A concept for a magnetic particle imaging scanner with Halbach arrays.

Magnetic particle imaging (MPI) is a new medical imaging technique visualizing the concentration distribution of superparamagnetic nanoparticles used as tracer material. MPI is not yet in clinical routine, since one of the challenges is the upscaling of scanners. Typically, the magnetic fields of MPI scanners are generated electromagnetically, resulting in an immense power consumption but providing high flexibility in terms of adjusting the field strengths and very fast image acquisition rates. Permanent magnets provide high flux densities and do not need any power supply. However, the flux density is not adjustable, and a mechanical movement is slow compared to electromagnetically varying fields. The MPI scanner concept proposed here uses permanent magnets and provides high flexibility, with the possibility to choose between fast overview scanning and detailed image acquisition. By mechanical rotation of magnetic rings in Halbach array configuration, it is possible to adjust the field or gradient strengths. The latter allows for determining the spatial resolution and the size of the field of view. A continuous mechanical rotation defines the coarseness of the scanning trajectory and image acquisition rate. This concept provides a comparable flexibility, as an alternating magnetic field and an adjustable field gradient can be applied as known from electromagnetically driven MPI systems, and therefore yields high potential for an enlarged system. We present the idea of an arrangement of Halbach arrays and how to calculate the generated magnetic fields. Simulations for an exemplary geometry are provided to show the potential of the proposed setup.

Hybrid system calibration for multidimensional magnetic particle imaging.

Magnetic particle imaging visualizes the spatial distribution of superparamagnetic nanoparticles. Because of its key features of excellent sensitivity, high temporal and spatial resolution and biocompatibility of the tracer material it can be used in multiple medical imaging applications. The common reconstruction technique for Lissajous-type trajectories uses a system matrix that has to be previously acquired in a time-consuming calibration scan, leading to long downtimes of the scanning device. In this work, the system matrix is determined by a hybrid approach. Using the hybrid system matrix for reconstruction, the calibration downtime of the scanning device can be neglected. Furthermore, the signal to noise ratio of the hybrid system matrix is much higher, since the size of the required nanoparticle sample can be chosen independently of the desired voxel size. As the signal to noise ratio influences the reconstruction process, the resulting images have better resolution and are less affected by artefacts. Additionally, a new approach is introduced to address the background signal in image reconstruction. The common technique of subtraction of the background signal is replaced by extending the system matrix with an entry that represents the background. It is shown that this approach reduces artefacts in the reconstructed images.

Two dimensional magnetic particle spectrometry.

Magnetic particle spectrometry (MPS) is an excellent and straight forward method to determine the response of magnetic nanoparticles to an oscillating magnetic field. Such fields are applied in magnetic particle imaging (MPI). However, state of the art MPS devices lack the ability to excite particles in multidimensional field sequences that are present in MPI devices. Especially the particle behavior caused by Lissajous sequences cannot be measured with only one excitation direction. This work presents a new kind of MPS which features two excitation directions to overcome this limitation. Both field coils can drive AC as well as DC currents and are thereby able to emulate the field sequences for arbitrary spatial positions inside an MPI device. Since the DC currents can be switched very fast, the device can be used as system calibration unit and acquire system matrices in very short time. These are crucial for MPI image reconstruction. As the signal-to-noise-ratio provided by the MPS is approximately 1000 times higher than that of actual imaging devices, the time space analysis of particle signals is more precise and easier done. Four system matrices are presented in this paper which have been measured with the realized multidimensional MPS. Additionally, a time space comparison of the particle signal for Lissajous, radial and spiral trajectories is given.

Using data redundancy gained by patch overlaps to reduce truncation artifacts in magnetic particle imaging.

The imaging technology magnetic particle imaging allows the detection of magnetic material, in particular superparamagnetic nanoparticles, by remagnetization of the material via magnetic fields. The application is aimed at medical imaging where the particles are applied as tracer directly into the blood stream. Medical safety considerations such as peripheral nerve stimulation limit the maximal amplitude of the magnetic fields and in turn the field of view size. To handle this constraint the concept of patches was introduced, which allows a shift of a field of view to different positions in order to enlarge the imaging area. If this is done statically an overlap of patches can be used to reduce truncation artifacts occurring at the adjacent edges. In this contribution, a differentiation of two different kinds of patch overlaps, i.e. a trajectory and a system matrix overlap, is made. Further, different concepts to combine the resulting redundant information are investigated with respect to the reduction of truncation artifacts. The methods are analyzed in detail in a simulation study and validated on experimental data.

Artifact free reconstruction with the system matrix approach by overscanning the field-free-point trajectory in magnetic particle imaging.

Magnetic particle imaging is a tracer-based imaging method that utilizes the non-linear magnetization response of iron-oxide for determining their spatial distribution. The method is based on a sampling scheme where a sensitive spot is moved along a trajectory that captured a predefined field-of-view (FOV). However, particles outside the FOV also contribute to the measurement signal due to their rotation and the non-sharpness of the sensitive spot. In the present work we investigate artifacts that are induced by particles not covered by the FOV and show that the artifacts can be mitigated by using a system matrix that covers not only the region of interest but also a certain area around the FOV. The findings are especially relevant when using a multi-patch acquisition scheme where the boundaries of neighboring patches have to be handled.

Weighted simultaneous algebraic reconstruction technique for tomosynthesis imaging of objects with high-attenuation features.

PURPOSE: This paper introduces a nonlinear weighting scheme into the backprojection operation within the simultaneous algebraic reconstruction technique (SART). It is designed for tomosynthesis imaging of objects with high-attenuation features in order to reduce limited angle artifacts. METHODS: The algorithm estimates which projections potentially produce artifacts in a voxel. The contribution of those projections into the updating term is reduced. In order to identify those projections automatically, a four-dimensional backprojected space representation is used. Weighting coefficients are calculated based on a dissimilarity measure, evaluated in this space. For each combination of an angular view direction and a voxel position an individual weighting coefficient for the updating term is calculated. RESULTS: The feasibility of the proposed approach is shown based on reconstructions of the following real three-dimensional tomosynthesis datasets: a mammography quality phantom, an apple with metal needles, a dried finger bone in water, and a human hand. Datasets have been acquired with a Siemens Mammomat Inspiration tomosynthesis device and reconstructed using SART with and without suggested weighting. Out-of-focus artifacts are described using line profiles and measured using standard deviation (STD) in the plane and below the plane which contains artifact-causing features. Artifacts distribution in axial direction is measured using an artifact spread function (ASF). The volumes reconstructed with the weighting scheme demonstrate the reduction of out-of-focus artifacts, lower STD (meaning reduction of artifacts), and narrower ASF compared to nonweighted SART reconstruction. It is achieved successfully for different kinds of structures: point-like structures such as phantom features, long structures such as metal needles, and fine structures such as trabecular bone structures. CONCLUSIONS: Results indicate the feasibility of the proposed algorithm to reduce typical tomosynthesis artifacts produced by high-attenuation features. The proposed algorithm assigns weighting coefficients automatically and no segmentation or tissue-classification steps are required. The algorithm can be included into various iterative reconstruction algorithms with an additive updating strategy. It can also be extended to computed tomography case with the complete set of angular data.

A generic framework for modeling brain deformation as a constrained parametric optimization problem to aid non-diffeomorphic image registration in brain tumor imaging.

OBJECTIVES: In the present paper a novel computational framework for modeling tumor induced brain deformation as a biophysical prior for non-rigid image registration is described. More precisely, we aim at providing a generic building block for non-rigid image registration that can be used to resolve inherent irregularities in non-diffeomorphic registration problems that naturally arise in serial and cross-population brain tumor imaging studies due to the presence (or progression) of pathology. METHODS: The model for the description of brain cancer dynamics on a tissue level is based on an initial boundary value problem (IBVP). The IBVP follows the accepted assumption that the progression of primary brain tumors on a tissue level is governed by proliferation and migration of cancerous cells into surrounding healthy tissue. The model of tumor induced brain deformation is phrased as a parametric, constrained optimization problem. As a basis of comparison and to demonstrate generalizability additional soft constraints (penalties) are considered. A back-tracking line search is implemented in conjunction with a limited memory Broyden-Fletcher-Goldfarb-Shanno (LBFGS) method in order to handle the numerically delicate log-barrier strategy for confining volume change. RESULTS: Numerical experiments are performed to test the flexible control of the computed deformation patterns in terms of varying model parameters. The results are qualitatively and quantitatively related to patterns in patient individual magnetic resonance imaging data. CONCLUSIONS: Numerical experiments demonstrate the flexible control of the computed deformation patterns. This in turn strongly suggests that the model can be adapted to patient individual imaging patterns of brain tumors. Qualitative and quantitative comparison of the computed cancer profiles to patterns in medical imaging data of an exemplary patient demonstrates plausibility. The designed optimization problem is based on computational tools widely used in non-rigid image registration, which in turn makes the model generally applicable for integration into non-rigid image registration algorithms.

[Magnetic particle imaging (MPI)]. / Magnetic Particle Imaging (MPI).

Magnetic particle imaging (MPI) displays the spatial distribution and concentration of superparamagnetic iron oxides (SPIOs). It is a quantitative, tomographic imaging method with high temporal and spatial resolution and allows work with high sensitivity yet without ionizing radiation. Thus, it may be a very promising tool for medical imaging. In this review, we describe the physical and technical basics and various concepts for clinical scanners. Furthermore, clinical applications such as cardiovascular imaging, interventional procedures, imaging and therapy of malignancies as well as molecular imaging are presented.

Weighted iterative reconstruction for magnetic particle imaging.

Magnetic particle imaging (MPI) is a new imaging technique capable of imaging the distribution of superparamagnetic particles at high spatial and temporal resolution. For the reconstruction of the particle distribution, a system of linear equations has to be solved. The mathematical solution to this linear system can be obtained using a least-squares approach. In this paper, it is shown that the quality of the least-squares solution can be improved by incorporating a weighting matrix using the reciprocal of the matrix-row energy as weights. A further benefit of this weighting is that iterative algorithms, such as the conjugate gradient method, converge rapidly yielding the same image quality as obtained by singular value decomposition in only a few iterations. Thus, the weighting strategy in combination with the conjugate gradient method improves the image quality and substantially shortens the reconstruction time. The performance of weighting strategy and reconstruction algorithms is assessed with experimental data of a 2D MPI scanner.

Trajectory analysis for magnetic particle imaging.

Recently a new imaging technique called magnetic particle imaging was proposed. The method uses the nonlinear response of magnetic nanoparticles when a time varying magnetic field is applied. Spatial encoding is achieved by moving a field-free point through an object of interest while the field strength in the vicinity of the point is high. A resolution in the submillimeter range is provided even for fast data acquisition sequences. In this paper, a simulation study is performed on different trajectories moving the field-free point through the field of view. The purpose is to provide mandatory information for the design of a magnetic particle imaging scanner. Trajectories are compared with respect to density, speed and image quality when applied in data acquisition. Since simulation of the involved physics is a time demanding task, moreover, an efficient implementation is presented utilizing caching techniques.

3D reconstruction of dental specimens from 2D histological images and microCT-scans.

Direct comparison of experimental and theoretical results in biomechanical studies requires a careful reconstruction of specimen surfaces to achieve a satisfactory congruence for validation. In this paper a semi-automatic approach is described to reconstruct triangular boundary representations from images originating from, either histological sections or microCT-, CT- or MRI-data, respectively. In a user-guided first step, planar 2D contours were extracted for every material of interest, using image segmentation techniques. In a second step, standard 2D triangulation algorithms were used to derive high quality mesh representations of the underlying surfaces. This was accomplished by converting the 2D meshes into 3D meshes by a novel lifting procedure. The meshes can be imported as is into finite element programme packages such as Marc/Mentat or COSMOS/M. Accuracy and feasibility of the algorithm is demonstrated by reconstructing several specimens as examples and comparing simulated results with available measurements performed on the original objects.

A novel navigation principle in computer-assisted surgery.

Novel methods of laser measurements and interventions in the framework of image-guided surgery are presented. The first innovation concerns the basis of error propagation studies in registration chains from CT via OR navigator to the surgical laser tool holder. Here, we propose a holographic ground truth. The second innovation concerns a laser-based inherent sound-guidance principle for burr hole depth measurement.

Landmark-based elastic registration using approximating thin-plate splines.

We consider elastic image registration based on a set of corresponding anatomical point landmarks and approximating thin-plate splines. This approach is an extension of the original interpolating thin-plate spline approach and allows to take into account landmark localization errors. The extension is important for clinical applications since landmark extraction is always prone to error. Our approach is based on a minimizing functional and can cope with isotropic as well as anisotropic landmark errors. In particular, in the latter case it is possible to include different types of landmarks, e.g., unique point landmarks as well as arbitrary edge points. Also, the scheme is general with respect to the image dimension and the order of smoothness of the underlying functional. Optimal affine transformations as well as interpolating thin-plate splines are special cases of this scheme. To localize landmarks we use a semi-automatic approach which is based on three-dimensional (3-D) differential operators. Experimental results are presented for two-dimensional as well as 3-D tomographic images of the human brain.