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- MATERIALS AND METHODS
In MRI of the human brain, subject motion is a major cause of magnetic resonance image quality degradation. To compensate for the effects of head motion during data acquisition, an in-bore optical motion tracking system is proposed. The system comprises two MR-compatible infrared cameras that are fixed on a holder right above and in front of the head coil. The resulting close proximity of the cameras to the object allows precise tracking of its movement. During image acquisition, the MRI scanner uses this tracking information to prospectively compensate for head motion by adjusting the gradient field direction and radio frequency (RF) phases and frequencies. Experiments performed on subjects demonstrate robust system performance with translation and rotation accuracies of 0.1 mm and 0.15°, respectively. Magn Reson Med, 2009. © 2009 Wiley-Liss, Inc.
Magnetic resonance imaging (MRI) has been widely used as a noninvasive clinical and research modality for the study of human anatomy. However, subject motion during scanning remains a severe problem and may degrade image quality below levels acceptable for clinical diagnosis. Recent improvements that yield high-spatial-resolution MRI of the brain (∼0.2 mm) make this problem more acute, since for this application the tolerance to motion is reduced, and scan time is increased. The latter makes it more difficult for the subject to maintain the same position throughout the scan, which is especially problematic for children and patients.
Several methods have been proposed to solve the head movement problem in MRI. All of these methods model head movement as rigid body motion with six degrees of freedom (DOF), namely three rotations and three translations along the MRI coordinate system. These parameters are then used to either retrospectively or prospectively compensate for the effects of motion on the image data.
Retrospective motion correction addresses motion artifacts after the acquisition of a complete set of raw image data. While this might work well for in-plane motion, it is generally inadequate for through-plane motion, primarily because it cannot correct for the effects of this motion on the local magnetization history (i.e., changes in the saturation level of longitudinal magnetization due to motion-induced changes in the image-slice location). To avoid this problem, prospective motion-correction techniques have been recently proposed (1–5). They track the head motion and rectify the acquisition planes correspondingly, by adjusting the gradient direction and the radio frequency (RF) phases and frequencies. This avoids problems related to the effects of motion on spin history.
The object motion parameters used by prospective and retrospective correction methods can be derived from MRI data using image- or navigator-based methods. Image-based methods use image registration algorithms to detect the motion parameters (3, 6–8). These techniques, while straightforward, can only calculate motion after acquiring a volume and therefore always lag the motion. Also, any ghosting and blurring artifacts that motion may cause may affect the accuracy of the registration algorithms.
Navigator-based motion-correction methods acquire a motion-sensitive reference signal with the image (1, 2, 4, 9, 10). The earliest navigator method was capable of detecting only one-dimensional (1D) translation (9) by employing a frequency-encoding gradient but no phase-encoding gradient before image acquisition. Soon after, orbital navigators (5, 10), spherical navigators (11), and cloverleaf navigators (4) were proposed to allow full tracking of 3D motion. However, the extra time required for measuring the navigator echoes led to an increased scan time.
Self-navigating methods can also be used to retrospectively correct motion when combined with particular image acquisition techniques including projection acquisition (12, 13), spiral acquisition (14–16), and Periodically Rotated Overlapping ParallEL Lines with Enhanced Reconstruction (PROPELLER) (17, 18). These all involve the collection of redundant MRI data, usually including the central region of k-space. The oversampled central region of k-space provides intrinsic averaging of image features that reduces motion artifacts, and can be further used to correct spatial inconsistencies in position, rotation, and phase between acquisitions. One drawback of these methods is the increased scan time related to redundant k-space sampling.
Alternatively, external motion tracking systems can be used for MRI motion correction. These tracking systems rely on additional hardware, such as miniature coils (19, 20), fiducial samples (21), optical reflectors (22), or stereo cameras (23–25). Among them, stereo optical systems have been shown to give good motion correction results with reasonable accuracy, retrospectively (24) as well as prospectively (23, 25). The stereo tracking system works parallel with the scanner thus needs no extra scan time for motion detection in the imaging sequence. However, there are limitations to the current tracking systems—the most important being that the tracking target required for monitoring object position is either uncomfortable for the subjects (e.g., a mouthpiece (25)) or hard to combine with close-fitting receiver arrays (e.g., a cap (24)).
To overcome these limitations, we developed and evaluated an in-bore video tracking system for prospective motion correction that monitors the position of a target on the subject's forehead. In the current implementation, this target consisted of visual feature points drawn on a small sticker. The system was designed to allow high-resolution (∼0.2 mm) MRI at 7T in the presence of substantial head motion.
- Top of page
- MATERIALS AND METHODS
In this study, a method for the prospective correction of 3D motion for high-resolution MRI was developed and evaluated. Preliminary data obtained on a human volunteer show substantial image quality improvement for high-resolution gradient-echo imaging at 7T, in particular under conditions of periodic head motion related to breathing and intentional head rotation.
To obtain adequate tracking accuracy, the cameras were put right above the MRI receiver coil, monitoring the human face from a very small distance. Since the error from stereo estimation is a function of the true distance (33), a translation accuracy much better than 0.1 mm and rotation better than 0.15° were achieved with our setup. Motion toward and away from the cameras can be detected with high accuracy, which is seemingly counterintuitive. However, due to the close proximity of the cameras to the object, such motion will lead to a significant perceived size change of the object in the camera images, which enhances detection accuracy. The stereo 3D reconstruction accuracy depends on the relative angle and distance between the two cameras, the best setup for the two cameras should be explored further for better accuracy.
The accuracy of the calibration between the tracker and the MRI scanner is also essential for system performance. Since all the motion parameters were estimated in the camera-coordinate frame, they had to be transformed precisely to the MRI-coordinate frame to be used in the sequence. However, calibration was difficult in this system because the tracker can only see the surface of the calibration object, whereas the MRI can only see its internal structure. A previous publication (25) dealt with this issue in an iterative way. They used three gel-filled reflective spheres for estimating the initial coordinate transformation, and then iteratively refined this transformation based on the internal structures of the phantom. This method, though accurate, took more than 30 min to accomplish. This limitation was addressed in our project by designing a phantom with high-resolution, well-ordered structures. Since every parameter of the phantom was known, the relationship between the external appearance (black-and-white checkerboard) and internal MR-visible structure was easily established, which simplified the calibration and shortened the calibration time to several minutes.
The use of phantom calibration data for head-motion correction assumed that the patient table could be reproducibly moved to the magnet isocenter. However, this was not the case for our long nonstandard table, which showed a misalignment error of 1–2 mm in the z-direction when the bed was returned to the isocenter position after being moved out of the scanner. From Eq. , it can be seen that this uncertainty in Tmc does not affect the calculated rotations, only the translations. The introduced translation error was (I – RmcRcRmc−1)Tmc, which was small when rotational motion was small (i.e., when Rc was close to an identity matrix). For larger rotations, however, e.g., a 10° rotation around the z-axis and a z-alignment error of 2 mm, would lead to translations of 0.25 mm, 0.09 mm, and 0.02 mm, in the x-, y-, and z-directions, respectively. This could partly explain the uncorrected residual in Figs. 7f and 8f compared to Figs. 7d and 8d, but needed to be explored further. A solution to this is simply to fix the cameras to the magnet bore.
Apart from the table uncertainty, a calibration accuracy test showed a 0.15 ± 0.13 mm error in the z-direction. But the contribution of this error to motion computation was much smaller than 0.1 mm, and could be neglected for our purposes. This suggests that even though calibration between the cameras and MRI scanner should be as accurate as possible, the motion calculation is rather tolerant for small imperfections in this calibration.
A different situation is encountered with regard to the accuracy of feature point localization. Accuracy of motion estimation is highly dependent on tracker noise. Under the conditions described above, at least 20 feature points needed to be tracked for adequate precision. Because of the setup of our cameras, which imaged only part of a subject's face, only a limited number of feature points could be identified with the cameras. This was remediated by using a sticker fixed to the forehead. However, facial twitching or frowning could introduce potential problems of relative motion between the sticker and the brain. Fast facial movements could be filtered out in our method by discarding and reacquiring k-space lines, but slow facial changes could compromise the effectiveness of the proposed methods. A potential improvement is to replace the rigid sticker used in the current implementation with a number of separate stickers or facial markings, and detect facial twitching from changes in the relative position of the stickers. This would allow a rescan of the corresponding k-space lines. An alternative solution is to increase the camera field of view to allow the incorporation of facial feature points that are less sensitive to skin movements caused by effects such as frowning and twitching.
Due to the tracker's limited (10 Hz) frame rate, some fast motion could not be adequately corrected. For this reason, a reacquisition function was added to the sequence in this project. The number of reacquired lines (reacquisition rate) was dependent on the type of head motion and was quite variable, e.g., 2.3% for the conditions presented in Fig. 5d and 10% for those in Fig. 7f. Considering the resulting improvement in image quality and the small increase in total imaging time, this sacrifice appears worthwhile. Nevertheless, improved tracking speed might reduce or eliminate the need for reacquisition. This was confirmed in the “slow motion” experiment, during which the reacquisition was turned off. Under this condition, the tracking speed exceeded the head motion speed, resulting in excellent image quality without any reacquisition, as seen in Fig. 8f and i. With the continuing increase of computational power, and improved motion tracking algorithms, it is therefore expected that the need for reacquisition will be reduced. One possible area for improved tracking is the use of predicative estimates for object motion, for example by using Kalman filtering.
Although the motion-correction scheme presented here was effective in reducing the effects of motion on image quality (e.g., compare Figs. 5d and 7f with correction vs. the uncorrected versions in Figs. 5c and 7e), there remain possibilities for improvement. For example, the motion-corrected images in Figs. 7f and 8f are slightly inferior to the corresponding images acquired during the minimal head motion (Figs. 7d and 8d). This apparently imperfect motion compensation could be due not only to the table uncertainty mentioned above, but also to a number of other factors, e.g., phase changes due to small changes in the local B0 amplitude, or subtle changes in the B1 amplitude and phase. The contribution of these effects and their potential mitigation remains to be investigated.