Robust stochastic optimization of needle configurations for robotic HDR prostate brachytherapy.

BACKGROUND: Ideally, inverse planning for HDR brachytherapy (BT) should include the pose of the needles which define the trajectory of the source. This would be particularly interesting when considering the additional freedom and accuracy in needle pose which robotic needle placement enables. However, needle insertion typically leads to tissue deformation, resulting in uncertainty regarding the actual pose of the needles with respect to the tissue. PURPOSE: To efficiently address uncertainty during inverse planning for HDR BT in order to robustly optimize the pose of the needles before insertion, that is, to facilitate path planning for robotic needle placement. METHODS: We use a form of stochastic linear programming to model the inverse treatment planning problem. To account for uncertainty, we consider random tissue displacements at the needle tip to simulate tissue deformation. Conventionally for stochastic linear programming, each simulated deformation is reflected by an addition to the linear programming problem which increases problem size and computational complexity substantially and leads to impractical runtime. We propose two efficient approaches for stochastic linear programming. First, we consider averaging dose coefficients to reduce the problem size. Second, we study weighting of the slack variables of an adjusted linear problem to approximate the full stochastic linear program. We compare different approaches to optimize the needle configurations and evaluate their robustness with respect to different amounts of tissue deformation. RESULTS: Our results illustrate that stochastic planning can improve the robustness of the treatment with respect to deformation. The proposed approaches approximating stochastic linear programming better conform to the tissue deformation compared to conventional linear programming. They show good correlation with the plans computed after deformation while reducing the runtime by two orders of magnitude compared to the complete stochastic linear program. Robust optimization of needle configurations takes on average 59.42 s. Skew needle configurations lead to mean coverage improvements compared to parallel needles from 0.39 to 2.94 percentage points, when 8 mm tissue deformation is considered. Considering tissue deformations from 4  to 10 mm during planning with weighted stochastic optimization and skew needles generally results in improved mean coverage from 1.77 to 4.21 percentage points. CONCLUSIONS: We show that efficient stochastic optimization allows selecting needle configurations which are more robust with respect to potentially negative effects of target deformation and displacement on the achievable prescription dose coverage. The approach facilitates robust path planning for robotic needle placement.

A Miniature Dual-Fiber Probe for Quantitative Optical Coherence Elastography.

OBJECTIVE: Optical coherence elastography (OCE) allows for high resolution analysis of elastic tissue properties. However, due to the limited penetration of light into tissue, miniature probes are required to reach structures inside the body, e.g., vessel walls. Shear wave elastography relates shear wave velocities to quantitative estimates of elasticity. Generally, this is achieved by measuring the runtime of waves between two or multiple points. For miniature probes, optical fibers have been integrated and the runtime between the point of excitation and a single measurement point has been considered. This approach requires precise temporal synchronization and spatial calibration between excitation and imaging. METHODS: We present a miniaturized dual-fiber OCE probe of 1 mm diameter allowing for robust shear wave elastography. Shear wave velocity is estimated between two optics and hence independent of wave propagation between excitation and imaging. We quantify the wave propagation by evaluating either a single or two measurement points. Particularly, we compare both approaches to ultrasound elastography. RESULTS: Our experimental results demonstrate that quantification of local tissue elasticities is feasible. For homogeneous soft tissue phantoms, we obtain mean deviations of 0.15 ms-1 and 0.02 ms-1 for single-fiber and dual-fiber OCE, respectively. In inhomogeneous phantoms, we measure mean deviations of up to 0.54 ms-1 and 0.03 ms-1 for single-fiber and dual-fiber OCE, respectively. CONCLUSION: We present a dual-fiber OCE approach that is much more robust in inhomogeneous tissues. Moreover, we demonstrate the feasibility of elasticity quantification in ex-vivo coronary arteries. SIGNIFICANCE: This study introduces an approach for robust elasticity quantification from within the tissue.

Real-Time Motion Analysis With 4D Deep Learning for Ultrasound-Guided Radiotherapy.

Motion compensation in radiation therapy is a challenging scenario that requires estimating and forecasting motion of tissue structures to deliver the target dose. Ultrasound offers direct imaging of tissue in real-time and is considered for image guidance in radiation therapy. Recently, fast volumetric ultrasound has gained traction, but motion analysis with such high-dimensional data remains difficult. While deep learning could bring many advantages, such as fast data processing and high performance, it remains unclear how to process sequences of hundreds of image volumes efficiently and effectively. We present a 4D deep learning approach for real-time motion estimation and forecasting using long-term 4D ultrasound data. Using motion traces acquired during radiation therapy combined with various tissue types, our results demonstrate that long-term motion estimation can be performed markerless with a tracking error of 0.35±0.2 mm and with an inference time of less than 5 ms. Also, we demonstrate forecasting directly from the image data up to 900 ms into the future. Overall, our findings highlight that 4D deep learning is a promising approach for motion analysis during radiotherapy.

Automated isocenter optimization approach for treatment planning for gyroscopic radiosurgery.

BACKGROUND: Radiosurgery is a well-established treatment for various intracranial tumors. In contrast to other established radiosurgery platforms, the new ZAP-X® allows for self-shielding gyroscopic radiosurgery. Here, treatment beams with variable beam-on times are targeted towards a small number of isocenters. The existing planning framework relies on a heuristic based on random selection or manual selection of isocenters, which often leads to a higher plan quality in clinical practice. PURPOSE: The purpose of this work is to study an improved approach for radiosurgery treatment planning, which automatically selects the isocenter locations for the treatment of brain tumors and diseases in the head and neck area using the new system ZAP-X® . METHODS: We propose a new method to automatically obtain the locations of the isocenters, which are essential in gyroscopic radiosurgery treatment planning. First, an optimal treatment plan is created based on a randomly selected nonisocentric candidate beam set. The intersections of the resulting subset of weighted beams are then clustered to find isocenters. This approach is compared to sphere-packing, random selection, and selection by an expert planner for generating isocenters. We retrospectively evaluate plan quality on 10 acoustic neuroma cases. RESULTS: Isocenters acquired by the method of clustering result in clinically viable plans for all 10 test cases. When using the same number of isocenters, the clustering approach improves coverage on average by 31 percentage points compared to random selection, 15 percentage points compared to sphere packing and 2 percentage points compared to the coverage achieved with the expert selected isocenters. The automatic determination of location and number of isocenters leads, on average, to a coverage of 97 ± 3% with a conformity index of 1.22 ± 0.22, while using 2.46 ± 3.60 fewer isocenters than manually selected. In terms of algorithm performance, all plans were calculated in less than 2 min with an average runtime of 75 ± 25 s. CONCLUSIONS: This study demonstrates the feasibility of an automatic isocenter selection by clustering in the treatment planning process with the ZAP-X® system. Even in complex cases where the existing approaches fail to produce feasible plans, the clustering method generates plans that are comparable to those produced by expert selected isocenters. Therefore, our approach can help reduce the effort and time required for treatment planning in gyroscopic radiosurgery.

Computer-aided classification of indirect immunofluorescence patterns on esophagus and split skin for the detection of autoimmune dermatoses.

Autoimmune bullous dermatoses (AIBD) are rare diseases that affect human skin and mucous membranes. Clinically, they are characterized by blister formation and/or erosions. Depending on the structures involved and the depth of blister formation, they are grouped into pemphigus diseases, pemphigoid diseases, and dermatitis herpetiformis. Classification of AIBD into their sub-entities is crucial to guide treatment decisions. One of the most sensitive screening methods for initial differentiation of AIBD is the indirect immunofluorescence (IIF) microscopy on tissue sections of monkey esophagus and primate salt-split skin, which are used to detect disease-specific autoantibodies. Interpretation of IIF patterns requires a detailed examination of the image by trained professionals automating this process is a challenging task with these highly complex tissue substrates, but offers the great advantage of an objective result. Here, we present computer-aided classification of esophagus and salt-split skin IIF images. We show how deep networks can be adapted to the specifics and challenges of IIF image analysis by incorporating segmentation of relevant regions into the prediction process, and demonstrate their high accuracy. Using this semi-automatic extension can reduce the workload of professionals when reading tissue sections in IIF testing. Furthermore, these results on highly complex tissue sections show that further integration of semi-automated workflows into the daily workflow of diagnostic laboratories is promising.

Towards fast adaptive replanning by constrained reoptimization for intra-fractional non-periodic motion during robotic SBRT.

BACKGROUND: Periodic and slow target motion is tracked by synchronous motion of the treatment beams in robotic stereotactic body radiation therapy (SBRT). However, spontaneous, non-periodic displacement or drift of the target may completely change the treatment geometry. Simple motion compensation is not sufficient to guarantee the best possible treatment, since relative motion between the target and organs at risk (OARs) can cause substantial deviations of dose in the OARs. This is especially evident when considering the temporally heterogeneous dose delivery by many focused beams which is typical for robotic SBRT. Instead, a reoptimization of the remaining treatment plan after a large target motion during the treatment could potentially reduce the actually delivered dose to OARs and improve target coverage. This reoptimization task, however, is challenging due to time constraints and limited human supervision. PURPOSE: To study the detrimental effect of spontaneous target motion relative to surrounding OARs on the delivered dose distribution and to analyze how intra-fractional constrained replanning could improve motion compensated robotic SBRT of the prostate. METHODS: We solve the inverse planning problem by optimizing a linear program. When considering intra-fractional target motion resulting in a change of geometry, we adapt the linear program to account for the changed dose coefficients and delivered dose. We reduce the problem size by only reweighting beams from the reference treatment plan without motion. For evaluation we simulate target motion and compare our approach for intra-fractional replanning to the conventional compensation by synchronous beam motion. Results are generated retrospectively on data of 50 patients. RESULTS: Our results show that reoptimization can on average retain or improve coverage in case of target motion compared to the reference plan without motion. Compared to the conventional compensation, coverage is improved from 87.83 % to 94.81 % for large target motion. Our approach for reoptimization ensures fixed upper constraints on the dose even after motion, enabling safer intra-fraction adaption, compared to conventional motion compensation where overdosage in OARs can lead to 21.79 % higher maximum dose than planned. With an average reoptimization time of 6 s for 200 reoptimized beams our approach shows promising performance for intra-fractional application. CONCLUSIONS: We show that intra-fractional constrained reoptimization for adaption to target motion can improve coverage compared to the conventional approach of beam translation while ensuring that upper dose constraints on VOIs are not violated.

Robotic Tissue Sampling for Safe Post-Mortem Biopsy in Infectious Corpses.

In pathology and legal medicine, the histopathological and microbiological analysis of tissue samples from infected deceased is a valuable information for developing treatment strategies during a pandemic such as COVID-19. However, a conventional autopsy carries the risk of disease transmission and may be rejected by relatives. We propose minimally invasive biopsy with robot assistance under CT guidance to minimize the risk of disease transmission during tissue sampling and to improve accuracy. A flexible robotic system for biopsy sampling is presented, which is applied to human corpses placed inside protective body bags. An automatic planning and decision system estimates optimal insertion point. Heat maps projected onto the segmented skin visualize the distance and angle of insertions and estimate the minimum cost of a puncture while avoiding bone collisions. Further, we test multiple insertion paths concerning feasibility and collisions. A custom end effector is designed for inserting needles and extracting tissue samples under robotic guidance. Our robotic post-mortem biopsy (RPMB) system is evaluated in a study during the COVID-19 pandemic on 20 corpses and 10 tissue targets, 5 of them being infected with SARS-CoV-2. The mean planning time including robot path planning is 5.72±167s. Mean needle placement accuracy is 7.19± 422mm.

AI-based optimization for US-guided radiation therapy of the prostate.

OBJECTIVES: Fast volumetric ultrasound presents an interesting modality for continuous and real-time intra-fractional target tracking in radiation therapy of lesions in the abdomen. However, the placement of the ultrasound probe close to the target structures leads to blocking some beam directions. METHODS: To handle the combinatorial complexity of searching for the ultrasound-robot pose and the subset of optimal treatment beams, we combine CNN-based candidate beam selection with simulated annealing for setup optimization of the ultrasound robot, and linear optimization for treatment plan optimization into an AI-based approach. For 50 prostate cases previously treated with the CyberKnife, we study setup and treatment plan optimization when including robotic ultrasound guidance. RESULTS: The CNN-based search substantially outperforms previous randomized heuristics, increasing coverage from 93.66 to 97.20% on average. Moreover, in some cases the total MU was also reduced, particularly for smaller target volumes. Results after AI-based optimization are similar for treatment plans with and without beam blocking due to ultrasound guidance. CONCLUSIONS: AI-based optimization allows for fast and effective search for configurations for robotic ultrasound-guided radiation therapy. The negative impact of the ultrasound robot on the plan quality can successfully be mitigated resulting only in minor differences.

Systematic analysis of volumetric ultrasound parameters for markerless 4D motion tracking.

OBJECTIVES: Motion compensation is an interesting approach to improve treatments of moving structures. For example, target motion can substantially affect dose delivery in radiation therapy, where methods to detect and mitigate the motion are widely used. Recent advances in fast, volumetric ultrasound have rekindled the interest in ultrasound for motion tracking. We present a setup to evaluate ultrasound based motion tracking and we study the effect of imaging rate and motion artifacts on its performance. METHODS: We describe an experimental setup to acquire markerless 4D ultrasound data with precise ground truth from a robot and evaluate different real-world trajectories and system settings toward accurate motion estimation. We analyze motion artifacts in continuously acquired data by comparing to data recorded in a step-and-shoot fashion. Furthermore, we investigate the trade-off between the imaging frequency and resolution. RESULTS: The mean tracking errors show that continuously acquired data leads to similar results as data acquired in a step-and-shoot fashion. We report mean tracking errors up to 2.01 mm and 1.36 mm on the continuous data for the lower and higher resolution, respectively, while step-and-shoot data leads to mean tracking errors of 2.52 mm and 0.98 mm. CONCLUSIONS: We perform a quantitative analysis of different system settings for motion tracking with 4D ultrasound. We can show that precise tracking is feasible and additional motion in continuously acquired data does not impair the tracking. Moreover, the analysis of the frequency resolution trade-off shows that a high imaging resolution is beneficial in ultrasound tracking.

Ultrasound Shear Wave Elasticity Imaging With Spatio-Temporal Deep Learning.

Ultrasound shear wave elasticity imaging is a valuable tool for quantifying the elastic properties of tissue. Typically, the shear wave velocity is derived and mapped to an elasticity value, which neglects information such as the shape of the propagating shear wave or push sequence characteristics. We present 3D spatio-temporal CNNs for fast local elasticity estimation from ultrasound data. This approach is based on retrieving elastic properties from shear wave propagation within small local regions. A large training data set is acquired with a robot from homogeneous gelatin phantoms ranging from 17.42 kPa to 126.05 kPa with various push locations. The results show that our approach can estimate elastic properties on a pixelwise basis with a mean absolute error of 5.01(437) kPa. Furthermore, we estimate local elasticity independent of the push location and can even perform accurate estimates inside the push region. For phantoms with embedded inclusions, we report a 53.93% lower MAE (7.50 kPa) and on the background of 85.24% (1.64 kPa) compared to a conventional shear wave method. Overall, our method offers fast local estimations of elastic properties with small spatio-temporal window sizes.

Vibration enhanced cell growth induced by surface acoustic waves as in vitro wound-healing model.

We report on in vitro wound-healing and cell-growth studies under the influence of radio-frequency (rf) cell stimuli. These stimuli are supplied either by piezoactive surface acoustic waves (SAWs) or by microelectrode-generated electric fields, both at frequencies around 100 MHz. Employing live-cell imaging, we studied the time- and power-dependent healing of artificial wounds on a piezoelectric chip for different cell lines. If the cell stimulation is mediated by piezomechanical SAWs, we observe a pronounced, significant maximum of the cell-growth rate at a specific SAW amplitude, resulting in an increase of the wound-healing speed of up to 135 ± 85% as compared to an internal reference. In contrast, cells being stimulated only by electrical fields of the same magnitude as the ones exposed to SAWs exhibit no significant effect. In this study, we investigate this effect for different wavelengths, amplitude modulation of the applied electrical rf signal, and different wave modes. Furthermore, to obtain insight into the biological response to the stimulus, we also determined both the cell-proliferation rate and the cellular stress levels. While the proliferation rate is significantly increased for a wide power range, cell stress remains low and within the normal range. Our findings demonstrate that SAW-based vibrational cell stimulation bears the potential for an alternative method to conventional ultrasound treatment, overcoming some of its limitations.

Feasibility and analysis of CNN-based candidate beam generation for robotic radiosurgery.

PURPOSE: Robotic radiosurgery offers the flexibility of a robotic arm to enable high conformity to the target and a steep dose gradient. However, treatment planning becomes a computationally challenging task as the search space for potential beam directions for dose delivery is arbitrarily large. We propose an approach based on deep learning to improve the search for treatment beams. METHODS: In clinical practice, a set of candidate beams generated by a randomized heuristic forms the basis for treatment planning. We use a convolutional neural network to identify promising candidate beams. Using radiological features of the patient, we predict the influence of a candidate beam on the delivered dose individually and let this prediction guide the selection of candidate beams. Features are represented as projections of the organ structures which are relevant during planning. Solutions to the inverse planning problem are generated for random and CNN-predicted candidate beams. RESULTS: The coverage increases from 95.35% to 97.67% for 6000 heuristically and CNN-generated candidate beams, respectively. Conversely, a similar coverage can be achieved for treatment plans with half the number of candidate beams. This results in a patient-dependent reduced averaged computation time of 20.28%-45.69%. The number of active treatment beams can be reduced by 11.35% on average, which reduces treatment time. Constraining the maximum number of candidate beams per beam node can further improve the average coverage by 0.75 percentage points for 6000 candidate beams. CONCLUSIONS: We show that deep learning based on radiological features can substantially improve treatment plan quality, reduce computation runtime, and treatment time compared to the heuristic approach used in clinics.

Analysis and optimization of the robot setup for robotic-ultrasound-guided radiation therapy.

PURPOSE: Robotic ultrasound promises continuous, volumetric, and non-ionizing tracking of organ motion during radiation therapy. However, placement of the robot is critical because it is radio-opaque and might severely influence the achievable dose distribution. METHODS: We propose two heuristic optimization strategies for automatic placement of an ultrasound robot around a patient. Considering a kinematically redundant robot arm, we compare a generic approach based on stochastic search and a more problem-specific segmentwise construction approach. The former allows for multiple elbow configurations while the latter is deterministic. Additionally, we study different objective functions guiding the search. Our evaluation is based on data for ten actual prostate cancer cases and we compare the resulting plan quality for both methods to manually chosen robot configurations previously proposed. RESULTS: The mean improvements in the treatment planning objective value with respect to the best manually selected robot position and a single elbow configuration range from 8.2 to 32.8% and 8.5 to 15.5% for segmentwise construction and stochastic search, respectively. Considering three different elbow configurations, the stochastic search results in better objective values in 80% of the cases, with 30% being significantly better. The optimization strategies are robust with respect to beam sampling and transducer orientation and using previous optimization results as starting point for stochastic search typically results in better solutions compared to random starting points. CONCLUSION: We propose a robust and generic optimization scheme, which can be used to optimize the robot placement for robotic ultrasound guidance in radiation therapy. The automatic optimization further mitigates the impact of robotic ultrasound on the treatment plan quality.

Money demand under free banking: Switzerland 1851-1906.

This paper studies money demand in Switzerland under free banking before the establishment of the Swiss National Bank. We find that, in addition to income, the banks' balance-sheet-to-GDP ratio and the number of banks were important determinants of long-run money demand. The former variable also played an important role in the monetary adjustment process. We also detect a strong positive long-run impact of real income on the bank's balance-sheet-total-to-GDP ratio and a strong long-run influence of real income and the interest rate spread on the number of banks.

Impact of robotic ultrasound image guidance on plan quality in SBRT of the prostate.

OBJECTIVE: Ultrasound provides good image quality, fast volumetric imaging and is established for abdominal image guidance. Robotic transducer placement may facilitate intrafractional motion compensation in radiation therapy. We consider integration with the CyberKnife and study whether the kinematic redundancy of a seven-degrees-of-freedom robot allows for acceptable plan quality for prostate treatments. METHODS: Reference treatment plans were generated for 10 prostate cancer cases previously treated with the CyberKnife. Considering transducer and prostate motion by different safety margins, 10 different robot poses, and 3 different elbow configurations, we removed all beams colliding with robot or transducer. For each combination, plans were generated using the same strict dose constraints and the objective to maximize the target coverage. Additionally, plans for the union of all unblocked beams were generated. RESULTS: In 9 cases the planning target coverage with the ultrasound robot was within 1.1 percentage points of the reference coverage. It was 1.7 percentage points for one large prostate. For one preferable robot position, kinematic redundancy decreased the average number of blocked beam directions from 23.1 to 14.5. CONCLUSION: The impact of beam blocking can largely be offset by treatment planning and using a kinematically redundant robot. Plan quality can be maintained by carefully choosing the ultrasound robot position and pose. For smaller planning target volumes the difference in coverage is negligible for safety margins of up to 35 mm. Advances in knowledge: Integrating a robot for online intrafractional image guidance based on ultrasound can be realized while maintaining acceptable plan quality for prostate cancer treatments with the CyberKnife.

Performance analysis of automated evaluation of Crithidia luciliae-based indirect immunofluorescence tests in a routine setting - strengths and weaknesses.

BACKGROUND: Antibodies directed against dsDNA are a highly specific diagnostic marker for the presence of systemic lupus erythematosus and of particular importance in its diagnosis. To assess anti-dsDNA antibodies, the Crithidia luciliae-based indirect immunofluorescence test (CLIFT) is one of the assays considered to be the best choice. To overcome the drawback of subjective result interpretation that inheres indirect immunofluorescence assays in general, automated systems have been introduced into the market during the last years. Among these systems is the EUROPattern Suite, an advanced automated fluorescence microscope equipped with different software packages, capable of automated pattern interpretation and result suggestion for ANA, ANCA and CLIFT analysis. METHODS: We analyzed the performance of the EUROPattern Suite with its automated fluorescence interpretation for CLIFT in a routine setting, reflecting the everyday life of a diagnostic laboratory. Three hundred and twelve consecutive samples were collected, sent to the Central Diagnostic Laboratory of the Maastricht University Medical Centre with a request for anti-dsDNA analysis over a period of 7 months. RESULTS: Agreement between EUROPattern assay analysis and the visual read was 93.3%. Sensitivity and specificity were 94.1% and 93.2%, respectively. The EUROPattern Suite performed reliably and greatly supported result interpretation. CONCLUSIONS: Automated image acquisition is readily performed and automated image classification gives a reliable recommendation for assay evaluation to the operator. The EUROPattern Suite optimizes workflow and contributes to standardization between different operators or laboratories.

Robotic ultrasound-guided SBRT of the prostate: feasibility with respect to plan quality.

PURPOSE: Advances in radiation therapy delivery systems have enabled motion compensated SBRT of the prostate. A remaining challenge is the integration of fast, non-ionizing volumetric imaging. Recently, robotic ultrasound has been proposed as an intra-fraction image modality. We study the impact of integrating a light-weight robotic arm carrying an ultrasound probe with the CyberKnife system. Particularly, we analyze the effect of different robot poses on the plan quality. METHODS: A method to detect the collision of beams with the robot or the transducer was developed and integrated into our treatment planning system. A safety margin accounts for beam motion and uncertainties. Using strict dose bounds and the objective to maximize target coverage, we generated a total of 7650 treatment plans for five different prostate cases. For each case, ten different poses of the ultrasound robot and transducer were considered. The effect of different sets of beam source positions and different motion margins ranging from 5 to 50 mm was analyzed. RESULTS: Compared to reference plans without the ultrasound robot, the coverage typically drops for all poses. Depending on the patient, the robot pose, and the motion margin, the reduction in coverage may be up to 50 % points. However, for all patient cases, there exist poses for which the loss in coverage was below 1 % point for motion margins of up to 20 mm. In general, there is a positive correlation between the number of treatment beams and the coverage. CONCLUSION: While the blocking of beam directions has a negative effect on the plan quality, the results indicate that a careful choice of the ultrasound robot's pose and a large solid angle covered by beam starting positions can offset this effect. Identifying robot poses that yield acceptable plan quality and allow for intra-fraction ultrasound image guidance, therefore, appears feasible.

Multiparametric serological testing in autoimmune encephalitis using computer-aided immunofluorescence microscopy (CAIFM).

Autoantibodies against neuronal cell surface antigens are tightly associated with immunotherapy-responsive autoimmune encephalitis, and a considerable number of corresponding autoantigens has been identified in recent years. Most patients initially present with overlapping symptoms, and a broad range of autoantibodies has to be considered to establish the correct diagnosis and initiate treatment as soon as possible to prevent irreversible and sometimes even life-threatening damage to the brain. Recombinant cell-based immunofluorescence allows to authentically present fragile membrane-associated surface antigens and, in combination with multiparametric analysis in the form of biochip mosaics, has turned out to be highly beneficial for parallel and prompt determination of anti-neuronal autoantibodies and comprehensive differential diagnostics. For the evaluation of recombinant cell-based IIFT, a semi-automated novel function was introduced into an established platform for computer-aided immunofluorescence microscopy. The system facilitates the microscopic analysis of the tests and supports the laboratory personnel in the rapid issuance of diagnostic findings, which is of major importance for autoimmune encephalitis patients since timely initiation of treatment may lead to their full recovery.

Automated Evaluation of Crithidia luciliae Based Indirect Immunofluorescence Tests: A Novel Application of the EUROPattern-Suite Technology.

Systemic lupus erythematosus (SLE) is a severe rheumatic autoimmune disease with various clinical manifestations. Anti-dsDNA antibodies are an important immunological hallmark of SLE and their occurrence represents a major criterion for the diagnosis. Among the commonly applied test systems for determination of anti-dsDNA antibodies, the indirect immunofluorescence test (IIFT) using the flagellated kinetoplastida Crithidia luciliae is considered to be highly disease specific at moderate sensitivity. Since IIFT, however, is claimed to be affected by subjective interpretation and a lack of standardization, there has been an increasing demand for automated pattern interpretation of immunofluorescence reactions in recent years. Corresponding platforms are already available for evaluation of anti-nuclear antibody (ANA) IIFT on HEp-2 cells, the recommended "gold standard" for ANA screening in the diagnosis of various systemic rheumatic autoimmune diseases. For one of these systems, the "EUROPattern-Suite" computer-aided immunofluorescence microscopy (CAIFM), automated interpretation of microscopic fluorescence patterns was extended to the Crithidia luciliae based anti-dsDNA IIFT.

Perioperative management of patients with severe pulmonary hypertension in major orthopedic surgery: experience-based recommendations.

INTRODUCTION: It is known that pulmonary hypertension is associated with worse outcome in both cardiac and non-cardiac surgery. The aims of our retrospective analysis were to evaluate the outcomes of our patients with pulmonary hypertension undergoing major orthopedic surgery and to give experience-based recommendations for the perioperative management. MATERIAL AND METHODS: From 92 patients with pulmonary hypertension undergoing different kinds of surgical procedures from 2011-2014 in a tertiary academic hospital we evaluated 16 patients with major orthopedic surgery for perioperative morbidity and mortality. RESULTS: Regarding the in-hospital morbidity and mortality, one patient died postoperatively due to pulmonary infection and right heart failure (6.25%) and 6 patients suffered significant postoperative complications (37.5%; bleeding = 1, infection = 1, wound healing deficits = 3; dysrhythmia = 1). CONCLUSION: Our data show that major orthopedic surgery is feasible with satisfactory outcome even in cases of severe pulmonary hypertension by an individualized, disease-adapted interdisciplinary treatment concept.