The propulsion of ferromagnetic objects by means of MRI gradients is a promising approach to enable new forms of therapy. In this work, necessary techniques are presented to make this approach work. This includes path planning algorithms working on MRI data, ferromagnetic artifact imaging and a tracking algorithm which delivers position feedback for the ferromagnetic objects, and a propulsion sequence to enable interleaved magnetic propulsion and imaging. Using a dedicated software environment, integrating path-planning methods and real-time tracking, a clinical MRI system is adapted to provide this new functionality for controlled interventional targeted therapeutic applications. Through MRI–based sensing analysis, this article aims to propose a framework to plan a robust pathway to enhance the navigation ability to reach deep locations in the human body. The proposed approaches are validated with different experiments.

Microscale robots introduce great perspectives into many medical applications such as drug delivery, minimally invasive surgery, and localized biometric diagnostics. Fully automatic microrobots' real-time detection and tracking using medical imagers are actually investigated for future clinical translation. Ultrasound (US) B-mode imaging has been employed to monitor single agents and collective swarms of microrobots in vitro and ex vivo in controlled experimental conditions. However, low contrast and spatial resolution still limit the effective employment of such a method in a medical microrobotic scenario due to uncertainties associated with the position of microrobots. The positioning error arises due to the inaccuracy of the US-based visual feedback, which is provided by the detection and tracking algorithms. The application of deep learning networks is a promising solution to detect and track real-time microrobots in noisy ultrasonic images. However, what is most striking is the performance gap among state-of-the-art microrobots deep learning detection and tracking research. A key factor of that is the unavailability of large-scale datasets and benchmarks. In this letter, we present the first publicly available B-mode ultrasound dataset for microrobots ( $\text{USmicroMagSet}$ ) with accurate annotations which contains more than 40000 samples of magnetic microrobots. In addition, for analyzing the performance of microrobots included in the proposed benchmark dataset, 4 deep learning detectors and 4 deep learning trackers are used.

Magnetic microrobotics is a promising technology for improving minimally invasive surgery (MIS) with the ambition of enhancing patient care and comfort. The potential benefits include limited incisions, less hemorrhaging and postoperative pain, and faster recovery time. To achieve this, a key issue relies on the design of a proper electromagnetic actuation (EMA) setup which is based on the use of magnetic sources. The magnetic field and its gradient generated by the EMA platform is then used to induce magnetic torque and force for microrobot manipulations inside the human body. Like any control systems, the EMA system must be adapted to the given controlled microrobot and customized for the application. With great research efforts on magnetic manipulating of microrobots, the EMA systems are approaching commercial applications, and their configurations are becoming more suitable to be employed in real medical surgeries. However, most of the proposed designs have not followed any specific rule allowing to take into account the biomedical applications constraints. Through reviewing the different proposed EMA systems in the literature, their various specifications and configurations are comprehensively discussed and analyzed. This study focus on EMA platforms that use electromagnets. From this review and based on the biomedical application specifications, the appropriate EMA system can be determined efficiently.

To enhance locoregional therapies for liver cancer treatment, we propose in this study a mathematical model to optimize the transcatheter arterial delivery of therapeutical agents. To maximize the effect of the treatment and minimize adverse effects on the patient, different mathematical models of the tumor growth are considered in this study to find the optimal number of the therapeutic drug-loaded magnetic nanoparticles to be administered. Three types of therapy models are considered, e.g., angiogenesis inhibition therapy, chemotherapy and radiotherapy. We use state-dependent Riccati equations (SDRE) as an optimal control methodology framework to the Hahnfeldt's tumor growth formulation. Based on this, design optimal rules are derived for each therapy to reduce the growth of a tumor through the administration of appropriate dose of antiangiogenic, radio- and chemo-therapeutic agents. Simulation results demonstrate the validity of the proposed optimal delivery approach, leading to reduced intervention time, low drug administration rates and optimal targeted delivery.