A Realistic Digital Phantom for Perfusion C-Arm CT Based on MRI Data - Aichert, André; Manhart, Michael; Navalpakkam, Bharath; Grimm, Robert; Hutter, Jana; Maier, Andreas; Hornegger, Joachim; Dörfler, Arnd (2013 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC), 2013)
CTP is an important imaging modality for diagnosis of ischemic stroke, which is computed from of a series of consecutive CT-scans during the injection of contrast agent. Contrast flow at any point in space can be tracked as minor changes in intensity over a period of about 40 seconds to one minute, represented as a time-attenuation curve (TAC) for every voxel. This work presents an isotropic, dense, physiologically realistic and dynamic brain phantom for CT perfusion. The phantom is based on MRI scans of a volunteer and is freely available for download. © 2013 IEEE.
UnivIS-Import:2015-04-16:Pub.2013.tech.IMMD.IMMD5.areali_2

Bridge to Real Data: Empirical Multiple Material Calibration for Learning-Based Material Decomposition - Lu, Yanye; Berger, Martin; Manhart, Michael; Choi, Jang-Hwan; Hoheisel, Martin; Kowarschik, Markus; Fahrig, Rebecca; Ren, Qiushi; Hornegger, Joachim; Maier, Andreas (Proceedings of the 2016 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2016)
In this study, we proposed an empirical multi-material calibration pipeline for learning-based material decomposition. We used realistic short scan CT data from a general metric phantom using a Siemens C-arm system, and built the corresponding numeric phantom data in a software framework. After that we applied registration approaches for matching the simulated data to the acquired data, which generates prior knowledge for the following material decomposition process, as well as the ground truth for quantitative evaluations. According to the preliminary decomposition results, we successfully decomposed the inserted phantom plugs of different materials using learning-based material decomposition process, which indicates that the proposed approach is valid for learning-based material decomposition.
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.bridge_9

Characterization of mixing in a T-mixer: A combined experimental and numerical study - Peukert, Wolfgang; Schwarzer, Hans-Christoph; Manhart, Michael (European Conference on Mixing, 2003)
UnivIS-Import:2015-04-16:Pub.2003.tech.ITC.mechve.charac
Keywords: mixing; simulation

Data Consistent Artifact Reduction for Limited Angle Tomography with Deep Learning Prior - Huang, Yixing; Preuhs, Alexander; Lauritsch, Guenter; Manhart, Michael; Huang, Xiaolin; Maier, Andreas (Machine Learning for Medical Image Reconstruction, 2019)
Robustness of deep learning methods for limited angle tomography is challenged by two major factors: a) due to insufficient training data the network may not generalize well to unseen data; b) deep learning methods are sensitive to noise.Thus, generating reconstructed images directly from a neural network appears inadequate. We propose to constrain the reconstructed images to be consistent with the measured projection data, while the unmeasured information is complemented by learning based methods. For this purpose, a data consistent artifact reduction (DCAR) method is introduced: First, a prior image is generated from an initial limited angle reconstruction via deep learning as a substitute for missing information. Afterwards, a conventional iterative reconstruction algorithm is applied, integrating the data consistency in the measured angular range and the prior information in the missing angular range. This ensures data integrity in the measured area, while inaccuracies incorporated by the deep learning prior lie only in areas where no information is acquired. The proposed DCAR method achieves significant image quality improvement: for 120-degree cone-beam limited angle tomography more than 10% RMSE reduction in noise-free case and more than 24% RMSE reduction in noisy case compared with a state-of-the-art U-Net based method.
Keywords: Deep learning; Limited angle tomography; Data consistency; Poisson noise; Robustness; Generalization ability

Denoising and artefact reduction in dynamic flat detector CT perfusion imaging using high speed acquisition: first experimental and clinical results - Manhart, Michael; Aichert, André; Struffert, Tobias; Deuerling-Zheng, Yu; Kowarschik, Markus; Maier, Andreas; Hornegger, Joachim; Dörfler, Arnd (Physics in Medicine and Biology, 2014)
Flat detector CT perfusion (FD-CTP) is a novel technique using C-arm angiography systems for interventional dynamic tissue perfusion measurement with high potential benefits for catheter-guided treatment of stroke. However, FD-CTP is challenging since C-arms rotate slower than conventional CT systems. Furthermore, noise and artefacts affect the measurement of contrast agent flow in tissue. Recent robotic C-arms are able to use high speed protocols (HSP), which allow sampling of the contrast agent flow with improved temporal resolution. However, low angular sampling of projection images leads to streak artefacts, which are translated to the perfusion maps. We recently introduced the FDK-JBF denoising technique based on Feldkamp (FDK) reconstruction followed by joint bilateral filtering (JBF). As this edge-preserving noise reduction preserves streak artefacts, an empirical streak reduction (SR) technique is presented in this work. The SR method exploits spatial and temporal information in the form of total variation and time-curve analysis to detect and remove streaks. The novel approach is evaluated in a numerical brain phantom and a patient study. An improved noise and artefact reduction compared to existing post-processing methods and faster computation speed compared to an algebraic reconstruction method are achieved. © 2014 Institute of Physics and Engineering in Medicine.
UnivIS-Import:2015-03-09:Pub.2014.tech.IMMD.IMMD5.denois_8
Keywords: 3D reconstruction; flat detector CT; noise reduction; perfusion imaging; stroke treatment

Dynamic Interventional Perfusion Imaging: Reconstruction Algorithms and Clinical Evaluation - Manhart, Michael (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2014)
Acute ischaemic stroke is a major cause for death and disabilities with increasing prevalence in aging societies. Novel interventional stroke treatment procedures have the potential to improve the clinical outcome of certain stroke-affected patients. Certainly, prompt diagnosis and treatment are required. Brain perfusion imaging with computed tomography (CT) or magnetic resonance imaging (MRI) is a routine method for stroke diagnosis. However, in the interventional room usually only CT imaging with flat detector C-arm systems is available, which do not support dynamic perfusion imaging yet. Enabling flat detector CT perfusion (FD-CTP) in clinical practice could support optimized stroke management. By stroke diagnosis in the interventional room precious time until the start of treatment could be saved. Recently, first promising clinical results for FD-CTP imaging under laboratory conditions have been presented. Based on this work, this dissertation introduces and evaluates novel technical contributions for noise reduction, artifact reduction and dynamic reconstruction in FD-CTP. Furthermore, the feasibility of FD-CTP imaging in clinical practice is demonstrated for the first time using data acquired during interventional stroke treatments. CT perfusion imaging requires measurement of dynamic contrast agent attenuation over time. The contrast agent signal in the brain tissue is very low and noise is a major problem. Thus a novel computationally fast noise reduction technique for perfusion data is introduced. Currently available C-arm systems have a comparably low rotation speed, which makes it challenging to reconstruct the dynamic change of contrast agent concentration over time. Therefore, a dynamic iterative reconstruction algorithm is proposed to utilize the high temporal resolution in the projection data for improved reconstruction of the contrast agent dynamics. Novel robotic C-arm systems (Artis zeego, Siemens Healthcare, Germany) provide a high speed rotation protocol (HSP) to improve the temporal acquisition of the contrast agent dynamics. However, the HSP suffers from angular under-sampling, which can lead to severe streak artifacts in the reconstructed perfusion maps. Thus a novel, computationally fast noise and streak artifact reduction approach for FD-CTP data is proposed. The feasibility of FD-CTP using the HSP is demonstrated with clinical data acquired during interventional treatment of two stroke cases. Furthermore, the design of a digital brain perfusion phantom for the thorough numerical evaluation of the proposed techniques is discussed. The quality of the perfusion maps acquired and reconstructed using the introduced novel approaches suggests that FD-CTP could be clinically available in the near future.<br
UnivIS-Import:2016-07-26:Pub.2014.tech.IMMD.IMMD5.dynami_6
Keywords: Medizinische Bildgebung; Computertomographie; Schlaganfall

Dynamic Iterative Reconstruction for Interventional 4-D C-Arm CT Perfusion Imaging - Manhart, Michael; Kowarschik, Markus; Fieselmann, Andreas; Deuerling-Zheng, Yu; Royalty, Kevin; Maier, Andreas; Hornegger, Joachim (IEEE Transactions on Medical Imaging, 2013)

Dynamic Reconstruction with Statistical Ray Weighting for C-Arm CT Perfusion Imaging - Manhart, Michael; Fieselmann, Andreas; Deuerling-Zheng, Yu; Maier, Andreas; Kowarschik, Markus (Proc. 12th Fully 3D, 2013)

Fast Adaptive Regularization for Perfusion Parameter Computation - Manhart, Michael; Maier, Andreas; Hornegger, Joachim; Dörfler, Arnd (Bildverarbeitung für die Medizin, 2015)
UnivIS-Import:2015-04-17:Pub.2015.tech.IMMD.IMMD5.fastad_9

Fast adaptive regularization for perfusion parameter computation: Tuning the Tikhonov regularization parameter to the SNR by regression - Manhart, Michael; Maier, Andreas; Hornegger, Joachim; Dörfler, Arnd (Informatik aktuell, 2015)
Computation of perfusion parameters by deconvolution from contrast-enhanced time-resolved CT or MR perfusion data sets is an illconditioned problem. Thus, adequate regularization and determination of corresponding regularization parameters is required. We present a novel method for Tikhonov regularization for perfusion imaging to locally adapt parameters to the SNR level by using a regression function. In an numerical evaluation our simple approach provided similar or even superior results compared to methods applying computationally more demanding L-curve analysis.
CRIS-Team Scopus Importer:2022-08-05

Fast Dynamic Reconstruction Algorithm with Joint Bilateral Filtering for Perfusion C-arm CT - Manhart, Michael; Kowarschik, Markus; Fieselmann, Andreas; Deuerling-Zheng, Yu; Hornegger, Joachim (NSS/MIC 2012 Conference Record, 2012)
Tissue perfusion measurement using C-arm angiographysystems is a novel technique with potential high benefitfor catheter-guided treatment of stroke in the interventionalsuite. However, perfusion C-arm CT (PCCT) is challenging:the slow C-arm rotation speed only allows measuring samplesof contrast time attenuation curves (TACs) every 5 - 6 s ifreconstruction algorithms for static data are used. Furthermore,the peaks of the tissue TACs typically lie in a range of 5- 30 HU, thus perfusion imaging is very sensitive to noise.We present a dynamic, iterative reconstruction (DIR) approachto reconstruct TACs described by a weighted sum of linearspline functions. The optimization problem is solved using anappropriate initialization and a Landweber-based optimizationstrategy with a modified backprojection step. To reduce noise anovel regularization technique based on Joint Bilateral Filtering(JBF) is introduced. The algorithm is evaluated using simulationdata created with a dynamic cylindrical phantom, a realisticdigital brain phantom and real measured data from an animalstudy with a canine stroke model. Results indicate that the DIRalgorithm qualitatively and quantitatively improves reconstructedTACs and perfusion maps compared to classical Feldkamp(FDK) reconstruction. For the brain phantom study the Pearsoncorrelation (PC) of the reconstructed cerebral blood flow (CBF)maps to the ground truth increased from 0.82 (FDK) to 0.87(DIR). For the canine study the PC of the CBF maps to coregisteredperfusion CT maps increased from 0.61 (FDK) to 0.73(DIR).

Fast Epipolar Consistency without the Need for Pseudo Matrix Inverses - Preuhs, Alexander; Manhart, Michael; Maier, Andreas (Proceedings of the Fifth International Conference on Image Formation in X-Ray Computed Tomography, 2018)
UnivIS-Import:2018-09-11:Pub.2018.tech.IMMD.IMMD5.fastep

Guided Noise Reduction for Spectral CT with Energy-Selective Photon Counting Detectors - Manhart, Michael; Fahrig, Rebecca; Hornegger, Joachim; Dörfler, Arnd; Maier, Andreas (Proceedings of the Third CT Meeting, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.guided_6

Guided Noise Reduction with Streak Removal for High Speed Flat Detector CT Perfusion - Manhart, Michael; Aichert, André; Kowarschik, Markus; Deuerling-Zheng, Yu; Struffert, Tobias; Dörfler, Arnd; Maier, Andreas; Hornegger, Joachim (Proceedings NSS/MIC 2013, 2013)
Tissue perfusion measurement using C-arm angiography systems capable of CT-like imaging (flat detector CT (FD-CT)) is a novel technique with high potential benefit for catheter-guided treatment of stroke in the interventional suite. New high speed protocols (HSP) with increased C-arm rotation speed enable fast acquisitions of FD-CT volumes and allow for sampling the contrast flow with improved temporal resolution. However, the peak contrast attenuation values of brain tissue typically lie in a range of 5-30 HU. Thus perfusion imaging is very sensitive to noise. Recently we introduced the FDK-JBF denoising technique based on Feldkamp (FDK) reconstruction followed by denoising in volume space using joint bilateral filtering (JBF). In the evaluation FDK-JBF achieved comparable results to algebraic techniques, but is computationally less costly. Yet the angular sampling of the projection data in the HSP is coarse, which leads to streak artifacts in the reconstructed volumes. Mask volumes are subtracted from the contrast agent enhanced (bolus) volumes and the streak artifacts are subtracted out if the patient does not move during the acquisition. However, in case of motion the streak artifacts will not be identical in the mask and bolus volumes. We show that these streaks can lead to severe artifacts in the perfusion maps and describe a novel technique for streak removal (SR), which is based on streak detection by using time-contrast curve analysis.We evaluated the FDK-SR-JBF algorithm in a phantom and a patient study and show that noise and streaks can be reduced within a short computation time. © 2013 IEEE.
UnivIS-Import:2015-04-16:Pub.2013.tech.IMMD.IMMD5.guided_5
Keywords: Flat detector CT; noise reduction; Perfusion imaging; stroke treatment

Lattice Boltzmann methods in porous media simulations: From laminar to turbulent flow - Fattahi, Ehsan; Waluga, Christian; Wohlmuth, B. I.; Rüde, Ulrich; Manhart, Michael; Helmig, Rainer (Computers & Fluids, 2016)

Material Decomposition for Energy Resolving Detectors using Weighted Levenberg-Marquardt Optimization - Manhart, Michael; Maier, Andreas; Hornegger, Joachim; Dörfler, Arnd (Proceedings of the 1st Conference on Image Guided Interventions, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.materi_5

Motion Gradients for Epipolar Consistency - Preuhs, Alexander; Manhart, Michael; Hoppe, Elisabeth; Kowarschik, Markus; Maier, Andreas (15th International Meeting on Fully Three-Dimensional Image Reconstruction, 2019)

Nanoparticle precipitation in a T-mixer: Coupling experimentaland numerical investigations of the fluid dynamics with the solid formation processes - Peukert, Wolfgang; Gradl, Johannes; Schwertfirm, Florian; Schwarzer, Hans-Christoph; Schmid, Hans-Joachim; Manhart, Michael (ASME 2006 2nd Joint U.S.-European Fluids Engineering Summer Meeting Collocated With the 14th International Conference on Nuclear Engineering, 2006)
Mixing and consequently fluid dynamic is a key parameter to tailor the particle size distribution (PSD) in nanoparticle precipitation. Due to fast and intensive mixing a static T-mixer configuration is capable .for synthesizing continuously nanoparticles. The flow and concentration field of the applied mixer is investigated experimentally at different flow rates by Particle Image Velocimetry (PIV) and Laser Induced Fluorescence (LIF). Due to the PIV measurements the flow field in the mixer was characterized qualitatively and the mixing process itself is quantified by the subsequent LIF-measurements. A special feature of the LIF set up is to detect structures in the flow field, which are smaller than the Batchelor length. Thereby a detailed insight into the mixing process in a static T-Mixer is given. In this study a CFD-based approach using Direct Numerical Simulation (DNS) in combination with the solid formation kinetics solving population balance equations (PBE) is applied, using barium sulfate as modeling material. A Lagrangian Particle Tracking strategy is used to couple the flow field information with a micro mixing model and with the classical theory of nucleation. We found that the DNS-PBE approach including macro and micro mixing, combined with the population balance is capable of predicting the full PSD in nanoparticle precipitation for different operating parameters. Additionally to the resulting PSD, this approach delivers a 3D-information about all running subprocesses in the mixer, i.e. supersaturation built-up or nucleation, which is visualized for different process variables. Copyright © 2006 by ASME.
Keywords: mixing; fluid; dynamic; nanoparticle; precipitation

Numerical and experimental investigation of the turbulent flow and mixing in a static T-mixer - Peukert, Wolfgang; Schwertfirm, Florian; Manhart, Michael; Gradl, Johannes; Schwarzer, Hans-Christoph (ASME 2006 2nd Joint U.S.-European Fluids Engineering Summer Meeting Collocated With the 14th International Conference on Nuclear Engineering, 2006)
Turbulent mixing plays an important role in chemical engineering, especially when the chemical reaction is fast compared to the mixing time. In this context a detailed knowledge of the flow field, the distribution of turbulent kinetic energy (TKE) and its dissipation rate is important, as these quantities are used for many mixing models. For this reason we conduct two direct numerical simulations (DNS) of a confined impinging jet reactor (CIJR) at Re = 500 and Sc = 1, with and without modelling the feeding pipes. The data is compared with particle image velocimetry (PIV) measurements and it is shown that only the simulation which includes the feeding pipes gives the correct flow field. This flow field is dominated by a stable vortex in the main mixing duct. High intensities of turbulent kinetic energy and dissipation are found in the impingement zone and decrease rapidly towards the exit of the CIJR. In the whole CIJR the turbulence is not in equilibrium. The mixing of the scalar is rapid in the impingement zone, but due to the fast decrease of the turbulent intensities and due to the relaminarisation of the flow the PDFs of the scalar distributions in cross sections of the CIJR take a bimodal form after the impingement zone. Copyright © 2006 by ASME.
Keywords: turbulence; mixing; kinetic; energy; simulation; jet; impinging

Precipitation of nanoparticles: A coupled LIF / PIV, DNS and PBE approach for prediction of the particle size distribution (PSD) - Peukert, Wolfgang; Gradl, Johannes; Schwertfirm, Florian; Schwarzer, Hans-Christoph; Manhart, Michael (2005)
Continuous precipitation is a promising process to generate nanoparticles with tailored properties. One challenge is to predict the PSD by considering all fluid dynamical, physiochemical and thermodynamical process parameters. A CFD-based approach using DNS in combination with Lagrangian Particle Tracking is applied coupled with micromixing and population balance models. The simulated flow field is validated by optical measurement methods (PIV and LIF). Whereas the mean particle size in dependence of the Re-number (over 6 orders of magnitude) can be predicted by a 1D approach, prediction of the complete PSD needs information from the fully resolved 3D fluid flow field.
Keywords: precipitation; continuous; mixing; prediction; simulation

Precipitation of nanoparticles in a T-mixer: Coupling the particle population dynamics with hydrodynamics through direct numerical simulation - Peukert, Wolfgang; Gradl, Johannes; Schwarzer, Hans-Christoph; Schwertfirm, Florian; Manhart, Michael (Chemical Engineering and Processing, 2006)
Mixing is a key parameter to tailor the particle size distribution (PSD) in nanoparticle precipitation. In this study we present two model approaches to simulate the impact of mixing on the PSD, using barium sulfate as exemplary material. In the first model, we combine a Lagrangian micromixing model with the population balance equation. This approach was found successful in predicting the influence of mixing on mean particle sizes but fails to predict the shape and width of the PSD. This is attributed to the neglect of spatial and temporal fluctuations in that model. Therefore, an improved CFD-based approach using direct numerical simulation (DNS) in combination with Lagrangian particle tracking strategy is applied. We found that the full DNS-approach, coupled to the population balance and a derived micromixing model, is capable of predicting the full PSD in nanoparticle precipitation. © 2006 Elsevier B.V. All rights reserved.
Keywords: Direct numeric simulation; Micromixing; Nanoparticle precipitation; Population balance; Supersaturation

Predictive simulation of nanoparticle precipitation based on the population balance equation - Peukert, Wolfgang; Schwarzer, Hans-Christoph; Schwertfirm, Florian; Manhart, Michael; Schmid, Hans-Joachim (Chemical Engineering Science, 2006)
Nanoparticle precipitation is an interesting process to generate particles with tailored properties. In this study we investigate the impact of various process steps such as solid formation, mixing and agglomeration on the resulting particle size distribution (PSD) as representative property using barium sulfate as exemplary material. Besides the experimental investigation, process simulations were carried out by solving the full 1D population balance equation coupled to a model describing the micromixing kinetics based on a finite-element Galerkin h-p-method. This combination of population balance and micromixing model was applied successfully to predict the influence of mixing on mean sizes (good quantitative agreement between experimental data and simulation results are obtained) and gain insights into nanoparticle precipitation: The interfacial energy was identified to be a critical parameter in predicting the particle size, poor mixing results in larger particles and the impact of agglomeration was found to increase with supersaturation due to larger particle numbers. Shear-induced agglomeration was found to be controllable through the residence time in turbulent regions and the intensity of turbulence, necessary for intense mixing but undesired due to agglomeration. By this approach, however, the distribution width is underestimated which is attributed to the large spectrum of mixing histories of fluid elements on their way through the mixer. Therefore, an improved computational fluid dynamics-based approach using direct numerical simulation with a Lagrangian particle tracking strategy is applied in combination with the coupled population balance-micromixing approach. We found that the full DNS-approach, coupled to the population balance and micromixing model is capable of predicting not only the mean sizes but the full PSD in nanoparticle precipitation. © 2005 Elsevier Ltd. All rights reserved.
Keywords: Computational fluid dynamics; Direct numerical simulation; Micromixing; Nanoparticle; Population balance; Precipitation

Projection-Based Denoising Method for Photon-Counting Energy-Resolving Detectors - Lu, Yanye; Manhart, Michael; Taubmann, Oliver; Zobel, Tobias; Yang, Qiao; Choi, Jang-Hwan; Wu, Meng; Dörfler, Arnd; Fahrig, Rebecca; Ren, Qiushi; Hornegger, Joachim; Maier, Andreas (Bildverarbeitung für die Medizin 2015, 2015)
UnivIS-Import:2017-12-18:Pub.2015.tech.IMMD.IMMD5.projec_7

Projection-based Material Decomposition by Machine Learning using Image-based Features for Computed Tomography - Lu, Yanye; Geret, Jan; Unberath, Mathias; Manhart, Michael; Ren, Qiushi; Fahrig, Rebecca; Hornegger, Joachim; Maier, Andreas (The 13th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2015)
UnivIS-Import:2017-12-18:Pub.2015.tech.IMMD.IMMD5.projec_5

Redundancies in X-ray images due to the epipolar geometry for transmission imaging - Aichert, André; Maass, Nicole; Deuerling-Zheng, Yu; Berger, Martin; Manhart, Michael; Hornegger, Joachim; Maier, Andreas; Dörfler, Arnd (Proceedings of the third international conference on image formation in x-ray computed tomography, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.redund_1
Keywords: GRK-1773

TV or not TV? That is the Question - Riess, Christian; Berger, Martin; Wu, Haibo; Manhart, Michael; Fahrig, Rebecca; Maier, Andreas (Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2013)