A surgical activity model of laparoscopic cholecystectomy for co-operation with collaborative robots.

BACKGROUND: Laparoscopic cholecystectomy is a very frequent surgical procedure. However, in an ageing society, less surgical staff will need to perform surgery on patients. Collaborative surgical robots (cobots) could address surgical staff shortages and workload. To achieve context-awareness for surgeon-robot collaboration, the intraoperative action workflow recognition is a key challenge. METHODS: A surgical process model was developed for intraoperative surgical activities including actor, instrument, action and target in laparoscopic cholecystectomy (excluding camera guidance). These activities, as well as instrument presence and surgical phases were annotated in videos of laparoscopic cholecystectomy performed on human patients (n = 10) and on explanted porcine livers (n = 10). The machine learning algorithm Distilled-Swin was trained on our own annotated dataset and the CholecT45 dataset. The validation of the model was conducted using a fivefold cross-validation approach. RESULTS: In total, 22,351 activities were annotated with a cumulative duration of 24.9 h of video segments. The machine learning algorithm trained and validated on our own dataset scored a mean average precision (mAP) of 25.7% and a top K = 5 accuracy of 85.3%. With training and validation on our dataset and CholecT45, the algorithm scored a mAP of 37.9%. CONCLUSIONS: An activity model was developed and applied for the fine-granular annotation of laparoscopic cholecystectomies in two surgical settings. A machine recognition algorithm trained on our own annotated dataset and CholecT45 achieved a higher performance than training only on CholecT45 and can recognize frequently occurring activities well, but not infrequent activities. The analysis of an annotated dataset allowed for the quantification of the potential of collaborative surgical robots to address the workload of surgical staff. If collaborative surgical robots could grasp and hold tissue, up to 83.5% of the assistant's tissue interacting tasks (i.e. excluding camera guidance) could be performed by robots.

Ensuring privacy protection in the era of big laparoscopic video data: development and validation of an inside outside discrimination algorithm (IODA).

BACKGROUND: Laparoscopic videos are increasingly being used for surgical artificial intelligence (AI) and big data analysis. The purpose of this study was to ensure data privacy in video recordings of laparoscopic surgery by censoring extraabdominal parts. An inside-outside-discrimination algorithm (IODA) was developed to ensure privacy protection while maximizing the remaining video data. METHODS: IODAs neural network architecture was based on a pretrained AlexNet augmented with a long-short-term-memory. The data set for algorithm training and testing contained a total of 100 laparoscopic surgery videos of 23 different operations with a total video length of 207 h (124 min ± 100 min per video) resulting in 18,507,217 frames (185,965 ± 149,718 frames per video). Each video frame was tagged either as abdominal cavity, trocar, operation site, outside for cleaning, or translucent trocar. For algorithm testing, a stratified fivefold cross-validation was used. RESULTS: The distribution of annotated classes were abdominal cavity 81.39%, trocar 1.39%, outside operation site 16.07%, outside for cleaning 1.08%, and translucent trocar 0.07%. Algorithm training on binary or all five classes showed similar excellent results for classifying outside frames with a mean F1-score of 0.96 ± 0.01 and 0.97 ± 0.01, sensitivity of 0.97 ± 0.02 and 0.0.97 ± 0.01, and a false positive rate of 0.99 ± 0.01 and 0.99 ± 0.01, respectively. CONCLUSION: IODA is able to discriminate between inside and outside with a high certainty. In particular, only a few outside frames are misclassified as inside and therefore at risk for privacy breach. The anonymized videos can be used for multi-centric development of surgical AI, quality management or educational purposes. In contrast to expensive commercial solutions, IODA is made open source and can be improved by the scientific community.

iPad-assisted percutaneous nephrolithotomy (PCNL): a matched pair analysis compared to standard PCNL.

PURPOSE: To compare iPad-assisted (Apple Inc., Cupertino, USA) percutaneous access to the kidney to the standard puncturing technique for percutaneous nephrolithotomy (PCNL). METHODS: For the iPad-assisted PCNL, a computed tomography is performed prior to surgery, using fiducial radiopaque markers. The important anatomical structures (i.e. kidney, stones) are segmented using specific software enabling the superimposition of images semi-transparently on the iPad by marker-based navigation. Twenty-two patients underwent an iPad-assisted percutaneous puncture of the kidney for PCNL. Twenty-two patients of the clinical database from the Urological Department SLK Hospital Heilbronn, who underwent the standard puncturing technique, were matched to these patients. Matching criteria were age, gender, stone volume, body mass index, stone site and the absence of anatomical variation. Puncture time, radiation exposure and number of attempts for a successful puncture were evaluated. All procedures were performed by two experienced urologists. The standard puncturing method consisted of a combination of ultrasound and fluoroscopy guidance. Chi-square and t test were used to ensure that there was no difference in the matching criteria between the groups. To compare the two methods, U test, Kruskal-Wallis and Chi-square test were used. RESULTS: Examination of radiation exposure showed a significant difference between the two groups in favour of the standard puncturing method (p < 0.01) and puncture time (p = 0.01). However, there was no significant difference in puncturing attempts (p = 0.45). CONCLUSION: The iPad-assisted navigation, with the objective being to puncture the renal collecting system, represents a new technique (IDEAL criteria 2b), which proved to be applicable in clinical practice, but still has potential for technical improvement.

Electromagnetic organ tracking allows for real-time compensation of tissue shift in image-guided laparoscopic rectal surgery: results of a phantom study.

BACKGROUND: Laparoscopic resection is a minimally invasive treatment option for rectal cancer but requires highly experienced surgeons. Computer-aided technologies could help to improve safety and efficiency by visualizing risk structures during the procedure. The prerequisite for such an image guidance system is reliable intraoperative information on iatrogenic tissue shift. This could be achieved by intraoperative imaging, which is rarely available. Thus, the aim of the present study was to develop and validate a method for real-time deformation compensation using preoperative imaging and intraoperative electromagnetic tracking (EMT) of the rectum. METHODS: Three models were compared and evaluated for the compensation of tissue deformation. For model A, no compensation was performed. Model B moved the corresponding points rigidly to the motion of the EMT sensor. Model C used five nested linear regressions with increasing level of complexity to compute the deformation (C1-C5). For evaluation, 14 targets and an EMT organ sensor were fit into a silicone-molded rectum of the OpenHELP phantom. Following a computed tomography, the image guidance was initiated and the rectum was deformed in the same way as during surgery in a total of 14 experimental runs. The target registration error (TRE) was measured for all targets in different positions of the rectum. RESULTS: The mean TRE without correction (model A) was 32.8 ± 20.8 mm, with only 19.6% of the measurements below 10 mm (80.4% above 10 mm). With correction, the mean TRE could be reduced using the rigid correction (model B) to 6.8 ± 4.8 mm with 78.7% of the measurements being <10 mm. Using the most complex linear regression correction (model C5), the error could be reduced to 2.9 ± 1.4 mm with 99.8% being below 10 mm. CONCLUSION: In laparoscopic rectal surgery, the combination of electromagnetic organ tracking and preoperative imaging is a promising approach to compensating for intraoperative tissue shift in real-time.

OpenHELP (Heidelberg laparoscopy phantom): development of an open-source surgical evaluation and training tool.

BACKGROUND: Apart from animal testing and clinical trials, surgical research and laparoscopic training mainly rely on phantoms. The aim of this project was to design a phantom with realistic anatomy and haptic characteristics, modular design and easy reproducibility. The phantom was named open-source Heidelberg laparoscopic phantom (OpenHELP) and serves as an open-source platform. METHODS: The phantom was based on an anonymized CT scan of a male patient. The anatomical structures were segmented to obtain digital three-dimensional models of the torso and the organs. The digital models were materialized via rapid prototyping. One flexible, using an elastic abdominal wall, and one rigid method, using a plastic shell, to simulate pneumoperitoneum were developed. Artificial organ production was carried out sequentially starting from raw gypsum models to silicone molds to final silicone casts. The reproduction accuracy was exemplarily evaluated for ten silicone rectum models by comparing the digital 3D surface of the original rectum with CT scan by calculating the root mean square error of surface variations. Haptic realism was also evaluated to find the most realistic silicone compositions on a visual analog scale (VAS, 0-10). RESULTS: The rigid and durable plastic torso and soft silicone organs of the abdominal cavity were successfully produced. A simulation of pneumoperitoneum could be created successfully by both methods. The reproduction accuracy of ten silicone rectum models showed an average root mean square error of 2.26 (0-11.48) mm. Haptic realism revealed an average value on a VAS of 7.25 (5.2-9.6) for the most realistic rectum. CONCLUSION: The OpenHELP phantom proved to be feasible and accurate. The phantom was consecutively applied frequently in the field of computer-assisted surgery at our institutions and is accessible as an open-source project at www.open-cas.org for the academic community.

Spectral characterization of intraoperative renal perfusion using hyperspectral imaging and artificial intelligence.

Accurate intraoperative assessment of organ perfusion is a pivotal determinant in preserving organ function e.g. during kidney surgery including partial nephrectomy or kidney transplantation. Hyperspectral imaging (HSI) has great potential to objectively describe and quantify this perfusion as opposed to conventional surrogate techniques such as ultrasound flowmeter, indocyanine green or the subjective eye of the surgeon. An established live porcine model under general anesthesia received median laparotomy and renal mobilization. Different scenarios that were measured using HSI were (1) complete, (2) gradual and (3) partial malperfusion. The differences in spectral reflectance as well as HSI oxygenation (StO2) between different perfusion states were compelling and as high as 56.9% with 70.3% (± 11.0%) for "physiological" vs. 13.4% (± 3.1%) for "venous congestion". A machine learning (ML) algorithm was able to distinguish between these perfusion states with a balanced prediction accuracy of 97.8%. Data from this porcine study including 1300 recordings across 57 individuals was compared to a human dataset of 104 recordings across 17 individuals suggesting clinical transferability. Therefore, HSI is a highly promising tool for intraoperative microvascular evaluation of perfusion states with great advantages over existing surrogate techniques. Clinical trials are required to prove patient benefit.

Aliado - A design concept of AI for decision support in oncological liver surgery.

BACKGROUND: The interest in artificial intelligence (AI) is increasing. Systematic reviews suggest that there are many machine learning algorithms in surgery, however, only a minority of the studies integrate AI applications in clinical workflows. Our objective was to design and evaluate a concept to use different kinds of AI for decision support in oncological liver surgery along the treatment path. METHODS: In an exploratory co-creation between design experts, surgeons, and data scientists, pain points along the treatment path were identified. Potential designs for AI-assisted solutions were developed and iteratively refined. Finally, an evaluation of the design concept was performed with n = 20 surgeons to get feedback on the different functionalities and evaluate the usability with the System Usability Scale (SUS). Participating surgeons had a mean of 14.0 ± 5.0 years of experience after graduation. RESULTS: The design concept was named "Aliado". Five different scenarios were identified where AI could support surgeons. Mean score of SUS was 68.2 ( ± 13.6 SD). The highest valued functionalities were "individualized prediction of survival, short-term mortality and morbidity", and "individualized recommendation of surgical strategy". CONCLUSION: Aliado is a design prototype that shows how AI could be integrated into the clinical workflow. Even without a fleshed out user interface, the SUS already yielded borderline good results. Expert surgeons rated the functionalities favorably, and most of them expressed their willingness to work with a similar application in the future. Thus, Aliado can serve as a surgical vision of how an ideal AI-based assistance could look like.

Crystalloid volume versus catecholamines for management of hemorrhagic shock during esophagectomy - assessment of microcirculatory tissue oxygenation of the gastric conduit in a porcine model using hyperspectral imaging - an experimental study.

INTRODUCTION: Oncologic esophagectomy is a two-cavity procedure with considerable morbidity and mortality. Complex anatomy and the proximity to major vessels constitute a risk for massive intraoperative hemorrhage. Currently, there is no conclusive consensus on the ideal anesthesiologic countermeasure in case of such immense blood loss. The objective of this work was to identify the most promising anesthesiologic management in case of intraoperative hemorrhage with regards to tissue perfusion of the gastric conduit during esophagectomy using hyperspectral imaging (HSI). MATERIAL AND METHODS: An established live porcine model (n=32) for esophagectomy was used with gastric conduit formation and simulation of a linear stapled side-to-side esophagogastrostomy. After a standardized procedure of controlled blood loss of about 1 L per pig, the four experimental groups (n=8 each) differed in anesthesiologic intervention i.e. (I) permissive hypotension, (II) catecholamine therapy using noradrenaline, (III) crystalloid volume supplementation and (IV) combined crystalloid volume supplementation with noradrenaline therapy. HSI tissue oxygenation (StO2) of the gastric conduit was evaluated and correlated with systemic perfusion parameters. Measurements were conducted before (T0) and after (T1) laparotomy, after hemorrhage (T2) and 60 minutes (T3) and 120 minutes (T4) after anesthesiologic intervention. RESULTS: StO2 values of the gastric conduit showed significantly different results between the four experimental groups with 63.3% (±7.6%) after permissive hypotension (I), 45.9% (±6.4%) after catecholamine therapy (II), 70.5% (±6.1%) after crystalloid volume supplementation (III) and 69.0% (±3.7%) after combined therapy (IV). StO2 values correlated strongly with systemic lactate values (r=-0.67; CI -0.77 to -0.54), which is an established prognostic factor. CONCLUSION: Crystalloid volume supplementation (III) yields the highest StO2 values and lowest systemic lactate values and therefore appears to be the superior primary treatment strategy after hemorrhage during esophagectomy with regards to microcirculatory tissue oxygenation of the gastric conduit.

Sources of performance variability in deep learning-based polyp detection.

PURPOSE: Validation metrics are a key prerequisite for the reliable tracking of scientific progress and for deciding on the potential clinical translation of methods. While recent initiatives aim to develop comprehensive theoretical frameworks for understanding metric-related pitfalls in image analysis problems, there is a lack of experimental evidence on the concrete effects of common and rare pitfalls on specific applications. We address this gap in the literature in the context of colon cancer screening. METHODS: Our contribution is twofold. Firstly, we present the winning solution of the Endoscopy Computer Vision Challenge on colon cancer detection, conducted in conjunction with the IEEE International Symposium on Biomedical Imaging 2022. Secondly, we demonstrate the sensitivity of commonly used metrics to a range of hyperparameters as well as the consequences of poor metric choices. RESULTS: Based on comprehensive validation studies performed with patient data from six clinical centers, we found all commonly applied object detection metrics to be subject to high inter-center variability. Furthermore, our results clearly demonstrate that the adaptation of standard hyperparameters used in the computer vision community does not generally lead to the clinically most plausible results. Finally, we present localization criteria that correspond well to clinical relevance. CONCLUSION: We conclude from our study that (1) performance results in polyp detection are highly sensitive to various design choices, (2) common metric configurations do not reflect the clinical need and rely on suboptimal hyperparameters and (3) comparison of performance across datasets can be largely misleading. Our work could be a first step towards reconsidering common validation strategies in deep learning-based colonoscopy and beyond.

Optimization of anastomotic technique and gastric conduit perfusion with hyperspectral imaging and machine learning in an experimental model for minimally invasive esophagectomy.

INTRODUCTION: Esophagectomy is the mainstay of esophageal cancer treatment, but anastomotic insufficiency related morbidity and mortality remain challenging for patient outcome. Therefore, the objective of this work was to optimize anastomotic technique and gastric conduit perfusion with hyperspectral imaging (HSI) for total minimally invasive esophagectomy (MIE) with linear stapled anastomosis. MATERIAL AND METHODS: A live porcine model (n = 58) for MIE was used with gastric conduit formation and simulation of linear stapled side-to-side esophagogastrostomy. Four main experimental groups differed in stapling length (3 vs. 6 cm) and simulation of anastomotic position on the conduit (cranial vs. caudal). Tissue oxygenation around the anastomotic simulation site was evaluated using HSI and was validated with histopathology. RESULTS: The tissue oxygenation (ΔStO2) after the anastomotic simulation remained constant only for the short stapler in caudal position (-0.4 ± 4.4%, n.s.) while it was impaired markedly in the other groups (short-cranial: -15.6 ± 11.5%, p = 0.0002; long-cranial: -20.4 ± 7.6%, p = 0.0126; long-caudal: -16.1 ± 9.4%, p < 0.0001). Tissue samples from avascular stomach as measured by HSI showed correspondent eosinophilic pre-necrotic changes in 35.7 ± 9.7% of the surface area. CONCLUSION: Tissue oxygenation at the site of anastomotic simulation of the gastric conduit during MIE is influenced by stapling technique. Optimal oxygenation was achieved with a short stapler (3 cm) and sufficient distance of the simulated anastomosis to the cranial end of the gastric conduit. HSI tissue deoxygenation corresponded to histopathologic necrotic tissue changes. The experimental model with HSI and ML allow for systematic optimization of gastric conduit perfusion and anastomotic technique while clinical translation will have to be proven.

Standardized assessment of new electromagnetic field generators in an interventional radiology setting.

PURPOSE: Two of the main challenges associated with electromagnetic (EM) tracking in computer-assisted interventions (CAIs) are (1) the compensation of systematic distance errors arising from the influence of metal near the field generator (FG) or the tracked sensor and (2) the optimized setup of the FG to maximize tracking accuracy in the area of interest. Recently, two new FGs addressing these issues were proposed for the well-established Aurora(®) tracking system [Northern Digital, Inc. (NDI), Waterloo, Canada]: the Tabletop 50-70 FG, a planar transmitter with a built-in shield that compensates for metal distortions emanating from treatment tables, and the prototypical Compact FG 7-10, a mobile generator designed to be attached to mobile imaging devices. The purpose of this paper was to assess the accuracy and precision of these new FGs in an interventional radiology setting. METHODS: A standardized assessment protocol, which uses a precisely machined base plate to measure relative error in position and orientation, was applied to the two new FGs as well as to the well-established standard Aurora(®) Planar FG. The experiments were performed in two different settings: a reference laboratory environment and a computed tomography (CT) scanning room. In each setting, the protocol was applied to three different poses of the measurement plate within the tracking volume of the three FGs. RESULTS: The two new FGs provided higher precision and accuracy within their respective measurement volumes as well as higher robustness with respect to the CT scanner compared to the established FG. Considering all possible 5 cm distances on the grid, the error of the Planar FG was increased by a factor of 5.94 in the clinical environment (4.4 mm) in comparison to the error in the laboratory environment (0.8 mm). In contrast, the mean values for the two new FGs were all below 1 mm with an increase in the error by factors of only 2.94 (Reference: 0.3 mm; CT: 0.9 mm) and 1.04 (both: 0.5 mm) in the case of the Tabletop FG and the Compact FG, respectively. CONCLUSIONS: Due to their high accuracy and robustness, the Tabletop FG and the Compact FG could eliminate the need for compensation of EM field distortions in certain CT-guided interventions.

[Technical innovations and future perspectives]. / Technische Innovationen und Blick in die Zukunft.

BACKGROUND: Digital systems have increasingly become integrated into the modern operating room in the last few decades. This has brought about a massive change, especially in minimally invasive surgery. OBJECTIVE: The article provides an overview of the current technical innovations and the perspectives of digitalization and artificial intelligence (AI) in surgery. MATERIAL AND METHODS: The article is based on a literature search via PubMed and research work by the participating coauthors. RESULTS: Current research is increasingly looking at machine learning techniques that take advantage of the complex data in surgery; however, the integration of artificial intelligence systems into the operating room and clinical practice has only just begun. DISCUSSION: Translational research of artificial intelligence in surgery is still in its infancy but has great potential to improve patient care; however, to accelerate the incorporation of intelligent systems into the clinical practice, the creation of interdisciplinary research groups led by surgeons is necessary.

[Surgery 4.0-are we ready?] / Wie weit ist Chirugie 4.0?

The increasing networking of data systems in medicine is not only leading to modern interdisciplinarity in the sense of cooperation between different medical departments, but also poses new challenges regarding the building and room infrastructure. The surgical operating room of the future expands or augments its reality, away from the pure building characteristics, towards an intelligent and communicative space platform. The building infrastructure (operating theatre) serves as sensor and actuator. Thus, it is possible to inform about missing diagnostics as well as to register them directly in the contextualization of the planned surgical intervention or to integrate them into the processes. Integrated operating theatres represent a comprehensive computer platform based on a corresponding system architecture with software-based protocols. An underlying modular system consisting of various modules for image acquisition and analysis, interaction and visualization supports the integration and merging of heterogeneous data that are generated in a hospital operation. Integral building data (e.g., air conditioning, lighting control, device registration) are merged with patient-related data (age, type of illness, concomitant diseases, existing diagnostic CT and MRI images). New systems coming onto the market, as well as already existing systems will have to be measured by the extent to which they will be able to guarantee this integration of information-similar to the development from mobile phone to smartphone. Cost reduction should not be the only legitimizing argument for the market launch, but the vision of a new quality of surgical perception and action.

Towards a mixed reality environment for preoperative planning of cardiac surgery.

We present a novel approach to studying physical heart models by coupling them with virtual 3D representations in a mixed reality environment. The limitations of standalone physical models (non-interactive, static) are overcome by the corresponding virtual models, which in turn become more natural to interact with. The potential of this approach is exemplified by a setup which enables cardiac surgeons to interactively trace the mitral annulus, a part of the cardiac skeleton playing a vital role in mitral valve surgery. We present results of a pilot study and discuss ways of improving and extending the system. The described mixed reality environment could easily be adapted to other fields and thus has the potential to become a new tool for investigating 3D medical data.

Creation and establishment of a respiratory liver motion simulator for liver interventions.

Image-guided surgery and navigation have resulted from convergent developments in radiology, teletransmission, and computer science and are well-established procedures in the surgical routine in orthopedic, neurosurgery, and head-and-neck surgery. In abdominal surgery, however, these tools have gained little attraction so far. The inability to transfer the methodology from orthopedic or neurosurgery is mainly a result of intraoperative organ movement and shifting. To practice and establish navigated interventions in the liver, a custom-designed respiratory liver motion simulator was built which models the human torso and is easy to recreate. To simulate breathing motion, an explanted porcine or human liver is mounted to the diaphragm model of the simulator, and a lung ventilator causes a periodic movement of the liver along the craniocaudal axis. Additionally, the liver can be connected to a circulating pump device which simulates hepatic perfusion and provides real surgical options to establish navigated interventions and simulate management of possible complications. Respiratory motion caused by the simulator was evaluated with an optical tracking system and it was shown that in vitro movement and deformation of a liver mounted to the device are similar to the liver movements in human or porcine bodies. Based on the tests, it is concluded that the novel respiratory liver motion simulator is suitable for in vitro evaluation of navigated systems and interventional and surgical procedures.

[Navigation in urological surgery: Possibilities and limits of current techniques]. / Navigierte urologische Chirurgie : Möglichkeiten und Grenzen aktueller Technik.

Surgical navigation describes the concept of real-time processing and presentation of preoperative and intraoperative data from different sources to intraoperatively provide surgeons with additional cognitive support. Imaging methods such as 3D ultrasound, magnetic resonance imaging (MRI) and computed tomography (CT) and data from optical, electromagnetic or mechanical tracking methods are used. The resulting information of the navigation system will be presented by the means of visual methods. Mostly virtual reality or augmented reality visualization is used. There are different guidance systems for various disciplines introduced. Mostly it operates on rigid structures (bone, brain). For soft tissue navigation motion compensation and deformation detection are necessary. Therefore, marker-based tracking methods are used in several urological application examples; however, the systems are often still under development and have not yet arrived in the clinical routine.

Toward knowledge-based liver surgery: holistic information processing for surgical decision support.

PURPOSE: Malignant neoplasms of the liver are among the most frequent cancers worldwide. Given the diversity of options for liver cancer therapy, the choice of treatment depends on various parameters including patient condition, tumor size and location, liver function, and previous interventions. To address this issue, we present the first approach to treatment strategy planning based on holistic processing of patient-individual data, practical knowledge (i.e., case knowledge), and factual knowledge (e.g., clinical guidelines and studies). METHODS: The contributions of this paper are as follows: (1) a formalized dynamic patient model that incorporates all the heterogeneous data acquired for a specific patient in the whole course of disease treatment; (2) a concept for formalizing factual knowledge; and (3) a technical infrastructure that enables storing, accessing, and processing of heterogeneous data to support clinical decision making. RESULTS: Our patient model, which currently covers 602 patient-individual parameters, was successfully instantiated for 184 patients. It was sufficiently comprehensive to serve as the basis for the formalization of a total of 72 rules extracted from studies on patients with colorectal liver metastases or hepatocellular carcinoma. For a subset of 70 patients with these diagnoses, the system derived an average of [Formula: see text] assertions per patient. CONCLUSION: The proposed concept paves the way for holistic treatment strategy planning by enabling joint storing and processing of heterogeneous data from various information sources.

The wireless monitoring of vital parameters: a design study.

This project shows the way vital parameters can be transmitted and visualized with no connecting cables necessary to the PDA. This was realized using a sensor developed with an integrated Bluetooth interface and a PDA, also equipped with Bluetooth. This radio connection can span up to 10 m, and parameters, such as pulse frequency, oxygen saturation in blood, ECG measurements and plethysmograms, can be transmitted. Using the software introduced in this work, the transmitted measurements can be displayed numerically or graphically on the PDA. The software simultaneously checks for any limits and sends a warning message if these limits are exceeded. All received data are additionally documented.

MITK-US: real-time ultrasound support within MITK.

PURPOSE: Intra-procedural acquisition of the patient anatomy is a key technique in the context of computer-assisted interventions (CAI). Ultrasound (US) offers major advantages as an interventional imaging modality because it is real time and low cost and does not expose the patient or physician to harmful radiation. To advance US-related research, the purpose of this paper was to develop and evaluate an open-source framework for US-based CAI applications. MATERIALS AND METHODS: We developed the open-source software module MITK-US for acquiring and processing US data as part of the well-known medical imaging interaction toolkit (MITK). To demonstrate its utility, we applied the module to implement a new concept for US-guided needle insertion. Performance of the US module was assessed by determining frame rate and latency for both a simple sample application and a more complex needle guidance system. RESULTS: MITK-US has successfully been used to implement both sample applications. Modern laptops achieve frame rates above 24 frames per second. Latency is measured to be approximately 250 ms or less. CONCLUSION: MITK-US can be considered a viable rapid prototyping environment for US-based CAI applications.