Coupled motion and activity estimation from PET and MR data with motion model-based parameter reduction

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.

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

Respiratory motion during positron emission tomography (PET)/computed tomography (CT) imaging can cause significant image blurring and underestimation of tracer concentration for both static and dynamic studies. In this paper, with the aim to eliminate both intra-cycle and inter-cycle motions, and apply to dynamic imaging, we developed a non-rigid event-by-event (NR-EBE) respiratory motion-compensated list-mode reconstruction algorithm. The proposed method consists of two components: the first component estimates a continuous non-rigid motion field of the internal organs using the internal-external motion correlation. This continuous motion field is then incorporated into the second component, non-rigid MOLAR (NR-MOLAR) reconstruction algorithm to deform the system matrix to the reference location where the attenuation CT is acquired. The point spread function (PSF) and time-of-flight (TOF) kernels in NR-MOLAR are incorporated in the system matrix calculation, and therefore are also deformed according to motion. We first validated NR-MOLAR using a XCAT phantom with a simulated respiratory motion. NR-EBE motion-compensated image reconstruction using both the components was then validated on three human studies injected with 18F-FPDTBZ and one with 18F-fluorodeoxyglucose (FDG) tracers. The human results were compared with conventional non-rigid motion correction using discrete motion field (NR-discrete, one motion field per gate) and a previously proposed rigid EBE motion-compensated image reconstruction (R-EBE) that was designed to correct for rigid motion on a target lesion/organ. The XCAT results demonstrated that NR-MOLAR incorporating both PSF and TOF kernels effectively corrected for non-rigid motion. The 18F-FPDTBZ studies showed that NR-EBE out-performed NR-Discrete, and yielded comparable results with R-EBE on target organs while yielding superior image quality in other regions. The FDG study showed that NR-EBE clearly improved the visibility of multiple moving lesions in the liver where some of them could not be discerned in other reconstructions, in addition to improving quantification. These results show that NR-EBE motion-compensated image reconstruction appears to be a promising tool for lesion detection and quantification when imaging thoracic and abdominal regions using PET.

IntroductionAccurate PET reconstruction in spinal cord PET/MRI is challenging due to the small size of the structure and interference from background activity. The aim of this study was to establish whether MR-guided PET reconstruction can improve the accuracy of measured uptake in the spinal cord.MethodsThe hybrid kernel expectation maximisation (HKEM) algorithm was evaluated on a digital anthropomorphic phantom (XNAT), and an implementation of a modified asymmetric Bowsher's prior incorporating both PET and MR data was evaluated on clinical test cases. The methods were compared against commonly used algorithms OSEM and Q.Clear.ResultsThe results demonstrated that the two algorithms lead to an increase in measured [18F]FDG PET tracer uptake in the spinal cord. Comparison to ground truth indicates that the improvement is insufficient to remove the bias in this small structure.DiscussionWith care taken to optimise for the desired application, novel PET image reconstruction algorithms using PET and MR data to inform iterative image updates lead to improved quantification and improved image quality compared to OSEM. Further work is needed to investigate the optimal parameters and identify strategies to reduce residual bias.

Integrated PET/MR systems are becoming increasingly popular in clinical and research applications. Quantitative PET reconstruction requires correction for γ-photon attenuations using an attenuation coefficient map (μ map) that is a measure of the electron density. One challenge of PET/MR, in contrast to PET/CT, lies in the accurate computation of μ maps. Unlike CT, MR imaging measures physical properties not directly related to electron density. Previous approaches have computed the attenuation coefficients using a segmentation of MR images or using deformable registration of atlas CT images to the space of the subject MR images. In this work, we propose a patch-based method to generate whole-head μ maps from ultrashort echo-time (UTE) MR imaging sequences. UTE images are preferred to other MR sequences because of the increased signal from bone. To generate a synthetic CT image, we use patches from a reference dataset, which consists of dual-echo UTE images and a coregistered CT scan from the same subject. Matching of patches between the reference and target images allows corresponding patches from the reference CT scan to be combined via a Bayesian framework. No registration or segmentation is required. For evaluation, UTE, CT, and PET data acquired from 5 patients under an institutional review board-approved protocol were used. Another patient (with UTE and CT data only) was selected to be the reference to generate synthetic CT images for these 5 patients. PET reconstructions were attenuation-corrected using the original CT, our synthetic CT, Siemens Dixon-based μ maps, Siemens UTE-based μ maps, and deformable registration-based CT. Our synthetic CT-based PET reconstruction showed higher correlation (average ρ = 0.996, R(2) = 0.991) to the original CT-based PET, as compared with the segmentation- and registration-based methods. Synthetic CT-based reconstruction had minimal bias (regression slope, 0.990), as compared with the segmentation-based methods (regression slope, 0.905). A peak signal-to-noise ratio of 35.98 dB in the reconstructed PET activity was observed, compared with 29.767, 29.34, and 27.43 dB for the Siemens Dixon-, UTE-, and registration-based μ maps. A patch-matching approach to synthesize CT images from dual-echo UTE images leads to significantly improved accuracy of PET reconstruction as compared with actual CT scans. The PET reconstruction is improved over segmentation- (Dixon and Siemens UTE) and registration-based methods, even in subjects with pathologic findings.

Intrafractional organ motion imposes considerable challenges to scanned ion beam therapy and demands for a thorough verification of the applied treatment. At the Heidelberg Ion-Beam Therapy Center (HIT), the scanned ion beam delivery is verified by means of postirradiation positron-emission-tomography (PET) imaging. This work presents a first clinical evaluation of PET-based treatment monitoring in ion beam therapy under consideration of target motion. Three patients with mobile liver lesions underwent scanned carbon ion irradiation at HIT and postirradiation PET/CT (x-ray-computed-tomography) imaging with a commercial scanner. Respiratory motion was recorded during irradiation and subsequent image acquisition. This enabled a time-resolved (4D) calculation of the expected irradiation-induced activity pattern and, for one patient where an additional 4D CT was acquired at the PET/CT scanner after treatment, a motion-compensated PET image reconstruction. For the other patients, PET data were reconstructed statically. To verify the treatment, calculated prediction and reconstructed measurement were compared with a focus on the ion beam range. Results in the current three patients suggest that for motion amplitudes in the order of 2 mm there is no benefit from incorporating respiratory motion information into PET-based treatment monitoring. For a target motion in the order of 10 mm, motion-related effects become more severe and a time-resolved modeling of the expected activity distribution can lead to an improved data interpretation if a sufficient number of true coincidences is detected. Benefits from motion-compensated PET image reconstruction could not be shown conclusively at the current stage. The feasibility of clinical PET-based treatment verification under consideration of organ motion has been shown for the first time. Improvements in noise-robust 4D PET image reconstruction are deemed necessary to enhance the clinical potential.

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.

To develop a high-scanning efficiency, motion-corrected imaging strategy for free-breathing pulmonary MRI by combining an iterative motion-compensation reconstruction with a ultrashort echo time (UTE) acquisition called iMoCo UTE. An optimized golden-angle ordering radial UTE sequence was used to continuously acquire data for 5 minutes. All readouts were grouped to different respiratory motion states based on self-navigator signals, and then motion-resolved data was reconstructed by XD golden-angle radial sparse parallel reconstruction. One state from the motion-resolved images was selected as a reference, and then motion fields from the other states to the reference were derived via nonrigid registration. Finally, all motion-resolved data and motion fields were reconstructed by using an iterative motion-compensation (MoCo) reconstruction with a total generalized variation sparse constraint. The iMoCo UTE strategy was evaluated in volunteers and nonsedated pediatric patient (4-6 years old) studies. Images reconstructed with iMoCo UTE provided sharper anatomical lung structures and higher apparent SNR and contrast-to-noise ratio compared to using other motion-correction strategies, such as soft-gating, motion-resolved reconstruction, and nonrigid MoCo. iMoCo UTE also showed promising results in an infant study. The proposed iMoCo UTE combines self-navigation, motion modeling, and a compressed sensing reconstruction to increase scan efficiency and SNR and to reduce respiratory motion in lung MRI. This proposed strategy shows improvements in free-breathing lung MRI scans, especially in very challenging application situations such as pediatric MRI studies.

Image reconstruction and motion model estimation in four dimensional cone-beam CT (4D-CBCT) are conventionally handled as two sequential steps. Due to the limited number of projections at each phase, the image quality of 4D-CBCT is degraded by view aliasing artifacts, and the accuracy of subsequent motion modeling is decreased by the inferior 4DCBCT. The objective of this work is to enhance both the image quality of 4D-CBCT and the accuracy of motion model estimation with a novel strategy enabling simultaneous motion estimation and image reconstruction (SMEIR). The proposed SMEIR algorithm consists of two alternating steps: 1) model-based iterative image reconstruction to obtain a motion-compensated primary CBCT (m-pCBCT) and 2) motion model estimation to obtain an optimal set of deformation vector fields (DVFs) between the m-pCBCT and other 4D-CBCT phases. The motion-compensated image reconstruction is based on the simultaneous algebraic reconstruction (SART) technique coupled with total variation minimization. During the forward- and back-projection of SART, measured projections from an entire set of 4D-CBCT are used for reconstruction of the m-pCBCT by utilizing the updated DVF. The DVF is estimated by matching the forward projection of the deformed m-pCBCT and measured projections of other phases of 4D-CBCT. The performance of the SMEIR algorithm is quantitatively evaluated on a 4D NCAT phantom. The SMEIR algorithm improves image reconstruction accuracy of 4D-CBCT and tumor motion trajectory estimation accuracy as compared to conventional sequential 4D-CBCT reconstruction and motion estimation.

Motion during PET data acquisition is either introduced by design (e.g., couch wobble to enrich data sampling) or through unintentional motion of the object under study (e.g., subject head motion). Prior to this present work, such effects have been considered within distinctly different frameworks in PET imaging, with a rather basic approach having been devised for the cases of intentional motion (such as couch wobble or bed translation) in contrast to the relatively advanced modeling approaches devised for object motion. This article unifies the treatment of these different types of acquisition motion within a generalized framework through the use of the PET supersets data format. From this general framework, a range of conventional motion-compensation methods can be examined as special cases, permitting a revealing investigation into both the suboptimality of commonly adopted approximations, as well as the beneficial impact of known acquisition motion on image quality. The PET superset data format is a data representation ideally suited for the (potentially lossless) combination of conventional PET data with complementary motion information. Different practical implementations for the reconstruction from PET superset data involving varying levels of approximation to facilitate computation are identified and their impact on the final reconstructed image quality is assessed. Three main simulated case studies are investigated: (i) Motion compensation for subject head motion for 18F-FDG imaging, (ii) motion exploitation using either couch wobble or random motion, and (iii) motion exploitation using involuntary object motion both with and without couch wobble occurring. The motion exploitation case study goes beyond merely correcting for motion: The motion is directly modeled by the iterative reconstruction to exploit the increased sampling which is available to the benefit of reconstructed image quality. Reconstruction from superset data was successfully demonstrated for the case of motion correction for a 18F-FDG brain phantom simulation. Building on this success, the methodology was then applied to the case of motion exploitation. The case study resulted in three important findings. First, only reconstruction implementations which model the motion directly within the iterative reconstruction can succeed in significantly improving image resolution and contrast recovery for a given reconstructed noise level. Therefore, the commonly adopted approximation of binning the motion-adjusted data (which is the only method reported to date in the literature) is suboptimal and underestimates the beneficial impact of methods such as wobble during acquisition. Second, similar improvements were found for both types of motion patterns: Periodic wobble motion as well as random motion. Finally, for the case of simulated realistic involuntary object motion, similar resolution improvements were found (both with and without couch wobble). The proposed superset framework allows comprehensive analysis of the commonly adopted approximations when considering motion in PET reconstruction. The findings demonstrate that the supersets data format successfully unify reconstruction in the presence of different sources of known acquisition motion into one framework, and as a result leads to hitherto unreported image quality improvements for all the cases tested where the object motion is accurately known.

This paper presents a three-dimensional method to reconstruct moving objects from cone-beam X-ray projections using an iterative reconstruction algorithm and a given motion vector field. For the image representation, adapted blobs are used, which can be implemented efficiently as basis functions. Iterative reconstruction requires the calculation of line integrals (forward projections) through the image volume, which are compared with the actual measurements to update the image volume. In the existence of a divergent motion vector field, a change in the volumes of the blobs has to be taken into account in the forward and backprojections. An efficient method to calculate the line integral through the adapted blobs is proposed. It solves the problem, how to compensate for the divergence in the motion vector field on a grid of basis functions. The method is evaluated on two phantoms, which are subject to three different known motions. Moreover, a motion-compensated filtered back-projection reconstruction method is used, and the reconstructed images are compared. Using the correct motion vector field with the iterative motion-compensated reconstruction, sharp images are obtained, with a quality that is significantly better than gated reconstructions.

Previous work has that deep learning (DL)-enhanced 4D cone beam computed tomography (4D-CBCT) images improve motion modeling and subsequent motion-compensated (MoCo) reconstruction for 4D-CBCT. However, building the motion model at treatment time via conventional deformable image registration (DIR) methods is not temporally feasible. This work aims to improve the efficiency of 4D-CBCT MoCo reconstruction using DL-based registration for the rapid generation of a motion model prior to treatment. 
Approach. An artifact-reduction DL model was first used to improve the initial 4D-CBCT reconstruction by reducing streaking artifacts. Based on the artifact-reduced phase images, a groupwise DIR employing DL was used to estimate the inter-phase motion model. Two DL DIR models using different learning strategies were employed: 1) a patient-specific one-shot DIR model which was trained from scratch only using the images to be registered, and 2) a population DIR model which was pre-trained using collected 4D-CT images from 35 patients. The registration accuracy of two DL DIR models was assessed and compared to a conventional groupwise DIR approach implemented in the Elastix toolbox using the publicly available DIR-Lab dataset, a Monte Carlo simulation dataset from the SPARE challenge, and two clinical cases. 
Main results. The patient-specific DIR model and the population DIR model demonstrated registration accuracy comparable to the conventional state-of-the-art methods on the DIR-Lab dataset. No significant difference in image quality was observed between the final MoCo reconstructions using the patient-specific model and population model for motion modeling, compared to using the conventional approach. The average runtime (hh:mm:ss) of the entire MoCo reconstruction on SPARE dataset was reduced from 01:37:26 using conventional DIR method to 00:10:59 using patient-specific model and 00:01:05 using the pre-trained population model. 
Significance. DL-based registration methods can improve the efficiency in generating motion models for 4D-CBCT without compromising the performance of final MoCo reconstruction.

EP-1492: A framework combining image registration, respiratory motion models, and motion compensated image reconstruction

MR-based PET attenuation correction using segmented MR images is a promising approach to compensating for the lack of a transmission source in PET/MR and solving the problem of attenuation correction. However, the method is prone to errors in the choice of patient-individual lung attenuation coefficients (LAC), which are difficult to determine from MR data. Maximum Likelihood reconstruction of Attenuation and Activity (MLAA) can be applied to reconstruct attenuation maps from PET emission data. We present a constrained MLAA variant using segmentation of the lungs and tissue classification of the rest of the body, and focus on the evaluation of estimated mean LACs. In simple simulation studies, mean LACs can be estimated with errors as low as 5%, while in realistic ones, uncorrected out-of-field accidental coincidences seem to introduce bias.

RISING: A new framework for model-based few-view CT image reconstruction with deep learning.