Plant-based model for the visual evaluation of electroporated area after irreversible electroporation and its comparison to in-vivo animal data.

Electroporation (EP) is widely used in medicine, such as cancer treatment, in form of electrochemotherapy or irreversible electroporation (IRE). For EP device testing, living cells or tissue inside a living organism (including animals) are needed. Plant-based models seem to be a promising alternative to substitute animal models in research. The aim of this study is to find a suitable plant-based model for visual evaluation of IRE, and to compare the geometry of electroporated areas with in-vivo animal data.For this purpose, a variety of fruit and vegetables were selected and visually evaluated after 0/1/2/4/6/8/12/16/24 h post-EP. Apple and potato were found to be suitable models as they enabled a visual evaluation of the electroporated area. For these models, the size of the electroporated area was determined after 0/1/2/4/6/8/12/16/24 h. For apples, a well-defined electroporated area was visual within two hours, while in potatoes it reached a plateau after eight hours only. The electroporated area of apple, which showed the fastest visual results was then compared to a retrospectively evaluated swine liver IRE dataset which had been obtained for similar conditions. The electroporated area of the apple and swine liver both showed a spherical geometry of comparable size. For all experiments, the standard protocol for human liver IRE was followed. To conclude, potato and apple were found to be suitable plant-based models for the visual evaluation of electroporated area after irreversible EP, with apple being the best choice for fast visual results. Given the comparable range, the size of the electroporated area of the apple may be promising as a quantitative predictor in animal tissue. Even if plant-based models cannot completely replace animal experiments, they can be used in the early stages of EP device development and testing, decreasing animal experiments to the necessary minimum.

Advancing Knowledge on Situation Comprehension in Dynamic Traffic Situations by Studying Eye Movements to Empty Spatial Locations.

OBJECTIVE: This study used the looking-at-nothing phenomenon to explore situation awareness (SA) and the effects of working memory (WM) load in driving situations. BACKGROUND: While driving, people develop a mental representation of the environment. Since errors in retrieving information from this representation can have fatal consequences, it is essential for road safety to investigate this process. During retrieval, people tend to fixate spatial positions of visually encoded information, even if it is no longer available at that location. Previous research has shown that this "looking-at-nothing" behavior can be used to trace retrieval processes. METHOD: In a video-based laboratory experiment with 2 (WM) x 3 (SA level) within-subjects design, participants (N = 33) viewed a reduced screen and evaluated auditory statements relating to different SA levels on previously seen dynamic traffic scenarios while eye movements were recorded. RESULTS: When retrieving information, subjects more frequently fixated emptied spatial locations associated with the information relevant for the probed SA level. The retrieval of anticipations (SA level 3) in contrast to the other SA level information resulted in more frequent gaze transitions that corresponded to the spatial dynamics of future driving behavior. CONCLUSION: The results support the idea that people build a visual-spatial mental image of a driving situation. Different gaze patterns when retrieving level-specific information indicate divergent retrieval processes. APPLICATION: Potential applications include developing new methodologies to assess the mental representation and SA of drivers objectively.

Implicit intention communication as a design opportunity for automated vehicles: Understanding drivers' interpretation of vehicle trajectory at narrow passages.

To ensure road safety in mixed traffic, automated vehicles (AVs) must be equipped with distinct and easy-to-understand communication strategies. For this, the communication design of AVs might be oriented toward implicit communication between manually driven vehicles. This research focused on how drivers interpret observable vehicle behavior (vehicle trajectory) to predict vehicle intentions and planned maneuvers. In addition, the processing and interpretation of contradictory cues were examined as a possible cause for misunderstandings and failed coordination. Two video-based experiments investigated the presumed intention (yielding or insisting on priority), intention recognition time, distinctiveness, and cooperativeness of a vehicle's implicit communication at narrow road passages. For this, two vehicles approached a bottleneck from opposite sides, and only one vehicle could pass at a time. The driving behavior of the oncoming vehicle consisted of longitudinal (decelerating, stopping, accelerating, maintaining speed) and lateral movements (driving to the center, driving to the edge of the road, no lateral deviation) at different timings (early, late). The results indicated advantages of lateral vehicle movements for distinct intention communication. Lateral movements were interpreted the fastest, perceived more distinct, and were more decisive for the presumed intention than longitudinal movements. Early communication was preferred by drivers but increased the intention recognition time, especially for longitudinal movements. However, early lateral communication was interpreted as fast as late longitudinal communication. Furthermore, lateral and longitudinal vehicle movements within a driving pattern that contradicted each other led to ambiguous perceptions. For AVs, early distinct communication via lateral vehicle movement is recommended. Minimizing ambiguity in the interpretation of communication cues might maximize the probability of the safest response from human drivers and contribute to accident prevention in mixed traffic.

Pedestrian assessment: Is displaying automated driving mode in self-driving vehicles as relevant as emitting an engine sound in electric vehicles?

Pedestrians rely on vehicle dynamics, engine sound, and driver cues. The lack of engine sound now constitutes an addressed pedestrian safety issue for (hybrid) electric vehicles ((H)EVs). Analogously, lacking driver cues may constitute a pedestrian safety issue for self-driving vehicles (SDVs). The purpose of this study was to systematically compare the relevance of substituting driver cues with an external human-machine interface among SDVs (no eHMI vs. eHMI) with the relevance of substituting engine sound with artificial sound among (H)EVs (no engine sound vs. engine sound). In a within-subject design, twenty-nine participants acting as pedestrians encountered a simulated SDV in a parking lot. The results revealed that both informational cues have equally large effects on subjective measures such as perceived safety. In semi-structured interviews, participants stated that it is equally crucial to equip SDVs with an eHMI as equipping (H)EVs with an artificial sound generator. We conclude that an eHMI for SDVs seems to be as relevant as an artificial sound for (H)EVs.

More Than a Feeling-Interrelation of Trust Layers in Human-Robot Interaction and the Role of User Dispositions and State Anxiety.

With service robots becoming more ubiquitous in social life, interaction design needs to adapt to novice users and the associated uncertainty in the first encounter with this technology in new emerging environments. Trust in robots is an essential psychological prerequisite to achieve safe and convenient cooperation between users and robots. This research focuses on psychological processes in which user dispositions and states affect trust in robots, which in turn is expected to impact the behavior and reactions in the interaction with robotic systems. In a laboratory experiment, the influence of propensity to trust in automation and negative attitudes toward robots on state anxiety, trust, and comfort distance toward a robot were explored. Participants were approached by a humanoid domestic robot two times and indicated their comfort distance and trust. The results favor the differentiation and interdependence of dispositional, initial, and dynamic learned trust layers. A mediation from the propensity to trust to initial learned trust by state anxiety provides an insight into the psychological processes through which personality traits might affect interindividual outcomes in human-robot interaction (HRI). The findings underline the meaningfulness of user characteristics as predictors for the initial approach to robots and the importance of considering users' individual learning history regarding technology and robots in particular.

Navigating with Augmented Reality - How does it affect drivers' mental load?

Drivers have been proven to easily understand Augmented Reality (AR) information. Especially in an ambiguous navigation task, drivers are expected to benefit from AR information. The driving simulator study was aimed at examining differences in mental load while navigating in an urban area with ambiguous intersection situations (N = 59). The navigation information was presented to the driver through a head-up display (HUD): a conventional HUD or an AR display, which relates information to the surroundings. Additionally, the driver had to solve a non-driving-related task (NDRT) which was an auditory cognitive, spatial task. Results showed that while driving with the AR display, participants performed better in the NDRT, which indicates a reduced mental load compared with the HUD. Participants drove on average 3 km/h slower with the HUD, showing compensation behaviour.

Sleep inertia in automated driving: Post-sleep take-over and driving performance.

Sleep is emerging as a new driver state in automated driving. Post-sleep performance impairments due to sleep inertia, the transitional phase from sleep to wakefulness that can take up to 30 min, are a potential safety issue. Take-over performance immediately after sleep is impaired and drivers perceive the take-over as critical. The aim of the presented study was to assess take-over behavior immediately after sleep and driving behavior during the 10 min after sleep. A study with N = 31 drivers was conducted in a high-fidelity driving simulator. Take-over performance and driving performance were assessed a) under alert baseline conditions and b) after awakening from electroencephalography-confirmed stable sleep. Take-over performance 15 s after awakening was impaired resulting in more driving errors compared to the alert baseline. Lane keeping was dramatically impaired in the first 3 min after sleep and recovered rapidly. Drivers drove slower after sleep and speed keeping was less stable for at least 10 min. The results suggest that human-machine interaction design should account for the drivers' impaired post-sleep driving performance.

What's Driving Me? Exploration and Validation of a Hierarchical Personality Model for Trust in Automated Driving.

OBJECTIVE: This paper presents a comprehensive investigation of personality traits related to trust in automated vehicles. A hierarchical personality model based on Mowen's (2000) 3M model is explored in a first and replicated in a second study. BACKGROUND: Trust in automation is established in a complex psychological process involving user-, system- and situation-related variables. In this process, personality traits have been viewed as an important source of variance. METHOD: Dispositional variables on three levels were included in an exploratory, hierarchical personality model (full model) of dynamic learned trust in automation, which was refined on the basis of structural equation modeling carried out in Study 1 (final model). Study 2 replicated the final model in an independent sample. RESULTS: In both studies, the personality model showed a good fit and explained a large proportion of variance in trust in automation. The combined evidence supports the role of extraversion, neuroticism, and self-esteem at the elemental level; affinity for technology and dispositional interpersonal trust at the situational level; and propensity to trust in automation and a priori acceptability of automated driving at the surface level in the prediction of trust in automation. CONCLUSION: Findings confirm that personality plays a substantial role in trust formation and provide evidence of the involvement of user dispositions not previously investigated in relation to trust in automation: self-esteem, dispositional interpersonal trust, and affinity for technology. APPLICATION: Implications for personalization of information campaigns, driver training, and user interfaces for trust calibration in automated driving are discussed.

Effects of explaining system failures during maneuver coordination while driving manual or automated.

To support the coordination of road users in situations like merging or turning left, an advanced driver assistance system for cooperative driving could be helpful whether driving manually or automated. This simulator study investigated the behavior of drivers being confronted with system failures. In two test situations with system failures (loss of communication of the system and change of traffic environment), the system could not complete the coordination properly and the driver was informed about the system failure and the abortion of maneuver coordination. The focus of this study was to analyze the effect of system failures on drivers' trust in the system and whether an explanatory message provided by the system would increase acceptance. Therefore, subjective data as well as gaze and physiological data of 32 participants were analyzed. The results revealed decreased trust in the system after experiencing a system failure, but no long term effect was found. The drivers evaluated the timing, as well as the content, of the explanatory message as appropriate. The explanations were perceived as helpful, but no effect on acceptance was found.

Comparing dynamic and static illustration of an HMI for cooperative driving.

The study analyses the Human-Machine-Interface (HMI) of a driver assistance system for cooperative driving, such as merging or turning left situations. Three versions of the HMI are varied as independent variables within subjects. Two versions, displayed in the instrument cluster, focus either on a dynamic or a static illustration of the current status of the system. The third HMI, developed in a preliminary study, serves as benchmark to compare the cluster-based HMIs. The benchmark HMI uses the same status messages and highlights the partner directly in the environment by augmented reality elements. The results of the present study show that the Benchmark best supported cooperative behavior. Both versions of the HMI located in the instrument cluster also support cooperative behavior and are accepted by the drivers. However, more glances are shifted from the relevant area in the driving scenario towards the cluster compared to the Benchmark HMI. With the static version, the participants felt more distracted compared to the dynamic HMI. In conclusion, as long as it is not technically possible to display the partner directly in the environment, a dynamic display in cooperation situations is a good alternative.

Sleep in highly automated driving: Takeover performance after waking up.

Takeover performance in automated driving is subject to investigation in the context of a variety of driver states such as distraction or drowsiness. New driver states will emerge with increasing automation level with drivers potentially being allowed to sleep while driving a highly automated vehicle. Still at some point during a drive, drivers will be required to or voluntarily take back control of the vehicle. A simulator study was conducted to investigate drivers' ability to take over the vehicle control after sleeping. In a within-subjects study design N = 25 test drivers completed a drive using a highly automated driving system a) during day time after a full night of sleep and b) early in the morning after a night of partial sleep deprivation. During the second drive, sleep was measured in drivers according to the American Academy of Sleep Medicine (AASM) standard using electroencephalography (EEG). In total, the participants had to handle four takeover requests (TORs) from the system, two while being awake (day drive) and two when being awakened from sleep stage N2 (morning drive). The objective criticality of the situations was assessed performing the Takeover Controllability rating (TOC-rating). The results indicate that the applied takeover time of 60 s was sufficient for drivers to reengage in driving after sleeping. Reaction times were extended by about 3 s after sleep compared to the wake condition. Takeover performance assessed with the TOC-rating however was clearly worse after sleep than after wakefulness which was also reflected in the drivers' subjective perception of the criticality of the situation. Further research is needed on how to deal with performance impairments after waking up from sleep during automated driving.

Tracing current explanations in memory: A process analysis based on eye-tracking.

Sequential abductive reasoning is the process of finding the best explanation for a set of observations. Explanations can be multicausal and require the retrieval of previously found ones from memory. The theory of abductive reasoning (TAR) allows detailed predictions on what information is stored and retrieved from memory during reasoning. In the research to date, however, these predictions have never been directly tested. In this study, we tested process assumptions such as the construction of a mental representation from TAR using memory indexing, an eye-tracking method that makes it possible to trace the retrieval of explanations currently held in working memory. Gaze analysis revealed that participants encode the presented evidence (i.e., observations) together with possible explanations into memory. When new observations are presented, the previously presented evidence and explanations are retrieved. Observations that are not explained immediately are encoded as abstractly explained. Abstract explanations enter a refinement process in which they become concrete before they enter the situation model. With the memory indexing method, we were able to assess the process of information retrieval in abductive reasoning, which was previously believed to be unobservable. We discuss the results in the light of TAR and other current theories on the diagnostic reasoning process.

Development and Testing of Psychological Conflict Resolution Strategies for Assertive Robots to Resolve Human-Robot Goal Conflict.

As service robots become increasingly autonomous and follow their own task-related goals, human-robot conflicts seem inevitable, especially in shared spaces. Goal conflicts can arise from simple trajectory planning to complex task prioritization. For successful human-robot goal-conflict resolution, humans and robots need to negotiate their goals and priorities. For this, the robot might be equipped with effective conflict resolution strategies to be assertive and effective but similarly accepted by the user. In this paper, conflict resolution strategies for service robots (public cleaning robot, home assistant robot) are developed by transferring psychological concepts (e.g., negotiation, cooperation) to HRI. Altogether, fifteen strategies were grouped by the expected affective outcome (positive, neutral, negative). In two online experiments, the acceptability of and compliance with these conflict resolution strategies were tested with humanoid and mechanic robots in two application contexts (public: n 1 = 61; private: n 2 = 93). To obtain a comparative value, the strategies were also applied by a human. As additional outcomes trust, fear, arousal, and valence, as well as perceived politeness of the agent were assessed. The positive/neutral strategies were found to be more acceptable and effective than negative strategies. Some negative strategies (i.e., threat, command) even led to reactance and fear. Some strategies were only positively evaluated and effective for certain agents (human or robot) or only acceptable in one of the two application contexts (i.e., approach, empathy). Influences on strategy acceptance and compliance in the public context could be found: acceptance was predicted by politeness and trust. Compliance was predicted by interpersonal power. Taken together, psychological conflict resolution strategies can be applied in HRI to enhance robot task effectiveness. If applied robot-specifically and context-sensitively they are accepted by the user. The contribution of this paper is twofold: conflict resolution strategies based on Human Factors and Social Psychology are introduced and empirically evaluated in two online studies for two application contexts. Influencing factors and requirements for the acceptance and effectiveness of robot assertiveness are discussed.

The More You Know: Trust Dynamics and Calibration in Highly Automated Driving and the Effects of Take-Overs, System Malfunction, and System Transparency.

OBJECTIVE: This paper presents a theoretical model and two simulator studies on the psychological processes during early trust calibration in automated vehicles. BACKGROUND: The positive outcomes of automation can only reach their full potential if a calibrated level of trust is achieved. In this process, information on system capabilities and limitations plays a crucial role. METHOD: In two simulator experiments, trust was repeatedly measured during an automated drive. In Study 1, all participants in a two-group experiment experienced a system-initiated take-over, and the occurrence of a system malfunction was manipulated. In Study 2 in a 2 × 2 between-subject design, system transparency was manipulated as an additional factor. RESULTS: Trust was found to increase during the first interactions progressively. In Study 1, take-overs led to a temporary decrease in trust, as did malfunctions in both studies. Interestingly, trust was reestablished in the course of interaction for take-overs and malfunctions. In Study 2, the high transparency condition did not show a temporary decline in trust after a malfunction. CONCLUSION: Trust is calibrated along provided information prior to and during the initial drive with an automated vehicle. The experience of take-overs and malfunctions leads to a temporary decline in trust that was recovered in the course of error-free interaction. The temporary decrease can be prevented by providing transparent information prior to system interaction. APPLICATION: Transparency, also about potential limitations of the system, plays an important role in this process and should be considered in the design of tutorials and human-machine interaction (HMI) concepts of automated vehicles.

Get Ready for Being Chauffeured : Passenger's Preferences and Trust While Being Driven by Human and Automation.

OBJECTIVE: We investigated passenger's trust and preferences using subjective, qualitative, and psychophysiological measures while being driven either by human or automation in a field study and a driving simulator experiment. BACKGROUND: The passenger's perspective has largely been neglected in autonomous driving research, although the change of roles from an active driver to a passive passenger is incontrovertible. Investigations of passenger's appraisals on self-driving vehicles often seem convoluted with active manual driving experiences instead of comparisons with being driven by humans. METHOD: We conducted an exploratory field study using an autonomous research vehicle (N = 11) and a follow-up experimental driving simulation (N = 24). Participants were driven on the same course by a human and an autonomous agent sitting on a passenger seat. Skin conductance, trust, and qualitative characteristics of the perceived driving situation were assessed. In addition, the effect of driving style (defensive vs. sporty) was evaluated in the simulator. RESULTS: Both investigations revealed a close relation between subjective trust ratings and skin conductance, with increased trust and by trend reduced arousal for human compared with automation in control. Even though driving behavior was equivalent in the simulator when being driven by human and automation, passengers most preferred and trusted the human-defensive driver. CONCLUSION: Individual preferences for driving style and human or autonomous vehicle control influence trust and subjective driving characterizations. APPLICATION: The findings are applicable in human-automation research, reminding to not neglect subjective attributions and psychophysiological reactions as a result of ascribed control duties in relation to specific execution characteristics.

Scared to Trust? - Predicting Trust in Highly Automated Driving by Depressiveness, Negative Self-Evaluations and State Anxiety.

The advantages of automated driving can only come fully into play if these systems are used in an appropriate way, which means that they are neither used in situations they are not designed for (misuse) nor used in a too restricted manner (disuse). Trust in automation has been found to be an essential psychological basis for appropriate interaction with automated systems. Well-balanced system use requires a calibrated level of trust in correspondence with the actual ability of an automated system. As for these far-reaching implications of trust for safe and efficient system use, the psychological processes, in which trust is dynamically calibrated prior and during the use of automated technology, need to be understood. At this point, only a restricted body of research investigated the role of personality and emotional states for the formation of trust in automated systems. In this research, the role of the personality variables depressiveness, self-efficacy, self-esteem, and locus of control for the experience of anxiety before the first experience with a highly automated driving system were investigated. Additionally, the relationship of the investigated personality variables and anxiety to subsequent formation of trust in automation was investigated. In a driving simulator study, personality variables and anxiety were measured before the interaction with an automated system. Trust in the system was measured after participants drove with the system for a while. Trust in the system was significantly predicted by state anxiety and the personality characteristics self-esteem and self-efficacy. The relationships of self-esteem and self-efficacy were mediated by state anxiety as supported by significant specific indirect effects. While for depression the direct relationship with trust in automation was not found to be significant, an indirect effect through the experience of anxiety was supported. Locus of control did not show a significant association to trust in automation. The reported findings support the importance of considering individual differences in negative self-evaluations and anxiety when being introduced to a new automated system for individual differences in trust in automation. Implications for future research as well as implications for the design of automated technology in general and automated driving systems are discussed.

Electroporation of the Liver: More Than 2 Concurrently Active, Curved Electrodes Allow New Concepts for Irreversible Electroporation and Electrochemotherapy.

Irreversible electroporation and electrochemotherapy are 2 innovative electroporation-based minimally invasive therapies for the treatment of cancer. Combining nonthermal effects of irreversible electroporation with local application of chemotherapy, electrochemotherapy is an established treatment modality for skin malignancies. Since the application of electrochemotherapy in solid organs is a promising approach, this article describes a novel electrode configuration and field generating method. For the treatment of hepatic malignancies, the shape of the electric field should resemble a spherical 3-dimensional geometry around the target tissue inside the liver. To adapt the actual shape of the field, the probe is designed in computer-aided design with a live link to a computer simulation software: Changes in design can be revalued quickly, regarding different quality criteria for field strength inside and outside the tumor. To rate these criteria, a set of formulas with weighting coefficients has been included. As a result of this design process, a needle-shaped prototype applicator has been built, designed for an intracorporal electroporation-based treatment. It can be used as percutaneous, image-guided, minimally invasive treatment option for malignant liver tumors. The shaft of the probe is used as central electrode and fitted with additional 4 expandable electrodes. These satellite electrodes are hollow, thus serving as injectors for chemotherapeutic agents within the area of the electric field. This configuration can be used for electrochemotherapy as well as irreversible electroporation. By placing 5 electrodes with just one needle, the procedure duration as well as the radiation dose can be reduced tremendously. Additionally, the probe offers an option to adapt the field geometry to the tumor geometry by connecting the 5 electrodes to 5 individually chosen electric potentials: By fine-tuning the ablation zone via the potentials instead of adjusting the location of the electrode(s), the procedure duration as well as the radiation dose will decrease further.

Establishment of a biophysical model to optimize endoscopic targeting of magnetic nanoparticles for cancer treatment.

Superparamagnetic iron oxide nanoparticles (SPION) may be used for local tumor treatment by coupling them to a drug and accumulating them locally with magnetic field traps, that is, a combination of permanent magnets and coils. Thereafter, an alternating magnetic field generates heat which may be used to release the thermosensitively bound drug and for hyperthermia. Until today, only superficial tumors can be treated with this method. Our aim was to transfer this method into an endoscopic setting to also reach the majority of tumors located inside the body. To find the ideal endoscopic magnetic field trap, which accumulates the most SPION, we first developed a biophysical model considering anatomical as well as physical conditions. Entities of choice were esophageal and prostate cancer. The magnetic susceptibilities of different porcine and rat tissues were measured with a superconducting quantum interference device. All tissues showed diamagnetic behavior. The evaluation of clinical data (computed tomography scan, endosonography, surgical reports, pathological evaluation) of patients gave insight into the topographical relationship between the tumor and its surroundings. Both were used to establish the biophysical model of the tumors and their surroundings, closely mirroring the clinical situation, in which we could virtually design, place and evaluate different electromagnetic coil configurations to find optimized magnetic field traps for each tumor entity. By simulation, we could show that the efficiency of the magnetic field traps can be enhanced by 38-fold for prostate and 8-fold for esophageal cancer. Therefore, our approach of endoscopic targeting is an improvement of the magnetic drug-targeting setups for SPION tumor therapy as it holds the possibility of reaching tumors inside the body in a minimal-invasive way. Future animal experiments must prove these findings in vivo.

Driver behavior following an automatic steering intervention.

The study investigated driver behavior toward an automatic steering intervention of a collision mitigation system. Forty participants were tested in a driving simulator and confronted with an inevitable collision. They performed a naïve drive and afterwards a repeated exposure in which they were told to hold the steering wheel loosely. In a third drive they experienced a false alarm situation. Data on driving behavior, i.e. steering and braking behavior as well as subjective data was assessed in the scenarios. Results showed that most participants held on to the steering wheel strongly or counter-steered during the system intervention during the first encounter. Moreover, subjective data collected after the first drive showed that the majority of drivers was not aware of the system intervention. Data from the repeated drive in which participants were instructed to hold the steering wheel loosely, led to significantly more participants holding the steering wheel loosely and thus complying with the instruction. This study seems to imply that without knowledge and information of the system about an upcoming intervention, the most prevalent driving behavior is a strong reaction with the steering wheel similar to an automatic steering reflex which decreases the system's effectiveness. Results of the second drive show some potential for countermeasures, such as informing drivers shortly before a system intervention in order to prevent inhibiting reactions.

Development, physical properties and clinical applicability of a mechanical Multileaf Collimator for the use in Cobalt-60 radiotherapy.

According to the Directory of Radiotherapy Centres (DIRAC) there are 2348 Cobalt-60 (Co-60) teletherapy units worldwide, most of them in low and middle income countries, compared to 11046 clinical accelerators. To improve teletherapy with Co-60, a mechanical Multi-Leaf Collimator (MLC) was developed, working with pneumatic pressure and thus independent of electricity supply. Instead of tungsten, brass was used as leaf material to make the mechanical MLC more affordable. The physical properties and clinical applicability of this mechanical MLC are presented here. The leakage strongly depends on the fieldsize of the therapy unit due to scatter effects. The maximum transmission through the leaves measured 2.5 cm from the end-to-end gap, within a field size of 20 cm × 30 cm defined by jaws of the therapy unit at 80 cm SAD, amounts 4.2%, normalized to an open 10 cm × 10 cm field, created by the mechanical MLC. Within a precollimated field size of 12.5 cm × 12.5 cm, the end-to-end leakage is 6.5% normalized to an open 10 cm × 10 cm field as well. This characteristic is clinically acceptable considering the criteria for non-IMRT MLCs of the International Electrotechnical Commission (IEC 60601-2-1). The penumbra for a 10 cm × 10 cm field was measured to be 9.14 mm in plane and 8.38 mm cross plane. The clinical applicability of the designed mechanical MLC was affirmed by measurements relating to all relevant clinical properties such as penumbra, leakage, output factors and field widths. Hence this novel device presents an apt way forward to make radiotherapy with conformal fields possible in low-infrastructure environments, using gantry based Co-60 therapy units.