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- DISCUSSIONS AND CONCLUSIONS
Patient motion in magnetic resonance imaging (MRI) remains problematic in both clinical routine and research. Even for a highly motivated patient, it is difficult to stay completely immobile for the duration of an MRI examination. This problem has existed since the early days of MRI and has been investigated in detail for a number of imaging protocols. Artifacts caused by breathing motion, cardiac pulsation, and involuntary head movements are common, especially during long image acquisitions. In routine structural imaging, motion artifacts often lead to reduced image quality, longer scan sessions, repeated examinations, or even a failed diagnosis.
It has been shown that prospective motion correction (PMC) can reduce motion artifacts by using data from external tracking devices or navigators to adapt the field of view dynamically to the changes in the patient's head position [1-5]. Recent improvements made in the development of motion tracking systems bring the technique closer to application .
However, in contrast to retrospective correction methods, during a PMC-enabled experiment no uncorrected image remains for comparison, which makes a quantification of the improvements achieved through PMC based on a single experiment impossible. Usually, a second measurement without PMC is done [2, 3, 7]. The images are then compared visually or image entropy or other image metrics [4, 8] are used.
However, often this is not a fair comparison, since the motion occurring in the two imaging experiments cannot be identical. Retrospective simulation of motion artifacts using the image or k-space data is strongly dependent on the sequences used and is currently too computationally intensive to be practical.
One possible solution to the problem of validation in PMC is to perform corrected and uncorrected data acquisitions in an interleaved manner for each k-space line . For sequences with short repetition times (TR), this enables one to acquire a corrected and an uncorrected image during almost identical motion. However, this doubles the duration of each scan, which makes it impractical for clinical studies. Moreover, the doubled scan time increases the likelihood and amplitude of involuntary motion. Additionally, the interleaved acquisition of corrected and uncorrected scans might result in unwanted spin history effects such as spin saturation or stimulated echoes. For example, such effect could be due to an overlap of corrected and uncorrected slices in subsequent 2D excitations.
Another way to quantify the effect of PMC on a specific magnetic resonance (MR) technique, would be the conduction of larger patient studies including image scoring by experienced radiologists and statistical analysis methods. However, for methodological developments a faster way of quantifying the improvements of PMC is desired.
In this work, we present a method that enables the reproduction of all motion artifacts that can be corrected with PMC. This was recently introduced and briefly described for the investigation of the continuous update during diffusion weighted imaging . However, the method is not specific to selected imaging methods and can be included in all sequences using PMC. Thus, the investigation of motion artifacts becomes much easier and facilitates the implementation of PMC in new sequences. The method can also be used to separately investigate the influence of different motion components (e.g., slow drifts, breathing, heart beat) on image artifacts.
With this technique, the effects of motion and PMC during MRI were investigated, to demonstrate the ability and flexibility of PMC to adapt MR acquisitions to patients' motion, and to reproduce artifacts suppressed by PMC for a quantitative validation of the correction method. In the presented article, the accuracy of the artifacts simulated is investigated in phantom experiments and in vivo. Additionally, the method is used to display the effect of various motions on different imaging sequences.
DISCUSSIONS AND CONCLUSIONS
- Top of page
- DISCUSSIONS AND CONCLUSIONS
The validation and fair comparison of PMC results has previously been problematic, since the resulting image cannot be compared to an uncorrected scan fulfilling the same imaging conditions. Especially when used in clinical routine, a quantification of the improvements needs to be done without interaction with the actual patient scan. Existing methods rely on the exact repetition of an acquisition [2, 3, 7] or double the duration of a single scan  to quantify the improvements made. The method presented here enables the reproduction of the prevented motion artifacts under controlled conditions on a phantom or a stationary volunteer and thus allows a fair comparison between different subjects. This can be investigated retrospectively if needed and does not cause any prolongation of the MR-investigation, or additional discomfort for the patient.
The new method was used to simulate arbitrary motion artifacts in different sequences. For the development of PMC sequences, this technique offers an important tool to investigate the effect of coordinate updates at different time points of the sequence and to understand the motion artifacts expected. Compared to an alternating acquisition of two images , this new method allows the accurate simulation of intrascan motion artifacts, for example, from motion during diffusion encoding periods  or during the echo train. This includes the possibility to investigate PMC-enabled sequences with respect to their sensitivity to inaccurate tracking data due to latencies or measurement noise . In this work, examples were presented for a RARE sequence. However, the method is not limited to these two imaging techniques and can be used whenever PMC is applied in a particular sequence.
The accuracy of the method was tested in phantom experiments and in vivo. The results of these measurements demonstrate the ability of the technique to accurately reproduce artifacts and underline the importance of short latencies when prospectively correcting for fast motion. With this method, motion artifacts due to arbitrary motion can be reproduced, as long as they can be prevented with PMC. For example, the effects of changing B0 field inhomogeneities or RF coil sensitivities during actual motions cannot be simulated using the proposed method. This might reduce the accuracy of the reproduced artifacts when parallel imaging techniques are used. Also, subject motion within nonlinear areas of the gradient system cannot be perfectly corrected by PMC and thus the related artifacts cannot be accurately reproduced by the presented method. Another reason for remaining differences between original and reproduced artifacts might be the discrete time grid used for PMC and simulations. In , a technique is presented to reduce the time between correction (or simulation) steps to about 2 ms; however, motion during these finite sequence elements is not taken into account. Conversely, for the purpose of a quantification of PMC-relevant improvements these effects are of minor interest as they are not taken into account by the correction method itself. In this work, the average edge strength was used to quantify differences between uncorrected scans and measurements using PMC as suggested in Refs. [4, 9]. However, the average edge strength is just one possible quantification metric and other approaches could be combined with the technique of reproducing motion artifacts.
A high-resolution scan was performed on a volunteer who was instructed to remain as still as possible. PMC was used to correct for slow position drifts and small head motion resulting from breathing and the heart beating. The observation of such microscopic head motion was reported [9, 10] but the effect of PMC has not been investigated so far. In this work, the artifacts prevented during this scan were then reproduced in a phantom separately for the slow drift, the breathing motion and the fast movements resulting from the cardiac cycle. The results display the importance of PMC during high resolution imaging but also show that a shorter latency might be necessary when fast motions occur. Long latencies make the correction of such fast motion impossible and can even lead to additional artifacts.
The problem of quantifying improvements achieved by prospective methods is not limited to the correction of motion artifacts. For example, real time shimming has been shown to reduce image artifacts resulting from B0 changes during a dynamic acquisition  or high resolution T2* imaging . While the method presented in our work was used to simulate artifacts by feeding back motion data to the scanner, the same principle could be used to reproduce artifacts prevented by real time shimming or other prospective correction methods. All parameters used to perform a specific prospective correction could be used to retrospectively investigate the improvements achieved. For navigator-based corrections, it might be possible to extend this method to apply the simulated motion during the navigators as well as during the imaging sequences, thereby allowing the testing of multiple variations of PMC methods on the same motion set.
While PMC can greatly reduce motion artifacts when correcting for the true position changes, it fails and can even create new artifacts when the applied update does not represent the true position of the measured object. This issue has to be kept in mind whenever PMC is used in new sequences. The presented technique provides a means to perform such investigations under controlled conditions. However, the simulated artifacts can only be as accurate as the tracking data provided. Corruption or inaccuracies of the tracking data (e.g., loss of tracking, nonrigid body motion, or measurement noise) collected during an in vivo scan would not only result in remaining artifacts in the corrected image but also lead to an imperfect simulation of the artifacts. The use of filters on the acquired tracking data helps to reduce such imperfections and can be applied to the tracking data prior to the artifact reproduction.
In conclusion, this work shows that the retrospective reproduction of motion artifacts is both a possible and advantageous extension to PMC. The technique presented enables a quantitative comparison of motion artifacts prevented, and accurate investigations of the effects of motion and correction on arbitrary sequences using PMC.