Body Motion Correction Research Articles

Improved motion correction in brain MRI using 3D radial trajectory and projection moment analysis.

To develop a generalized rigid body motion correction method in 3D radial brain MRI to deal with continuous motion pattern through projection moment analysis. An assumption was made that the multichannel coil moves with the head, which was achieved by using a flexible head coil. A two-step motion correction scheme was proposed to directly extract the motion parameters from the acquired k-space data using the analysis of center-of-mass with high noise robustness, which were used for retrospective motion correction. A recursive least-squares model was introduced to recursively estimate the motion parameters for every single spoke, which used the smoothness of motion and resulted in high temporal resolution and low computational cost. Five volunteers were scanned at 3 T using a 3D radial multidimensional golden-means trajectory with instructed motion patterns. The performance was tested through both simulation and in vivo experiments. Quantitative image quality metrics were calculated for comparison. The proposed method showed good accuracy and precision in both translation and rotation estimation. A better result was achieved using the proposed two-step correction compared to traditional one-step correction without significantly increasing computation time. Retrospective correction showed substantial improvements in image quality among all scans, even for stationary scans. The proposed method provides an easy, robust, and time-efficient tool for motion correction in brain MRI, which may benefit clinical diagnosis of uncooperative patients as well as scientific MRI researches.

Body motion detection and correction in cardiac PET: Phantom and human studies.

Patient body motion during a cardiac positron emission tomography (PET) scan can severely degrade image quality. We propose and evaluate a novel method to detect, estimate, and correct body motion in cardiac PET. Our method consists of three key components: motion detection, motion estimation, and motion-compensated image reconstruction. For motion detection, we first divide PET list-mode data into 1-s bins and compute the center of mass (COM) of the coincidences' distribution in each bin. We then compute the covariance matrix within a 25-s sliding window over the COM signals inside the window. The sum of the eigenvalues of the covariance matrix is used to separate the list-mode data into "static" (i.e., body motion free) and "moving" (i.e. contaminated by body motion) frames. Each moving frame is further divided into a number of evenly spaced sub-frames (referred to as "sub-moving" frames), in which motion is assumed to be negligible. For motion estimation, we first reconstruct the data in each static and sub-moving frame using a rapid back-projection technique. We then select the longest static frame as the reference frame and estimate elastic motion transformations to the reference frame from all other static and sub-moving frames using nonrigid registration. For motion-compensated image reconstruction, we reconstruct all the list-mode data into a single image volume in the reference frame by incorporating the estimated motion transformations in the PET system matrix. We evaluated the performance of our approach in both phantom and human studies. Visually, the motion-corrected (MC) PET images obtained using the proposed method have better quality and fewer motion artifacts than the images reconstructed without motion correction (NMC). Quantitative analysis indicates that MC yields higher myocardium to blood pool concentration ratios. MC also yields sharper myocardium than NMC. The proposed body motion correction method improves image quality of cardiac PET.

Data-driven voluntary body motion detection and non-rigid event-by-event correction for static and dynamic PET

PET has the potential to perform absolute in vivo radiotracer quantitation. This potential can be compromised by voluntary body motion (BM), which degrades image resolution, alters apparent tracer uptakes, introduces CT-based attenuation correction mismatch artifacts and causes inaccurate parameter estimates in dynamic studies. Existing body motion correction (BMC) methods include frame-based image-registration (FIR) approaches and real-time motion tracking using external measurement devices. FIR does not correct for motion occurring within a pre-defined frame and the device-based method is generally not practical in routine clinical use, since it requires attaching a tracking device to the patient and additional device set up time. In this paper, we proposed a data-driven algorithm, centroid of distribution (COD), to detect BM. In this algorithm, the central coordinate of the time-of-flight (TOF) bin, which can be used as a reasonable surrogate for the annihilation point, is calculated for every event, and averaged over a certain time interval to generate a COD trace. We hypothesized that abrupt changes on the COD trace in lateral direction represent BMs. After detection, BM is estimated using non-rigid image registrations and corrected through list-mode reconstruction. The COD-based BMC approach was validated using a monkey study and was evaluated against FIR using four human and one dog studies with multiple tracers. The proposed approach successfully detected BMs and yielded superior correction results over conventional FIR approaches.

Patient body motion correction for dynamic cardiac PET-CT by attenuation-emission alignment according to projection consistency conditions.

Patient body motion is known to cause large deviations in the determination of myocardial blood flow (MBF) with errors exceeding 300%. Accurate correction for patient whole-body motion is still a largely unsolved problem in cardiac positron emission tomography (PET) imaging. This study evaluated the efficacy of using Natterer's formulation of the Helgason-Ludwig consistency conditions on the two-dimensional Radon transform to align computed tomography to PET projection data in multiple time frames of a dynamic sequence for the purpose of frame-by-frame correction of rigid whole-body motion. The correction algorithm was evaluated with digital NCAT phantoms using realistic noise added by the analytical simulator. Count rates used in the simulation were derived from clinical patient data. In addition, a proof of concept test using measured data with a cardiac torso phantom was conducted. Motion correction resulted in significant improvement in the accuracy of MBF estimates, especially for high count-rate acquisitions. Maximum errors for 2cm of motion dropped from 325% to 25% and from 250% to 25% using global and regional partial-volume correction, respectively. Median MBF errors dropped from 33% to 4.5% and 27% to 3.8%, respectively. Importantly, the correction algorithm performed equally well to compensate for body motion in both early and late time frames. Cardiac PET-CT data used for attenuation correction (CTAC) alignment using projection consistency conditions was effective for reducing errors in MBF measurements due to simulated patient motion, and can be integrated into the image reconstruction workflow.

Ｍｕｌｔｉｖａｎｅにおける画像特性の基礎的検討

Deterioration in the image because of a patient's movement always becomes a problem in the MRI inspection. To solve this problem, the imaging procedure named Multivane was developed. The principle is similar to the periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) method. As for Multivane, the effect of the body motion correction is high. However, the filling method of k space is different than a past Cartesian method. A basic examination of the image characteristic of Multivane and Cartesian was utilized along with geostationary phantom. The examination items are SNR, CNR, and a spatial resolution. As a result, Multivane of SNR was high. Cartesian of the contrast and the spatial resolution was also high. It is important to recognize these features and to use Multivane.

Theoretical and Numerical Study of MLEM and OSEM Reconstruction Algorithms for Motion Correction in Emission Tomography

Patient body-motion and respiratory-motion impacts the image quality of cardiac SPECT and PET perfusion images. Several algorithms exist in the literature to correct for motion within the iterative maximum-likelihood reconstruction framework. In this work, three algorithms are derived starting with Poisson statistics to correct for patient motion. The first one is a motion compensated MLEM algorithm (MC-MLEM). The next two algorithms called MGEM-1 and MGEM-2 (short for Motion Gated OSEM, 1 and 2) use the motion states as subsets, in two different ways. Experiments were performed with NCAT phantoms (with exactly known motion) as the source and attenuation distributions. Experiments were also performed on an anthropomorphic phantom and a patient study. The SIMIND Monte Carlo simulation software was used to create SPECT projection images of the NCAT phantoms. The projection images were then modified to have Poisson noise levels equivalent to that of clinical acquisition. We investigated application of these algorithms to correction of (1) a large body-motion of 2 cm in Superior-Inferior (SI) and Anterior-Posterior (AP) directions each and (2) respiratory motion of 2 cm in SI and 0.6 cm in AP. We determined the bias with respect to the NCAT phantom activity for noiseless reconstructions as well as the bias-variance for noisy reconstructions. The MGEM-1 advanced along the bias-variance curve faster than the MC-MLEM with iterations. The MGEM-1 also lowered the noiseless bias (with respect to NCAT truth) faster with iterations, compared to the MC-MLEM algorithms, as expected with subset algorithms. For the body motion correction with two motion states, after the 9th iteration the bias was close to that of MC-MLEM at iteration 17, reducing the number of iterations by a factor of 1.89. For the respiratory motion correction with 9 motion states, based on the noiseless bias, the iteration reduction factor was approximately 7. For the MGEM-2, however, bias-plot or the bias-variance-plot saturated with iteration because of successive interpolation error. SPECT data was acquired simulating respiratory motion of 2 cm amplitude with an anthropomorphic phantom. A patient study acquired with body motion in a second rest was also acquired. The motion correction was applied to these acquisitions with the anthropomorphic phantom and the patient study, showing marked improvements of image quality with the estimated motion correction.

Real‐time rigid body motion correction and shimming using cloverleaf navigators

Subject motion during scanning can greatly reduce MRI image quality and is a major reason for discarding data in both clinical and research scanning. The quality of the high-resolution structural data used for morphometric analysis is especially compromised by subject movement because high-resolution scans are of longer duration. A method is presented that measures and corrects rigid body motion and associated first-order shim changes in real time, using a pulse sequence with embedded cloverleaf navigators and a feedback control mechanism. The procedure requires a 12-s preliminary mapping scan. A single-path, 4.2-ms cloverleaf navigator is inserted every repetition time (TR) after the readout of a 3D fast low-angle shot (FLASH) sequence, requiring no additional RF pulses and minimally impacting scan duration. Every TR, a rigid body motion estimate is made and a correction is fed back to adjust the gradients and shim offsets. Images are corrected and reconstructed on the scanner computer for immediate access. Correction for between-scan motion can be accomplished by using the same reference map for each scan repetition. Human and phantom tests demonstrated a consistent improvement in image quality if motion occurred during the acquisition.

Correction of MR image distortions induced by metallic objects using a 3D cubic B‐spline basis set: Application to stereotactic surgical planning

Metallic implants in MRI cause spin-echo (SE) images to be distorted in the slice and frequency-encoding directions. Chang and Fitzpatrick (IEEE Trans Med Imaging 1992;11:319-329) proposed a distortion correction method (termed the CF method) based on the magnitude images from two SE acquisitions that differ only in the polarity of the frequency-encoding and slice-selection gradients. In the present study we solved some problems with the CF method, primarily by modeling the field inhomogeneities as a single 3D displacement field built by 3D cubic B-splines. The 3D displacement field was applied in the actual distortion direction in the slice/frequency-encoding plane. To account for patient head motion, a 3D rigid body motion correction was also incorporated in the model. Experiments on a phantom containing an aneurysm clip showed that the knot spacing between the B-splines is a very important factor in both the final image quality and the processing speed. Depending on the knot spacing and the image volume size, the number of unknowns range from a few thousands to over 100,000, leading to processing times ranging from minutes to days. Optimal knot spacing, a means of increasing the processing speed, and other parameters are investigated and discussed.

2D-PACE呼吸同期MRCPにおける呼吸ペースメーカーの開発と画質改善効果

Two-dimensional prospective acquisition correction (2D-PACE) is a human body motion-correction technique that uses the navigator echo sequence. Clinically, this respiratory-gated technique is used to improve the image quality of magnetic resonance cholangiopancreatography (MRCP). However respiratory motion artifact caused by a mismatch in respiratory rhythm has been experienced. The newly developed breath pacemaker was developed with the aim of providing the best match between the breath cycle and 2D-PACE trigger. The breath pacemaker is composed of two stimulators, an auto voice (AV) system and an auto light (AL) system. The two systems can be controlled separately or concurrently. Using the new breath pacemaker, improvement in MRCP image quality was evaluated in normal volunteers, and definite improvement was noted. Particularly good efficacy was obtained with concurrent control of the AV system and AL system. The results clearly demonstrated that the breath pacemaker can provide good synchronization of 2D-PACE and can lead to enhanced quality of MRCP images.

The effects of motion on parametric fMRI analysis techniques

Subject motion during the time course of functional activation studies has been shown to cause spurious signals which can mimic `true' activation. Therefore, the importance of motion correction has been widely recognized. Correction with post-processing using image registration software is common practice in functional imaging and analysis. Many image registration algorithms, developed for analysis requirements other than fMRI, assume rigid body motion. Although these techniques are now routinely used by a number of groups, rigid body co-registration has not yet been shown to reduce the effects of motion to an acceptable level in fMRI analysis, i.e. the effects on resulting correlation analysis directly. In this paper we have used volume data to assess rigid body co-registration in terms of motion artefacts for the different correlation approaches used in fMRI. We have developed a new way of visualizing motion effects in correlation analysis based on generating a scatter plot of correlation score against local image gradient. This technique has been tested on fMRI data sets from a functional paradigm suffering from motion correlated artefacts, with and without rigid body motion correction. Although we do not attempt to estimate the actual residual motion, this technique can be used to verify the results of analysis and select regions of relatively unambiguous activation. This paper assesses directly the rigid body assumption and proves the need for, and effectiveness of, co-registration for all correlation based analysis techniques. The specific differences between the popular correlation forms used are investigated and explained. We show that for certain forms of correlation analysis the effects of motion, while not removed altogether, are effectively statistically eliminated.