Volume 13 Supplement 1
Motion Correction of Whole-Body PET Data with a Joint PET-MRI Registration Functional
© Fieseler et al.; licensee BioMed Central Ltd. 2014
Published: 28 February 2014
Respiratory motion is known to degrade image quality in PET imaging. The necessary acquisition time of several minutes per bed position will inevitably lead to a blurring effect due to organ motion. A lot of research has been done with regards to motion correction of PET data. As full-body PET-MRI became available recently, the anatomical data provided by MRI is a promising source of motion information. Current PET-MRI-based motion correction approaches, however, do not take into account the available information provided by PET data. PET data, though, may add valuable additional information to increase motion estimation robustness and precision.
In this work we propose a registration functional that is capable of performing motion detection in gated data of two modalities simultaneously. Evaluation is performed using phantom data. We demonstrate that performing a joint registration of both modalities does improve registration accuracy and PET image quality.
KeywordsMotion Correction PET-MRI Image Registration Joint Registration Multi-Modal Data Positron Emission Tomography Magnetic Resonance Imaging
Respiratory motion is known to impair image quality as well as quantification in positron emission tomography (PET) . As the acquisition of PET takes several minutes per bed position, organ motion due to respiration cannot be avoided and will thus result in blurred images. By using gating methods, the acquired PET data can be divided into different motion phases. Gating reduces the amount of motion contained within each gate to a large extent, yet at the expense of reduced statistics and thereby image quality [2, 3]. To alleviate this, motion between gates can be estimated and subsequently be used to correct PET data for motion, resulting in a single image volume with reduced motion artifacts and full statistics. Various approaches for motion estimation of gated PET data have been studied, including optical flow , B-spline based methods , and registration methods including mass-preservation .
The use of 4D-CT data for motion correction has been proposed . The advantage of this approach lies in the usage of anatomical data, which is independent of tracer uptake. Acquisition of 4D-CT data, however, increases the radiation burden for the patient.
Whole body PET-MRI is promising regarding PET motion correction. High resolution MR data may allow for a precise motion estimation independent of tracer uptake and without additional radiation burden for the patient. The feasibility of PET-MRI-based motion correction of PET data has been demonstrated already, e.g., using hardware phantoms , animals , and simulation data . A commonality of current approaches to MR-based motion correction is that a 4D MR dataset is acquired from which motion is estimated and subsequently used in the correction of PET data. Strategies for the generation of 4D MR include acquisition of 2D slices with subsequent reordering , fast, consecutive acquisitions of 3D volumes , and sorting of k-space data during or after acquisition [13, 14]. Fayad et al. propose an approach where motion and image data are estimated simultaneously from the acquired MR data .
The motion information contained in PET data, however, remains unused in these approaches. In clinical routine, time for the acquisition of MR data needed for motion correction may be limited, as clinical protocols may demand for further, diagnostic sequences . Since time is proportional to image quality in MR, limited acquisition time may not allow to exploit the full potential of MR, resulting in poorer image quality than technically possible. Accordingly, all available information for motion estimation, including PET data, should be used.
Further, both modalities may contribute valuable information to the motion detection process. In MR, e.g., the lungs give relatively little signal due to their low proton density . Integrating information from PET data, if, e.g., active lesions are present in the lungs, may benefit motion estimation. Using information from both modalities should result in more reliable registration results. In the present work we propose an approach that uses information from both modalities by combining two registration functionals into a joint functional.
For motion correction approaches in PET-MRI, proper synchronisation of MR and PET is mandatory. The motion determined from MR has to be related to the PET data with respect to time. For the approach described in the following, we assume a gated PET dataset as well as a series of MR datasets. We assume that each PET gate corresponds to one MR gate with respect to its motion phase. The feasibility of creating corresponding phases has been demonstrated, e.g., in .
In the following we describe the proposed registration functional, followed by a description of the phantom data used in this work.
where is a distance functional, is the reference volume, is the template volume to be registered, and is a regularizer penalizing unfavourable transformations. The scalar value α weights the influence of the regularizer. For non-rigid transformations, y is chosen as a non-parametric transformation (one vector per voxel).
Here, and denote two reference volumes and and the template volumes. The scalar value β allows to weight the influence of the data term for PET.
In the registration functional in Equation (2), the deformation is represented by a common grid y for both modalities. Since the input data will not necessarily share the same resolution, resampling to a common grid is performed. Here, we chose a common grid of 2 mm3 voxel size.
where yref is a reference grid, given by the identity transformation in our case. The three summands control changes in length, surface area, and volume. Parameters for the hyperelastic regularizer were chosen empirically as α l = 1, α a = 0.1, α v = 1. Throughout all experiments in this paper, we keep α l , α a , α v fixed and vary the regularization strength by changing α in Eq. (2). For further details regarding the regularizer we refer to .
As the distance functional , we choose the sum of squared differences (SSD) for both PET and MR. The functional was implemented using the Matlab-based FAIR toolbox . For all registration experiments, linear interpolation was used and a multi-level approach using a downscaling factor of 0.5 was applied. Optimiziation was performed using a Gauss-Newton scheme with a preconditioned conjugate gradient solver.
In the present work we use data generated using a software phantom for evaluation. This allows us to compare motion estimates against ground-truth motion data. The XCAT phantom  is widely used as the basis for the simulation of imaging modalities and the evaluation of correction methods, e.g., in [6, 20, 21]. Using the XCAT phantom, we created an artificial PET-MRI dataset as described in the following.
The XCAT phantom was set to a maximum diaphragm motion of 2 cm. We selected eight frames representing the full range from inspiration to expiration .
For the creation of MR data a labelled XCAT dataset of 1 mm3 resolution was created. This dataset and known tissue values for T 1, T 2, and proton density [23, 24] were used as an input to the freely available MR simulation software SIMRI . We simulated an MR acquisition of stacked 2D slices covering the thorax. For respiratory motion, the largest amount of motion can be expected in the cranio-caudal and anterior-posterior directions. Thus, a sagittal slice orientation was chosen to capture these directions in-plane. For the phantom dataset used here, the maximum extents of motion are 2.5 mm (left-right), 10.9 mm (anterior-posterior), and 25.2 mm (head-feet). The following MR parameters were used: gradient echo, TE/TR 10 ms/30 ms, 12° flip angle, 256 × 256 pixels, 2 mm pixel size in-plane, slice spacing 1 cm, slice thickness 1 cm.
Using the phantom data described above allows for evaluation based on ground-truth motion as well as ground-truth activity data. First, we set β = 0, thereby performing an MR-based registration and use varying values of α to determine the best result for an MR-based registration.
Using the determined registration parameters, we compute registrations for increasing values of β, thus adding increasing amounts of PET information to the registration.
where Ω is the image domain, y i the i-th component of vector y, and yGT the ground-truth vector. Here, we examine averaged values of the AEE for all gates.
Further, the computed motion estimates are used to perform a motion correction of the dataset. The PET gates are warped using the computed motion and averaged. We evaluate correlation values of the corrected images as well as the recovered activities in the three lesion regions. For all registrations, attenuated PET data are used. For evaluation of correlation coefficients and activity recovery, attenuation corrected PET data are used.
Recovered Activity in Lesions
Using the joint registration approach, local improvements are observable. For the heart, the lungs, and the lesions (lungs and liver), improvements in terms of registration error (AEE) are achieved. In particular the lesions in the lungs show a large reduction in registration error when PET data are added to the registration. For the liver region, an increase of the AEE is observable. For the area of the lesion added to the liver, though, the AEE is decreased. With the exception of the liver region in toto, those regions that exhibit tracer uptake in PET seem to contribute to a better motion estimation result. Correlation of PET data is improved if PET data is added to the registration, indicating a benefit. Additionally, the lower registration error leads to a slightly better recovery of the activity in the lesions. Globally, a slight increase of the AEE is observable with increasing values of β.
The results presented here indicate that a benefit of using motion information from both modalities, MR and PET, is achievable. Certainly, the extent of the benefit will depend on many factors, with one major factor being the image quality of the two image modalities. The amount of remaining motion-induced blurring within the individual MR and PET gates will certainly limit the precision to which motion can be estimated. The extent to which PET can contribute will as well depend on the image quality, determined by factors like, e.g., type of injected tracer, injected dose, tracer uptake, and acquisition time. The phantom data used here does not contain artifacts other than noise. Particularly, we did not simulate motion artifacts which are likely to occur during MR acquisitions.
We have presented a joint registration functional that makes use of motion information derived from PET and MR data simultaneously. In this approach, motion information from both modalities is used. We demonstrated that the proposed method leads to a lower local registration error and better recovery of lesion activity, thus using information from both modalities simultaneously is beneficial regarding motion correction. As a result, clinical scenarios involving lesion quantification might in particular benefit from the proposed method.
In future work, we will evaluate our approach for a broader range of data, including cardiac motion. This will include the addition of mass-preservation . Certainly, the phantom data used in this work does only approximate reality. Thus, we will evaluate the proposed approach on real phantom data.
This work was partly funded by the German Research Foundation (Deutsche Forschungsgemeinschaft), Sonderforschungsbereich SFB 656 MoBil (project B3), and a research grant from Siemens Healthcare, Erlangen, Germany. We acknowledge support by Deutsche Forschungsgemeinschaft and Open Access Publication Fund of University of Muenster.
This article has been published as part of BioMedical Engineering OnLine Volume 13 Supplement 1, 2014: Selected articles from the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Workshop on Current Challenging Image Analysis and Information Processing in Life Sciences. The full contents of the supplement are available online at http://www.biomedical-engineering-online.com/supplements/13/S1
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