To propose a two stage semi-automatic imaging coregistration pipeline to coregister glioblastoma brain MRI between time points of preoperative, postoperative and progression.

There is an expanding research interest in high grade gliomas due to their significant population burden and poor survival despite the extensive standard multimodal treatment. One of the obstacles is the lack of individualized monitoring of tumor characteristics and treatment response before, during and after treatment.3,4 We developed a semi-automatic method (SAC) to coregister MRI scans before and after surgical and after adjuvant treatment of high grade gliomas.

Coregistration methods Coregistration was performed using a two stage semi-automatic coregistration (SAC) (Figure 1). Conventional contrast-enhanced T1-weighted images were coregistered using tools from the FMRIB Software Library (FSL) version 5.0.0.5 First stage was the coregistration of the binary masks consisting of outer contour of the brain, ventricles, and contrast enhancing area (presurgical MRI images) or resection area (follow up MRI images) of each subject between different time points to create a transformation matrix (using FLIRT function). The contrast enhancing area is targeted for resection and so forms most of the resection cavity on the direct postoperative and later follow-up MRI scans. Therefore, this stage of coregistration allowed for optimal correction of variable brain shift at different time points. Secondly, further applied this transformation matrix as input for a nonlinear transformation matrix of the brain images (using the FNIRT functions). Standard linear coregistration (FLIRT) and standard non-linear coregistration (FNIRT) were done separately for comparison. Validation methods Validation was performed using a targeted registration error method for calculating the error in the x and y direction for the cerebral aqueduct and septum pellucidum on the same axial slice (Figure 2A), and in the y and z direction (Figure 2B) for the upper anterior boundary of the third ventricle at the level of the foramen of Monro (Figure 2C) on the same coronal slice. The central point of the tumor/cavity was also targeted for registration error as the location were expected most errors could be. All targeted registration errors were calculated for the SAC method and compared to FLIRT and FNIRT. In addition, we created a 3D structural similarity map using Matlab to compare images in different time points. Targeted registration error The targeted registration error showed good performance of the SAC method with a clear benefit over FLIRT and standard FNIRT coregistration (Table 1). In the coregistration of postoperative to preoperative, the SAC showed a smaller deviation of vector of cerebral aqueduct (1.1 versus 1.6, p=0.015), septum pallucidum y coordinate (1.3 versus 2.0, p=0.029) and vector (1.8 versus 2.6, p=0.021), and upper most of third ventricle y, z coordinate and vector (0.4 versus 2.2, p<0.001; 1.2 versus 1.9, p=0.043; 1.3 versus 3.3, p<0.001). The SAC also outperformed the default FNIRT coregistration, small deviation can be seen in most of the coordinate and vector of the error targeted points. This coregistration benefit can also be seen when coregistered recurrent images to preoperative images (Table 2). The cerebral aqueduct and the third ventricle both displayed smaller errors for the SAC coregistration than FLIRT and FNIRT. 3D structural similarity Mean 3D structural similarity showed the relative performance of the coregistration method (Figure 3). The peripheral areas including the frontal and parietal areas demonstrated the best performance. A good performance is also seen at the periventricular regions. A relatively lower overlap between the coregistered and reference preoperative scan was seen in the midsagittal and central areas, the centrum semiovale and central cerebellum. Most coregistration methods focus on coregistration of different sequences of the same scan time point with linear and nonlinear registration methods in healthy subjects and brain tumor patients. They demonstrate good performances for this intrasubject coregistration of data from the same time1,2 or to a standardized brain atlas.6 We developed and validated a reproducible two stages semi-automatic coregistration method using the FSL which is free available for the coregistration in different time points. By using the semi-automatic derived mask of the outer brain contour, ventricles and lesion allowed an optimal estimation of the changes in brain shift and postoperative changes. The semi-automatic non-linear coregistration allowed for optimal correction of variable brain shift at different time points as evaluated by minimal targeted registration error. This allows for accurate evaluation of treatment response, essential for the growing research area of brain tumor imaging and treatment response evaluation in large sets of patients.