Optimal Spatial Sensor Design for Magnetic Tracking in a Myokinetic Control Interface.

Abstract Background and Objectives Magnetic tracking involves the use of magnetic sensors to localize one or more magnetic objectives, in those applications in which a free line-of-sight between them and the operator is hampered. We applied this concept to prosthetic hands, which could be controlled by tracking permanent magnets implanted in the forearm muscles of amputees (the myokinetic control interface). Concerning the system design, the definition of a sensor distribution which maximizes the information, while minimizing the computational cost of localization, is still an open problem. We present a simple yet effective strategy to define an optimal sensor set for tracking multiple magnets, which we called the Peaks method. Methods We simulated a proximal amputation using a 3D CAD model of a human forearm, and the implantation of 11 magnets in the residual muscles. The Peaks method was applied to select a subset of sensors from an initial grid of 480 elements. The approach involves setting an appropriate threshold to select those sensors associated with the peaks in the magnetic flux density and its gradient distributions. Selected sensors were used to track the magnets during muscle contraction. For validating our strategy, an alternative method based on state-of-the-art solutions was implemented. We finally proposed a calibration phase to customize the sensor distribution on the specific patient's anatomy. Results 80 sensors were selected with the Peaks method, and 101 with the alternative one. A localization accuracy below 0.22 mm and 1.86° for position and orientation, respectively, was always achieved. Unlike alternative methods from the literature, neither iterative or analytical solution, nor a-priori knowledge on the magnet positions or trajectories were required, and yet the outcomes achieved with the two strategies proved statistically comparable. The calibration phase proved useful to adapt the sensors to the patient's stump and to increase the signal-to-noise ratio against intrinsic noise. Conclusions We demonstrated an efficient and general solution for solving the design optimization problem (i.e. identifying an optimal sensor set) and reducing the computational cost of localization. The optimal sensor distribution mirrors the field shape traced by the magnets on the sensing surface, being an intuitive and fast way of achieving the same results of more complex and application-specific methods. Several applications in the (bio)medical field involving magnetic tracking will benefit from the outcomes of this work

Development of an Embedded Myokinetic Prosthetic Hand Controller

The quest for an intuitive and physiologically appropriate human machine interface for the control of dexterous prostheses is far from being completed. In the last decade, much effort has been dedicated to explore innovative control strategies based on the electrical signals generated by the muscles during contraction. In contrast, a novel approach, dubbed myokinetic interface, derives the control signals from the localization of multiple magnetic markers (MMs) directly implanted into the residual muscles of the amputee. Building on this idea, here we present an embedded system based on 32 magnetic field sensors and a real time computation platform. We demonstrate that the platform can simultaneously localize in real-time up to five MMs in an anatomically relevant workspace. The system proved highly linear (R2 = 0.99) and precise (1% repeatability), yet exhibiting short computation times (4 ms) and limited cross talk errors (10% the mean stroke of the magnets). Compared to a previous PC implementation, the system exhibited similar precision and accuracy, while being ~75% faster. These results proved for the first time the viability of using an embedded system for magnet localization. They also suggest that, by using an adequate number of sensors, it is possible to increase the number of simultaneously tracked MMs while introducing delays that are not perceivable by the human operator. This could allow to control more degrees of freedom than those controllable with current technologies

Enhancing Catheter Segmentation in 2D X-RayFluoroscopy Using CNNs trained on SyntheticData

Minimally invasive endovascular procedures require accurate tracking and localization of tools under fluoroscopic guidance. In this work, a novel method to fullyautomatically and in real-time segment catheters and guidewires in 2D X-Ray images based on Deep Convolutional Neural Networks is presented. A transfer learning approach is followed and the training process is carried out by using synthetic images to perform the bulk of training. A small number of annotated data is then used to fine-tune an adapted U-net model, a particular Deep Architecture that has shown promising results in medical image segmentation tasks. The network takes as input a single grayscale image and outputs the catheter and guidewire segmentation within an average time of 6 ms. Two different experiments are presented in which the network is trained on synthetic and ex-vivo fluoroscopy images, respectively. In the latter, ex-vivo data indicate four fluoroscopic sequences acquired on a silicon aorta phantom during catheter insertion. After the training step, by fine-tuning the deepest layers of the network on a small amount of annotated data, accurate segmentation performance, with an average Dice Coefficient higher than 50%, can be obtained on in-vivo fluoroscopic images and sequences. Since the two experiments, i.e. the training on synthetic (experiment-1) and on ex-vivo frames (experiment- 2), give comparable results, it can be argued that it is possible to reduce the need of annotated data in the training phase. This represents an important advantage because acquiring pixel-level annotated images is considered a key bottleneck in building Deep Architecture segmentation models

WEISS Catheter Segmentation in Fluoroscopy Dataset

This dataset contains fluoroscopy images extracted from four videos of canulation experiments with an aorta phantom and six videos of in-vivo catheterisation procedures: four Transcatheter Aortic Valve Implantations (TAVI) and two diagnostic catheterisation procedures. Please refer to the README.docxThe Phantom.hdf5 file contains the 2000 (Dataset-2 in the paper) images extracted from the four fluoroscopy videos from catheterization experiments carried out on a silicon aorta phantom in an angiography suite.The T1T2.hdf5 and T3-T6.hdf5 files contain images extracted from the six fluoroscopy videos during in-vivo endovascular operations (Dataset-3 in the paper). Specifically, 836 frames were extracted from TAVI (data groups T1, T2, T3 andT4) and 371 from diagnostic catheterization (data groups T5 andT6). Each data group contains the following number of images: T1 – 286, T2 – 150, T3 – 200, T4 – 200, T5 – 143, T6 – 228.Binary segmentation masks of the interventional catheter are provided as ground truth. A semiautomated tracking method with manual initialisation (http://ieeexplore.ieee.org/document/7381624/) was employed to obtain the catheter annotations as the 2D coordinates of the catheter restricted to a manually selected region of interest (ROI). The method employs a b-spline tube model as a prior for the catheter shape to restrict the search space and deal with potential missing measurements. This is combined with a probabilistic framework that estimates the pixel-wise posteriors between the foreground (catheter) and background delimited by the b-spline tube contour. The output of the algorithm was manually checked and corrected to provide the final catheter segmentation.The annotations are provided in the files: “Phantom_label.hdf5”, “T1T2_label.hdf5” and “T3-T6_label.hdf5”. All annotations consist of full-scale (256x256 px) binary masks where background pixels have a “0” value, while a value equal to “1” denotes the catheter pixels.Example python code (MAIN.py) is provided to access the data and the labels and visualize them.Citing the datasetThe dataset should be cited using its DOI whenever research making use of this dataset is reported in any academic publication or research report. Please also cite the following publication:Marta Gherardini, Evangelos Mazomenos, Arianna Menciassi, Danail Stoyanov, “Catheter segmentation in X-ray fluoroscopy using synthetic data and transfer learning with light U-nets”, Computer Methods and Programs in Biomedicine, Volume 192, Aug 2020, 105420, doi:10.1016/j.cmpb.2020.105420.To find out more about our research team, visit the Surgical Robot Vision and Wellcome/EPSRC Centre for Interventional and Surgical Science websites.</p

The Myokinetic Control Interface: How Many Magnets Can be Implanted in an Amputated Forearm? Evidence From a Simulated Environment

We recently introduced the concept of a new human-machine interface (the myokinetic control interface) to control hand prostheses. The interface tracks muscle contractions via permanent magnets implanted in the muscles and magnetic field sensors hosted in the prosthetic socket. Previously we showed the feasibility of localizing several magnets in non-realistic workspaces. Here, aided by a 3D CAD model of the forearm, we computed the localization accuracy simulated for three different below-elbow amputation levels, following general guidelines identified in early work. To this aim we first identified the number of magnets that could fit and be tracked in a proximal (T1), middle (T2) and distal (T3) representative amputation, starting from 18, 20 and 23 eligible muscles, respectively. Then we ran a localization algorithm to estimate the poses of the magnets based on the sensor readings. A sensor selection strategy (from an initial grid of 840 sensors) was also implemented to optimize the computational cost of the localization process. Results showed that the localizer was able to accurately track up to 11 (T1), 13 (T2) and 19 (T3) magnetic markers (MMs) with an array of 154, 205 and 260 sensors, respectively. Localization errors lower than 7% the trajectory travelled by the magnets during muscle contraction were always achieved. This work not only answers the question: "how many magnets could be implanted in a forearm and successfully tracked with a the myokinetic control approach?", but also provides interesting insights for a wide range of bioengineering applications exploiting magnetic tracking

Non‐neuronal kappa‐opioid receptor activation enhances epidermal keratinocyte proliferation, and modulates mast cell functions in human skin ex vivo

Kappa‐opioid receptor (KOR) activation reportedly elicits anti‐inflammatory responses and can downregulate neuropeptide release from sensory nerve fibers. While this renders KOR agonists (KORAs) potentially interesting therapeutics in skin diseases associated with neurogenic inflammation, it remains poorly understood how KOR agonists impact on human skin and dermal mast cells (MCs) ex vivo, in the absence of functional innervation. The KORA 5a was administrated to the culture medium (200 nmol/L and 1 µmol/L) in human skin organ culture, thus mimicking a “systemic” mode of application. We show that KORA significantly increased epidermal thickness and upregulated the number and proliferation of epidermal keratinocytes. Unexpectedly, it also stimulated epidermal keratinocyte apoptosis in situ, compared with vehicle. Moreover, KORA significantly decreased the number of c‐Kit‐positive MCs, but did not significantly alter the number or degranulation of mature (tryptase‐ or toluidine blue‐positive) MCs. These pilot observations render the tested KORA (5a) an interesting candidate for the management of inflammatory dermatoses in which MC‐dependent neurogenic skin inflammation plays an important role (e.g. atopic dermatitis, psoriasis)

Tissue-resident macrophages can be generated de novo in adult human skin from resident progenitor cells during substance P-mediated neurogenic inflammation ex vivo

Besides monocyte (MO)-derived macrophages (MACs), self-renewing tissue-resident macrophages (trMACs) maintain the intracutaneous MAC pool in murine skin. Here, we have asked whether the same phenomenon occurs in human skin using organ-cultured, full-thickness skin detached from blood circulation and bone marrow. Skin stimulation ex vivo with the neuropeptide substance P (SP), mimicking neurogenic skin inflammation, significantly increased the number of CD68+MACs in the papillary dermis without altering intracutaneous MAC proliferation or apoptosis. Since intraluminal CD14+MOs were undetectable in the non-perfused dermal vasculature, new MACs must have differentiated from resident intracutaneous progenitor cells in human skin. Interestingly, CD68+MACs were often seen in direct cell-cell-contact with cells expressing both, the hematopoietic stem cell marker CD34 and SP receptor (neurokinin-1 receptor [NK1R]). These cell-cell contacts and CD34+cell proliferation were up-regulated in SP-treated skin samples. Collectively, our study provides the first evidence that resident MAC progenitors, from which mature MACs can rapidly differentiate within the tissue, do exist in normal adult human skin. That these NK1R+trMAC-progenitor cells quickly respond to a key stress-associated neuroinflammatory stimulus suggests that this may satisfy increased local MAC demand under conditions of wounding/stress