Günther, Matthias
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Günther, Matthias
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Günther, Matthias
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Guenther, Matthias
Guenther, Joerg Matthias
Guenther, Jörg Matthias
Günther, Joerg Matthias
Günther, Jörg Matthias
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Item-typ:Veröffentlichung, Diffusionsgewichtete und Diffusionstensor-Bildgebung in der Magnetischen Resonanztomographie - Sequenzentwicklung und -optimierung im Fokus der klinischen Anwendung(2010-11-19); ; ; The importance of Magnetic Resonance Tomography (MRT) arises from the high frequency of water protons in living tissue, the non-invasiveness of the technique and the straightforward spatial encoding of protons by means of additional locally varying magnetic field gradients. During the last two decades, diffusion-weighted imaging (DWI) has added an essential contribution to clinical MR tomography of structural T1- and T2-weighted imaging and to mapping of physiological processes like perfusion and functional activity. DWI uses the self-diffusion (Brownian motion) of water molecules in an inhomogeneous static magnetic field that can be encoded by locally dependent gradient fields. The key feature and importance of MR-DWI results from the fact that the random translatory motion of molecules scans the microscopic tissue structures far beyond the spatial resolution of MR imaging techniques. Furthermore, diffusion as a physical process is independent of magnetic resonance phenomena, but offers similar advantages like high contrast and spatial resolution. The intent of this dissertation covers projects of sequence development and optimization of clinical DWI applications that illustrate the fast evolving development of DWI in medical MR imaging. One focus is set on the combination of DW echo-planar imaging (EPI) and the fluid-attenuated inversion recovery (FLAIR) prepa¬ration. After technical validation, issues of measurement accuracy and signal-to-noise ratio and their implications on estimated contrast parameters like diffusion anisotropy are discussed. A proposed correction term enables immediate acquisition and comparison of standard DW-EPI and FLAIR-DWI in volunteer and patient studies. A preliminary study on patients with astrocytoma reveals the advantages of FLAIR-prepared DW imaging protocols. The second topic of this dissertation explores the extension of the linear diffusion tensor model to high angular resolution DWI (HARDI). The high complexity of white matter fiber structures limits the linear DTI model and requires improved acquisition schemes as well as enhanced quantitative estimates. An approach for visualizing deviations from the linear DT model by means of 2D polar and contour plots is proposed. Additionally, four examples of clinical protocols are described in detail showing important current contribu¬tions of methodological developments in DW and DT imaging. Neurological diagnostic questions concerning early stroke dynamics, transient global amnesia, and side effects of electroconvulsive therapy require a robust DWI strategy that is independent of diffusion anisotropy, whereas the directional information of diffusion tensor imaging enables the delineation of main white matter fiber tracts and a reliable description of small focal lacunar infarct lesions. This adds important diagnostic details to the patient s symptoms and prognosis. The stimulus to improvements and innovations in the field of DW MRI and all post-processing disciplines is still unbowed. Improved technical aspects of MR scanners like higher field strength, parallel acquisition techniques and development of alternative effective sampling strategies allow continuously improving image quality, stronger diffusion weighting and/or the realization of theoretically desired DW schemes of pulsed, short and strong DW gradients. On the other side, recent progress and increased benefits of post-processing strategies contribute essentially to the visualization of processed diffusion tensor data. The techniques of tracking algorithms, formerly based on the linear DT model and today extended to tensors of higher order and probabilistic algorithms, suggest the visualization of structural connectivity across fiber tracts. Merging these analyses with functional connectivity studies and other physiological data assigns DTI a small, yet important role in the investigation of human brain structure and function.Dissertation331 119 - Some of the metrics are blocked by yourconsent settings
Item-typ:Veröffentlichung, Extraction of time-dependent properties from medical ultrasound image series(2025-03-25); ; ; Medical ultrasound offers the unique possibility to gather real-time image series, providing insights into dynamic processes of the human body. The interpretation of the acquired sequences, however, can be challenging, especially when the dynamic property of interest is superimposed by, for example, respiratory motion. This thesis investigates how to automate the extraction of time-dependent properties from motion-affected ultrasound image series considering two concrete use cases. The first part deals with extracting two image features from contrast-enhanced ultrasound (CEUS) acquisitions of liver lesions, which are relevant for diagnosis. Both features characterise the distribution of the contrast agent in the lesion compared to normal liver tissue over time. Deep learning-based classifiers are exploited on a large collection of 500 labelled heterogeneous CEUS acquisitions. The influence of aspects such as motion compensation and data representation on the classification result is systematically analysed. In the second part, a use case from physiotherapy is explored in which segmental stabilising exercises are incorporated to treat low back pain. During those exercises, the contraction status of the abdominal muscles can be monitored via ultrasound imaging. Automating the extraction of this status has the potential to enable wearable ultrasound biofeedback devices which can be used for example during home training. Several deep learning-based segmentation algorithms for the three relevant abdominal muscles are evaluated, using time series acquired from volunteers performing exercises.Also, different strategies to assess the contraction state from the obtained segmentations are explored. Both use cases showed that motion can affect the assessment of dynamic image features in ultrasound. Using effective algorithms, it can be controlled to some extent, enabling the use of information along the sequence to retrieve the desired properties.Dissertation116 136 - Some of the metrics are blocked by yourconsent settings
Item-typ:Veröffentlichung, Motion Tracking for Medical Applications using Hierarchical Filter Models(2018-11-01); ; ; A medical intervention often requires relating treatment to the situation, which it was planned on. In order to circumvent undesirable effects of motion during the intervention, positional differences must be detected in real-time. To this end, in this thesis a hierarchical Particle Filter based tracking algorithm is developed in three stages. Initially, a model description of the individual nodes in the aspired hierarchical tree is presented. Using different approaches, properties of such a node are derived and approximated, leading to a parametrization scheme. Secondly, transformations and appearance of the data are described by a fixed hierarchical tree. A sparse description for typical landmarks in medical image data is presented. A static tree model with two levels is developed and investigated. Finally, the notion of 'association' between landmarks and nodes is introduced in order to allow for dynamic adaptation to the underlying structure of the data. Processes for tree maintenance using clustering and sequential reinforcement are implemented. The function of the full algorithm is demonstrated on data of abdominal breathing motion.Dissertation384 166 - Some of the metrics are blocked by yourconsent settings
Item-typ:Veröffentlichung, Adaption in Dynamic Contrast-Enhanced MRI(2015-03-25); ; ; In breast DCE MRI, dynamic data are acquired to assess signal changes caused by contrast agent injection in order to classify lesions. Two approaches are used for data analysis. One is to fit a pharmacokinetic model, such as the Tofts model, to the data, providing physiological information. For accurate model fitting, fast sampling is needed. Another approach is to evaluate architectural features of the contrast agent distribution, for which high spatial resolution is indispensable. However, high temporal and spatial resolution are opposing aims and a compromise has to be found. A new area of research are adaptive schemes, which sample data at combined resolutions to yield both, accurate model fitting and high spatial resolution morphological information. In this work, adaptive sampling schemes were investigated with the objective to optimize fitting accuracy, whilst providing high spatial resolution images. First, optimal sampling design was applied to the Tofts model. By that it could be determined, based on an assumed parameter distribution, that time points during the onset and the initial fast kinetics, lasting for approximately two minutes, are most relevant for fitting. During this interval, fast sampling is required. Later time points during wash-out can be exploited for high spatial resolution images. To achieve fast sampling during the initial kinetics, data acquisition has to be accelerated. A common way to increase imaging speed is to use view-sharing methods, which omit certain k-space data and interpolate the missing data from neighboring time frames. In this work, based on phantom simulations, the influence of different view-sharing techniques during the initial kinetics on fitting accuracy was investigated. It was found that all view-sharing methods imposed characteristic systematic errors on the fitting results of Ktrans. The best fitting performance was achieved by the scheme ``modTRICKS'', which is a combination of the often used schemes keyhole and TRICKS. It is not known prior to imaging, when the contrast agent will arrive in the lesion or when the wash-out begins. Currently used adaptive sequences change resolutions a fixed time points. However, missing time points on the upslope may cause fitting errors and missing the signal peak may lead to a loss in morphological information. This problem was addressed with a new automatic resolution adaption (AURA) sequence. Acquired dynamic data were analyzed in real-time to find the onset and the beginning of the wash-out and consequently the temporal resolution was automatically adapted. Using a perfusion phantom it could be shown that AURA provides both, high fitting accuracy and reliably high spatial resolution images close to the signal peak. As alternative approach to AURA, a sequence which allows for retrospective resolution adaption, was assesses. Advantages are that adaption does not have to be a global process, and can be tailored regionally to local sampling requirements. This can be useful for heterogeneous lesions. For that, a 3D golden angle radial sequence was used, which acquires contrast information with each line and the golden angles allow arbitrary resolutions at arbitrary time points. Using a perfusion phantom, it could be shown that retrospective resolution adaption yields high fitting accuracy and relatively high spatial resolution maps.Dissertation414 179 - Some of the metrics are blocked by yourconsent settings
Item-typ:Veröffentlichung, Towards motion-insensitive Arterial Spin Labeling perfusion imaging(2022-03-31); ; ; Perfusion measurements in brain and liver are of high clinical interest. Arterial spin labeling (ASL) magnetic resonance imaging (MRI) has the potential to be an alternative to invasive measurements of perfusion using contrast agent-based techniques. However, the clinical application of ASL is currently limited due to a severe sensitivity to subject motion. The goal of this thesis was to develop novel methods which address the motion sensitivity of ASL sequences. The developed techniques include novel optimized approaches for background suppression, an automatic detection of breathholds during ASL experiments to suppress respiratory motion artifacts, prospective correction of respiratory motion during free-breathing scans as well as three-dimensional retrospective motion correction using a 3D GRASE PROPELLER (3DGP) readout. In addition, a novel 3DGP reconstruction, allowing joint estimation of motion and geometric distortion, is presented. Algorithms are implemented and validated in brain and liver ASL perfusion imaging using healthy volunteers. Finally, recommendations for future improvements of the developed techniques are given.Dissertation714 238 - Some of the metrics are blocked by yourconsent settings
Item-typ:Veröffentlichung, Robust and time-effcient determination of perfusion parameters using time-encoded Arterial Spin Labeling MRIIn clinical routine, arterial spin labeling (ASL) faces many challenges, such as time pressure, patient- and disease-specific artifacts, e.g., in steno-occlusive and Moya-Moya disease. In addition, individually tailored parametrization of the MR pulse-sequence is frequently required. Time-encoded ASL-techniques like Hadamard time-encoded pseudocontinuous ASL (H-pCASL) offers a time and signal efficient way to measure accurately both perfusion and arterial transit-times. However, it relies on the decoding of a series of volumes. If even a single volume is corrupted this might, via the decoding process, lead to artifacts in the entire dataset and in the worst case result in the loss of the data. In this thesis a general introduction to time encoded ASL is given and three methods are introduced to increase the robustness of time-encoded ASL against image artifacts and to detect corrupted images. The first method is Walsh-ordered time-encoded H-pCASL (WH-pCASL). It proposes the Walsh-ordering of Hadamard encoding-matrices. In contrast to conventional H-pCASL, this makes perfusion-weighted images accessible during a running experiment and even from incomplete sets of encoded images. An optional additional averaging strategy is based on a mirrored matrix and results in more perfusion-weighted images without any penalty in time. The feasibility of the method is shown using five volunteer datasets. As a second method non-decoded time-encoded ASL is introduced. This novel model-based approach to quantification avoids the decoding step altogether. It models the non-decoded time encoded signal. Therefore it uses the convolution of the tissue response function with a model of the true encoded arterial input function, which is determined by the employed encoding matrix. The model was implemented in a Bayesian model-based ASL analysis framework to fit maps for hemodynamic parameters. The feasibility of the method is demonstrated in a study with five volunteers. The last method is an algorithm for the automated detection of outliers and corrupted images, which is based on variational Bayesian inference (VB). Using the variance of the posterior normal distributions, the algorithm measures the quality of a fit directly and without the need for a separate reference dataset. Its performance and feasibility is demonstrated using volunteer data and a clinical dataset.Dissertation752 216 - Some of the metrics are blocked by yourconsent settings
Item-typ:Veröffentlichung, New Approaches to Simultaneous Multislice Magnetic Resonance Imaging : Sequence Optimization and Deep Learning based Image Reconstruction(2019-04-09); ; ; Magnetic resonance imaging (MRI) is a versatile imaging modality in clinical diagnostics. Despite the impressive range of application, a main drawback of MRI is its inherently low acquisition speed. However, scan time is crucial for many applications and also for an efficient utilization of MRI in clinical routine. Two developments have influenced MRI recently: Simultaneous multislice imaging (SMS) and deep learning (DL). Simultaneous multislice imaging is a paradigm shift in MRI which has re-emerged in the early 2010'. It yields improved image quality compared to in-plane parallel imaging, because it benefits from increased signal-to-noise ratio and robustness for higher accelerations. SMS sequences accelerate data acquisition by undersampling along the slice dimension and specific algorithms allow reconstruction of these undersampled data. In the first part, SMS was extended to measure multiple image contrasts in contrast-enhanced dynamic MRI. Therefore, a bespoke MRI sequence was developed to accelerate segmented echo-planar imaging of three echoes. Dynamic in-vivo data with sufficient spatial coverage were acquired in an animal model. Data acquisition were fast enough to sample the arterial input function which is essential for pharmacokinetic modeling. Imperfections in the excitation of multiple slice and their relevance for reconstruction algorithms were closely investigated and evaluated for processing of multi-contrast data. This work connects SMS and deep learning. Today, the application of deep learning in medicine assists decision making in medical diagnosis, analysis of radiologic data or personalized medicine in genomics. In MRI however, deep learning has just entered the stage. With two abstracts matching the search term 'deep learning' at the ISMRM 2016, the number of abstracts rose to 42 in 2017 and to 139 in 2018. Most of the early contributions to DL in MRI concern image processing and data evaluation. Image reconstruction itself is mostly conducted in standard fashioned way. Common algorithmic approaches applying deep neural networks for (some) processing steps have shown impressive results and can often be generalized to similar problems. In the second part, the separation of overlapping slice content after SMS was performed by an artificial neural network. This novel reconstruction technique, termed SMSnet, does not require any reference data for calibration of the MR machine's receiver characteristics. Omitting the need for reference data could extend the use of modern accelerated imaging sequences to a broad spectrum of applications. Potential and limitations of this approach were investigated in various experiments accounting for image quality, robustness, sensitivity and how the network generalizes. The discussion at the end summarizes and relates the results of this work to state-of-the-art techniques and recent developments in MRI and gives an outlook to future work on SMS and DL-based reconstructions.Dissertation525 225 - Some of the metrics are blocked by yourconsent settings
Item-typ:Veröffentlichung, Advancing the applicability to extract physiological parameters using Arterial Spin Labeling(2025-03-27); ; ; Arterial Spin Labeling (ASL) is a non-invasive MRI technique for measuring perfusion without the need for contrast agents, as it utilizes the blood itself. ASL can quantify not only blood flow but also other hemodynamical parameters, such as transit times and exchange rates within organs. The first part of this work introduces an extended quantification model, incorporating an additional intravoxel transit time parameter that differentiates between transit and exchange mechanisms. Conducted simulations show that neglecting transit effects leads to a compensation in the exchange parameter. The second part of this thesis explores the use of velocity selective ASL for robust extracerebral measurements. This sophisticated technique offers flexibility for imaging organs with low blood flow velocities or unclear feeding vessels. The focus is on breast and liver imaging.Dissertation132 156 - Some of the metrics are blocked by yourconsent settings
Item-typ:Veröffentlichung, Efficient Simulation of Magnetic Resonance Imaging(2015-11-18); ; ; Simulation of Magnetic Resonance Imaging (MRI) is based on the Bloch equation. Solving the Bloch equation numerically is not difficult, but realistic imaging experiments bear a high computational burden. In his dissertation, Cristoffer Cordes presents simulation methods that exploit hardware restrictions and the common structure of MRI sequences while not enforcing any approximations. These strategies use the reoccurrence of radiofrequency pulses, partial availability of analytical solutions, a reformulation of the problem in Fourier space and finally an inclusion of the reconstruction process to perform MRI simulation in image space, titled Sequence Response Kernel approach. The algorithmic efficiencies of the methods are investigated and applied to realistic imaging experiments. The properties and potential of the algorithms are exemplified, with an emphasis on the Sequence Response Kernel approach. This book is aimed at physicists and mathematicians with an MRI background.Dissertation628 173 - Some of the metrics are blocked by yourconsent settings
Item-typ:Veröffentlichung, Computer-aided image quality assessment in automated 3D breast ultrasound images(2016-01-22); ; ; Automated 3D breast ultrasound (ABUS) is a valuable, non-ionising adjunct to X-ray mammography for breast cancer screening and diagnosis for women with dense breasts. High image quality is an important prerequisite for diagnosis and has to be guaranteed at the time of acquisition. The high throughput of images in a screening scenario demands for automated solutions. In this work, an automated image quality assessment system rating ABUS scans at the time of acquisition was designed and implemented. Quality assessment of present diagnostic ultrasound images has rarely been performed demanding thorough analysis of potential image quality aspects in ABUS. Therefore, a reader study was initiated, making two clinicians rate the quality of clinical ABUS images. The frequency of specific quality aspects was evaluated revealing that incorrect positioning and insufficiently applied contact fluid caused the most relevant image quality issues. The relative position of the nipple in the image, the acoustic shadow caused by the nipple as well as the shape of the breast contour reflect patient positioning and ultrasound transducer handling. Morphological and histogram-based features utilized for machine learning to reproduce the manual classification as provided by the clinicians. At 97 % specificity, the automatic classification achieved sensitivities of 59 %, 45 %, and 46 % for the three aforementioned aspects, respectively. The nipple is an important landmark in breast imaging, which is generally---but not always correctly---pinpointed by the technicians. An existing nipple detection algorithm was extended by probabilistic atlases and exploited for automatic detection of incorrectly annotated nipple marks. The nipple detection rate was increased from 82 % to 85 % and the classification achieved 90 % sensitivity at 89 % specificity. A lack of contact fluid between transducer and skin can induce reverberation patterns and acoustic shadows, which can possibly obscure lesions. Parameter maps were computed in order to localize these artefact regions and yielded a detection rate of 83 % at 2.6 false positives per image. Parts of the presented work were integrated to clinical workflow making up a novel image quality assessment system that supported technicians in their daily routine by detecting images of insufficient quality and indicating potential improvements for a repeated scan while the patient was still in the examination room. First evaluations showed that the proposed method sensitises technicians for the radiologists' demands on diagnostically valuable images.Dissertation324 151