Accuracy of head motion compensation for the HRRT: Comparison of methods

Advances in PET technology have led to improved resolution capability in modern PET scanners, andhence the impact of motion has become increasingly significant in order to produce high resolutionPET images. This thesis aims to advance the utility of motion correction in combined PET and MR byimproving the capabilities of motion correction, and by easing the effort required in order to applythem. In particular, PET-based data-driven motion tracking methods, which are able to track motionwithout requiring additional hardware, are investigated and applied to the head. First, existing techniques for motion correction, especially in the context of PET/MR, are reviewedand a general framework for PET motion correction is presented. The framework provides a top-levelperspective on PET motion correction, simplifying the process of comparing and contrasting variousmotion correction techniques. Next, the implementation of a patient-based orientation system withinthe open-source PET reconstruction packages the Software for Tomographic Image Reconstruction(STIR) and the Synergistic Image Reconstruction Software (SIRF) is described. Additionally, animplementation of the general PET motion correction framework platform is introduced. To investigate current technological limitations in combined PET and MR motion correction, theefficacy of a commercial PET/MR motion correction product, Brain COMPASS (herein just COMPASSfor brevity), was assessed. An experiment introduced a methodology for measuring motion blur invivo and uses this to measure reduction in motion blur after applying COMPASS. N=10 subjects worea head-mounted apparatus with fiducial markers affixed. COMPASS reduced blur in the directionof motion, measured by the principal component of the full-width at half-maximum (FWHM) ofthe source point-spread-function (PSF) distribution, only when the motion was sufficient to causethat blur to exceed 1.9 mm. However, blur across all directions almost always increased (mean=0.3mm) when COMPASS was used. Results suggested that any error in estimating the patient posewas low and was not the cause of the blur. Increases in overall blur were therefore suspected to becaused by interpolation when resampling transformed reconstructions. A second experiment measuredneocortical standardised uptake value ratio (SUVr) in an 18F-Florbetaben (18F-FBB) study of N=42young, healthy, asymptomatic volunteers with high polygenetic risk of developing Alzheimer’s Disease.No significant changes in neocortical SUVr were observed. This cohort exhibited low motion, andthe neocortex is a large region-of-interest, so is less sensitive to motion corruption than other cohorts.Hence, it was concluded that COMPASS did not aid this investigation, but also does not have adiscernible negative impact despite low motion. During the COMPASS investigation, MR acquisitions were the time-limiting factor of the session,meaning that the MR acquisitions introduced for PET motion correction increased the overall sessiontime. Because MR is not always available for motion tracking, alternative PET-alone motion correctiontechniques were investigated. Several data-driven tracking techniques are available; however, it is notclear which are most appropriate. A phantom investigation was conducted on a Biograph mCT Flowto determine which data-driven head motion tracking techniques were most appropriate. Of the fourmethodologies investigated principal component analysis (PCA) was found to be the most promisingtechnique for PET data-driven motion tracking of head motion. The PET-alone motion correction technique outlined above had two key limitations. Firstly, theyprovide frame-based motion correction with low temporal resolution. However, the type of motionseen in the COMPASS investigation were continuous “drooping” of the head, is not modelled in adiscrete, frame-based scheme. Secondly, the PET reconstructions without attenuation provide noisypose estimates compared to those from MR volume registration. Therefore, a scheme was proposedthat, unlike previous techniques, models head motion directly from motion surrogates. Here, a radialbasis function (RBF) regression relates the endogenous tracking signal to the exogenous rigid poseparameters, which are supplied by temporal-sparse MR acquisitions. A proof-of-concept is presentedin which low temporal resolution MR pose estimates are upsampled. The technique is compared tolinear and nearest-neighbour interpolation, and for the regression, both linear and RBF are investigated.The proposed technique was found to introduce some negligible blurring when no motion is present (<0.1 mm mean-of-maximum displacement (MMD)) but reduce blurring by 0.7 mm MMD when motionis present. This demonstrates the capability of the technique to reduce error in motion estimation byimproving the temporal resolution. In a clinical setting, motion correction is likely to become increasingly important in the future. Thework in this thesis will make motion correction easier to apply in a clinical setting, so that it may beapplied more regularly, providing wider benefit.

Cardiac x-ray CT imaging is still challenging due to heart motion, which cannot be ignored even with the current rotation speed of the equipment. In response, many algorithms have been developed to compensate remaining motion artifacts by estimating the motion using projection data or reconstructed images. In these algorithms, accurate motion estimation is critical to the compensated image quality. In addition, since the scan range is directly related to the radiation dose, it is preferable to minimize the scan range in motion estimation. In this paper, the authors propose a novel motion estimation and compensation algorithm using a sinogram with a rotation angle of less than 360°. The algorithm estimates the motion of the whole heart area using two opposite 3D partial angle reconstructed (PAR) images and compensates the motion in the reconstruction process. A CT system scans the thoracic area including the heart over an angular range of 180° + α + β, where α and β denote the detector fan angle and an additional partial angle, respectively. The obtained cone-beam projection data are converted into cone-parallel geometry via row-wise fan-to-parallel rebinning. Two conjugate 3D PAR images, whose center projection angles are separated by 180°, are then reconstructed with an angular range of β, which is considerably smaller than a short scan range of 180° + α. Although these images include limited view angle artifacts that disturb accurate motion estimation, they have considerably better temporal resolution than a short scan image. Hence, after preprocessing these artifacts, the authors estimate a motion model during a half rotation for a whole field of view via nonrigid registration between the images. Finally, motion-compensated image reconstruction is performed at a target phase by incorporating the estimated motion model. The target phase is selected as that corresponding to a view angle that is orthogonal to the center view angles of two conjugate PAR images. To evaluate the proposed algorithm, digital XCAT and physical dynamic cardiac phantom datasets are used. The XCAT phantom datasets were generated with heart rates of 70 and 100 bpm, respectively, by assuming a system rotation time of 300 ms. A physical dynamic cardiac phantom was scanned using a slowly rotating XCT system so that the effective heart rate will be 70 bpm for a system rotation speed of 300 ms. In the XCAT phantom experiment, motion-compensated 3D images obtained from the proposed algorithm show coronary arteries with fewer motion artifacts for all phases. Moreover, object boundaries contaminated by motion are well restored. Even though object positions and boundary shapes are still somewhat different from the ground truth in some cases, the authors see that visibilities of coronary arteries are improved noticeably and motion artifacts are reduced considerably. The physical phantom study also shows that the visual quality of motion-compensated images is greatly improved. The authors propose a novel PAR image-based cardiac motion estimation and compensation algorithm. The algorithm requires an angular scan range of less than 360°. The excellent performance of the proposed algorithm is illustrated by using digital XCAT and physical dynamic cardiac phantom datasets.

Simultaneous PET and MR imaging is a promising new technique allowing the fusion of functional (PET) and anatomic/functional (MR) information. In the thoracic-abdominal regions, respiratory motion is a major challenge leading to reduced quantitative and qualitative image accuracy. Correction methodologies include the use of gated frames that lead to low signal-to-noise ratio considering the associated low statistics. More advanced correction approaches, previously developed for PET/CT imaging, consist of either registering all the reconstructed gated frames to the reference frame or incorporating motion parameters into the iterative reconstruction process to produce a single motion-compensated PET image. The goal of this work was to compare these two—previously implemented in PET/CT—correction approaches within the context of PET/MR motion correction for oncology applications using clinical 4-dimensional PET/MR acquisitions. Two different correction approaches were evaluated comparing the incorporation of elastic transformations extracted from 4-dimensional MR imaging datasets during PET list-mode image reconstruction to a postreconstruction image-based approach. Eleven patient datasets acquired on a PET/MR system were used. T1-weighted 4D MR images were registered to the end-expiration image using a nonrigid B-spline registration algorithm to derive deformation matrices accounting for respiratory motion. The derived matrices were subsequently incorporated within a PET image reconstruction of the original emission list-mode data (reconstruction space [RS] method). The corrected images were compared with those produced by applying the deformation matrices in the image space (IS method) followed by summing the realigned gated frames, as well as with uncorrected motion-averaged images. Both correction techniques led to significant improvement in accounting for respiratory motion artifacts when compared with uncorrected motion-averaged images. These improvements included signal-to-noise ratio (mean increase of 28.0% and 24.2% for the RS and IS methods, respectively), lesion size (reduction of 60.4% and 47.9%, respectively), lesion contrast (increase of 70.1% and 57.2%, respectively), and lesion position (changes of 60.9% and 46.7%, respectively). Our results demonstrate significant respiratory motion compensation using both methods, with superior results from a 4D PET RS approach.

To develop and test a real-time motion compensation algorithm for contrast-enhanced ultrasound imaging of tumor angiogenesis on a clinical ultrasound system. The Administrative Institutional Panel on Laboratory Animal Care approved all experiments. A new motion correction algorithm measuring the sum of absolute differences in pixel displacements within a designated tracking box was implemented in a clinical ultrasound machine. In vivo angiogenesis measurements (expressed as percent contrast area) with and without motion compensated maximum intensity persistence (MIP) ultrasound imaging were analyzed in human colon cancer xenografts (n=64) in mice. Differences in MIP ultrasound imaging signal with and without motion compensation were compared and correlated with displacements in x- and y-directions. The algorithm was tested in an additional twelve colon cancer xenograft-bearing mice with (n=6) and without (n=6) anti-vascular therapy (ASA-404). In vivo MIP percent contrast area measurements were quantitatively correlated with ex vivo microvessel density (MVD) analysis. MIP percent contrast area was significantly different (P<0.001) with and without motion compensation. Differences in percent contrast area correlated significantly (P<0.001) with x- and y-displacements. MIP percent contrast area measurements were more reproducible with motion compensation (ICC=0.69) than without (ICC=0.51) on two consecutive ultrasound scans. Following anti-vascular therapy, motion-compensated MIP percent contrast area significantly (P=0.03) decreased by 39.4±14.6% compared to non-treated mice and correlated well with ex vivo MVD analysis (Rho=0.70; P=0.05). Real-time motion-compensated MIP ultrasound imaging allows reliable and accurate quantification and monitoring of angiogenesis in tumors exposed to breathing-induced motion artifacts.

Degradation of image quality caused by cardiac and respiratory motions hampers the diagnostic quality of cardiac PET. It has been shown that improved diagnostic accuracy of myocardial defect can be achieved by tagged MR (tMR) based PET motion correction using simultaneous PET-MR. However, one major hurdle for the adoption of tMR-based PET motion correction in the PET-MR routine is the long acquisition time needed for the collection of fully sampled tMR data. In this work, the authors propose an accelerated tMR acquisition strategy using parallel imaging and/or compressed sensing and assess the impact on the tMR-based motion corrected PET using phantom and patient data. Fully sampled tMR data were acquired simultaneously with PET list-mode data on two simultaneous PET-MR scanners for a cardiac phantom and a patient. Parallel imaging and compressed sensing were retrospectively performed by GRAPPA and kt-FOCUSS algorithms with various acceleration factors. Motion fields were estimated using nonrigid B-spline image registration from both the accelerated and fully sampled tMR images. The motion fields were incorporated into a motion corrected ordered subset expectation maximization reconstruction algorithm with motion-dependent attenuation correction. Although tMR acceleration introduced image artifacts into the tMR images for both phantom and patient data, motion corrected PET images yielded similar image quality as those obtained using the fully sampled tMR images for low to moderate acceleration factors (<4). Quantitative analysis of myocardial defect contrast over ten independent noise realizations showed similar results. It was further observed that although the image quality of the motion corrected PET images deteriorates for high acceleration factors, the images were still superior to the images reconstructed without motion correction. Accelerated tMR images obtained with more than 4 times acceleration can still provide relatively accurate motion fields and yield tMR-based motion corrected PET images with similar image quality as those reconstructed using fully sampled tMR data. The reduction of tMR acquisition time makes it more compatible with routine clinical cardiac PET-MR studies.

Following continuous improvement in PET spatial resolution, respiratory motion correction has become an important task. Two of the most common approaches that utilize all detected PET events to motion-correct PET data are the reconstruct-transform-average method (RTA) and motion-compensated image reconstruction (MCIR). In RTA, separate images are reconstructed for each respiratory frame, subsequently transformed to one reference frame and finally averaged to produce a motion-corrected image. In MCIR, the projection data from all frames are reconstructed by including motion information in the system matrix so that a motion-corrected image is reconstructed directly. Previous theoretical analyses have explained why MCIR is expected to outperform RTA. It has been suggested that MCIR creates less noise than RTA because the images for each separate respiratory frame will be severely affected by noise. However, recent investigations have shown that in the unregularized case RTA images can have fewer noise artefacts, while MCIR images are more quantitatively accurate but have the common salt-and-pepper noise. In this paper, we perform a realistic numerical 4D simulation study to compare the advantages gained by including regularization within reconstruction for RTA and MCIR, in particular using the median-root-prior incorporated in the ordered subsets maximum a posteriori one-step-late algorithm. In this investigation we have demonstrated that MCIR with proper regularization parameters reconstructs lesions with less bias and root mean square error and similar CNR and standard deviation to regularized RTA. This finding is reproducible for a variety of noise levels (25, 50, 100 million counts), lesion sizes (8 mm, 14 mm diameter) and iterations. Nevertheless, regularized RTA can also be a practical solution for motion compensation as a proper level of regularization reduces both bias and mean square error.

This paper proposes a new motion compensation method using "sliced blocks" in DCT-based hybrid video coding. In H.264 | MPEG-4 Advance Video Coding, a brand-new international video coding standard, motion compensation can be performed by splitting macroblocks into multiple square or rectangular regions. In the proposed method, on the other hand, macroblocks or sub-macroblocks are divided into two regions (sliced blocks) by an arbitrary line segment. The result is that the shapes of the segmented regions are not limited to squares or rectangles, allowing the shapes of the segmented regions to better match the boundaries between moving objects. Thus, the proposed method can improve the performance of the motion compensation. In addition, adaptive prediction of the shape according to the region shape of the surrounding macroblocks can reduce overheads to describe shape information in the bitstream. The proposed method also has the advantage that conventional coding techniques such as mode decision using rate-distortion optimization can be utilized, since coding processes such as frequency transform and quantization are performed on a macroblock basis, similar to the conventional coding methods. The proposed method is implemented in an H.264-based P-picture codec and an improvement in bit rate of 5% is confirmed in comparison with H.264.

Motion estimation and motion compensation (MEMC) has been applied inside the wavelet-transformed domain for video compression at low bit rates. However, pixel-based motion estimation in the wavelet domain is not as straightforward as in the spatial domain due to the shift variance property caused by decimation. The paper finds a new wavelet-based motion estimation and compensation method, named as the sub-decimation decomposition method, to perform shift invariant motion estimation inside the wavelet transformed domain, so that better matching blocks are compensated to achieve more efficient video compression.

In this paper, a fuzzy inference digital image stabilization method is proposed to adaptively determine better motion compensation method through the selection of two different motion compensation methods. The experimental results show the good performance. It solves the problem of lag and overshoot of motion trajectory.

A fine image motion compensation method for the panoramic TDI CCD camera in remote sensing applications

Respiratory motion during PET imaging causes the resulting images to become corrupted by artifacts. Respiratory motion models have found application in estimating and correcting for the effects of motion in a wide range of applications. In this work we evaluate the performance of an MR-derived motion model for motion-correction of PET data that is suitable for use in a simultaneous PET-MR imaging system. The technique is based on the formation of a subject-specific respiratory motion model from near real-time dynamic MR images and is capable of making real-time motion estimates based on an MR navigator signal. This approach has an advantage over gating based approaches in that individual motion corrections can be applied at each time point, avoiding the need to combine large amounts of PET data for correction by a single transformation for each gate. In our investigation, we evaluate the effects of the MR-derived motion model and PET motion compensation using dynamic PET simulations based on dynamic MR data (105 real-time frames, 0.7s each) of the thorax acquired from a healthy volunteer. This large amount of data, acquired during different breathing patterns, allows us to more accurately simulate the complex respiratory motion variation that is observed in real PET acquisitions. PET images are corrected for motion using two approaches: reconstruct-transform-average (RTA) and motion compensated image reconstruction (MCIR). We compare the methods in terms of quantification and visualization of three tumors that were artificially added to the emission and attenuation maps of the volunteer close to the diaphragm. It is demonstrated that our motion model can be used to correct successfully for motion in real-time dynamic PET-MR acquisitions. The correction is feasible using either RTA or MCIR, although the latter provides

A motion compensation method that is aimed at correcting motion artifacts of cardiac valves is proposed. The primary focus is the aorticvalve. The method is based around partial angle reconstructions and a cost function including the image entropy. A motion model is applied to approximate the cardiac motion in the temporal and spatial domain. Based on characteristic values for velocities and strain during cardiac motion, penalties for the velocity and spatial derivatives are introduced to maintain anatomically realistic motion vector fields and avoid distortions. The model addresses global elastic deformation, but not the finer and more complicated motion of the valve leaflets. The method is verified based on clinical data. Image quality was improved for most artifact-impaired reconstructions. An image quality study with Likert scoring of the motion artifact severity on a scale from 1 (highest image quality) to 5 (lowest image quality/extreme artifact presence) was performed. The biggest improvements after applying motion compensation were achieved for strongly artifact-impaired initial images scoring 4 and 5, resulting in an average change of the scores by and , respectively. In the case of artifact-free images, a chance to introduce blurring was observed and their average score was raised by 0.42 ± 0.03. Motion artifacts were consistently removed and image qualityimproved.

The ability to manipulate image contrast and thus to obtain complementary information is one of the main advantages of MRI. Motion consistency within the whole data set is a key point in the context of multi contrast imaging. In cardiac and abdominal MRI, the acquisition strategy uses multiple breath-holds and often relies on acceleration methods that inherently suffer from a signal to-noise ratio loss. The aim of this work is to propose a free-breathing multi-contrast acquisition and reconstruction workflow to improve image quality and the subsequent data analysis. We extended a previously proposed motion-compensated image reconstruction method for multi-contrast imaging. Shared information throughout the imaging protocol is now exploited by the image reconstruction in the form of an additional constraint based on image gradient sparsity. This constraint helps to minimize the amount of data needed for efficient non-rigid motion correction. T₁and T₂weighted images were reconstructed from free-breathing acquisitions in 4 healthy volunteers and in a phantom. The impact of multi-contrast motion correction was evaluated in a phantom in terms of precision and accuracy of T₁and T₂quantification. In the phantom, the proposed method achieved an accuracy of 97.5 % on the quantified parameters against 88.0 % before motion correction. In volunteers, motion inconsistency in T₁and T₂quantification were noticeably reduced within 5 min of free-breathing acquisition. An efficient, free-breathing, multi-contrast imaging method has been demonstrated that does not require prior assumptions about contrast and that is applicable to a wide range of examinations.

In encoding of video sequences, similarity between subsequent frames can be exploited for compression. By many aspects, video coding schemes can be regarded as extensions of still image coding schemes as presented in the previous chapter, however techniques of motion compensation play a key role to achieve high compression performance. Highest redundancy between adjacent frames will be found along the motion trajectory; also coding errors will be less visible if they are consistent with the motion of the scene. This chapter starts by introducing more simple schemes which are aiming to exploit the temporal redundancy without motion compensation. Then, the combination of motion-compensated prediction with spatial transform coding is investigated, which is denoted as hybrid coding. This is the type of video coding scheme which is implemented in most of today’s video coding standards, for which the foundations are investigated in detail. Methods of advanced motion compensation are discussed, scalable and multiple-description coding concepts are introduced. Hybrid coders are based on a temporally recursive DPCM loop, which imposes a number of constraints in encoding and enforces provisions to prevent possible error propagations. As a possible alternative, motion-compensated wavelet methods are introduced, which extend methods of wavelet coding into the dimension of the temporal axis. Recent research has shown that no limitation exists in principle to combine wavelet filtering along motion trajectories with any type of motion compensation. This in particular provides flexible scalability of video streams and gives significantly higher robustness in case of data losses as compared to the hybrid solution. The chapter concludes by investigating methods for motion vector encoding, which is of particular importance as the data rate to be spent for motion information can reach a significant amount in the more sophisticated classes of video coding schemes.KeywordsMotion VectorMotion EstimationVideo CodeMotion CompensationEnhancement LayerThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Recently published methods for motion correction in neurological PET include the multiple acquisition frame (MAF) and LOR rebinning methods. The aim of the present work was to compare the accuracy of reconstructions obtained with these methods when multiple, arbitrary movements were applied to a Hoffman brain phantom during 3D list mode acquisition. A reflective target attached to the phantom enabled a Polaris optical motion tracking system to monitor the phantom position and orientation in the scanner coordinate frame. The motion information was used in the motion correction algorithms. The MAF method was applied to the list-mode data after sorting them into a series of dynamic frames, while the LOR rebinning method was applied directly to the list-mode data. A proportion of the list mode events had to be discarded during rebinning because the application of the corrective spatial transformation removed them from the 3D projection space. A correction for these 'lost' events was implemented as a global post-reconstruction scale factor, based on the overall fraction of lost events. Reconstructions from both motion correction methods were compared with a motion-free reference scan of the same phantom. Motion correction produced a marked improvement in image clarity and reduced errors with respect to the reference scan. LOR rebinning with global loss correction was found to be more accurate than the MAF method