Fabian Gigengack

Motion Correction in Dual Gated Cardiac PET Using Mass-Preserving Image Registration

Respiratory and cardiac motion leads to image degradation in positron emission tomography (PET) studies of the human heart. In this paper we present a novel approach to motion correction based on dual gating and mass-preserving hyperelastic image registration. Thereby, we account for intensity modulations caused by the highly nonrigid cardiac motion. This leads to accurate and realistic motion estimates which are quantitatively validated on software phantom data and carried over to clinically relevant data using a hardware phantom. For patient data, the proposed method is first evaluated in a high statistic (20 min scans) dual gating study of 21 patients. It is shown that the proposed approach properly corrects PET images for dual-cardiac as well as respiratory-motion. In a second study the list mode data of the same patients is cropped to a scan time reasonable for clinical practice (3 min). This low statistic study not only shows the clinical applicability of our method but also demonstrates its robustness against noise obtained by hyperelastic regularization.

Motion Correction of Whole-Body PET Data with a Joint PET-MRI Registration Functional

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.

A mass conservation-based optical flow method for cardiac motion correction in 3D-PET

PURPOSE Cardiac positron emission tomography (PET) images usually show two kinds of artifacts: the limited resolution of PET leads to partial volume effects and the motion of the heart induces blurring. These phenomena degrade the PET images and induce errors in the quantification. One method of reducing this problem is to use gated PET data. However, the reduction of information per phase leads to an increase in noise on the reconstructed images. Alternatively, the PET data have to be corrected for motion and partial volume effects. METHODS Optical flow methods have been shown to accurately estimate the motion between PET image frames. These methods assume that the brightness of the objects remains constant between the frames. This condition is not fulfilled in cardiac PET data because the brightness of the cardiac muscle tissue (myocardium) is not accurately resolved due to the partial volume effect. Therefore, the use of a newly developed optical flow method based upon the conservation of mass condition is proposed to correct the cardiac PET data. Mass conservation is applicable to PET images as the total activity in the field of view may be assumed to remain almost constant, if the data are precorrected for radioactive decay. Two variants of the method using the quadratic and the nonquadratic penalization are presented. The methods were evaluated with respect to correlation coefficient, myocardial thickness and the blood pool activity in the left ventricle on phantom data and on 14 patient image volumes. RESULTS The proposed methods showed that the cardiac motion can be efficiently corrected despite partial volume effects. The correlation coefficient between the image volumes increased from 0.87 to 0.98 on average. The change in myocardial thickness was reduced from 28% to 3%. The variation in blood pool activity was reduced from 80% to 8%. The algorithm needed only about 4 s for execution. CONCLUSIONS A mass preserving optical flow method of cardiac motion correction in 3D PET data has been presented and tested on phantom as well as patient data. The results show that the motion was corrected for all datasets effectively.

Motion correction of cardiac PET using mass-preserving registration

Cardiac motion leads to image quality degradation in positron emission tomography (PET). In the literature gated listmode acquisition along with motion estimation techniques, such as optical flow or image registration, proved successful for respiratory motion correction. Cardiac gated PET images, however, are affected by the partial volume effect (PVE) which is expressed in strongly varying local intensities. This fact complicates the motion estimation process. To overcome this problem, the mass-preserving nature of PET images is identified and included into the image registration problem. We show that our mass-preserving registration approach allows cardiac motion correction with high accuracy due to realistic motion estimates. In addition, neglecting the mass-preserving property of PET is proven to entail unrealistic results.

A Simplified Pipeline for Motion Correction in Dual Gated Cardiac PET

Positron Emission Tomography (PET) is a nuclear imaging technique of increasing importance e.g. in cardiovascular investigations. However, cardiac and respiratory motion of the patient degrade the image quality due to acquisition times in the order of minutes. Reconstructions without motion compensation are prone to spatial blurring and affected attenuation correction. These effects can be reduced by gating, motion correction and finally summation of the transformed images. This paper describes a new and systematic approach for the correction of both cardiac and respiratory motion. Key contribution is the splitting of the motion into respiratory and cardiac components, which are then corrected individually. For the considered gating scheme the number of registration problems is reduced by a factor of 3, which considerably simplifies the motion correction pipeline compared to previous approaches. The subproblems are stabilized by averaging cardiac gates for respiratory motion estimation and vice versa. The potential of the novel pipeline is evaluated in a group study on data of 21 human patients.

Biomedical Imaging: A Computer Vision Perspective

Many computer vision algorithms have been successfully adapted and applied to biomedical imaging applications. However, biomedical computer vision is far beyond being only an application field. Indeed, it is a wide field with huge potential for developing novel concepts and algorithms and can be seen as a driving force for computer vision research. To emphasize this view of biomedical computer vision we consider a variety of important topics of biomedical imaging in this paper and exemplarily discuss some challenges, the related concepts, techniques, and algorithms.

Atlas-Based whole-body PET-CT segmentation using a passive contour distance

In positron emission tomography (PET) imaging, the segmentation of organs is necessary for many quantitative image analysis tasks, e.g., estimation of individual organ concentration or partial volume correction. To this end we present a fully automated approach for wholebody segmentation which enables large-scale and reproducible studies. The approach is based on joint segmentation and atlas registration. The classical active contour approach by Chan and Vese is modified to a novel passive contour energy term with implicitly incorporated information about shape and location of the organs. This new energy is added to a registration functional which is based on both functional (PET) and morphological (CT) data. The proposed method is applied to medical data, given by 13 PET-CT data sets of mice, and quantitatively compared to manually drawn VOIs. An average Dice coefficient of 0.73 ± 0.10 for the left ventricle, 0.88 ± 0.05 for the bladder, and 0.76 ± 0.07 for the kidneys shows the high accuracy of our method.

Motion correction in PET-MRI: A human torso phantom study

Respiratory and cardiac motion as degrading factors in PET imaging have been tackled by gating and subsequent PET-based motion detection techniques. The limitation of these approaches, however, is that only regions with high uptake yield sufficient motion information. Motion detection for tiny structures, e.g. arteriosclerotic plaques or small tumors, can prove difficult. In PET-MRI, the simultaneously acquired MR data are a promising source of motion information. In the present work, we give preliminary results for motion correction of PET data using information derived from MR data. A human torso phantom capable of simulating cardiac and respiratory motion was used to generate realistic data. Our preliminary results show that motion correction in PET-MRI is a promising approach.

Mass-preserving motion correction of PET: Displacement field vs. spline transformation

In Positron Emission Tomography (PET), motion due to the cardiac and respiratory cycle causes blurred images. Different approaches for motion correction in PET vary in the general concept (optical flow or image registration) or, e.g., in the discretization of motion. Given our mass-preserving transformation model, we evaluate different motion models in this work: dense displacement field (compute for each voxel an individual displacement) vs. spline transformation (i.e. free-form deformation). Thereby a focus is put on the parametrization of the spline transformations where we optimize the number of spline coefficients and the regularization parameter. We make a quantitative comparison of the motion estimates of the different motion models based on data of the established XCAT software phantom. For both motion models (Displacement Field (DF) and Spline Transformation (ST)) the registration results are evaluated by 1) the total processing time and 2) the Euclidean distance to the ground-truth vectors provided by the XCAT phantom. We found that the spline transformation model is superior to the displacement field strategy in terms of processing time and accuracy.

Motion Correction in Thoracic Positron Emission Tomography

Respiratory and cardiac motion leads to image degradation in Positron Emission Tomography (PET), which impairs quantification. In this book, the authors present approaches to motion estimation and motion correction in thoracic PET. The approaches for motion estimation are based on dual gating and mass-preserving image registration (VAMPIRE) and mass-preserving optical flow (MPOF). With mass-preservation, image intensity modulations caused by highly non-rigid cardiac motion are accounted for. Within the image registration framework different data terms, different variants of regularization and parametric and non-parametric motion models are examined. Within the optical flow framework, different data terms and further non-quadratic penalization are also discussed. The approaches for motion correction particularly focus on pipelines in dual gated PET. A quantitative evaluation of the proposed approaches is performed on software phantom data with accompanied ground-truth motion information. Further, clinical applicability is shown on patient data. The book concludes with an outlook of recent developments and potential future advances in the field of PET motion correction.