Mr. Christian Wachinger Profile

Christian Wachinger

at Technische Univ Munchen

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Publications (3)

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Proceedings Article | 11 March 2011 Paper

Manifold learning for image-based breathing gating in MRI

Mehmet Yigitsoy, Christian Wachinger, Nassir Navab

Proceedings Volume 7962, 796210 (2011) https://doi.org/10.1117/12.878027

KEYWORDS: Magnetic resonance imaging, Liver, Data acquisition, Signal detection, Image registration, Radiotherapy, Image restoration, 3D image processing, Ultrasonography, Radon

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Respiratory motion is a challenging factor for image-guided procedures in the abdominal region. Target localization, an important issue in applications like radiation therapy, becomes difficult due to this motion. Therefore, it is necessary to detect the respiratory signal to have a higher accuracy in planning and treatment. We propose a novel image-based breathing gating method to recover the breathing signal directly from the image data. For the gating we use Laplacian eigenmaps, a manifold learning technique, to determine the low-dimensional manifold embedded in the high-dimensional space. Since Laplacian eigenmaps assign each 2D MR slice a coordinate in a low-dimensional space by respecting the neighborhood relationship, they are well suited for analyzing the respiratory motion. We perform the manifold learning on MR slices acquired from a fixed location. Then, we use the resulting respiratory signal to derive a similarity criterion to be used in applications like 4D MRI reconstruction. We perform experiments on liver data using one and three dimensions as the dimension of the manifold and compare the results. The results from the first case show that using only one dimension as the dimension of the manifold is not enough to represent the complex motion of the liver caused by respiration. We successfully recover the changes due to respiratory motion by using three dimensions. The proposed method has the potential of reducing the processing time for the 4D reconstruction significantly by defining a search window for a subsequent registration approach. It is fully automatic and does not require any prior information or training data.

Proceedings Article | 27 March 2009 Paper

Sphere extraction in MR images with application to whole-body MRI

Christian Wachinger, Simon Baumann, Jochen Zeltner, Ben Glocker, Nassir Navab

Proceedings Volume 7259, 72594J (2009) https://doi.org/10.1117/12.811603

KEYWORDS: Optical spheres, Distortion, Magnetic resonance imaging, Hough transforms, Whole body imaging, Image registration, Distance measurement, Image filtering, Magnetism, Sensors

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Recent technological advances in magnetic resonance imaging (MRI) lead to shorter acquisition times and consequently make it an interesting whole-body imaging modality. The acquisition time can further be reduced by acquiring images with a large field-of-view (FOV), making less scan stations necessary. Images with a large FOV are however disrupted by severe geometric distortion artifacts, which become more pronounced closer to the boundaries. Also the current trend in MRI, towards shorter and wider bore magnets, makes the images more prone to geometric distortion. In a previous work,⁴ we proposed a method to correct for those artifacts using simultaneous deformable registration. In the future, we would like to integrate previous knowledge about the distortion field into the process. For this purpose we scan a specifically designed phantom consisting of small spheres arranged in a cube. In this article, we focus on the automatic extraction of the centers of the spheres, wherein we are particularly interested, for the calculation of the distortion field. The extraction is not trivial because of the significant intensity inhomogeneity within the images. We propose to use the local phase for the extraction purposes. The phase has the advantage that it provides structural information invariant to intensity. We use the monogenic signal to calculate the phase. Subsequently, we once apply a Hough transform and once a direct maxima search, to detect the centers. Moreover, we use a gradient and variance based approach for the radius estimation. We performed our extraction on several phantom scans and obtained good results.

Proceedings Article | 11 March 2008 Paper

Ultrasound specific similarity measures for three-dimensional mosaicing

Christian Wachinger, Nassir Navab

Proceedings Volume 6914, 69140F (2008) https://doi.org/10.1117/12.768820

KEYWORDS: Ultrasonography, Image registration, Speckle pattern, Mathematical modeling, Speckle, Transducers, Motion models, Chromium, Motion estimation, 3D image processing

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The introduction of 2D array ultrasound transducers enables the instantaneous acquisition of ultrasound volumes in the clinical practice. The next step coming along is the combination of several scans to create compounded volumes that provide an extended field-of-view, so called mosaics. The correct alignment of multiple images, which is a complex task, forms the basis of mosaicing. Especially the simultaneous intensity-based registration has many properties making it a good choice for ultrasound mosaicing in comparison to the pairwise one. Fundamental for each registration approach is a suitable similarity measure. So far, only standard measures like SSD, NNC, CR, and MI were used for mosaicing, which implicitly assume an additive Gaussian distributed noise. For ultrasound images, which are degraded by speckle patterns, alternative noise models based on multiplicative Rayleigh distributed noise were proposed in the field of motion estimation. Setting these models into the maximum likelihood estimation framework, which enables the mathematical modeling of the registration process, led us to ultrasound specific bivariate similarity measures. Subsequently, we used an extension of the maximum likelihood estimation framework, which we developed in a previous work, to also derive multivariate measures. They allow us to perform ultrasound specific simultaneous registration for mosaicing. These measures have a higher potential than afore mentioned standard measures since they are specifically designed to cope with problems arising from the inherent contamination of ultrasound images by speckle patterns. The results of the experiments that we conducted on a typical mosaicing scenario with only partly overlapping images confirm this assumption.

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