Christian Buerger

Thoracic respiratory motion estimation from MRI using a statistical model and a 2-D image navigator

Respiratory motion models have potential application for estimating and correcting the effects of motion in a wide range of applications, for example in PET-MR imaging. Given that motion cycles caused by breathing are only approximately repeatable, an important quality of such models is their ability to capture and estimate the intra- and inter-cycle variability of the motion. In this paper we propose and describe a technique for free-form nonrigid respiratory motion correction in the thorax. Our model is based on a principal component analysis of the motion states encountered during different breathing patterns, and is formed from motion estimates made from dynamic 3-D MRI data. We apply our model using a data-driven technique based on a 2-D MRI image navigator. Unlike most previously reported work in the literature, our approach is able to capture both intra- and inter-cycle motion variability. In addition, the 2-D image navigator can be used to estimate how applicable the current motion model is, and hence report when more imaging data is required to update the model. We also use the motion model to decide on the best positioning for the image navigator. We validate our approach using MRI data acquired from 10 volunteers and demonstrate improvements of up to 40.5% over other reported motion modelling approaches, which corresponds to 61% of the overall respiratory motion present. Finally we demonstrate one potential application of our technique: MRI-based motion correction of real-time PET data for simultaneous PET-MRI acquisition.

Hierarchical adaptive local affine registration for fast and robust respiratory motion estimation

Non-rigid image registration techniques are commonly used to estimate complex tissue deformations in medical imaging. A range of non-rigid registration algorithms have been proposed, but they typically have high computational complexity. To reduce this complexity, combinations of multiple less complex deformations have been proposed such as hierarchical techniques which successively split the non-rigid registration problem into multiple locally rigid or affine components. However, to date the splitting has been regular and the underlying image content has not been considered in the splitting process. This can lead to errors and artefacts in the resulting motion fields. In this paper, we propose three novel adaptive splitting techniques, an image-based, a similarity-based, and a motion-based technique within a hierarchical framework which attempt to process regions of similar motion and/or image structure in single registration components. We evaluate our technique on free-breathing whole-chest 3D MRI data from 10 volunteers and two publicly available CT datasets. We demonstrate a reduction in registration error of up to 49.1% over a non-adaptive technique and compare our results with a commonly used free-form registration algorithm.

Fast generation of 4D PET-MR data from real dynamic MR acquisitions

We have implemented and evaluated a framework for simulating simultaneous dynamic PET-MR data using the anatomic and dynamic information from real MR acquisitions. PET radiotracer distribution is simulated by assigning typical FDG uptake values to segmented MR images with manually inserted additional virtual lesions. PET projection data and images are simulated using analytic forward projections (including attenuation and Poisson statistics) implemented within the image reconstruction package STIR. PET image reconstructions are also performed with STIR. The simulation is validated with numerical simulation based on Monte Carlo (GATE) which uses more accurate physical modelling, but has 150× slower computation time compared to the analytic method for ten respiratory positions and is 7000× slower when performing multiple realizations. Results are validated in terms of region of interest mean values and coefficients of variation for 65 million coincidences including scattered events. Although some discrepancy is observed, agreement between the two different simulation methods is good given the statistical noise in the data. In particular, the percentage difference of the mean values is 3.1% for tissue, 17% for the lungs and 18% for a small lesion. The utility of the procedure is demonstrated by simulating realistic PET-MR datasets from multiple volunteers with different breathing patterns. The usefulness of the toolkit will be shown for performance investigations of the reconstruction, motion correction and attenuation correction algorithms for dynamic PET-MR data.

Simultaneous PET–MR acquisition and MR-derived motion fields for correction of non-rigid motion in PET

ObjectivePositron emission tomography (PET) provides an accurate measurement of radiotracer concentration in vivo, but performance can be limited by subject motion which degrades spatial resolution and quantitative accuracy. This effect may become a limiting factor for PET studies in the body as PET scanner technology improves. In this work, we propose a new approach to address this problem by employing motion information from images measured simultaneously using a magnetic resonance (MR) scanner.MethodsThe approach is demonstrated using an MR-compatible PET scanner and PET–MR acquisition with a purpose-designed phantom capable of non-rigid deformations. Measured, simultaneously acquired MR data were used to correct for motion in PET, and results were compared with those obtained using motion information from PET images alone.ResultsMotion artefacts were significantly reduced and the PET image quality and quantification was significantly improved by the use of MR motion fields, whilst the use of PET-only motion information was less successful.ConclusionsCombined PET–MR acquisitions potentially allow PET motion compensation in whole-body acquisitions without prolonging PET acquisition time or increasing radiation dose. This, to the best of our knowledge, is the first study to demonstrate that simultaneously acquired MR data can be used to estimate and correct for the effects of non-rigid motion in PET.

Nonrigid Motion Modeling of the Liver From 3-D Undersampled Self-Gated Golden-Radial Phase Encoded MRI

Magnetic resonance imaging (MRI) has been commonly used for guiding and planning image guided interventions since it provides excellent soft tissue visualization of anatomy and allows motion modeling to predict the position of target tissues during the procedure. However, MRI-based motion modeling remains challenging due to the difficulty of acquiring multiple motion-free 3-D respiratory phases with adequate contrast and spatial resolution. Here, we propose a novel retrospective respiratory gating scheme from a 3-D undersampled high-resolution MRI acquisition combined with fast and robust image registrations to model the nonrigid deformation of the liver. The acquisition takes advantage of the recently introduced golden-radial phase encoding (G-RPE) trajectory. G-RPE is self-gated, i.e., the respiratory signal can be derived from the acquired data itself, and allows retrospective reconstructions of multiple respiratory phases at any arbitrary respiratory position. Nonrigid motion modeling is applied to predict the liver deformation of an average breathing cycle. The proposed approach was validated on 10 healthy volunteers. Motion model accuracy was assessed using similarity-, surface-, and landmark-based validation methods, demonstrating precise model predictions with an overall target registration error of TRE = 1.70 ± 0.94 mm which is within the range of the acquired resolution.

The effect of regularization in motion compensated PET image reconstruction: a realistic numerical 4D simulation study

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.

Investigation of MR-Based Attenuation Correction and Motion Compensation for Hybrid PET/MR

In hybrid PET/MR systems, attenuation maps can be derived from MR to correct for attenuation in PET. However, MR-based attenuation correction (AC) in abdominal applications remains challenging (i) because of poor signal from important tissue types in common MR sequences (e.g., cortical bone) and (ii) because of respiratory motion which results in misalignments between the derived attenuation maps and the PET emissions. Furthermore, respiratory motion also leads to motion-blurring artefacts in the final PET reconstructions. In this paper, we compute an MR-based 4D attenuation map including cortical bone by combining an Ultrashort Echo Time (UTE) acquisition with a subject-specific motion model derived from a second near real-time 3D MR image acquisition. This model allows us to create attenuation maps at any respiratory position which are used for AC in the reconstruction of different respiratory resolved PET images. The inverse of the model is used for motion compensation (MC) of these images. We demonstrate our approach on MR data from 5 healthy volunteers including 3 manually inserted artificial lesions. The impact of bone tissue and respiratory motion on AC is investigated in PET simulations (i) by misclassifying bone to soft tissue in the attenuation maps leading to errors of up to 26.0% in mean uptake for lesions close to bone, and (ii) by using a non-moving attenuation map leading to errors of up to 24.2%. The impact of respiratory motion on MC showed errors of up to 50.7% in areas of strong motion if MC was not performed. The results show that the effect of motion has to be considered both for attenuation correction and for motion-compensating PET emissions. This additive effect of motion is larger than the effect of a wrong AC.

Accelerated motion corrected three‐dimensional abdominal MRI using total variation regularized SENSE reconstruction

Develop a nonrigid motion corrected reconstruction for highly accelerated free‐breathing three‐dimensional (3D) abdominal images without external sensors or additional scans.

Real-time respiratory motion correction for simultaneous PET-MR using an MR-derived motion model

Respiratory motion during PET imaging causes the resulting PET images to become corrupted by artefacts. In this paper we describe a technique for motion-correction of PET data based on MR imaging 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 a one-dimensional MR navigator, allowing additional MR imaging to take place at the same time as PET imaging. The model estimates the complex freeform deformations present in the human thorax during respiration. We validate our motion compensation approach using PET simulations based on real MR data of the thorax acquired from a healthy volunteer. Qualitative results show a clear improvement in visualisation of the myocardium and three tumours that were artificially added to the emission/attenuation map of the volunteer close to the diaphragm. Quantitative analysis was based on computing DICE coefficients between true regions of interest (myocardium and the three artificial tumours) and regions manually segmented from a ‘no motion’ PET image, an uncorrected PET image and a motion-corrected PET image. The DICE coefficients over all 4 regions were 0.8 ± 0.03 (‘no motion’), 0.24 ± 0.11 (uncorrected) and 0.6 ± 0.08 (corrected), indicating that a significant improvement in PET resolution and quantification is achievable by applying our motion-correction technique.

Prospective high-resolution respiratory-resolved whole-heart MRI for image-guided cardiovascular interventions

Cardiovascular diseases, including arrhythmias and heart failure, are commonly treated with percutaneous procedures guided by X‐ray fluoroscopy. The visualization of the targeted structures can be enhanced using preacquired respiratory‐resolved anatomic data (dynamic roadmap), which is displayed as an overlay onto X‐ray fluoroscopy images. This article demonstrates how dynamic roadmaps using an affine motion model can be obtained from one respiratory‐resolved three‐dimensional whole‐heart acquisition using the previously introduced Radial Phase Encoding‐Phase Ordering with Automatic Window Selection method. Respiratory motion in different regions of the heart was analyzed in 10 volunteers, and it was shown that the use of dynamic roadmaps can reduce misalignment errors from more than 10 down to less than 1.5 mm. Furthermore, the results suggest that reliable motion information can be obtained from highly undersampled images due to the advantageous undersampling properties of the radial phase encoding trajectory. Finally, results of a three‐dimensional dynamic roadmap obtained from a patient before catheter ablation for atrial fibrillation treatment are presented. Magn Reson Med, 2012.