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- three-dimensional imaging (2)
- GRAPPA (1)
- GaSb/AlAsSb (1)
- RAKI (1)
- anatomy (1)
- biomedical engineering (1)
- bone imaging (1)
- complex‐valued machine learning (1)
- data augmentation (1)
- deep learning (1)
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- Physikalisches Institut (6)
- Institut für diagnostische und interventionelle Neuroradiologie (ehem. Abteilung für Neuroradiologie) (2)
- Institut für diagnostische und interventionelle Radiologie (Institut für Röntgendiagnostik) (1)
- Klinik und Poliklinik für Hals-, Nasen- und Ohrenkrankheiten, plastische und ästhetische Operationen (1)
- Medizinische Klinik und Poliklinik I (1)
Angle-resolved photoemission spectroscopy (ARPES) is a method that measures orbital and band structure contrast through the momentum distribution of photoelectrons. Its simplest interpretation is obtained in the plane-wave approximation, according to which photoelectrons propagate freely to the detector. The photoelectron momentum distribution is then essentially given by the Fourier transform of the real-space orbital. While the plane-wave approximation is remarkably successful in describing the momentum distributions of aromatic compounds, it generally fails to capture kinetic-energy-dependent final-state interference and dichroism effects. Focusing our present study on quasi-freestanding monolayer graphene as the archetypical two-dimensional (2D) material, we observe an exemplary E\(_{kin}\)-dependent modulation of, and a redistribution of spectral weight within, its characteristic horseshoe signature around the \(\bar {K}\) and \(\bar {K´}\) points: both effects indeed cannot be rationalized by the plane-wave final state. Our data are, however, in remarkable agreement with ab initio time-dependent density functional simulations of a freestanding graphene layer and can be explained by a simple extension of the plane-wave final state, permitting the two dipole-allowed partial waves emitted from the C 2p\(_z\) orbitals to scatter in the potential of their immediate surroundings. Exploiting the absolute photon flux calibration of the Metrology Light Source, this scattered-wave approximation allows us to extract E\(_{kin}\)-dependent amplitudes and phases of both partial waves directly from photoemission data. The scattered-wave approximation thus represents a powerful yet intuitive refinement of the plane-wave final state in photoemission of 2D materials and beyond.
We demonstrate monolithic high contrast gratings (MHCG) based on GaSb/AlAs0.08Sb0.92 epitaxial structures with sub-wavelength gratings enabling high reflection of unpolarized mid-infrared radiation at the wavelength range from 2.5 to 5 µm. We study the reflectivity wavelength dependence of MHCGs with ridge widths ranging from 220 to 984 nm and fixed 2.6 µm grating period and demonstrate that peak reflectivity of above 0.7 can be shifted from 3.0 to 4.3 µm for ridge widths from 220 to 984 nm, respectively. Maximum reflectivity of up to 0.9 at 4 µm can be achieved. The experiments are in good agreement with numerical simulations, confirming high process flexibility in terms of peak reflectivity and wavelength selection. MHCGs have hitherto been regarded as mirrors enabling high reflection of selected light polarization. With this work, we show that thoughtfully designed MHCG yields high reflectivity for both orthogonal polarizations simultaneously. Our experiment demonstrates that MHCGs are promising candidates to replace conventional mirrors like distributed Bragg reflectors to realize resonator based optical and optoelectronic devices such as resonant cavity enhanced light emitting diodes and resonant cavity enhanced photodetectors in the mid-infrared spectral region, for which epitaxial growth of distributed Bragg reflectors is challenging.
Automated analysis of the inner ear anatomy in radiological data instead of time-consuming manual assessment is a worthwhile goal that could facilitate preoperative planning and clinical research. We propose a framework encompassing joint semantic segmentation of the inner ear and anatomical landmark detection of helicotrema, oval and round window. A fully automated pipeline with a single, dual-headed volumetric 3D U-Net was implemented, trained and evaluated using manually labeled in-house datasets from cadaveric specimen (N = 43) and clinical practice (N = 9). The model robustness was further evaluated on three independent open-source datasets (N = 23 + 7 + 17 scans) consisting of cadaveric specimen scans. For the in-house datasets, Dice scores of 0.97 and 0.94, intersection-over-union scores of 0.94 and 0.89 and average Hausdorf distances of 0.065 and 0.14 voxel units were achieved. The landmark localization task was performed automatically with an average localization error of 3.3 and 5.2 voxel units. A robust, albeit reduced performance could be
attained for the catalogue of three open-source datasets. Results of the ablation studies with 43 mono-parametric variations of the basal architecture and training protocol provided task-optimal parameters for both categories. Ablation studies against single-task variants of the basal architecture showed a clear performance beneft of coupling landmark localization with segmentation and a dataset-dependent performance impact on segmentation ability.
Minimally invasive endovascular interventions have become an important tool for the treatment of cardiovascular diseases such as ischemic heart disease, peripheral artery disease, and stroke. X-ray fluoroscopy and digital subtraction angiography are used to precisely guide these procedures, but they are associated with radiation exposure for patients and clinical staff. Magnetic Particle Imaging (MPI) is an emerging imaging technology using time-varying magnetic fields combined with magnetic nanoparticle tracers for fast and highly sensitive imaging. In recent years, basic experiments have shown that MPI has great potential for cardiovascular applications. However, commercially available MPI scanners were too large and expensive and had a small field of view (FOV) designed for rodents, which limited further translational research. The first human-sized MPI scanner designed specifically for brain imaging showed promising results but had limitations in gradient strength, acquisition time and portability. Here, we present a portable interventional MPI (iMPI) system dedicated for real-time endovascular interventions free of ionizing radiation. It uses a novel field generator approach with a very large FOV and an application-oriented open design enabling hybrid approaches with conventional X-ray-based angiography. The feasibility of a real-time iMPI-guided percutaneous transluminal angioplasty (PTA) is shown in a realistic dynamic human-sized leg model.
To evaluate an iterative learning approach for enhanced performance of robust artificial‐neural‐networks for k‐space interpolation (RAKI), when only a limited amount of training data (auto‐calibration signals [ACS]) are available for accelerated standard 2D imaging.
Methods
In a first step, the RAKI model was tailored for the case of limited training data amount. In the iterative learning approach (termed iterative RAKI [iRAKI]), the tailored RAKI model is initially trained using original and augmented ACS obtained from a linear parallel imaging reconstruction. Subsequently, the RAKI convolution filters are refined iteratively using original and augmented ACS extracted from the previous RAKI reconstruction. Evaluation was carried out on 200 retrospectively undersampled in vivo datasets from the fastMRI neuro database with different contrast settings.
Results
For limited training data (18 and 22 ACS lines for R = 4 and R = 5, respectively), iRAKI outperforms standard RAKI by reducing residual artifacts and yields better noise suppression when compared to standard parallel imaging, underlined by quantitative reconstruction quality metrics. Additionally, iRAKI shows better performance than both GRAPPA and standard RAKI in case of pre‐scan calibration with varying contrast between training‐ and undersampled data.
Conclusion
RAKI benefits from the iterative learning approach, which preserves the noise suppression feature, but requires less original training data for the accurate reconstruction of standard 2D images thereby improving net acceleration.
The great progress in organic photovoltaics (OPV) over the past few years has been largely achieved by the development of non‐fullerene acceptors (NFAs), with power conversion efficiencies now approaching 20%. To further improve device performance, loss mechanisms must be identified and minimized. Triplet states are known to adversely affect device performance, since they can form energetically trapped excitons on low‐lying states that are responsible for non‐radiative losses or even device degradation. Halogenation of OPV materials has long been employed to tailor energy levels and to enhance open circuit voltage. Yet, the influence on recombination to triplet excitons has been largely unexplored. Using the complementary spin‐sensitive methods of photoluminescence detected magnetic resonance and transient electron paramagnetic resonance corroborated by transient absorption and quantum‐chemical calculations, exciton pathways in OPV blends are unravelled employing the polymer donors PBDB‐T, PM6, and PM7 together with NFAs Y6 and Y7. All blends reveal triplet excitons on the NFA populated via non‐geminate hole back transfer and, in blends with halogenated donors, also by spin‐orbit coupling driven intersystem crossing. Identifying these triplet formation pathways in all tested solar cell absorber films highlights the untapped potential for improved charge generation to further increase plateauing OPV efficiencies.