ORCID Profile
0000-0003-1923-6198
Current Organisation
KU Leuven
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Publisher: IOP Publishing
Date: 12-02-2015
DOI: 10.1088/0031-9155/60/5/2047
Abstract: We propose a method to compensate for six degree-of-freedom rigid motion in helical CT of the head. The method is demonstrated in simulations and in helical scans performed on a 16-slice CT scanner. Scans of a Hoffman brain phantom were acquired while an optical motion tracking system recorded the motion of the bed and the phantom. Motion correction was performed by restoring projection consistency using data from the motion tracking system, and reconstructing with an iterative fully 3D algorithm. Motion correction accuracy was evaluated by comparing reconstructed images with a stationary reference scan. We also investigated the effects on accuracy of tracker s ling rate, measurement jitter, interpolation of tracker measurements, and the synchronization of motion data and CT projections. After optimization of these aspects, motion corrected images corresponded remarkably closely to images of the stationary phantom with correlation and similarity coefficients both above 0.9. We performed a simulation study using volunteer head motion and found similarly that our method is capable of compensating effectively for realistic human head movements. To the best of our knowledge, this is the first practical demonstration of generalized rigid motion correction in helical CT. Its clinical value, which we have yet to explore, may be significant. For ex le it could reduce the necessity for repeat scans and resource-intensive anesthetic and sedation procedures in patient groups prone to motion, such as young children. It is not only applicable to dedicated CT imaging, but also to hybrid PET/CT and SPECT/CT, where it could also ensure an accurate CT image for lesion localization and attenuation correction of the functional image data.
Publisher: Springer Science and Business Media LLC
Date: 25-11-2016
Publisher: Springer Science and Business Media LLC
Date: 24-02-2040
Publisher: IOP Publishing
Date: 20-09-2016
DOI: 10.1088/0031-9155/61/19/7074
Abstract: Motion compensation (MC) in PET brain imaging of awake small animals is attracting increased attention in preclinical studies since it avoids the confounding effects of anaesthesia and enables behavioural tests during the scan. A popular MC technique is to use multiple external cameras to track the motion of the animal's head, which is assumed to be represented by the motion of a marker attached to its forehead. In this study we have explored several methods to improve the experimental setup and the reconstruction procedures of this method: optimising the camera-marker separation improving the temporal synchronisation between the motion tracker measurements and the list-mode stream post-acquisition smoothing and interpolation of the motion data and list-mode reconstruction with appropriately selected subsets. These techniques have been tested and verified on measurements of a moving resolution phantom and brain scans of an awake rat. The proposed techniques improved the reconstructed spatial resolution of the phantom by 27% and of the rat brain by 14%. We suggest a set of optimal parameter values to use for awake animal PET studies and discuss the relative significance of each parameter choice.
Publisher: Wiley
Date: 14-03-2013
DOI: 10.1118/1.4794481
Abstract: To establish a practical and accurate motion tracking method for the development of rigid motion correction methods in helical x-ray computed tomography (CT). A commercially available optical motion tracking system provided 6 degrees of freedom pose measurements at 60 Hz. A 4 × 4 calibration matrix was determined to convert raw pose data acquired in tracker coordinates to a fixed CT coordinate system with origin at the isocenter of the scanner. Two calibration methods, absolute orientation (AO), and a new method based on image registration (IR), were compared by means of landmark analysis and correlation coefficient in phantom images coregistered using the derived motion transformations. Transformations calculated using the IR-derived calibration matrix were found to be more accurate, with positional errors less than 0.5 mm (mean RMS), and highly correlated image voxel intensities. The AO-derived calibration matrix yielded larger mean RMS positional errors (≈ 1.0 mm), and poorer correlation coefficients. The authors have demonstrated the feasibility of accurate motion tracking for retrospective motion correction in helical CT. Their new IR-based calibration method based on image registration and function minimization was simpler to perform and delivered more accurate calibration matrices. This technique is a useful tool for future work on rigid motion correction in helical CT and potentially also other imaging modalities.
Publisher: IOP Publishing
Date: 25-01-2016
DOI: 10.1088/0031-9155/61/4/1416
Abstract: Correction for rigid object motion in helical CT can be achieved by reconstructing from a modified source-detector orbit, determined by the object motion during the scan. This ensures that all projections are consistent, but it does not guarantee that the projections are complete in the sense of being sufficient for exact reconstruction. We have previously shown with phantom measurements that motion-corrected helical CT scans can suffer from data-insufficiency, in particular for severe motions and at high pitch. To study whether such data-insufficiency artefacts could also affect the motion-corrected CT images of patients undergoing head CT scans, we used an optical motion tracking system to record the head movements of 10 healthy volunteers while they executed each of the 4 different types of motion ('no', slight, moderate and severe) for 60 s. From these data we simulated 354 motion-affected CT scans of a voxelized human head phantom and reconstructed them with and without motion correction. For each simulation, motion-corrected (MC) images were compared with the motion-free reference, by visual inspection and with quantitative similarity metrics. Motion correction improved similarity metrics in all simulations. Of the 270 simulations performed with moderate or less motion, only 2 resulted in visible residual artefacts in the MC images. The maximum range of motion in these simulations would encompass that encountered in the vast majority of clinical scans. With severe motion, residual artefacts were observed in about 60% of the simulations. We also evaluated a new method of mapping local data sufficiency based on the degree to which Tuy's condition is locally satisfied, and observed that areas with high Tuy values corresponded to the locations of residual artefacts in the MC images. We conclude that our method can provide accurate and artefact-free MC images with most types of head motion likely to be encountered in CT imaging, provided that the motion can be accurately determined.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Wiley
Date: 28-09-2016
DOI: 10.1118/1.4963218
Abstract: Although current computed tomography (CT) systems can scan the head in a very short time, patient motion sometimes still induces artifacts. If motion occurs, one has to repeat the scan to avoid motion, sedation or anesthesia is sometimes applied. The authors propose a method to iteratively estimate and compensate this motion during the reconstruction. In every iteration, the rigid motion was estimated view-by-view and then used to update the system matrix. A multiresolution scheme was used to speed up the convergence of this joint estimation of the image and the motion of the subject. A final iterative reconstruction was performed with the last motion estimate. The method was evaluated on simulations, patient scans, and a phantom study. The quality of the reconstructed images was improved substantially after the compensation. In simulation and phantom studies, root-mean-square error was reduced and mean structural similarity was increased. In the patient studies, most of the motion blurring in the reconstructed images disappeared after the compensation. The proposed method effectively eliminated motion-induced artifacts in head CT scans. Since only measured raw data are needed for the motion estimation and compensation, the proposed method can be applied retrospectively to clinical helical CT scans affected by motion.
No related grants have been discovered for Johan Nuyts.