Hanno Schumacher

Combined Reconstruction and Motion Correction in SPECT Imaging

Due to the long imaging times in SPECT, patient motion is inevitable and constitutes a serious problem for any reconstruction algorithm. The measured inconsistent projection data lead to reconstruction artifacts which can significantly affect the diagnostic accuracy of SPECT if not corrected. To address this problem a new approach for motion correction is introduced. It is purely based on the measured SPECT data and therefore belongs to the data-driven motion correction algorithm class. However, it does overcome some of the shortcomings of conventional methods. This is mainly due to the innovative idea to combine reconstruction and motion correction in one optimization problem. The scheme allows for the correction of abrupt and gradual patient motion. To demonstrate the performance of the proposed scheme extensive 3D tests with numerical phantoms for 3D rigid motion are presented. In addition, a test with real patient data is shown. Each test shows an impressive improvement of the quality of the reconstructed image. In this note, only rigid movements are considered. The extension to non-linear motion, as for example breathing or cardiac motion, is straightforward and will be investigated in a forthcoming paper.

A fully parallel algorithm for multimodal image registration using normalized gradient fields

We present a super fast variational algorithm for the challenging problem of multimodal image registration. It is capable of registering full-body CT and PET images in about a second on a standard CPU with virtually no memory requirements. The algorithm is founded on a Gauss-Newton optimization scheme with specifically tailored, mathematically optimized computations for objective function and derivatives. It is fully parallelized and perfectly scalable, thus directly suitable for usage in many-core environments. The accuracy of our method was tested on 21 PET-CT scan pairs from clinical routine. The method was able to correct random distortions in the range from -10 cm to 10 cm translation and from -15° to 15° degree rotation to subvoxel accuracy. In addition, it exhibits excellent robustness to noise.

A New Flexible Reconstruction Framework for Motion Correction in SPECT Imaging

Due to the long imaging times in SPECT, patient motion is inevitable and constitutes a serious problem for any reconstruction algorithm. The measured inconsistent projection data lead to reconstruction artifacts which can significantly affect the diagnostic accuracy of SPECT if not corrected. Among the most promising attempts for addressing this cause of artifacts is the so-called data-driven motion correction methodology, implemented, for example, in the OSEM scheme. At present, this algorithm is restricted to the exclusive use of a dual-head SPECT system with perpendicular heads and incorporating in a subset only projection data obtained between a patient movement. The utilization within other SPECT systems may lead to unsatisfactory results. In this note we present a new reconstruction framework which overcomes these two shortcomings. Within the new framework, the user may choose any set of projection for the reconstruction and the scheme works for any SPECT system. As a byproduct, the well-known EM and OSEM reconstruction schemes may be written in terms of the new framework and therefore are included in the theoretical considerations. The paper is supplemented by a large set of test examples, underscoring the potential power of the proposed novel approach. Using both an academic example and images from a double-head detector we studied the extent of defects induced by simulated motion and validated the new schemes.

A New Approach for Motion Correction in SPECT Imaging

Due to the long imaging times in SPECT, patient motion is inevitable and constitutes a serious problem for any reconstruction algorithm. The measured inconsistent projection data lead to reconstruction artifacts which can significantly affect the diagnostic accuracy of SPECT if not corrected. Among the most promising attempts for addressing this cause of artifacts is the so-called data-driven motion correction methodology. But even this algorithm is restricted to the correction of abrupt rigid patient motion and exclusive correction of gradual motion, which may lead to unsatisfactory results. In this note we present for the first time a motion correction approach which overcomes the mentioned restrictions. The new approach is based on the super-resolution methodology. To demonstrate the performance of the proposed scheme, corrections of abrupt and gradual motion are presented.

Automatic detection of abrupt patient motion in SPECT data acquisition

Due to the long imaging times in SPECT, patient motion is inevitable and constitutes a serious problem for any reconstruction algorithm. The measured inconsistent projection data leads to reconstruction artefacts which can significantly affect the diagnostic accuracy of SPECT, if not corrected. Among the most promising attempts for addressing this cause of artefacts, is the so-called data-driven motion correction methodology. To use this approach it is necessary to automatically detect patient motion and to subdivide the acquired data in projection sets accordingly. In this note, we propose three different schemes for automatically detecting patient motion. All methods were tested on 3D academic examples with different rigid motions, motion times, and camera systems. On the whole, every method was tested with approximately 400 to 600 test cases. One of the proposed new methods does show promising results.

Weighted medical image registration with automatic mask generation

Registration of images is a crucial part of many medical imaging tasks. The problem is to find a transformation which aligns two given images. The resulting displacement fields may be for example described as a linear combination of pre-selected basis functions (parametric approach), or, as in our case, they may be computed as the solution of an associated partial differential equation (non-parametric approach). Here, the underlying functional consists of a smoothness term ensuring that the transformation is anatomically meaningful and a distance term describing the similarity between the two images. To be successful, the registration scheme has to be tuned for the problem under consideration. One way of incorporating user knowledge is the employment of weighting masks into the distance measure, and thereby enhancing or hiding dedicated image parts. In general, these masks are based on a given segmentation of both images. We present a method which generates a weighting mask for the second image, given the mask for the first image. The scheme is based on active contours and makes use of a gradient vector flow method. As an example application, we consider the registration of abdominal computer tomography (CT) images used for radiation therapy. The reference image is acquired well ahead of time and is used for setting up the radiation plan. The second image is taken just before the treatment and its processing is time-critical. We show that the proposed automatic mask generation scheme yields similar results as compared to the approach based on a pre-segmentation of both images. Hence for time-critical applications, as intra-surgery registration, we are able to significantly speed up the computation by avoiding a pre-segmentation of the second image.

A Fast and Flexible Image Registration Toolbox

In the last decades there has been tremendous research towards the design of fully automatic non-rigid registration schemes. However, apart from the ITK based implementation of Rueckerts B-spline oriented approach, there is a lack of sound publicly available implementations of the modern schemes. The Flexible Image Registration Toolbox (FLIRT) is an attempt to close this gap. It focuses on non-parametric schemes as popularized in the book by Modersitzki [1]. To be successful, it is crucial for any registration scheme to reflect the special properties of the underlying registration problem. Consequently, FLIRT has an open object-oriented architecture which allows for the incorporation of user prescribed building blocks. In its present form, most of the prominent blocks are already implemented. They may be arranged in a consistent way and cover a wide range of applications. Apart from the flexibility issue, great care has been taken towards fast execution times. The most computationally intensive part, the solution of the underlying linear systems, is implemented by state-of-the-art solution techniques.

Iterative Reconstruction of SPECT Images Using Adaptive Multi-level Refinement

We present a novel method for iterative reconstruction of high resolution images. Our method is based on the observation that constant regions in an image can be represented at much lower resolution than region with fine details. Therefore, we combine adaptive re-finement based on quadtrees with iterative reconstruction to reduce the computational costs. In our experiments we found a speed up factor of approximately two compared to a standard multi-level method.

Improved elastic medical image registration using mutual information

One of the future-oriented areas of medical image processing is to develop fast and exact algorithms for image registration. By joining multi-modal images we are able to compensate the disadvantages of one imaging modality with the advantages of another modality. For instance, a Computed Tomography (CT) image containing the anatomy can be combined with metabolic information of a Positron Emission Tomography (PET) image. It is quite conceivable that a patient will not have the same position in both imaging systems. Furthermore some regions for instance in the abdomen can vary in shape and position due to different filling of the rectum. So a multi-modal image registration is needed to calculate a deformation field for one image in order to maximize the similarity between the two images, described by a so-called distance measure. In this work, we present a method to adapt a multi-modal distance measure, here mutual information (MI), with weighting masks. These masks are used to enhance relevant image structures and suppress image regions which otherwise would disturb the registration process. The performance of our method is tested on phantom data and real medical images.

Wahl eines gewichteten Distanzmaßes für monomodale Bilder in der nicht-parametrischen Registrierung

In der Bildregistrierung ist es haufig notwendig, die Methoden den speziellen Problemanforderungen der zu bearbeitenden Bilder anzupassen. Ein Weg, um zusatzliches Wissen in eine Registrierung einzubringen, ist die Nutzung gewichteter Distanzmase, um damit die Bedeutung ausgewahlter Bildbereiche zu verstarken, abzuschwachen oder auszublenden. Im Fall der parameterfreien Registrierung sind zwei gewichtete Distanzmase, SSDmix und MIadd, bekannt. Diese beiden Distanzmase werden hier gegenubergestellt und ihre Wirkung auf monomodalen Bildern verglichen. Zusatzlich wird SSDmix mit ungewichtetem MI verglichen. Die Ergebnisse verdeutlichen, dass SSDmix und MIadd bessere Ergebnisse als SSD und MI liefern. Weiterhin zeigt sich, dass SSDmix und MIadd fur monomodale Bilder gleichmachtig sind.