@article{WinterAndelovicKampfetal.2021, author = {Winter, Patrick M. and Andelovic, Kristina and Kampf, Thomas and Hansmann, Jan and Jakob, Peter Michael and Bauer, Wolfgang Rudolf and Zernecke, Alma and Herold, Volker}, title = {Simultaneous measurements of 3D wall shear stress and pulse wave velocity in the murine aortic arch}, series = {Journal of Cardiovascular Magnetic Resonance}, volume = {23}, journal = {Journal of Cardiovascular Magnetic Resonance}, number = {1}, doi = {10.1186/s12968-021-00725-4}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-259152}, pages = {34}, year = {2021}, abstract = {Purpose Wall shear stress (WSS) and pulse wave velocity (PWV) are important parameters to characterize blood flow in the vessel wall. Their quantification with flow-sensitive phase-contrast (PC) cardiovascular magnetic resonance (CMR), however, is time-consuming. Furthermore, the measurement of WSS requires high spatial resolution, whereas high temporal resolution is necessary for PWV measurements. For these reasons, PWV and WSS are challenging to measure in one CMR session, making it difficult to directly compare these parameters. By using a retrospective approach with a flexible reconstruction framework, we here aimed to simultaneously assess both PWV and WSS in the murine aortic arch from the same 4D flow measurement. Methods Flow was measured in the aortic arch of 18-week-old wildtype (n = 5) and ApoE\(^{-/-}\) mice (n = 5) with a self-navigated radial 4D-PC-CMR sequence. Retrospective data analysis was used to reconstruct the same dataset either at low spatial and high temporal resolution (PWV analysis) or high spatial and low temporal resolution (WSS analysis). To assess WSS, the aortic lumen was labeled by semi-automatically segmenting the reconstruction with high spatial resolution. WSS was determined from the spatial velocity gradients at the lumen surface. For calculation of the PWV, segmentation data was interpolated along the temporal dimension. Subsequently, PWV was quantified from the through-plane flow data using the multiple-points transit-time method. Reconstructions with varying frame rates and spatial resolutions were performed to investigate the influence of spatiotemporal resolution on the PWV and WSS quantification. Results 4D flow measurements were conducted in an acquisition time of only 35 min. Increased peak flow and peak WSS values and lower errors in PWV estimation were observed in the reconstructions with high temporal resolution. Aortic PWV was significantly increased in ApoE\(^{-/-}\) mice compared to the control group (1.7 ± 0.2 versus 2.6 ± 0.2 m/s, p < 0.001). Mean WSS magnitude values averaged over the aortic arch were (1.17 ± 0.07) N/m\(^2\) in wildtype mice and (1.27 ± 0.10) N/m\(^2\) in ApoE\(^{-/-}\) mice. Conclusion The post processing algorithm using the flexible reconstruction framework developed in this study permitted quantification of global PWV and 3D-WSS in a single acquisition. The possibility to assess both parameters in only 35 min will markedly improve the analyses and information content of in vivo measurements.}, language = {en} } @article{WinterKampfHelluyetal.2013, author = {Winter, Patrick and Kampf, Thomas and Helluy, Xavier and Gutjahr, Fabian T. and Meyer, Cord B. and Rommel, Eberhard and Bauer, Wolfgang R. and Jakob, Peter M. and Herold, Volker}, title = {Fast retrospectively triggered local pulse-wave velocity measurements in mice with CMR-microscopy using a radial trajectory}, series = {Journal of Cardiovascular Magnetic Resonance}, journal = {Journal of Cardiovascular Magnetic Resonance}, doi = {10.1186/1532-429X-15-88}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-96602}, year = {2013}, abstract = {Background The aortic pulse-wave velocity (PWV) is an important indicator of cardiovascular risk. In recent studies MRI methods have been developed to measure this parameter noninvasively in mice. Present techniques require additional hardware for cardiac and respiratory gating. In this work a robust self-gated measurement of the local PWV in mice without the need of triggering probes is proposed. Methods The local PWV of 6-months-old wild-type C57BL/6J mice (n=6) was measured in the abdominal aorta with a retrospectively triggered radial Phase Contrast (PC) MR sequence using the flow-area (QA) method. A navigator signal was extracted from the CMR data of highly asymmetric radial projections with short repetition time (TR=3 ms) and post-processed with high-pass and low-pass filters for retrospective cardiac and respiratory gating. The self-gating signal was used for a reconstruction of high-resolution Cine frames of the aortic motion. To assess the local PWV the volume flow Q and the cross-sectional area A of the aorta were determined. The results were compared with the values measured with a triggered Cartesian and an undersampled triggered radial PC-Cine sequence. Results In all examined animals a self-gating signal could be extracted and used for retrospective breath-gating and PC-Cine reconstruction. With the non-triggered measurement PWV values of 2.3±0.2 m/s were determined. These values are in agreement with those measured with the triggered Cartesian (2.4±0.2 m/s) and the triggered radial (2.3±0.2 m/s) measurement. Due to the strong robustness of the radial trajectory against undersampling an acceleration of more than two relative to the prospectively triggered Cartesian sampling could be achieved with the retrospective method. Conclusion With the radial flow-encoding sequence the extraction of a self-gating signal is feasible. The retrospective method enables a robust and fast measurement of the local PWV without the need of additional trigger hardware.}, language = {en} } @article{WechAnkenbrandBleyetal.2022, author = {Wech, Tobias and Ankenbrand, Markus Johannes and Bley, Thorsten Alexander and Heidenreich, Julius Frederik}, title = {A data-driven semantic segmentation model for direct cardiac functional analysis based on undersampled radial MR cine series}, series = {Magnetic Resonance in Medicine}, volume = {87}, journal = {Magnetic Resonance in Medicine}, number = {2}, doi = {10.1002/mrm.29017}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-257616}, pages = {972-983}, year = {2022}, abstract = {Purpose Image acquisition and subsequent manual analysis of cardiac cine MRI is time-consuming. The purpose of this study was to train and evaluate a 3D artificial neural network for semantic segmentation of radially undersampled cardiac MRI to accelerate both scan time and postprocessing. Methods A database of Cartesian short-axis MR images of the heart (148,500 images, 484 examinations) was assembled from an openly accessible database and radial undersampling was simulated. A 3D U-Net architecture was pretrained for segmentation of undersampled spatiotemporal cine MRI. Transfer learning was then performed using samples from a second database, comprising 108 non-Cartesian radial cine series of the midventricular myocardium to optimize the performance for authentic data. The performance was evaluated for different levels of undersampling by the Dice similarity coefficient (DSC) with respect to reference labels, as well as by deriving ventricular volumes and myocardial masses. Results Without transfer learning, the pretrained model performed moderately on true radial data [maximum number of projections tested, P = 196; DSC = 0.87 (left ventricle), DSC = 0.76 (myocardium), and DSC =0.64 (right ventricle)]. After transfer learning with authentic data, the predictions achieved human level even for high undersampling rates (P = 33, DSC = 0.95, 0.87, and 0.93) without significant difference compared with segmentations derived from fully sampled data. Conclusion A 3D U-Net architecture can be used for semantic segmentation of radially undersampled cine acquisitions, achieving a performance comparable with human experts in fully sampled data. This approach can jointly accelerate time-consuming cine image acquisition and cumbersome manual image analysis.}, language = {en} } @article{GramGenslerWinteretal.2022, author = {Gram, Maximilian and Gensler, Daniel and Winter, Patrick and Seethaler, Michael and Arias-Loza, Paula Anahi and Oberberger, Johannes and Jakob, Peter Michael and Nordbeck, Peter}, title = {Fast myocardial T\(_{1P}\) mapping in mice using k-space weighted image contrast and a Bloch simulation-optimized radial sampling pattern}, series = {Magnetic Resonance Materials in Physics, Biology and Medicine}, volume = {35}, journal = {Magnetic Resonance Materials in Physics, Biology and Medicine}, number = {2}, issn = {1352-8661}, doi = {10.1007/s10334-021-00951-y}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-268903}, pages = {325-340}, year = {2022}, abstract = {Purpose T\(_{1P}\) dispersion quantification can potentially be used as a cardiac magnetic resonance index for sensitive detection of myocardial fibrosis without the need of contrast agents. However, dispersion quantification is still a major challenge, because T\(_{1P}\) mapping for different spin lock amplitudes is a very time consuming process. This study aims to develop a fast and accurate T\(_{1P}\) mapping sequence, which paves the way to cardiac T1ρ dispersion quantification within the limited measurement time of an in vivo study in small animals. Methods A radial spin lock sequence was developed using a Bloch simulation-optimized sampling pattern and a view-sharing method for image reconstruction. For validation, phantom measurements with a conventional sampling pattern and a gold standard sequence were compared to examine T\(_{1P}\) quantification accuracy. The in vivo validation of T\(_{1P}\) mapping was performed in N = 10 mice and in a reproduction study in a single animal, in which ten maps were acquired in direct succession. Finally, the feasibility of myocardial dispersion quantification was tested in one animal. Results The Bloch simulation-based sampling shows considerably higher image quality as well as improved T\(_{1P}\) quantification accuracy (+ 56\%) and precision (+ 49\%) compared to conventional sampling. Compared to the gold standard sequence, a mean deviation of - 0.46 ± 1.84\% was observed. The in vivo measurements proved high reproducibility of myocardial T\(_{1P}\) mapping. The mean T\(_{1P}\) in the left ventricle was 39.5 ± 1.2 ms for different animals and the maximum deviation was 2.1\% in the successive measurements. The myocardial T\(_{1P}\) dispersion slope, which was measured for the first time in one animal, could be determined to be 4.76 ± 0.23 ms/kHz. Conclusion This new and fast T\(_{1P}\) quantification technique enables high-resolution myocardial T\(_{1P}\) mapping and even dispersion quantification within the limited time of an in vivo study and could, therefore, be a reliable tool for improved tissue characterization.}, language = {en} } @article{GramGenslerAlbertovaetal.2022, author = {Gram, Maximilian and Gensler, Daniel and Albertova, Petra and Gutjahr, Fabian Tobias and Lau, Kolja and Arias-Loza, Paula-Anahi and Jakob, Peter Michael and Nordbeck, Peter}, title = {Quantification correction for free-breathing myocardial T1ρ mapping in mice using a recursively derived description of a T\(_{1p}\)\(^{*}\) relaxation pathway}, series = {Journal of Cardiovascular Magnetic Resonance}, volume = {24}, journal = {Journal of Cardiovascular Magnetic Resonance}, number = {1}, doi = {10.1186/s12968-022-00864-2}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-300491}, year = {2022}, abstract = {Background Fast and accurate T1ρ mapping in myocardium is still a major challenge, particularly in small animal models. The complex sequence design owing to electrocardiogram and respiratory gating leads to quantification errors in in vivo experiments, due to variations of the T\(_{1p}\) relaxation pathway. In this study, we present an improved quantification method for T\(_{1p}\) using a newly derived formalism of a T\(_{1p}\)\(^{*}\) relaxation pathway. Methods The new signal equation was derived by solving a recursion problem for spin-lock prepared fast gradient echo readouts. Based on Bloch simulations, we compared quantification errors using the common monoexponential model and our corrected model. The method was validated in phantom experiments and tested in vivo for myocardial T\(_{1p}\) mapping in mice. Here, the impact of the breath dependent spin recovery time T\(_{rec}\) on the quantification results was examined in detail. Results Simulations indicate that a correction is necessary, since systematically underestimated values are measured under in vivo conditions. In the phantom study, the mean quantification error could be reduced from - 7.4\% to - 0.97\%. In vivo, a correlation of uncorrected T\(_{1p}\) with the respiratory cycle was observed. Using the newly derived correction method, this correlation was significantly reduced from r = 0.708 (p < 0.001) to r = 0.204 and the standard deviation of left ventricular T\(_{1p}\) values in different animals was reduced by at least 39\%. Conclusion The suggested quantification formalism enables fast and precise myocardial T\(_{1p}\) quantification for small animals during free breathing and can improve the comparability of study results. Our new technique offers a reasonable tool for assessing myocardial diseases, since pathologies that cause a change in heart or breathing rates do not lead to systematic misinterpretations. Besides, the derived signal equation can be used for sequence optimization or for subsequent correction of prior study results.}, language = {en} }