Medical Imaging of Microrobots: Toward In Vivo Applications

Medical microrobots (MRs) have been demonstrated for a variety of non-invasive biomedical applications, such as tissue engineering, drug delivery, and assisted fertilization, among others. However, most of these demonstrations have been carried out in in vitro settings and under optical microscopy, being significantly different from the clinical practice. Thus, medical imaging techniques are required for localizing and tracking such tiny therapeutic machines when used in medical-relevant applications. This review aims at analyzing the state of the art of microrobots imaging by critically discussing the potentialities and limitations of the techniques employed in this field. Moreover, the physics and the working principle behind each analyzed imaging strategy, the spatiotemporal resolution, and the penetration depth are thoroughly discussed. The paper deals with the suitability of each imaging technique for tracking single or swarms of MRs and discusses the scenarios where contrast or imaging agent's inclusion is required, either to absorb, emit, or reflect a determined physical signal detected by an external system. Finally, the review highlights the existing challenges and perspective solutions which could be promising for future in vivo applications

Localization and Tracking of Intestinal Paths for Wireless Capsule Endoscopy

Wireless capsule endoscopy (WCE) is a non-invasive technology used for visual inspection of the human gastrointestinal (GI) tract. Localization of the capsule is a vital component of the system, as this enables physicians to identify the position of abnormalities. Several approaches exist that use the received signal strength (RSS) of the radio frequency (RF) signals for localization. However, few of these utilize the sparseness of the signals. Due to intestinal motility, the capsule positions will change with time. The distance travelled by the capsule in the intestine, however, remains more or less constant with time. In this thesis, a compressive sensing (CS) based localization algorithm is presented, that utilize signal sparsity in the RSS measurements. Different L1-minimization algorithms are used to find the sparse location vector. The performance is evaluated by electromagnetic (EM) simulations performed on a human voxel model, using narrow-band (NB) and ultra wide-band (UWB) signals. From intestinal positions, the distance the capsule has travelled is estimated by use of Kalman- and particle filters. It was found that localization accuracy of a few millimeters is possible under ideal conditions, when the RSS measurements are generated from a path loss model. When using path loss data from the EM simulations, localization accuracy on the order of 20-30 mm was achievable for NB signals. Use of UWB signals resulted in localization errors between 35-60 mm, depending on frequency range and bandwidth. From generated intestinal positions, the travelled distance was estimated with a minimum accuracy of a few millimeters, when using a VNL Kalman filter and moderate amounts of observation noise. The results are found from a limited amount of data. In order to increase the confidence in the presented results, the performance of the localization algorithm and the filters should be evaluated with a larger number of datasets