TRACING THE DEFORMED MIDLINE ON BRAIN CT

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1 305 TRACING THE DEFORMED MIDLINE ON BRAIN CT CHUN-CHIH LIAO 1,2,3, I-JEN CHIANG 1,3, FUREN XIAO 3,4, JAU-MIN WONG 3,4 1 Graduate Institute of Medical Informatics, Taipei Medical University, 2 Taipei Hospital, Department of Health, 3 Institute of Biomedical Engineering, National Taiwan University, 4 National Taiwan University Hospital, Taipei, Taiwan Biomed. Eng. Appl. Basis Commun : Downloaded from ABSTRACT Midline shift (MLS) is the most important quantitative feature clinicians use to evaluate the severity of brain compression by various pathologies. We proposed a model of the deformed midline according to the biomechanical properties of different types of intracranial tissue. The model comprised three segments. The upper and lower straight segments represented parts of the tough meninges separating two hemispheres, and the central curved segment, formed by a quadratic Bezier curve, represented the intervening soft brain tissue. For each point of the model, the intensity difference was calculated over 48 adjacent point pairs at each side. The deformed midline was considered ideal as summed square of the difference across all midline points approaches global minimum, simulating maximal bilateral symmetry. Genetic algorithm was applied to optimize the values of the three control points of the Bezier curve. Our system was tested on images containing various pathologies from 81 consecutive patients treated in a single institute over one-year period. The deformed midlines itself as well as the amount of midline shift were evaluated by human experts, with satisfactory results. Biomed Eng Appl Basis Comm, 2006(December); 18: Keywords: medical image analysis; computed tomography; midline shift; brain deformation; symmetry detection; decision support system; genetic algorithm; pathological image 1. INTRODUCTION Human head is roughly bilateral symmetric. Although there is functional difference between hemispheres of the brain, the gross morphology follows the rule. Both cerebrum and cerebellum are symmetric with lobes, ventricles and deep nuclei of similar size and shape in both hemispheres. From pathological examinations, physicians have already known that intracranial mass can cause brain shift, Received: July 26, 2006; Accepted: Sep. 25, 2006 Correspondence: I-Jen Chiang, Professor Graduate Institute of Medical Informatics, Taipei Medical University, No. 250, Wusing st., Taipei 110, Taiwan ijchiang@tmu.edu.tw follow by herniation, brainstem compression and death. Therefore, they rely on midline shift (MLS) to quantify the change of symmetry for diagnosis and outcome prediction. In the early 20th century, shift of calcified pineal body on plain X-ray was used to measure MLS. Ultrasound, computed tomography (CT), and magnetic resonance imaging (MRI) were invented later with greatly improved resolution and tissue contrast [1]. Despite these technical improvements, midline shift continue to be one of the jargon most commonly used by physicians, including neurologists, neurosurgeons, and neuroradiologists. 1.1 Clinical Relevance Shortly after the invention of CT, its value on the diagnosis of traumatic brain injury was well 30

2 BIOMEDICAL ENGINEERING- APPLICATIONS, BASIS & COMMUNICATIONS demonstrated. Analysis of Traumatic Coma Data Bank (TCDB) results revealed midline shift more than 15 mm as an important outcome predictor regardless of the clinical condition [2]. Absent or compressed basal cisterns on the CT scan is another ominous predictor of outcome in severe head injury [3]. Midline shift and basal cistern effacement are both indicators of mass effect, the degree of brain compression by intracranial mass. Mass effect is usually a better predictor of outcome than the size of the mass. Quantification studies were performed by Ropper to detect the earliest CT changes associated with depression of consciousness as soon as the intracranial lesion was detected [4]. Horizontal displacement of the pineal body of 0 to 3 mm from the midline was associated with alertness, 3 to 4 mm with drowsiness, 6 to 8.5 mm with stupor, and 8 to 13 mm with coma. The dose-dependent relationship between MLS and neurological condition as well as clinical outcome seemed to be established. Considering the three-dimensional (3-D) anatomy, however, a different story was told. MLS continued to be an important parameter on analysis of coronal MRI images, but Ropper failed to demonstrate the relationship between vertical brain shift and clinical condition on coronal MRI [5]. Also using axial CT images, other authors confirmed midline shift at septum pellucidum as a significant predictor of outcome, but not shift of pineal body or cerebral aqueduct [6]. These seemingly contradictory results highlighted the complexity of MLS measurement, which results from the interaction between different types of intracranial tissue. In addition to pathological masses, there are three different types of tissue within the rigid cranial cavity normally: brain, cerebrospinal fluid (CSF) and blood. Blood flows within arteries and veins, but these vessels are difficult to model volumetrically. Furthermore, the brain itself, although can be considered homogeneous, is separated into three major compartments: cerebral hemispheres and cerebellum, by the cerebral falx and cerebellar tentorium [7]. These tough infoldings of the meninges, guides the direction of brain deformation despite their very small, negligible volume. 1.2 Biomechanical Modeling To model brain deformation, one must have an estimation of the biomechanical properties of the brain and the CSF spaces, namely the ventricles and the basal cisterns. Using finite-element model (FEM) calculations, Miga deformed the preoperative image database to generate a reliable representation of the surgical focus during an operation, reducing the 5.7- mm shift to 1.2 mm [8]. The same authors used data 306 obtained in the operation room and the preoperative neuroanatomic image volume of the patient to generate a highly resolved, heterogeneous, FEM [9]. To have sufficient accuracy for localized force applied at brain retraction and lesion resection, the biomechanical properties of the cerebral falx were incorporated to the upgraded model. Although very complex and only used in one patient, this model does not take CSF into account, and may fail with lesions around the ventricles. Furthermore, the 15-mm deformation commonly encountered in real patients [2-4] may exceed the capability of this model. Nevertheless, simulating the change of ventricular shape is a even more computationally expensive task. To date, there are only two reports applying FEM to 2-D images [10-11], and there is even no attempt to investigate the response of ventricles to intracranial mass]. Unfortunately, one of the most commonly used landmarks for MLS measurement is septum pellucidum, the structure between both frontal horns of the lateral ventricles. 1.3 Related Works So far, there has not been any published work focusing on automated detection of MLS. The most closely related work is detection of ideal midsagittal plane (imsp), defined as the one best superposing some structures on one side and their counterparts on the other side by reflective symmetry. Except the earliest one based on longitudinal fissure [12], all works are based on symmetry as the key feature. Ardekani applied a multi-resolution, intensity based method [13], and Prima identify homologous anatomical structures, by way of an intensity-based block matching procedure [14]. Liu used an edgebased approach, apparently relying on the outline of the skull and the brain [15]. With these edge pixels, the author demonstrated successful tracing of a deformed midline on a single slice of brain CT by active contour. The line is completely formed by the falx. This method would fail, however, at slices where the midline contains multiple anatomical structures with different intensity, such as one described in previously. Furthermore, all these imsp detection depend on a large number of samples to ensure their stability, and thus not directly applicable to single slices. The difficulties encountered by engineers seem not to demolish the reliability of MLS among clinicians, as it remained widely used in CT and MRI with good validity, and will be so. Manual measurement of MLS is usually done in an axial slice containing septum pellucidum, foramen of Monro between lateral and third ventricles, and/or pineal body. We design a simple parametric model simulating 31

3 307 Biomed. Eng. Appl. Basis Commun : Downloaded from this process and test it on patient images. CT images are used in this paper because CT is the imaging modality of choice in patients with critical illness due to its speed and virtually no interference on other medical equipments, such as ventilators and infusion pumps. 2. MATERIALS From July 2003 to June 2004, 86 patients were admitted to the intensive care unit (ICU) of Taipei Hospital, Department of Health, due to head injury (54 patients) or spontaneous intracranial hemorrhage (27 patients). There were 60 males and 26 females with their age ranging from 11 to 97 (51.2 +/- 19) years. Five of them had no CT images available. Test images of the remaining 81 patients were downloaded from the PACS (UniSight, EBM technologies, Taipei, Taiwan). We only selected one CT slice containing foramen of Monro in each patient. The CT scanner used was GE LightSpeed Qx/i and all brain CT scans were done with a standard protocol. The gantry was oriented parallel to the OM line. The distance between slices was 0.75 cm at cerebral region, and 0.5 cm at cerebellar region. The field of view (FOV) was 25x25 cm. Each image was 512x512 pixels in size, resulting in a resolution of 0.5 mm per pixel. The original CT number was transformed with brain window (center 40, width 150) into 256 gray levels and downloaded to a personal computer in JPEG format. 3. METHODS Following definition of imsp in 3-D images [14-15], we define the ideal midline (iml) of an axial brain CT slice as the intersection line between the slice and imsp. The deformed midline (dml) is defined as a line, whether curved or straight, best superposing the points on one side and their counterparts on the other side by reflective symmetry. To extend the definition of symmetry without losing accuracy, we parameterize the dml so that only one point is allowed on the tangential line of each point on the iml. Onedimensional reflective symmetry is calculated based on the intensity difference between corresponding neighbors of each point. The sum of these intensity differences reaches global minimum on the dml, while having biological and physical constraints, such as continuity. Fig. 1. Our model of the deformed midline. Points A, B, and C are the control points of a quadratic Bezier curve. The parallel lines beside the deformed midline reveal the approximate range for computing the intensity difference. 3.1 Our Parametric Model of Deformed Midline According to the biomechanical parameters of 3-D FEM [9], we decompose the dml into three segments: upper and lower segments of the falx, the middle segment formed by the intervening brain. From preliminary studies with downsized images with reduced gray level [16], we have demonstrated that the falx segments can be treated at straight lines, and a quadratic Bezier curve can be used to fit the curved segment in between [17]. A Bezier curve has the form of and N = 3 for a quadratic Bezier curve.. The upper- and lower-most points of the falx should be attached to the center of the skull, and thus locate on the iml. Three control points on the 2-D test image are required in this model. Deformed midlines can be traced by exhaustive search and the results are concordant to human experts. To apply this model to full-sized images in large numbers, automated search in a large parametric space is required. Therefore, we further reduce the number of parameters to four by forcing the falx segments to overlap with the iml. There is no loss of generality, since the largest amount of MLS is always formed the deformation of the soft brain tissue rather than the falx, which has Young s modulus about 100 times higher. The tissue contrast around the falx is also lower, contributing less to the determination of the dml. A schematic view of our 32

4 BIOMEDICAL ENGINEERING- APPLICATIONS, BASIS & COMMUNICATIONS 308 Biomed. Eng. Appl. Basis Commun : Downloaded from revised model of the dml is shown on Fig 1. In fact, this model may be applied to any supratentorial slices of CT or MRI images. 3.2 Removal of Extracranial Pixels and Alignment of the Ideal Midline The histogram of the test image obtained first. Three distinct peaks representing air, brain and skull bone can be easily separated. The skull can be recognized by finding the largest region after applying region-growing algorithm on all pixels with bony density. Applying region growing again for pixels with brain density at the center of the skull image can define the brain region. Pixels outside the skull, the extracranial pixels, are discarded. We approximate the iml of a test image with the axis of the skull, defined by a line interconnecting the center of the attachment of the falx. Although the outline of the skull may be asymmetric due to anatomical variations, falx cerebri separating the cerebral hemispheres is always used as the reference midsagittal plane when there is no MLS. With mass effect, the free edge of the falx may be displaced, but its attachment to the skull remained fixed and robust in most cases. Furthermore, the skull at the point of attachment is thicker. Therefore, the skull axis is sought by finding the focal symmetric thickening of the skull by an exhaustive search with rotation around the center of the image from -60 degrees to 60 degrees, with a 0.25-degree precision. The skull axis is then determined by connecting the thickest points of the skull at upper and lower parts. This method works in about two thirds of the cases. The results are reviewed by a human expert and corrected if necessary. The image of brain and skull is then centered to its center of gravity and reoriented to turn the skull axis vertical, with a 5x5 super-sampling (Fig 2A and 2B). 3.3 Generation of Symmetry Map After rotating the iml into a vertical position, the symmetry map of the test is generated. Because of the bilateral symmetry of the brain and the head, only onedimensional difference along the horizontal direction is taken into account. Typically, the diameter of the brain is about 300 pixels. Weighted sum of squared difference of the 48 pixels at each side is calculated for each brain pixel. This corresponds to about 2.4 cm laterally and usually covers anatomical structures around the midline, such as frontal horns of the lateral ventricles and the basal ganglia. The sum is purely intensity-based without any preprocessing. There is no pre-computation such as edge detection [15]. To emphasize the region most close to the dml, the A C Fig. 2. Calculation of the deformed midline in 4 steps. This is a CT slice from a patient with traumatic epidural hematoma causing midline shift. A. The original image. B. The image after removing extracranial pixels, centering and rotation. Stippled area denotes the skull. C. The symmetry map generated according to B, the central portion is brighter, representing better symmetry. D. The final result. Dashed line is the deformed midline and the solid line is the ideal midline weight decreased linearly as the distance increased. The final formula is Si,j = (Pi+k,j - Pi-k,j ) 2 * (48 - k) / 48), k=1 to 47 Where Pi,j is the intensity at point (i,j) of the image, and S forms a symmetry map that can be used to trace the deformed midline according to the model proposed, obviating the need to calculate the intensity difference repeatedly. Figure 2C is an example of symmetry map. 3.4 Determining the Parameters of Deformed Midline by Genetic Algorithm Due to the increased complexity with the increased size and number of parameters, exhaustive search is no longer feasible. Among the techniques of pattern recognition, genetic algorithm is favored over B D 33

5 309 Fig. 3. Failure of our algorithm to recognize the deformed midline in a CT slice from a patient with spontaneous intracerebral and intraventricular hemorrhage causing midline shift. A. The original image. B. The result. Dashed line is the deformed midline and the solid line is the ideal midline. Black line is the deformed midline traced manually by a human expert. others because of its ability to jump out of the local minimum, which is very common during the search of the dml. The target function is simple: minimizing the summed score of each point of the deformed midline on symmetry map. One point of the dml is found for each Y coordinate. If the value of Y lies within the range of the control points of the Bezier curve, we use interpolation to find the corresponding X coordinate. Otherwise, X is set to 255, or the iml. The parameters for the genetic algorithm are determined according to standard reference, with a mutation rate of 0.03, a crossover rate of 0.25, and a population size of 64 [18]. Each individual (chromosome) comprised 34 bits formed by concatenation of the four parameters (ax, bx, by, cy) without special transformation. The four parameters: ax, bx, by, cy, shown in Fig 1, has different ranges. The upper and lower control points should lie at the upper and lower half of the image, and are coded with 8 bits, representing and , respectively. The X and Y coordinates of the central control point, bx and by, are allowed to freely move within the image, and are coded with 9 bits. There is no checkup for eligibility after mutation and crossover stage. During evaluation, the eligibility of the chromosome is checked first. If any point on the generated curve goes outside the brain area, the target function is set to maximum, representing worst fitness. The summation is aborted as long as it becomes larger than eight times of the minimal difference in last generation, to accelerate the calculation. Finally, fitness is calculated among all eligible chromosomes and a new generation is produced according to the fitness of current generation, which is defined as the difference between the value of each chromosome and largest sum (worst result) of the eligible chromosome. In other words, the fitness become higher as the target function becomes smaller, this means better overall symmetry along each horizontal line segments. The stopping criterion for genetic algorithm is defined as no improvement for 1024 epochs. This is usually reached with less than 5000 epochs totally. It takes about 3 minutes to find the dml of a test image on a Pentium IV 2.6G computer equipped with Microsoft Windows XP and Visual Basic 6.0. MLS can be calculated simply by subtracting BX by 255, and then divided by 2. Fig. 4. The distribution of MLS measured automatically and manually. 4. RESULTS The deformed midlines of these 81 images are calculated and the result was analyzed by a human expert. The distribution of MLS measured automatically and manually is shown in Figure 4. Good results, defined as MLS difference of less than 1mm (2 pixels) between automated and manual measurement, were noted in 58 (71.6%) images. Otherwise, poor results were considered, and were noted in 23 (28.4%) images. Examples of good and poor results are shown in Figures 2 and 3. The distribution of MLS, measured automatically and manually, is shown in figure 4. Although the amount of MLS was not different between trauma and nontrauma groups ( vs mm), analysis with Chi-square tests revealed significantly better results for the group of head trauma (Table ). As to the magnitude of MLS significantly worse result was also noted in cases with larger MLS more than 5 mm (Figure 5). 5. DISCUSSION Midline, whether shifted or not, is usually a global feature requiring a large number of data points to 34

6 BIOMEDICAL ENGINEERING- APPLICATIONS, BASIS & COMMUNICATIONS Table 1. Results by clinical diagnosis Good Poor Total Trauma Non-trauma Total p<0.001 Fig. 5. Accuracy of automatic detection of midline shift expressed in percentage shown in the center of each bar. Refer to figure 17 for number in each group. Accuracy becomes worse as the magnitude of midline shift increases. compute. Even with the preprocessing of rigid registration, application of warping or nonlinear registration algorithms to detect significant midline shift can result in failure because of its sensitivity to local changes, which is very common when there is mass effect and midline shift. This paper presents automatic detection of midline shift systematically in a significant number of real images, which was not reported before. Our method is simple. Only one image is required and there is no preprocessing to detect certain features. However, it is robust enough to allow windowing of the CT number and lossy image compression, making it suitable for conditions with limited data transfer rate or storage space, such as teleconsultation. The overall results were worse in patients with spontaneous intracerebral hemorrhage (ICH), or the non trauma group, as we expected. In contrast to traumatic hemorrhage that often appear on the surface of the brain, spontaneous ICH occurs mostly around midline at the basal ganglia, and the ventricles are also frequently involved, as shown in Fig 3A. We are working on methods that can automatically detect ICH and omit it on calculation of symmetry, in order to improve the results in this group. For images with MLS > 5 mm, our results were also worse, as more deformation occurred, causing loss of symmetry. The result cannot be further improved if the overall symmetry is lost. Therefore, a self-validation process may be required. 310 The success rate of iml (or imsp) detection in single images in this paper is about 70%, and manual correction required in the remaining images. For images of significant brain shift and loss of simple reflective symmetry, there is no feature other than the skull that is robust to represent the axis of the image. With 3-D models and areas less affected by brain shift, such as the skull base bones and the high convexity area, the skull axis could be better defined by imsp [14, 15] and the result may be further improved. In fact, human skull is usually not perfectly symmetric, and this is why these imsp algorithms require the complete 3-D model, especially the skull base area. Another problem with asymmetric skull is that measurements of MLS taken from lateral skull edge can become inaccurate. Another problem of our algorithm is the requirement of selecting the 'correct' CT slice by human expert. Automation of this step requires techniques for image retrieval, and is currently under development. Although the vertical component of the midline shift in coronal plane seems to be not clinical relevant now, generalization of our model into three-dimension may be an interesting development. Adding more parameters is not a problem to genetic algorithm, but adding another control point at MSP at high convexity may be enough. Good results are obtained without any preprocessing in this paper, but it is interesting whether feature-detection may improve the results. Edge may not be a good candidate because some structure may be fragmented, compressed or even destroyed. Pixel classifications may be useful, such as rule-based CT recognition [19]. However, this is more difficult for CT compared to MRI, which has several pulses sequence and is very robust to detect ventricles. Clinically, our simple algorithm may be written as an online script or plug-in program on CT consoles or PACS system. This is especially for the emergency room running continuously and specialists are not always available. The potential application of our program on trauma patients is even more feasible given the better results in this group (good validity in 90%). This is due to different distribution of various types of intracranial lesions in trauma patients compared to others. Extra-axial (outside the brain) lesions such as epidural and subdural hematoma are more common in the former while intracerebral hematoma, usually at basal ganglia near midline, is more common in nontrauma patients. 6. CONCLUSION A novel method is proposed to automatically detect the deformed midline on single slices of brain 35

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