banner image
No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
Mutual-information-based image to patient re-registration using intraoperative ultrasound in image-guided neurosurgery
Rent this article for


Image of FIG. 1.
FIG. 1.

Coordinate systems used in the image re-registration. Solid arrows indicate transformations determined from calibration, and are fixed. Dashed arrows indicate transformations determined from registration, and are subject to adjustment through the re-registration process. A transformation reversing the arrow direction is obtained by matrix inversion. Intraoperative US images obtained from single and sequence acquisitions before dural opening for a typical patient case are overlaid on the patient head, demonstrating the difference in the two types of acquisitions as well as the incomplete scanning and/or nonuniform sampling of the tumor volume resulting from free-hand scanning.

Image of FIG. 2.
FIG. 2.

A typical iUS image before (a) and after (b) preprocessing. The iUS image was Gaussian blurred, and only pixels within the dashed lines were included for nMI evaluation, while those in regions with few anatomical features or nonimaging regions (exterior to the solid line) were discarded. The outer boundary (i.e., image mask) was determined by manually identifying the corners of the actual US image and removing areas within a radius of approximately relative to the scan-head probe tip. This was performed before surgery during US calibration, and, therefore, did not compromise the automation of the image preprocessing in this study.

Image of FIG. 3.
FIG. 3.

Schematic for approximating the “ground truth” transformation between the set of iUS points generated from the fiducial-based registration and that averaged from . Arrows indicate the ordered steps in the procedure (see text for details).

Image of FIG. 4.
FIG. 4.

Overlay of iUS with corresponding oblique pMR image for each patient using the nMI re-registration procedure ( in Fig. 3). The resulting tumor surface cross sections from pMR (solid thin lines) and those generated by the fiducial-based registration (dashed thin lines) are shown (cross sections of the cyst for patient 2 due to its improved contrast). The alignment between tumor boundary in iUS (thick open lines) and tumor surface in pMR was significantly improved by the nMI re-registration. Shown also are proper alignment of tentorium (patient 1), ventricle (patients 1, 3, and 5) and gyrus (patient 6) in iUS with respect to their pMR counterparts (arrows). Note that segmented tumor boundaries in regions of poor contrast (all patients) or near the transducer probe tip (patients 1 and 2) were discarded.

Image of FIG. 5.
FIG. 5.

Scatter plot showing the average distance error relative to the initial translational (a) and rotational (b) misalignment for each patient (legend shows patient markers). Each subfigure contains 1200 data points, but average distance errors greater than are not shown. NMI-based re-registration was successful when the average distance error was less than the pMR voxel body diagonal (; dashed horizontal lines).

Image of FIG. 6.
FIG. 6.

Feature space of the negation of nMI relative to the translational (ac; in and directions) and rotational (bd; about and axes passing through ) misalignment centered at the “ground truth” values for patient 1 (ab) and 5 (cd). With increased number of iUS pixels and pMR voxels (ab) to enhance the sampling across the tumor volume, nMI smoothness is significantly improved [compare (ab) to (cd)], leading to larger capture ranges (Table V). Shown also are the isocontours at levels near the local minimum to indicate the nMI function smoothness and the distance from the local minimum to the “ground truth” values as indicated by the vertical lines (see Sec. II E).

Image of FIG. 7.
FIG. 7.

Registration success rate relative to the initial translational (a) and rotational (b) misalignment for all six patients. Capture range was determined as the corresponding initial misalignment when the horizontal dashed line at 90% intersected with the success rate curve. As a comparison, success rate curves with the average distance error threshold of are shown, where is the pMR voxel body diagonal of .


Generic image for table

Summary of patient information and the fiducial-based FRE (in mm).

Generic image for table

Number of available iUS images, as well as number of single, sequence of, and selected iUS images for each patient. Each single acquisition included one iUS image, while each sequence of acquisitions included 20 iUS images acquired continuously in approximately . Two images were selected for each sequence of acquisitions, while all single acquisitions were selected, provided that the corresponding spatial location of the respective US scan-head was available. Numbers in parentheses indicate the iUS images selected from each type of iUS image acquisition, which added to the total number of selected iUS images in the bottom row.

Generic image for table

Summary of nMI re-registration results. NMI-corrected CPPD (using ) was significantly lower than with fiducial-based registration . Shown also are the registration execution time, the number of iUS pixels, and pMR voxels used for nMI evaluation, the number of iUS tumor boundary points segmented from iUS, and the empirical measure of tumor feature prominence .

Generic image for table

Correlation coefficients between the nMI-corrected CPPD and the number of iUS pixels, number of pMR voxels, tumor size, and .

Generic image for table

Translational and rotational capture ranges for each patient. Capture ranges were determined as the largest initial misalignment at or below which the registration success rate was at least 90%. A successful registration was one that had an average distance error of less than the pMR voxel body diagonal of .

Generic image for table

Correlation coefficients between the translational and rotational capture ranges with the number of iUS pixels, number of pMR voxels, and tumor size.


Article metrics loading...


Full text loading...

This is a required field
Please enter a valid email address
752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Mutual-information-based image to patient re-registration using intraoperative ultrasound in image-guided neurosurgery