Enantioselective Hydroaminations Catalyzed by Yttrium(III)-Bis(sulfoximine) Complexes.

Bis(sulfoximine) yttrium complexes as catalysts for enantioselective intramolecular hydroaminations were prepared for the first time and characterized by a multinuclear NMR study including the nuclei 1H, 13C, 15N and 89Y. The stoichiometries of the complexes were confirmed by a Job plot. In addition to the experimental results, the 89Y NMR shifts and the complex structures were elucidated by DFT calculations.

Phase retrieval algorithm applied to high-energy ultrafast lasers.

A standardized phase retrieval algorithm is presented and applied to an industry-grade high-energy ultrashort pulsed laser to uncover its spatial phase distribution. We describe in detail how to modify the well-known algorithm in order to characterize particularly strong light sources from intensity measurements only. With complete information about the optical field of the unknown light source at hand, virtual back propagation can reveal weak points in the light path such as apertures or damaged components.

Robotic-assisted versus manual Uro Dyna-CT-guided puncture in an ex-vivo kidney phantom.

INTRODUCTION AND OBJECTIVES: Challenging percutaneous renal punctures to gain access to the kidney requiring guidance by cross-sectional imaging. To test the feasibility of robotic-assisted CT-guided punctures (RP) and compare them with manual laser-guided punctures (MP) with Uro Dyna-CT (Siemens Healthcare Solutions, Erlangen, Germany). MATERIAL AND METHODS: The silicon kidney phantom contained target lesions of three sizes. RP were performed using a robotic assistance system (guidoo, BEC GmbH, Pfullingen, Germany) with a robotic arm (LBR med R800, KUKA AG, Augsburg, Germany) and a navigation software with a cone-beam-CT Artis zeego (Siemens Healthcare GmbH, Erlangen, Germany). MP were performed using the syngo iGuide Uro-Dyna Artis Zee Ceiling CT (Siemens Healthcare Solutions). Three urologists with varying experience performed 20 punctures each. Success rate, puncture accuracy, puncture planning time (PPT), and needle placement time (NPT) were measured and compared with ANOVA and Chi-Square Test. RESULTS: One hundred eighteen punctures with a success rate of 100% for RP and 78% for MP were included. Puncture accuracy was significantly higher for RP. PPT (RP: 238 ± 90s, MP: 104 ± 21s) and NPT (RP: 128 ± 40s, MP: 81 ± 18s) were significantly longer for RP. The outcome variables did not differ significantly with regard to levels of investigators' experience. CONCLUSION: The accuracy of RP was superior to that of MP. This study paves the way for first in-human application of this robotic puncture system.

2,5-[C4+C2] Ringtransformation of Pyrylium Salts with α-Sulfinylacetaldehydes.

A rapid synthesis of chiral sulfoxide-functionalized meta-terphenyl derivatives by a 2,5-[C4+C2] ring transformation reaction of pyrylium salts with in situ generated enantiomerically pure α-sulfinylacetaldehydes is described in this paper. This synthetic method demonstrates, for the first time, the use of α-sulfinylacetaldehydes in a reaction sequence initiated by the nucleophilic attack of pyrylium salts by α-sulfinylcarbanions to generate chiral aromatic systems. The method presented shows a broad applicability starting with various methyl sulfoxides and a number of functionalized pyrylium salts, furnishing meta-terphenyls with complex substitution patterns from readily accessible starting compounds.

Measurement of stress-induced birefringence in a thin-disk laser with full-Stokes polarimetry.

Stress-induced birefringence leads to losses in solid-state laser resonators and amplifiers with polarized output beams. A model of stress-induced birefringence in thin disks is presented, as well as measurements of stress-induced birefringence in a thin disk in a multi-kilowatt oscillator. A full-Stokes imaging polarimeter was developed to enable fast and accurate polarimetric measurements. Experimental and simulated results are in good agreement qualitatively and quantitatively and show that the polarization loss due to stress-induced birefringence is negligible for ytterbium-doped thin disks with a thickness around 100 µm but becomes relevant in thicker disks. It is concluded that stress-induced birefringence should be taken into consideration when designing a thin-disk laser system.

Synthetic Arterial Spin Labeling MRI of the Kidneys for Evaluation of Data Processing Pipeline.

Accurate quantification of perfusion is crucial for diagnosis and monitoring of kidney function. Arterial spin labeling (ASL), a completely non-invasive magnetic resonance imaging technique, is a promising method for this application. However, differences in acquisition (e.g., ASL parameters, readout) and processing (e.g., registration, segmentation) between studies impede the comparison of results. To alleviate challenges arising solely from differences in processing pipelines, synthetic data are of great value. In this work, synthetic renal ASL data were generated using body models from the XCAT phantom and perfusion was added using the general kinetic model. Our in-house developed processing pipeline was then evaluated in terms of registration, quantification, and segmentation using the synthetic data. Registration performance was evaluated qualitatively with line profiles and quantitatively with mean structural similarity index measures (MSSIMs). Perfusion values obtained from the pipeline were compared to the values assumed when generating the synthetic data. Segmentation masks obtained by semi-automated procedure of the processing pipeline were compared to the original XCAT organ masks using the Dice index. Overall, the pipeline evaluation yielded good results. After registration, line profiles were smoother and, on average, MSSIMs increased by 25%. Mean perfusion values for cortex and medulla were close to the assumed perfusion of 250 mL/100 g/min and 50 mL/100 g/min, respectively. Dice indices ranged 0.80-0.93, 0.78-0.89, and 0.64-0.84 for whole kidney, cortex, and medulla, respectively. The generation of synthetic ASL data allows flexible choice of parameters and the generated data are well suited for evaluation of processing pipelines.

Fast CBCT Reconstruction using Convolutional Neural Networks for Arbitrary Robotic C-arm Orbits.

Cone-beam CT (CBCT) with non-circular acquisition orbits has the potential to improve image quality, increase the field-of view, and facilitate minimal interference within an interventional imaging setting. Because time is of the essence in interventional imaging scenarios, rapid reconstruction methods are advantageous. Model-Based Iterative Reconstruction (MBIR) techniques implicitly handle arbitrary geometries; however, the computational burden for these approaches is particularly high. The aim of this work is to extend a previously proposed framework for fast reconstruction of non-circular CBCT trajectories. The pipeline combines a deconvolution operation on the backprojected measurements using an approximate, shift-invariant system response prior to processing with a Convolutional Neural Network (CNN). We trained and evaluated the CNN for this approach using 1800 randomized arbitrary orbits. Noisy projection data were formed from 1000 procedurally generated tetrahedral phantoms as well as anthropomorphic data in the form of 800 CT and CBCT images from the Lung Image Database Consortium Image Collection (LIDC). Using this proposed reconstruction pipeline, computation time was reduced by 90% as compared to MBIR with only minor differences in performance. Quantitative comparisons of nRMSE, FSIM and SSIM are reported. Performance was consistent for projection data simulated with acquisition orbits the network has not previously been trained on. These results suggest the potential for fast processing of arbitrary CBCT trajectory data with reconstruction times that are clinically relevant and applicable - facilitating the application of non-circular orbits in CT image-guided interventions and intraoperative imaging.

Development of an abdominal phantom for the validation of an oligometastatic disease diagnosis workflow.

PURPOSE: The liver is a common site for metastatic disease, which is a challenging and life-threatening condition with a grim prognosis and outcome. We propose a standardized workflow for the diagnosis of oligometastatic disease (OMD), as a gold standard workflow has not been established yet. The envisioned workflow comprises the acquisition of a multimodal image data set, novel image processing techniques, and cone beam computed tomography (CBCT)-guided biopsy for subsequent molecular subtyping. By combining morphological, molecular, and functional information about the tumor, a patient-specific treatment planning is possible. We designed and manufactured an abdominal liver phantom that we used to demonstrate multimodal image acquisition, image processing, and biopsy of the OMD diagnosis workflow. METHODS: The anthropomorphic abdominal phantom contains a rib cage, a portal vein, lungs, a liver with six lesions, and a hepatic vessel tree. This phantom incorporates three different lesion types with varying visibility under computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography CT (PET-CT), which reflects clinical reality. The phantom is puncturable and the size of the corpus and the organs is comparable to those of a real human abdomen. By using several modern additive manufacturing techniques, the manufacturing process is reproducible and allows to incorporate patient-specific anatomies. As a first step of the OMD diagnosis workflow, a preinterventional CT, MRI, and PET-CT data set of the phantom was acquired. The image information was fused using image registration and organ information was extracted via image segmentation. A CBCT-guided needle puncture experiment was performed, where all six liver lesions were punctured with coaxial biopsy needles. RESULTS: Qualitative observation of the image data and quantitative evaluation using contrast-to-noise ratio (CNR) confirms that one lesion type is visible only in MRI and not CT. The other two lesion types are visible in CT and MRI. The CBCT-guided needle placement was performed for all six lesions, including those visible only in MRI and not CBCT. This was possible by successfully merging multimodal preinterventional image data. Lungs, bones, and liver vessels serve as realistic inhibitions during needle path planning. CONCLUSIONS: We have developed a reusable abdominal phantom that has been used to validate a standardized OMD diagnosis workflow. Utilizing the phantom, we have been able to show that a multimodal imaging pipeline is advantageous for a comprehensive detection of liver lesions. In a CBCT-guided needle placement experiment we have punctured lesions that are invisible in CBCT using registered preinterventional MRI scans for needle path planning.

An anthropomorphic pelvis phantom for MR-guided prostate interventions.

PURPOSE: To design and manufacture a pelvis phantom for magnetic resonance (MR)-guided prostate interventions, such as MRGB (MR-guided biopsy) or brachytherapy seed placement. METHODS: The phantom was designed to mimic the human pelvis incorporating bones, bladder, prostate with four lesions, urethra, arteries, veins, and six lymph nodes embedded in ballistic gelatin. A hollow rectum enables transrectal access to the prostate. To demonstrate the feasibility of the phantom for minimal invasive MRI-guided interventions, a targeted inbore MRGB was performed. The needle probe was rectally inserted and guided using an MRI-compatible remote controlled manipulator (RCM). RESULTS: The presented pelvis phantom has realistic imaging properties for MR imaging (MRI), computed tomography (CT) and ultrasound (US). In the targeted inbore MRGB, a prostate lesion was successfully hit with an accuracy of 3.5 mm. The experiment demonstrates that the limited size of the rectum represents a realistic impairment for needle placements. CONCLUSION: The phantom provides a valuable platform for evaluating the performance of MRGB systems. Interventionalists can use the phantom to learn how to deal with challenging situations, without risking harm to patients.

Automated Screening for Abdominal Aortic Aneurysm in CT Scans under Clinical Conditions Using Deep Learning.

Abdominal aortic aneurysms (AAA) may remain clinically silent until they enlarge and patients present with a potentially lethal rupture. This necessitates early detection and elective treatment. The goal of this study was to develop an easy-to-train algorithm which is capable of automated AAA screening in CT scans and can be applied to an intra-hospital environment. Three deep convolutional neural networks (ResNet, VGG-16 and AlexNet) were adapted for 3D classification and applied to a dataset consisting of 187 heterogenous CT scans. The 3D ResNet outperformed both other networks. Across the five folds of the first training dataset it achieved an accuracy of 0.856 and an area under the curve (AUC) of 0.926. Subsequently, the algorithms performance was verified on a second data set containing 106 scans, where it ran fully automated and resulted in an accuracy of 0.953 and an AUC of 0.971. A layer-wise relevance propagation (LRP) made the decision process interpretable and showed that the network correctly focused on the aortic lumen. In conclusion, the deep learning-based screening proved to be robust and showed high performance even on a heterogeneous multi-center data set. Integration into hospital workflow and its effect on aneurysm management would be an exciting topic of future research.

Drug-Related Problems on a Palliative Care Unit.

Pharmacotherapy is essential in palliative medicine. Besides potential benefits, pharmacotherapy also poses potential risks that need to be minimized for patient safety. Pharmacists can play an important role in identifying, solving, and avoiding drug-related problems (DRPs). The aim of this study was to evaluate pharmaceutical interventions on safety of drug therapy in patients in an inpatient palliative care unit. All patients admitted to a palliative care unit over a 12-month period were screened for eligibility (ie, life expectancy >4 weeks). To identify and assess DRPs, patients' pharmacotherapy was evaluated by a pharmacist according to various aspects (eg, drug selection, dose selection, or treatment duration according to the Pharmaceutical Care Network Europe classification for DRPs). During the 12-month period, 41 patients were included. Patients received a median of 11 (range, 1-22) different drugs. Overall, 207 DRPs were documented (median 5 DRPs/patient). After recording a DRP, the pharmacist directly intervened 290 times in order to solve the DRP, which was successful in 181/207 (88%). Clinically relevant DRPs are common in palliative medicine. The systematic assessment can support therapy decisions. This can result in optimized drug therapy, subsequently having a positive effect on symptom control and quality of life.

Quantitative Comparison of Deep Learning-Based Image Reconstruction Methods for Low-Dose and Sparse-Angle CT Applications.

The reconstruction of computed tomography (CT) images is an active area of research. Following the rise of deep learning methods, many data-driven models have been proposed in recent years. In this work, we present the results of a data challenge that we organized, bringing together algorithm experts from different institutes to jointly work on quantitative evaluation of several data-driven methods on two large, public datasets during a ten day sprint. We focus on two applications of CT, namely, low-dose CT and sparse-angle CT. This enables us to fairly compare different methods using standardized settings. As a general result, we observe that the deep learning-based methods are able to improve the reconstruction quality metrics in both CT applications while the top performing methods show only minor differences in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). We further discuss a number of other important criteria that should be taken into account when selecting a method, such as the availability of training data, the knowledge of the physical measurement model and the reconstruction speed.

[MRI-guided minimally invasive treatment of prostate cancer]. / MRT-gestützte minimal-invasive Therapie des Prostatakarzinoms.

CLINICAL/METHODOLOGICAL ISSUE: Multiparametric magnetic resonance imaging (mpMRI) of the prostate plays a crucial role in the diagnosis and local staging of primary prostate cancer. STANDARD RADIOLOGICAL METHODS: Image-guided biopsy techniques such as MRI-ultrasound fusion not only allow guidance for targeted tissue sampling of index lesions for diagnostic confirmation, but also improve the detection of clinically significant prostate cancer. METHODOLOGICAL INNOVATIONS: Minimally invasive, focal therapies of localized prostate cancer complement the treatment spectrum, especially for low- and intermediate-risk patients. PERFORMANCE: In patients of low and intermediate risk, MR-guided, minimally invasive therapies could enable local tumor control, improved functional outcomes and possible subsequent therapy escalation. Further study results related to multimodal approaches and the application of artificial intelligence (AI) by machine and deep learning algorithms will help to leverage the full potential of focal therapies for prostate cancer in the upcoming era of precision medicine. ACHIEVEMENTS: Completion of ongoing randomized trials comparing each minimally invasive therapy approach with established whole-gland procedures is needed before minimally invasive therapies can be implemented into existing treatment guidelines. PRACTICAL RECOMMENDATIONS: This review article highlights minimally invasive therapies of prostate cancer and the key role of mpMRI for planning and conducting these therapies.

Generation of annotated multimodal ground truth datasets for abdominal medical image registration.

PURPOSE: Sparsity of annotated data is a major limitation in medical image processing tasks such as registration. Registered multimodal image data are essential for the diagnosis of medical conditions and the success of interventional medical procedures. To overcome the shortage of data, we present a method that allows the generation of annotated multimodal 4D datasets. METHODS: We use a CycleGAN network architecture to generate multimodal synthetic data from the 4D extended cardiac-torso (XCAT) phantom and real patient data. Organ masks are provided by the XCAT phantom; therefore, the generated dataset can serve as ground truth for image segmentation and registration. Realistic simulation of respiration and heartbeat is possible within the XCAT framework. To underline the usability as a registration ground truth, a proof of principle registration is performed. RESULTS: Compared to real patient data, the synthetic data showed good agreement regarding the image voxel intensity distribution and the noise characteristics. The generated T1-weighted magnetic resonance imaging, computed tomography (CT), and cone beam CT images are inherently co-registered. Thus, the synthetic dataset allowed us to optimize registration parameters of a multimodal non-rigid registration, utilizing liver organ masks for evaluation. CONCLUSION: Our proposed framework provides not only annotated but also multimodal synthetic data which can serve as a ground truth for various tasks in medical imaging processing. We demonstrated the applicability of synthetic data for the development of multimodal medical image registration algorithms.

Thin-disk laser system operating above 10 kW at near fundamental mode beam quality.

We report on a thin-disk laser system with more than 10 kW of output power and a beam quality of M2=1.76 at an overall optical-to-optical efficiency of 51%. The system consists of two thin-disk laser oscillators and a thin-disk multi-pass amplifier system. To reach high output powers while maintaining good beam quality, the output beams of two identical laser oscillators are polarization-combined. Subsequently, the beam is amplified in a multi-pass system. To the best of our knowledge, this is the highest output power achieved for a thin-disk laser system with a beam quality close to fundamental mode.

Implementation of a renal pharmacist consultant service - Information sharing in paper versus digital form.

WHAT IS KNOWN AND OBJECTIVE: Renal impairment (RI) and renal drug-related problems (rDRP) often remain unrecognized in the community setting. A "renal pharmacist consultant service" (RPCS) at hospital admission can support patient safety by detecting rDRP. However, the efficient information sharing from pharmacists to physicians is still discussed. The aim of the study was to test the implementation of a RPCS and its effectiveness on prescription changes and to evaluate two ways of written information sharing with physicians. METHODS: Urological patients with eGFRnon-indexed of 15-59 ml/min and ≥1 drug were reviewed for manifest and potential rDRP at admission by a pharmacist. Written recommendations for dose or drug adaptation were forwarded to physicians comparing two routes: July-September 2017 paper form in handwritten chart; November 2017-January 2018 digital PDF document in the electronic patient information system and e-mail alert. Prescription changes regarding manifest rDRP were evaluated and compared with a previous retrospective study without RPCS. RESULTS AND DISCUSSION: The RPCS detected rDRP in 63 of 234 (26.9%) patients and prepared written recommendations (median 1 rDRP (1-5) per patient) concerning 110 of 538 (20.5%) drugs at admission. For manifest rDRP, acceptance rates of recommendations were 62.5% (paper) vs 42.9% (digital) (P = 0.16). Compared with the retrospective study without RPCS (prescription changes in 21/76 rDRP; 27.6%), correct prescribing concerning manifest rDRP significantly increased by 27.1%. WHAT IS NEW AND CONCLUSION: A RPCS identifies patients at risk for rDRP and significantly increases appropriate prescribing by physicians. In our hospital (no electronic order entry, electronic chart or ward pharmacists), consultations in paper form seem to be superior to a digital PDF document.

23 Na MRI in ischemic stroke: Acquisition time reduction using postprocessing with convolutional neural networks.

Quantitative 23 Na magnetic resonance imaging (MRI) provides tissue sodium concentration (TSC), which is connected to cell viability and vitality. Long acquisition times are one of the most challenging aspects for its clinical establishment. K-space undersampling is an approach for acquisition time reduction, but generates noise and artifacts. The use of convolutional neural networks (CNNs) is increasing in medical imaging and they are a useful tool for MRI postprocessing. The aim of this study is 23 Na MRI acquisition time reduction by k-space undersampling. CNNs were applied to reduce the resulting noise and artifacts. A retrospective analysis from a prospective study was conducted including image datasets from 46 patients (aged 72 ± 13 years; 25 women, 21 men) with ischemic stroke; the 23 Na MRI acquisition time was 10 min. The reconstructions were performed with full dataset (FI) and with a simulated dataset an image that was acquired in 2.5 min (RI). Eight different CNNs with either U-Net-based or ResNet-based architectures were implemented with RI as input and FI as label, using batch normalization and the number of filters as varying parameters. Training was performed with 9500 samples and testing included 400 samples. CNN outputs were evaluated based on signal-to-noise ratio (SNR) and structural similarity (SSIM). After quantification, TSC error was calculated. The image quality was subjectively rated by three neuroradiologists. Statistical significance was evaluated by Student's t-test. The average SNR was 21.72 ± 2.75 (FI) and 10.16 ± 0.96 (RI). U-Nets increased the SNR of RI to 43.99 and therefore performed better than ResNet. SSIM of RI to FI was improved by three CNNs to 0.91 ± 0.03. CNNs reduced TSC error by up to 15%. The subjective rating of CNN-generated images showed significantly better results than the subjective image rating of RI. The acquisition time of 23 Na MRI can be reduced by 75% due to postprocessing with a CNN on highly undersampled data.

Convolutional Neural Network Ensemble Segmentation With Ratio-Based Sampling for the Arteries and Veins in Abdominal CT Scans.

OBJECTIVE: Three-dimensional (3D) blood vessel structure information is important for diagnosis and treatment in various clinical scenarios. We present a fully automatic method for the extraction and differentiation of the arterial and venous vessel trees from abdominal contrast enhanced computed tomography (CE-CT) volumes using convolutional neural networks (CNNs). METHODS: We used a novel ratio-based sampling method to train 2D and 3D versions of the U-Net, the V-Net and the DeepVesselNet. Networks were trained with a combination of the Dice and cross entropy loss. Performance was evaluated on 20 IRCAD subjects. Best performing networks were combined into an ensemble. We investigated seven different weighting schemes. Trained networks were additionally applied to 26 BTCV cases to validate the generalizability. RESULTS: Based on our experiments, the optimal configuration is an equally weighted ensemble of 2D and 3D U- and V-Nets. Our method achieved Dice similarity coefficients of 0.758 ± 0.050 (veins) and 0.838 ± 0.074 (arteries) on the IRCAD data set. Application to the BTCV data set showed a high transfer ability. CONCLUSION: Abdominal vascular structures can be segmented more accurately using ensembles than individual CNNs. 2D and 3D networks have complementary strengths and weaknesses. Our ensemble of 2D and 3D U-Nets and V-Nets in combination with ratio-based sampling achieves a high agreement with manual annotations for both artery and vein segmentation. Our results surpass other state-of-the-art methods. SIGNIFICANCE: Our segmentation pipeline can provide valuable information for the planning of living donor organ transplantations.

Ultrafast green thin-disk laser exceeding 1.4 kW of average power.

We present an ultrafast laser with a near-diffraction-limited beam quality delivering more than 1.4 kW of average power in the visible spectral range. The laser is based on second harmonic generation in a lithium triborate crystal of a Yb:YAG thin-disk multipass amplifier emitting more than 2 kW of average power in the infrared.

Ultrafast thin-disk multipass amplifier with 720 mJ operating at kilohertz repetition rate for applications in atmospheric research.

We present an ultrafast thin-disk based multipass amplifier operating at a wavelength of 1030 nm, designed for atmospheric research in the framework of the Laser Lightning Rod project. The CPA system delivers a pulse energy of 720 mJ and a pulse duration of 920 fs at a repetition rate of 1 kHz. The 240 mJ seed pulses generated by a regenerative amplifier are amplified to the final energy in a multipass amplifier via four industrial thin-disk laser heads. The beam quality factor remains ∼ 2.1 at the output. First results on horizontal long-range filament generation are presented.