Smart Sensor Control and Monitoring of an Automated Cell Expansion Process.

Immune therapy for cancer patients is a new and promising area that in the future may complement traditional chemotherapy. The cell expansion phase is a critical part of the process chain to produce a large number of high-quality, genetically modified immune cells from an initial sample from the patient. Smart sensors augment the ability of the control and monitoring system of the process to react in real-time to key control parameter variations, adapt to different patient profiles, and optimize the process. The aim of the current work is to develop and calibrate smart sensors for their deployment in a real bioreactor platform, with adaptive control and monitoring for diverse patient/donor cell profiles. A set of contrasting smart sensors has been implemented and tested on automated cell expansion batch runs, which incorporate advanced data-driven machine learning and statistical techniques to detect variations and disturbances of the key system features. Furthermore, a 'consensus' approach is applied to the six smart sensor alerts as a confidence factor which helps the human operator identify significant events that require attention. Initial results show that the smart sensors can effectively model and track the data generated by the Aglaris FACER bioreactor, anticipate events within a 30 min time window, and mitigate perturbations in order to optimize the key performance indicators of cell quantity and quality. In quantitative terms for event detection, the consensus for sensors across batch runs demonstrated good stability: the AI-based smart sensors (Fuzzy and Weighted Aggregation) gave 88% and 86% consensus, respectively, whereas the statistically based (Stability Detector and Bollinger) gave 25% and 42% consensus, respectively, the average consensus for all six being 65%. The different results reflect the different theoretical approaches. Finally, the consensus of batch runs across sensors gave even higher stability, ranging from 57% to 98% with an average consensus of 80%.

Acquiring Process Knowledge in Extrusion-Based Additive Manufacturing via Interpretable Machine Learning.

Additive manufacturing (AM), especially the extrusion-based process, has many process parameters which influence the resulting part properties. Those parameters have complex interdependencies and are therefore difficult if not impossible to model analytically. Machine learning (ML) is a promising approach to find suitable combinations of process parameters for manufacturing a part with desired properties without having to analytically model the process in its entirety. However, ML-based approaches are typically black box models. Therefore, it is difficult to verify their output and to derive process knowledge from such approaches. This study uses interpretable machine learning methods to derive process knowledge from interpreted data sets by analyzing the model's feature importance. Using fused layer modeling (FLM) as an exemplary manufacturing technology, it is shown that the process can be characterized entirely. Therefore, sweet spots for process parameters can be determined objectively. Additionally, interactions between parameters are discovered, and the basis for further investigations is established.

Young's Modulus-Independent Determination of Fibre Parameters for Rayleigh-Based Optical Frequency Domain Reflectometry from Cryogenic Temperatures up to 353 K.

The magnetic spectrometer AMS-100, which includes a superconducting coil, is designed to measure cosmic rays and detect cosmic antimatter in space. This extreme environment requires a suitable sensing solution to monitor critical changes in the structure such as the beginning of a quench in the superconducting coil. Rayleigh-scattering-based distributed optical fibre sensors (DOFS) fulfil the high requirements for these extreme conditions but require precise calibration of the temperature and strain coefficients of the optical fibre. Therefore, the fibre-dependent strain and temperature coefficients KT and Kϵ for the temperature range from 77 K to 353 K were investigated in this study. The fibre was integrated into an aluminium tensile test sample with well-calibrated strain gauges to determine the fibre's Kϵ independently of its Young's modulus. Simulations were used to validate that the strain caused by changes in temperature or mechanical conditions was the same in the optical fibre as in the aluminium test sample. The results indicated a linear temperature dependence of Kϵ and a non-linear temperature dependence of KT. With the parameters presented in this work, it was possible to accurately determine the strain or temperature of an aluminium structure over the entire temperature range from 77 K to 353 K using the DOFS.

Transformative Materials to Create 3D Functional Human Tissue Models In Vitro in a Reproducible Manner.

Recreating human tissues and organs in the petri dish to establish models as tools in biomedical sciences has gained momentum. These models can provide insight into mechanisms of human physiology, disease onset, and progression, and improve drug target validation, as well as the development of new medical therapeutics. Transformative materials play an important role in this evolution, as they can be programmed to direct cell behavior and fate by controlling the activity of bioactive molecules and material properties. Using nature as an inspiration, scientists are creating materials that incorporate specific biological processes observed during human organogenesis and tissue regeneration. This article presents the reader with state-of-the-art developments in the field of in vitro tissue engineering and the challenges related to the design, production, and translation of these transformative materials. Advances regarding (stem) cell sources, expansion, and differentiation, and how novel responsive materials, automated and large-scale fabrication processes, culture conditions, in situ monitoring systems, and computer simulations are required to create functional human tissue models that are relevant and efficient for drug discovery, are described. This paper illustrates how these different technologies need to converge to generate in vitro life-like human tissue models that provide a platform to answer health-based scientific questions.

Adaptive phase contrast microscopy to compensate for the meniscus effect.

Phase contrast is one of the most important microscopic methods for making visible transparent, unstained cells. Cell cultures are often cultivated in microtiter plates, consisting of several cylindrical wells. The surface tension of the culture medium forms a liquid lens within the well, causing phase contrast conditions to fail in the more curved edge areas, preventing cell observation. Adaptive phase contrast microscopy is a method to strongly increase the observable area by optically compensating for the meniscus effect. The microscope's condenser annulus is replaced by a transmissive LCD to allow dynamic changes. A deformable, liquid-filled prism is placed in the illumination path. The prism's surface angle is adaptively inclined to refract transmitted light so that the tangential angle of the liquid lens can be compensated. Besides the observation of the phase contrast image, a beam splitter allows to simultaneously view condenser annulus and phase ring displacement. Algorithms analyze the displacement to dynamically adjust the LCD and prism to guarantee phase contrast conditions. Experiments show a significant increase in observable area, especially for small well sizes. For 96-well-plates, more than twelve times the area can be examined under phase contrast conditions instead of standard phase contrast microscopy.

Optical coherence tomography combined with convolutional neural networks can differentiate between intrahepatic cholangiocarcinoma and liver parenchyma ex vivo.

PURPOSE: Surgical resection with complete tumor excision (R0) provides the best chance of long-term survival for patients with intrahepatic cholangiocarcinoma (iCCA). A non-invasive imaging technology, which could provide quick intraoperative assessment of resection margins, as an adjunct to histological examination, is optical coherence tomography (OCT). In this study, we investigated the ability of OCT combined with convolutional neural networks (CNN), to differentiate iCCA from normal liver parenchyma ex vivo. METHODS: Consecutive adult patients undergoing elective liver resections for iCCA between June 2020 and April 2021 (n = 11) were included in this study. Areas of interest from resection specimens were scanned ex vivo, before formalin fixation, using a table-top OCT device at 1310 nm wavelength. Scanned areas were marked and histologically examined, providing a diagnosis for each scan. An Xception CNN was trained, validated, and tested in matching OCT scans to their corresponding histological diagnoses, through a 5 × 5 stratified cross-validation process. RESULTS: Twenty-four three-dimensional scans (corresponding to approx. 85,603 individual) from ten patients were included in the analysis. In 5 × 5 cross-validation, the model achieved a mean F1-score, sensitivity, and specificity of 0.94, 0.94, and 0.93, respectively. CONCLUSION: Optical coherence tomography combined with CNN can differentiate iCCA from liver parenchyma ex vivo. Further studies are necessary to expand on these results and lead to innovative in vivo OCT applications, such as intraoperative or endoscopic scanning.

LIFTOSCOPE: development of an automated AI-based module for time-effective and contactless analysis and isolation of cells in microtiter plates.

BACKGROUND: The cultivation, analysis, and isolation of single cells or cell cultures are fundamental to modern biological and medical processes. The novel LIFTOSCOPE technology aims to integrate analysis and isolation into one versatile, fully automated device. METHODS: LIFTOSCOPE's three core technologies are high-speed microscopy for rapid full-surface imaging of cell culture vessels, AI-based semantic segmentation of microscope images for localization and evaluation of cells, and laser-induced forward transfer (LIFT) for contact-free isolation of cells and cell clusters. LIFT transfers cells from a standard microtiter plate (MTP) across an air gap to a receiver plate, from where they can be further cultivated. The LIFT laser is integrated into the optical path of an inverse microscope, allowing to switch quickly between microscopic observation and cell transfer. RESULTS: Tests of the individual process steps prove the feasibility of the concept. A prototype setup shows the compatibility of the microscope stage with the LIFT laser. A specifically designed MTP adapter to hold a receiver plate has been designed and successfully used for material transfers. A suitable AI algorithm has been found for cell selection. CONCLUSION: LIFTOSCOPE speeds up cell cultivation and analysis with a target process time of 10 minutes, which can be achieved if the cell transfer is sped up using a more efficient path-finding algorithm. Some challenges remain, like finding a suitable cell transfer medium. SIGNIFICANCE: The LIFTOSCOPE system can be used to extend existing cell cultivation systems and microscopes for fully automated biotechnological applications.

Optical coherence tomography and convolutional neural networks can differentiate colorectal liver metastases from liver parenchyma ex vivo.

PURPOSE: Optical coherence tomography (OCT) is an imaging technology based on low-coherence interferometry, which provides non-invasive, high-resolution cross-sectional images of biological tissues. A potential clinical application is the intraoperative examination of resection margins, as a real-time adjunct to histological examination. In this ex vivo study, we investigated the ability of OCT to differentiate colorectal liver metastases (CRLM) from healthy liver parenchyma, when combined with convolutional neural networks (CNN). METHODS: Between June and August 2020, consecutive adult patients undergoing elective liver resections for CRLM were included in this study. Fresh resection specimens were scanned ex vivo, before fixation in formalin, using a table-top OCT device at 1310 nm wavelength. Scanned areas were marked and histologically examined. A pre-trained CNN (Xception) was used to match OCT scans to their corresponding histological diagnoses. To validate the results, a stratified k-fold cross-validation (CV) was carried out. RESULTS: A total of 26 scans (containing approx. 26,500 images in total) were obtained from 15 patients. Of these, 13 were of normal liver parenchyma and 13 of CRLM. The CNN distinguished CRLM from healthy liver parenchyma with an F1-score of 0.93 (0.03), and a sensitivity and specificity of 0.94 (0.04) and 0.93 (0.04), respectively. CONCLUSION: Optical coherence tomography combined with CNN can distinguish between healthy liver and CRLM with great accuracy ex vivo. Further studies are needed to improve upon these results and develop in vivo diagnostic technologies, such as intraoperative scanning of resection margins.

Needle to needle robot-assisted manufacture of cell therapy products.

Advanced therapeutic medicinal products (ATMPs) have emerged as novel therapies for untreatable diseases, generating the need for large volumes of high-quality, clinically-compliant GMP cells to replace costly, high-risk and limited scale manual expansion processes. We present the design of a fully automated, robot-assisted platform incorporating the use of multiliter stirred tank bioreactors for scalable production of adherent human stem cells. The design addresses a needle-to-needle closed process incorporating automated bone marrow collection, cell isolation, expansion, and collection into cryovials for patient delivery. AUTOSTEM, a modular, adaptable, fully closed system ensures no direct operator interaction with biological material; all commands are performed through a graphic interface. Seeding of source material, process monitoring, feeding, sampling, harvesting and cryopreservation are automated within the closed platform, comprising two clean room levels enabling both open and closed processes. A bioprocess based on human MSCs expanded on microcarriers was used for proof of concept. Utilizing equivalent culture parameters, the AUTOSTEM robot-assisted platform successfully performed cell expansion at the liter scale, generating results comparable to manual production, while maintaining cell quality postprocessing.

Toward Rapid, Widely Available Autologous CAR-T Cell Therapy - Artificial Intelligence and Automation Enabling the Smart Manufacturing Hospital.

CAR-T cell therapy is a promising treatment for acute leukemia and lymphoma. CAR-T cell therapies take a pioneering role in autologous gene therapy with three EMA-approved products. However, the chance of clinical success remains relatively low as the applicability of CAR-T cell therapy suffers from long, labor-intensive manufacturing and a lack of comprehensive insight into the bioprocess. This leads to high manufacturing costs and limited clinical success, preventing the widespread use of CAR-T cell therapies. New manufacturing approaches are needed to lower costs to improve manufacturing capacity and shorten provision times. Semi-automated devices such as the Miltenyi Prodigy® were developed to reduce hands-on production time. However, these devices are not equipped with the process analytical technology necessary to fully characterize and control the process. An automated AI-driven CAR-T cell manufacturing platform in smart manufacturing hospitals (SMH) is being developed to address these challenges. Automation will increase the cost-effectiveness and robustness of manufacturing. Using Artificial Intelligence (AI) to interpret the data collected on the platform will provide valuable process insights and drive decisions for process optimization. The smart integration of automated CAR-T cell manufacturing platforms into hospitals enables the independent manufacture of autologous CAR-T cell products. In this perspective, we will be discussing current challenges and opportunities of the patient-specific but highly automated, AI-enabled CAR-T cell manufacturing. A first automation concept will be shown, including a system architecture based on current Industry 4.0 approaches for AI integration.

Cell Cluster Sorting in Automated Differentiation of Patient-specific Induced Pluripotent Stem Cells Towards Blood Cells.

Induced pluripotent stem cells (iPS cells) represent a particularly versatile stem cell type for a large array of applications in biology and medicine. Taking full advantage of iPS cell technology requires high throughput and automated iPS cell culture and differentiation. We present an automated platform for efficient and robust iPS cell culture and differentiation into blood cells. We implemented cell cluster sorting for analysis and sorting of iPS cell clusters in order to establish clonal iPS cell lines with high reproducibility and efficacy. Patient-specific iPS cells were induced to differentiate towards hematopoietic cells via embryoid body (EB) formation. EB size impacts on iPS cell differentiation and we applied cell cluster sorting to obtain EB of defined size for efficient blood cell differentiation. In summary, implementing cell cluster sorting into the workflow of iPS cell cloning, growth and differentiation represent a valuable add-on for standard and automated iPS cell handling.

From Single Batch to Mass Production-Automated Platform Design Concept for a Phase II Clinical Trial Tissue Engineered Cartilage Product.

Advanced Therapy Medicinal Products (ATMP) provide promising treatment options particularly for unmet clinical needs, such as progressive and chronic diseases where currently no satisfying treatment exists. Especially from the ATMP subclass of Tissue Engineered Products (TEPs), only a few have yet been translated from an academic setting to clinic and beyond. A reason for low numbers of TEPs in current clinical trials and one main key hurdle for TEPs is the cost and labor-intensive manufacturing process. Manual production steps require experienced personnel, are challenging to standardize and to scale up. Automated manufacturing has the potential to overcome these challenges, toward an increasing cost-effectiveness. One major obstacle for automation is the control and risk prevention of cross contaminations, especially when handling parallel production lines of different patient material. These critical steps necessitate validated effective and efficient cleaning procedures in an automated system. In this perspective, possible technologies, concepts and solutions to existing ATMP manufacturing hurdles are discussed on the example of a late clinical phase II trial TEP. In compliance to Good Manufacturing Practice (GMP) guidelines, we propose a dual arm robot based isolator approach. Our novel concept enables complete process automation for adherent cell culture, and the translation of all manual process steps with standard laboratory equipment. Moreover, we discuss novel solutions for automated cleaning, without the need for human intervention. Consequently, our automation concept offers the unique chance to scale up production while becoming more cost-effective, which will ultimately increase TEP availability to a broader number of patients.

Daratumumab therapy for post-HSCT immune-mediated cytopenia: experiences from two pediatric cases and review of literature.

BACKGROUND: Immune-mediated cytopenias (AIC) are challenging complications following allogeneic hematopoietic stem cell transplantation (HSCT). While broad-acting immunosuppressive agents like corticosteroids are often standard of care, several novel therapies which target specific immunological pathways have recently been developed and provide hope for patients with steroid-refractory courses and may limit long-term toxicity. The successful off-label use of the plasma cell depleting anti-CD38 antibody daratumumab was published in several case reports, suggesting efficacy, i.e., in patients with antibody-mediated AIC refractory to previous B cell depletion. We want to share our experience with two children, whom we treated with daratumumab, including one fatal course with uncontrolled disease. Given the absence of substantial data from HSCT registries or prospective trials, we furthermore provide a critical review of the literature on daratumumab treatment of AIC. CASE PRESENTATIONS: Patient 1 (P1), an 11-year-old girl with lipopolysaccharide-responsive and beige-like anchor protein (LRBA) deficiency who developed immune-mediated thrombocytopenia (AIT) from day +58 after HSCT, showed a complete response to daratumumab after the fourth of six total daratumumab doses. She remains transfusion independent for over a year of follow-up. Previously, her thrombocytopenia was refractory to corticosteroids, rituximab, intravenous immunoglobulins (IVIG), eltrombopag, cyclosporine A, and sirolimus. Patient 2 (P2), a 6-year-old boy with CD40 ligand (CD40L) deficiency, developed both AIT and hemolytic anemia (AIHA) after HSCT on days +58 and +83, respectively, and was also treated with daratumumab after being previously refractory to prednisolone, rituximab, and IVIG. Yet, he did neither respond to daratumumab nor the concomitantly administered methyprednisolone pulse, plasmapheresis, and eculizumab and succumbed due to refractory disease. CONCLUSION: Reviewing the literature on the use of daratumumab for refractory AIC post-HSCT, we consider daratumumab a promising agent for this life-threatening disorder: ten of the twelve patients reached transfusion independency in the literature. However, treatment failures are likely to be underreported. Thus, controlled trials are needed to explore the safety and efficacy of daratumumab in this rare post-HSCT complication.

Deep-learning-based multi-class segmentation for automated, non-invasive routine assessment of human pluripotent stem cell culture status.

Human induced pluripotent stem cells (hiPSCs) are capable of differentiating into a variety of human tissue cells. They offer new opportunities for personalized medicine and drug screening. This requires large quantities of high quality hiPSCs, obtainable only via automated cultivation. One of the major requirements of an automated cultivation is a regular, non-invasive analysis of the cell condition, e.g. by whole-well microscopy. However, despite the urgency of this requirement, there are currently no automatic, image-processing-based solutions for multi-class routine quantification of this nature. This paper describes a method to fully automate the cell state recognition based on phase contrast microscopy and deep-learning. This approach can be used for in process control during an automated hiPSC cultivation. The U-Net based algorithm is capable of segmenting important parameters of hiPSC colony formation and can discriminate between the classes hiPSC colony, single cells, differentiated cells and dead cells. The model achieves more accurate results for the classes hiPSC colonies, differentiated cells, single hiPSCs and dead cells than visual estimation by a skilled expert. Furthermore, parameters for each hiPSC colony are derived directly from the classification result such as roundness, size, center of gravity and inclusions of other cells. These parameters provide localized information about the cell state and enable well based treatment of the cell culture in automated processes. Thus, the model can be exploited for routine, non-invasive image analysis during an automated hiPSC cultivation. This facilitates the generation of high quality hiPSC derived products for biomedical purposes.

The StemCellFactory: A Modular System Integration for Automated Generation and Expansion of Human Induced Pluripotent Stem Cells.

While human induced pluripotent stem cells (hiPSCs) provide novel prospects for disease-modeling, the high phenotypic variability seen across different lines demands usage of large hiPSC cohorts to decipher the impact of individual genetic variants. Thus, a much higher grade of parallelization, and throughput in the production of hiPSCs is needed, which can only be achieved by implementing automated solutions for cell reprogramming, and hiPSC expansion. Here, we describe the StemCellFactory, an automated, modular platform covering the entire process of hiPSC production, ranging from adult human fibroblast expansion, Sendai virus-based reprogramming to automated isolation, and parallel expansion of hiPSC clones. We have developed a feeder-free, Sendai virus-mediated reprogramming protocol suitable for cell culture processing via a robotic liquid handling unit that delivers footprint-free hiPSCs within 3 weeks with state-of-the-art efficiencies. Evolving hiPSC colonies are automatically detected, harvested, and clonally propagated in 24-well plates. In order to ensure high fidelity performance, we have implemented a high-speed microscope for in-process quality control, and image-based confluence measurements for automated dilution ratio calculation. This confluence-based splitting approach enables parallel, and individual expansion of hiPSCs in 24-well plates or scale-up in 6-well plates across at least 10 passages. Automatically expanded hiPSCs exhibit normal growth characteristics, and show sustained expression of the pluripotency associated stem cell marker TRA-1-60 over at least 5 weeks (10 passages). Our set-up enables automated, user-independent expansion of hiPSCs under fully defined conditions, and could be exploited to generate a large number of hiPSC lines for disease modeling, and drug screening at industrial scale, and quality.

Automation, Monitoring, and Standardization of Cell Product Manufacturing.

Although regenerative medicine products are at the forefront of scientific research, technological innovation, and clinical translation, their reproducibility and large-scale production are compromised by automation, monitoring, and standardization issues. To overcome these limitations, new technologies at software (e.g., algorithms and artificial intelligence models, combined with imaging software and machine learning techniques) and hardware (e.g., automated liquid handling, automated cell expansion bioreactor systems, automated colony-forming unit counting and characterization units, and scalable cell culture plates) level are under intense investigation. Automation, monitoring and standardization should be considered at the early stages of the developmental cycle of cell products to deliver more robust and effective therapies and treatment plans to the bedside, reducing healthcare expenditure and improving services and patient care.

Leveraging spatial uncertainty for online error compensation in EMT.

PURPOSE: Electromagnetic tracking (EMT) can potentially complement fluoroscopic navigation, reducing radiation exposure in a hybrid setting. Due to the susceptibility to external distortions, systematic error in EMT needs to be compensated algorithmically. Compensation algorithms for EMT in guidewire procedures are only practical in an online setting. METHODS: We collect positional data and train a symmetric artificial neural network (ANN) architecture for compensating navigation error. The results are evaluated in both online and offline scenarios and are compared to polynomial fits. We assess spatial uncertainty of the compensation proposed by the ANN. Simulations based on real data show how this uncertainty measure can be utilized to improve accuracy and limit radiation exposure in hybrid navigation. RESULTS: ANNs compensate unseen distortions by more than 70%, outperforming polynomial regression. Working on known distortions, ANNs outperform polynomials as well. We empirically demonstrate a linear relationship between tracking accuracy and model uncertainty. The effectiveness of hybrid tracking is shown in a simulation experiment. CONCLUSION: ANNs are suitable for EMT error compensation and can generalize across unseen distortions. Model uncertainty needs to be assessed when spatial error compensation algorithms are developed, so that training data collection can be optimized. Finally, we find that error compensation in EMT reduces the need for X-ray images in hybrid navigation.

BabyLux device: a diffuse optical system integrating diffuse correlation spectroscopy and time-resolved near-infrared spectroscopy for the neuromonitoring of the premature newborn brain.

The BabyLux device is a hybrid diffuse optical neuromonitor that has been developed and built to be employed in neonatal intensive care unit for the noninvasive, cot-side monitoring of microvascular cerebral blood flow and blood oxygenation. It integrates time-resolved near-infrared and diffuse correlation spectroscopies in a user-friendly device as a prototype for a future medical grade device. We present a thorough characterization of the device performance using test measurements in laboratory settings. Tests on solid phantoms report an accuracy of optical property estimation of about 10%, which is expected when using the photon diffusion equation as the model. The measurement of the optical and dynamic properties is stable during several hours of measurements within 3% of the average value. In addition, these measurements are repeatable between different days of measurement, showing a maximal variation of 5% in the optical properties and 8% for the particle diffusion coefficient on a liquid phantom. The variability over test/retest evaluation is < 3 % . The integration of the two modalities is robust and without any cross talk between the two. We also perform in vivo measurements on the adult forearm during arterial cuff occlusion to show that the device can measure a wide range of tissue hemodynamic parameters. We suggest that this platform can form the basis of the next-generation neonatal neuromonitors to be developed for extensive, multicenter clinical testing.

High-precision evaluation of electromagnetic tracking.

PURPOSE: Navigation in high-precision minimally invasive surgery (HP-MIS) demands high tracking accuracy in the absence of line of sight (LOS). Currently, no tracking technology can satisfy this requirement. Electromagnetic tracking (EMT) is the best tracking paradigm in the absence of LOS despite limited accuracy and robustness. Novel evaluation protocols are needed to ensure high-precision and robust EMT for navigation in HP-MIS. METHODS: We introduce a novel protocol for EMT measurement evaluation featuring a high-accuracy phantom based on LEGO[Formula: see text], which is calibrated by a coordinate measuring machine to ensure accuracy. Our protocol includes relative sequential positions and an uncertainty estimation of positioning. We show effects on distortion compensation using a learned interpolation model. RESULTS: Our high-precision protocol clarifies properties of errors and uncertainties of EMT for high-precision use cases. For EMT errors reaching clinically relevant 0.2 mm, our design is 5-10 times more accurate than previous protocols with 95% confidence margins of 0.02 mm. This high-precision protocol ensures the performance improvement in compensated EMT by 0.05 mm. CONCLUSION: Our protocol improves the reliability of EMT evaluations because of significantly lower protocol-inherent uncertainties. To reduce patient risk in HP-MIS and to evaluate magnetic field distortion compensation, more high-accuracy protocols such as the one proposed here are required.