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

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

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 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.

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.

We propose a technique that estimates both the image and the respiratory motion states of all PET gates during PET reconstruction. This is done using a gradient analytically-derived from the Poisson log-likelihood, and involves a parameterised motion model. This is incorporated into reconstruction using the motion-compensated image reconstruction (MCIR) framework, coupled with optimisation of the analytical gradient. The use of the motion model in this way reduces the number of additional parameters to be optimised for motion correction to one per PET gate. This was tested using a 3D PET simulation derived from segmented UTE MR image volumes, warped using real MR-derived motion measurements.

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.

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.

One of the most important parameters reducing the sensitivity and specificity in the thoracic and abdominal areas is respiratory motion and associated deformations which represent today an important challenge in medical imaging. The negative impact of respiratory motion is not only evident in the field of cancer diagnostic imaging but also in therapy applications, particularly in the field of radiation therapy. Modelling of the respiratory motion is therefore very important for the efficacy of both diagnostic and therapeutic imaging applications. All respiratory motion models presented to date are patient specific and require a 4D CT acquisition for every patient implying an associated increased dose which cannot be easily justified for all patients. The objective of this study is the description of a framework for the creation of a global generic model from 4D CT images of different patients. This global model is based on principal component analysis (PCA) and can be adapted to a given patient anatomy needing only one static CT image in combination with respiratory synchronised images of the patient surface. In this work 4D CT images of three patients were used to create the model which was tested by subsequently adapting it on the static CT images of two different patients. Our comparative results between the model derived and the corresponding acquired 4D CT images demonstrate the efficiency of the developed generic motion model.

Respiratory motion degrades quantitative and qualitative analysis of medical images. Estimation and, hence, correction of motion commonly uses static correspondence models between an external surrogate signal and internal motion. This paper presents a patient specific respiratory motion model with the ability to adapt in the presence of irregular motion via a Kalman filter with expectation maximization for parameter estimation. The adaptive approach introduces generalizability allowing the model to account for a broader variety of motion. This may be required in the presence of irregular breathing and with different sensors monitoring the external surrogate signal. The motion model framework utilizing an adaptive Kalman filter approach is tested on dynamic magnetic resonance imaging data of nine volunteers and compared to a state-of-the-art static total least squares approach. Results demonstrate the framework is capable of reducing motion to the order of ${p} ) more effective in the presence of irregular motion, assessed using the ${F}$ -test for model comparison. Utilizing the total sum of squares of estimated vector field error from the calculated ground truth, we observe approximately a fifty percent reduction in root mean square error and thirty percent reduction in standard deviation utilizing the Kalman model (EKF) in comparison to a static counterpart.

MR-guided radiotherapy has different requirements for the images than diagnostic radiology, thus requiring development of novel imaging sequences. MRI simulation is an excellent tool for optimizing these new sequences; however, currently available software does not provide all the necessary features. In this paper, we present a digital framework for testing MRI sequences that incorporates anatomical structure, respiratory motion, and realistic presentation of MR physics. The extended Cardiac-Torso (XCAT) software was used to create T1 , T2 , and proton density maps that formed the anatomical structure of the phantom. Respiratory motion model was based on the XCAT deformation vector fields, modified to create a motion model driven by a respiration signal. MRI simulation was carried out with JEMRIS, an open source Bloch simulator. We developed an extension for JEMRIS, which calculates the motion of each spin independently, allowing for deformable motion. The performance of the framework was demonstrated through simulating the acquisition of a two-dimensional (2D) cine and demonstrating expected motion ghosts from T2 weighted spin echo acquisitions with different respiratory patterns. All simulations were consistent with behavior previously described in literature. Simulations with deformable motion were not more time consuming than with rigid motion. We present a deformable four-dimensional (4D) digital phantom framework for MR sequence development. The framework incorporates anatomical structure, realistic breathing patterns, deformable motion, and Bloch simulation to achieve accurate simulation of MRI. This method is particularly relevant for testing novel imaging sequences for the purpose of MR-guided radiotherapy in lungs and abdomen.

Respiratory motion modeling of both tumor and surrounding tissues is a key element in minimizing errors and uncertainties in radiation therapy. Different continuous motion models have been previously developed. However, most of these models are based on the use of parameters such as amplitude and phase extracted from 1D external respiratory signal. A potentially reduced correlation between the internal structures (tumor and healthy organs) and the corresponding external surrogates obtained from such 1D respiratory signal is a limitation of these models. The objective of this work is to describe a continuous patient specific respiratory motion model, accounting for the irregular nature of respiratory signals, using patient external surface information as surrogate measures rather than a 1D respiratory signal. Ten patients were used in this study having each one 4D CT series, a synchronized RPM signal and patient surfaces extracted from the 4D CT volumes using a threshold based segmentation algorithm. A patient specific model based on the use of principal component analysis was subsequently constructed. This model relates the internal motion described by deformation matrices and the external motion characterized by the amplitude and the phase of the respiratory signal in the case of the RPM or using specific regions of interest (ROI) in the case of the patients' external surface utilization. The capability of the different models considered to handle the irregular nature of respiration was assessed using two repeated 4D CT acquisitions (in two patients) and static CT images acquired at extreme respiration conditions (end of inspiration and expiration) for one patient. Both quantitative and qualitative parameters covering local and global measures, including an expert observer study, were used to assess and compare the performance of the different motion estimation models considered. Results indicate that using surface information [correlation coefficient (CC): 0.998 ± 0.0006 and model error (ME): 1.35 ± 0.21 mm] is superior to the use of both motion phase and amplitude extracted from a 1D respiratory signal (CC and ME of 0.971 ± 0.02 and 1.64 ± 0.28 mm). The difference in performance was more substantial compared to the use of only one parameter (phase or amplitude) used in the motion model construction. Similarly, the patient surface based model was better in estimating the motion in the repeated 4D CT acquisitions and those CT images acquired at the full inspiration (FI) and the full expiration (FE). Once more, within this context the use of both amplitude and phase in the model building was substantially more robust than the use of phase or amplitude only. The present study demonstrates the potential of using external patient surfaces for the construction of patient specific respiratory motion models. Such information can be obtained using different devices currently available. The use of external surface information led to the best performance in estimating internal structure motion. On the other hand, the use of both amplitude and phase parameters derived from an 1D respiration signal led to largely superior model performance relative to the use of only one of these two parameters in the model building process.

We propose a bilinear model of respiratory organ motion. The advantages of classical statistical shape modelling are combined with a preconditioned trajectory basis for separately modelling the shape and motion components of the data. The separation of a linear basis into bilinear form leads to a more compact representation of the underlying physical process and the resulting model respects the temporal regularity within the training data, which is an important property for modelling quasi-periodic data. Bilinear modelling is combined with a Bayesian reconstruction algorithm for sparse data under observation noise. By applying the model to liver motion data, we show that our bilinear formulation of respiratory motion is significantly more parsimonious and can even outperform linear PCA-based models.

We propose a new type of computational phantom, the “4D voxel phantom,” for realistic modeling of continuous respiratory motion in Monte Carlo dose calculation. In this phantom, continuous respiratory motion is realized by linear interpolation of the deformation vector fields (DVFs) between the neighboring original phases in the 4D CT data of a patient and by subsequent application of the DVFs to the phase images or to the reference image to produce multiple inter-phase images between the neighboring original phase images. A 4D voxel phantom is a combination of high-temporal-resolution voxel phantoms and on-the-fly dose registration to the reference phase image. In the course of particle transport simulation, the dose or deposited energy is directly registered to the reference phase image on-the-fly (i.e., after each event) using a DVF for dose registration. In the present study, we investigated two methods - DRP (DIR [deformable image registration] with respect to Reference Phase image) and DNP (DIR with respect to Neighboring original Phase image) - for production of multiple inter-phase images or high-temporal-resolution voxel phantoms. Utilizing these two methods, two 4D voxel phantoms each with 100 phases were produced from the original 10-phase images of the 4D CT data of a real patient in order to compare the two methods and to test the feasibility of the 4D voxel phantom methodology in general. We found that it is possible to produce a 4D voxel phantom very rapidly (i.e., <;40 min on a 4-core personal computer for a 100-phase phantom) in a fully automated process. The dose calculation results showed that the constructed 100-phase 4D voxel phantoms provide cumulative-dose distributions very similar to those of the conventional 10-phase approach for stationary proton-beam irradiation. The passing rates of the dose distributions of the 4D voxel phantoms were higher than 99.9% according to the 3% and 3 mm gamma criteria, which results validate the 4D voxel phantom methodology. The point-and dose-tracking analysis data showed that the DRP method, which uses the minimal number of DIR operations but uses inverse DVFs, provides significantly better results than those of the DNP method, which uses only DIR to generate the DVFs for inter-phase image generation and dose registration. The present study also showed that the computation time does not significantly increase when the number of phases in the 4D voxel phantom is increased for more realistic representation of continuous respiratory motion; the only significant increase is in the memory occupancy, which grows almost linearly with the number of phases.

In this thesis, we develop constrained reconstructions methods to reconstruct high quality MR images from down-sampled k-space data by exploiting sparsity of MR images under the theory of Compressed Sensing. In the first work, we present an orthonormal-expansion L1 optimization technique to improve computational efficiency of existing methods without sacrificing the reconstruction accuracy. In order to achieve more accurate reconstructions especially in high under-sampling ratios, a novel method for motion-compensated reference-driven MR image reconstruction is presented in the second work with two sources of constraints: sparsity and prior information from a reference image. In the third work, we focus on reconstructions of MR images that have metallic implants. In order to reduce scan time incurred to fully correct metal artefacts and meanwhile improve SNR of results brought by the state-of-the-art methods, we propose a projection on convex set reconstruction technique to combine CS denoising and parallel MRI.

MRI-guided radiotherapy systems enable real-time 2D cine acquisitions for target monitoring, but cannot provide volumetric information due to spatio-temporal constraints. Hence, respiratory motion models coupled with a temporal predictive mechanism are a suitable solution to enable ahead-of-time 3D tumor and anatomy tracking in combination with real-time online plan adaptation. We propose a novel subject-specific probabilistic model to enable 3D+t predictions from image-based surrogates during radiotherapy treatments. The model is trained end-to-end to simultaneously capture and learn a distribution of realistic motion fields over a population dataset. Furthermore, the distribution is conditioned on a sequence of partial observations, which can be extrapolated in time using a \( {seq2seq}\)-inspired mechanism allowing for scalable predictive horizon. Based on the generative properties of conditional variational autoencoders, it integrates anatomical features and temporal information to construct an interpretable latent space with respiratory phase discrimination. The choice of a probabilistic framework allows improving uncertainty estimation during the volume generation phase. Experimental validation on 25 subjects demonstrates the potential of the proposed model, which achieves a mean landmark error of \(1.4 \pm 1.1\) mm, yielding statistically significant improvements over state-of-the-art methods.