients. Overall, the mean residual displacement remaining after non-rigid registration was 1.82 mm. There is a statistically significant improvement in alignment accuracy utilizing our non-rigid registration in comparison to the currently used technology (p<0.001).

CONCLUSIONS

We were able to achieve intra-operative rigid and non-rigid registration of (1) pre-operative structural MRI with intra-operative T1w MRI; (2) pre-operative fMRI with intra-operative T1w MRI, and (3) pre-operative DT-MRI with intra-operative T1w MRI. The registration algorithms as implemented were sufficiently robust and rapid to meet the hard real-time constraints of intra-operative surgical decision making. The validation experiments demonstrate that we can accurately compensate for the deformation of the brain and thus can construct an augmented reality visualization to aid the surgeon.

A deformable registration method for automated morphometry of MRI brain images in neuropsychiatric research

Image registration methods play a crucial role in computational neuroanatomy. This paper mainly contributes to the field of image registration with the use of nonlinear spatial transformations. Particularly, problems connected to matching magnetic resonance imaging (MRI) brain image data obtained from various subjects and with various imaging conditions are solved here. Registration is driven by local forces derived from multimodal point similarity measures which are estimated with the use of joint intensity histogram and tissue probability maps. A spatial deformation model imitating principles of continuum mechanics is used. Five similarity measures are tested in an experiment with image data obtained from the Simulated Brain Database and a quantitative evaluation of the algorithm is presented. Results of application of the method in automated spatial detection of anatomical abnormalities in first-episode schizophrenia are presented.

Non-rigid registration of pre-procedural MR images with intra-procedural unenhanced CT images for improved targeting of tumors during liver radiofrequency ablations

In the United States, unenhanced CT is currently the most common imaging modality used to guide percutaneous biopsy and tumor ablation. The majority of liver tumors such as hepatocellular carcinomas are visible on contrast-enhanced CT or MRI obtained prior to the procedure. Yet, these tumors may not be visible or may have poor margin conspicuity on unenhanced CT images acquired during the procedure. Non-rigid registration has been used to align images accurately, even in the presence of organ motion. However, to date, it has not been used clinically for radiofrequency ablation (RFA), since it requires significant computational infrastructure and often these methods are not sufficient robust. We have already introduced a novel finite element based method (FEM) that is demonstrated to achieve good accuracy and robustness for the problem of brain shift in neurosurgery. In this current study, we adapt it to fuse pre-procedural MRI with intra-procedural CT of liver. We also compare its performance with conventional rigid registration and two non-rigid registration methods: b-spline and demons on 13 retrospective datasets from patients that underwent RFA at our institution. FEM non-rigid registration technique was significantly better than rigid (p < 10-5), non-rigid b-spline (p < 10-4) and demons (p < 10-4) registration techniques. The results of our study indicate that this novel technology may be used to optimize placement of RF applicator during CT-guided ablations.

Phenomenological model of diffuse global and regional atrophy using finite-element methods

The main goal of this work is the generation of ground-truth data for the validation of atrophy measurement techniques, commonly used in the study of neurodegenerative diseases such as dementia. Several techniques have been used to measure atrophy in cross-sectional and longitudinal studies, but it is extremely difficult to compare their performance since they have been applied to different patient populations. Furthermore, assessment of performance based on phantom measurements or simple scaled images overestimates these techniques' ability to capture the complexity of neurodegeneration of the human brain. We propose a method for atrophy simulation in structural magnetic resonance (MR) images based on finite-element methods. The method produces cohorts of brain images with known change that is physically and clinically plausible, providing data for objective evaluation of atrophy measurement techniques. Atrophy is simulated in different tissue compartments or in different neuroanatomical structures with a phenomenological model. This model of diffuse global and regional atrophy is based on volumetric measurements such as the brain or the hippocampus, from patients with known disease and guided by clinical knowledge of the relative pathological involvement of regions and tissues. The consequent biomechanical readjustment of structures is modelled using conventional physics-based techniques based on biomechanical tissue properties and simulating plausible tissue deformations with finite-element methods. A thermoelastic model of tissue deformation is employed, controlling the rate of progression of atrophy by means of a set of thermal coefficients, each one corresponding to a different type of tissue. Tissue characterization is performed by means of the meshing of a labelled brain atlas, creating a reference volumetric mesh that will be introduced to a finite-element solver to create the simulated deformations. Preliminary work on the simulation of acquisition artefacts is also presented. Cross-sectional and longitudinal sets of simulated data are shown and a visual classification protocol has been used by experts to rate real and simulated scans according to their degree of atrophy. Results confirm the potential of the proposed methodology.

An enhanced block matching algorithm for fast elastic registration in adaptive radiotherapy

Image registration has many medical applications in diagnosis, therapy planning and therapy. Especially for time-adaptive radiotherapy, an efficient and accurate elastic registration of images acquired for treatment planning, and at the time of the actual treatment, is highly desirable. Therefore, we developed a fully automatic and fast block matching algorithm which identifies a set of anatomical landmarks in a 3D CT dataset and relocates them in another CT dataset by maximization of local correlation coefficients in the frequency domain. To transform the complete dataset, a smooth interpolation between the landmarks is calculated by modified thin-plate splines with local impact. The concept of the algorithm allows separate processing of image discontinuities like temporally changing air cavities in the intestinal track or rectum. The result is a fully transformed 3D planning dataset (planning CT as well as delineations of tumour and organs at risk) to a verification CT, allowing evaluation and, if necessary, changes of the treatment plan based on the current patient anatomy without time-consuming manual re-contouring. Typically the total calculation time is less than 5 min, which allows the use of the registration tool between acquiring the verification images and delivering the dose fraction for online corrections. We present verifications of the algorithm for five different patient datasets with different tumour locations (prostate, paraspinal and head-and-neck) by comparing the results with manually selected landmarks, visual assessment and consistency testing. It turns out that the mean error of the registration is better than the voxel resolution (2 x 2 x 3 mm(3)). In conclusion, we present an algorithm for fully automatic elastic image registration that is precise and fast enough for online corrections in an adaptive fractionated radiation treatment course.

A Novel Robotic Laser Ablation System for Precision Neurosurgery with Intraoperative 5-ALA-Induced PpIX Fluorescence Detection

We developed a combined system of tumor detection by 5-ALA-induced PpIX fluorescence and precise ablation by micro laser for the first time, with an automatic focusing and robotic scanning mechanism for the brain surface. 5-ALA accumulates on tumors to be metabolized to become PpIX that is a fluorescent. Intra-operative detection of 5-ALA induced PpIX fluorescence provides useful information for tumor detection. The wavelength of the micro laser is 2.8 microm close to the absorption band of water. This laser is effective only on the surface of brain tissue, enabling precise ablation at the boundary between tumor and normal tissue identified by intra-operative 5-ALA induced fluorescence. Combination tests of the fluorescence measurement and the laser ablation were performed, and it was possible to extract the area with fluorescence appropriately from the measurement data, and the micro laser with automatically scanning selectively ablated the extracted area.