Kerstin Müller received her diploma degree in Electrical-Electronic Communication Engineering in 2010 and her doctoral degree in medical imaging in 2014 from the Friedrich-Alexander Universität Erlangen-Nürnberg. The PhD project focused on motion estimation and compensation of cardiac chambers in interventional radiology. The research project was in close collaboration with the Siemens AG, Healthcare Sector, Forchheim.

Honors & Awards

  • Student Award, Erlangen Graduate School in Advanced Optical Technologies (SAOT) (2013)

Professional Education

  • Doctor, Friedrich Alexander Universitat (2014)
  • Diplom, Friedrich Alexander Universitat (2011)

Stanford Advisors


Journal Articles

  • Image artefact propagation in motion estimation and reconstruction in interventional cardiac C-arm CT PHYSICS IN MEDICINE AND BIOLOGY Mueller, K., MAIER, A. K., Schwemmer, C., Lauritsch, G., De Buck, S., Wielandts, J., Hornegger, J., Fahrig, R. 2014; 59 (12): 3121-3138


    The acquisition of data for cardiac imaging using a C-arm computed tomography system requires several seconds and multiple heartbeats. Hence, incorporation of motion correction in the reconstruction step may improve the resulting image quality. Cardiac motion can be estimated by deformable three-dimensional (3D)/3D registration performed on initial 3D images of different heart phases. This motion information can be used for a motion-compensated reconstruction allowing the use of all acquired data for image reconstruction. However, the result of the registration procedure and hence the estimated deformations are influenced by the quality of the initial 3D images. In this paper, the sensitivity of the 3D/3D registration step to the image quality of the initial images is studied. Different reconstruction algorithms are evaluated for a recently proposed cardiac C-arm CT acquisition protocol. The initial 3D images are all based on retrospective electrocardiogram (ECG)-gated data. ECG-gating of data from a single C-arm rotation provides only a few projections per heart phase for image reconstruction. This view sparsity leads to prominent streak artefacts and a poor signal to noise ratio. Five different initial image reconstructions are evaluated: (1) cone beam filtered-backprojection (FDK), (2) cone beam filtered-backprojection and an additional bilateral filter (FFDK), (3) removal of the shadow of dense objects (catheter, pacing electrode, etc) before reconstruction with a cone beam filtered-backprojection (cathFDK), (4) removal of the shadow of dense objects before reconstruction with a cone beam filtered-backprojection and a bilateral filter (cathFFDK). The last method (5) is an iterative few-view reconstruction (FV), the prior image constrained compressed sensing combined with the improved total variation algorithm. All reconstructions are investigated with respect to the final motion-compensated reconstruction quality. The algorithms were tested on a mathematical phantom data set with and without a catheter and on two porcine models using qualitative and quantitative measures. The quantitative results of the phantom experiments show that if no dense object is present within the scan field of view, the quality of the FDK initial images is sufficient for motion estimation via 3D/3D registration. When a catheter or pacing electrode is present, the shadow of these objects needs to be removed before the initial image reconstruction. An additional bilateral filter shows no major improvement with respect to the final motion-compensated reconstruction quality. The results with respect to image quality of the cathFDK, cathFFDK and FV images are comparable. In conclusion, in terms of computational complexity, the algorithm of choice is the cathFDK algorithm.

    View details for DOI 10.1088/0031-9155/59/12/3121

    View details for Web of Science ID 000337176600015

    View details for PubMedID 24840084

  • Interventional heart wall motion analysis with cardiac C-arm CT systems PHYSICS IN MEDICINE AND BIOLOGY Mueller, K., Maier, A. K., Zheng, Y., Wang, Y., Lauritsch, G., Schwemmer, C., Rohkohl, C., Hornegger, J., Fahrig, R. 2014; 59 (9): 2265-2284


    Today, quantitative analysis of three-dimensional (3D) dynamics of the left ventricle (LV) cannot be performed directly in the catheter lab using a current angiographic C-arm system, which is the workhorse imaging modality for cardiac interventions. Therefore, myocardial wall analysis is completely based on the 2D angiographic images or pre-interventional 3D/4D imaging. In this paper, we present a complete framework to study the ventricular wall motion in 4D (3D+t) directly in the catheter lab. From the acquired 2D projection images, a dynamic 3D surface model of the LV is generated, which is then used to detect ventricular dyssynchrony. Different quantitative features to evaluate LV dynamics known from other modalities (ultrasound, magnetic resonance imaging) are transferred to the C-arm CT data. We use the ejection fraction, the systolic dyssynchrony index a 3D fractional shortening and the phase to maximal contraction (ϕi, max) to determine an indicator of LV dyssynchrony and to discriminate regionally pathological from normal myocardium. The proposed analysis tool was evaluated on simulated phantom LV data with and without pathological wall dysfunctions. The LV data used is publicly available online at In addition, the presented framework was tested on eight clinical patient data sets. The first clinical results demonstrate promising performance of the proposed analysis tool and encourage the application of the presented framework to a larger study in clinical practice.

    View details for DOI 10.1088/0031-9155/59/9/2265

    View details for Web of Science ID 000334598100013

    View details for PubMedID 24731942

  • Towards Clinical Application of a Laplace Operator-Based Region of Interest Reconstruction Algorithm in C-Arm CT IEEE TRANSACTIONS ON MEDICAL IMAGING Xia, Y., Hofmann, H., Dennerlein, F., Mueller, K., Schwemmer, C., Bauer, S., Chintalapani, G., Chinnadurai, P., Hornegger, J., Maier, A. 2014; 33 (3): 593-606


    It is known that a reduction of the field-of-view in 3-D X-ray imaging is proportional to a reduction in radiation dose. The resulting truncation, however, is incompatible with conventional reconstruction algorithms. Recently, a novel method for region of interest reconstruction that uses neither prior knowledge nor extrapolation has been published, named approximated truncation robust algorithm for computed tomography (ATRACT). It is based on a decomposition of the standard ramp filter into a 2-D Laplace filtering and a 2-D Radon-based residual filtering step. In this paper, we present two variants of the original ATRACT. One is based on expressing the residual filter as an efficient 2-D convolution with an analytically derived kernel. The second variant is to apply ATRACT in 1-D to further reduce computational complexity. The proposed algorithms were evaluated by using a reconstruction benchmark, as well as two clinical data sets. The results are encouraging since the proposed algorithms achieve a speed-up factor of up to 245 compared to the 2-D Radon-based ATRACT. Reconstructions of high accuracy are obtained, e.g., even real-data reconstruction in the presence of severe truncation achieve a relative root mean square error of as little as 0.92% with respect to nontruncated data.

    View details for DOI 10.1109/TMI.2013.2291622

    View details for Web of Science ID 000332599500001

    View details for PubMedID 24595336

  • CONRAD--a software framework for cone-beam imaging in radiology. Medical physics Maier, A., Hofmann, H. G., Berger, M., Fischer, P., Schwemmer, C., Wu, H., Müller, K., Hornegger, J., Choi, J., Riess, C., Keil, A., Fahrig, R. 2013; 40 (11): 111914-?


    In the community of x-ray imaging, there is a multitude of tools and applications that are used in scientific practice. Many of these tools are proprietary and can only be used within a certain lab. Often the same algorithm is implemented multiple times by different groups in order to enable comparison. In an effort to tackle this problem, the authors created CONRAD, a software framework that provides many of the tools that are required to simulate basic processes in x-ray imaging and perform image reconstruction with consideration of nonlinear physical effects.CONRAD is a Java-based state-of-the-art software platform with extensive documentation. It is based on platform-independent technologies. Special libraries offer access to hardware acceleration such as OpenCL. There is an easy-to-use interface for parallel processing. The software package includes different simulation tools that are able to generate up to 4D projection and volume data and respective vector motion fields. Well known reconstruction algorithms such as FBP, DBP, and ART are included. All algorithms in the package are referenced to a scientific source.A total of 13 different phantoms and 30 processing steps have already been integrated into the platform at the time of writing. The platform comprises 74.000 nonblank lines of code out of which 19% are used for documentation. The software package is available for download at To demonstrate the use of the package, the authors reconstructed images from two different scanners, a table top system and a clinical C-arm system. Runtimes were evaluated using the RabbitCT platform and demonstrate state-of-the-art runtimes with 2.5 s for the 256 problem size and 12.4 s for the 512 problem size.As a common software framework, CONRAD enables the medical physics community to share algorithms and develop new ideas. In particular this offers new opportunities for scientific collaboration and quantitative performance comparison between the methods of different groups.

    View details for DOI 10.1118/1.4824926

    View details for PubMedID 24320447

  • Residual motion compensation in ECG-gated interventional cardiac vasculature reconstruction PHYSICS IN MEDICINE AND BIOLOGY Schwemmer, C., Rohkohl, C., Lauritsch, G., Mueller, K., Hornegger, J. 2013; 58 (11): 3717-3737
  • Evaluation of interpolation methods for surface-based motion compensated tomographic reconstruction for cardiac angiographic C-arm data. Medical physics Müller, K., Schwemmer, C., Hornegger, J., Zheng, Y., Wang, Y., Lauritsch, G., Rohkohl, C., Maier, A. K., Schultz, C., Fahrig, R. 2013; 40 (3): 031107-?


    For interventional cardiac procedures, anatomical and functional information about the cardiac chambers is of major interest. With the technology of angiographic C-arm systems it is possible to reconstruct intraprocedural three-dimensional (3D) images from 2D rotational angiographic projection data (C-arm CT). However, 3D reconstruction of a dynamic object is a fundamental problem in C-arm CT reconstruction. The 2D projections are acquired over a scan time of several seconds, thus the projection data show different states of the heart. A standard FDK reconstruction algorithm would use all acquired data for a filtered backprojection and result in a motion-blurred image. In this approach, a motion compensated reconstruction algorithm requiring knowledge of the 3D heart motion is used. The motion is estimated from a previously presented 3D dynamic surface model. This dynamic surface model results in a sparse motion vector field (MVF) defined at control points. In order to perform a motion compensated reconstruction, a dense motion vector field is required. The dense MVF is generated by interpolation of the sparse MVF. Therefore, the influence of different motion interpolation methods on the reconstructed image quality is evaluated.Four different interpolation methods, thin-plate splines (TPS), Shepard's method, a smoothed weighting function, and a simple averaging, were evaluated. The reconstruction quality was measured on phantom data, a porcine model as well as on in vivo clinical data sets. As a quality index, the 2D overlap of the forward projected motion compensated reconstructed ventricle and the segmented 2D ventricle blood pool was quantitatively measured with the Dice similarity coefficient and the mean deviation between extracted ventricle contours. For the phantom data set, the normalized root mean square error (nRMSE) and the universal quality index (UQI) were also evaluated in 3D image space.The quantitative evaluation of all experiments showed that TPS interpolation provided the best results. The quantitative results in the phantom experiments showed comparable nRMSE of ≈0.047 ± 0.004 for the TPS and Shepard's method. Only slightly inferior results for the smoothed weighting function and the linear approach were achieved. The UQI resulted in a value of ≈ 99% for all four interpolation methods. On clinical human data sets, the best results were clearly obtained with the TPS interpolation. The mean contour deviation between the TPS reconstruction and the standard FDK reconstruction improved in the three human cases by 1.52, 1.34, and 1.55 mm. The Dice coefficient showed less sensitivity with respect to variations in the ventricle boundary.In this work, the influence of different motion interpolation methods on left ventricle motion compensated tomographic reconstructions was investigated. The best quantitative reconstruction results of a phantom, a porcine, and human clinical data sets were achieved with the TPS approach. In general, the framework of motion estimation using a surface model and motion interpolation to a dense MVF provides the ability for tomographic reconstruction using a motion compensation technique.

    View details for DOI 10.1118/1.4789593

    View details for PubMedID 23464287

  • Reconstruction from Truncated Projections in Cone-beam CT using an Efficient 1D Filtering MEDICAL IMAGING 2013: PHYSICS OF MEDICAL IMAGING Xia, Y., Maier, A., Hofmann, H. G., Dennerlein, F., Mueller, K., Hornegger, J. 2013; 8668

    View details for DOI 10.1117/12.2007484

    View details for Web of Science ID 000322002700044

  • Automatic 3D Motion Estimation of Left Ventricle from C-arm Rotational Angiocardiography Using a Prior Motion Model and Learning Based Boundary Detector MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION (MICCAI 2013), PT III Chen, M., Zheng, Y., Wang, Y., Mueller, K., Lauritsch, G. 2013; 8151: 90-97
  • 4-D Motion Field Estimation by Combined Multiple Heart Phase Registration (CMHPR) for Cardiac C-arm Data 2012 IEEE NUCLEAR SCIENCE SYMPOSIUM AND MEDICAL IMAGING CONFERENCE RECORD (NSS/MIC) Mueller, K., Rohkohl, C., Lauritsch, G., Schwemmer, C., Heidbuechel, H., De Buck, S., Nuyens, D., Kyriakou, Y., Koehler, C., Hornegger, J. 2012: 3707-3712
  • Automatic Extraction of 3D Dynamic Left Ventricle Model from 2D Rotational Angiocardiogram MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION, MICCAI 2011, PT III Chen, M., Zheng, Y., Mueller, K., Rohkohl, C., Lauritsch, G., Boese, J., Funka-Lea, G., Hornegger, J., Comaniciu, D. 2011; 6893: 471-478

Conference Proceedings

  • Catheter artifact reduction (CAR) in dynamic cardiac chamber imaging with interventional C-arm CT Third international conference on image formation in x-ray computed tomography Müller, K., Lauritsch, G., Schwemmer, C., Maier, A., Taubmann, O., Abt, B., Köhler, H., Nöttling, A., Hornegger, J., Fahrig, R. 2014: 418-421

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