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Aziz A, Pane S, Iacovacci V, Koukourakis N, Czarske J, Menciassi A, Medina-Sánchez M, Schmidt OG. Medical Imaging of Microrobots: Toward In Vivo Applications. ACS NANO 2020; 14:10865-10893. [PMID: 32869971 DOI: 10.1021/acsnano.0c05530] [Citation(s) in RCA: 103] [Impact Index Per Article: 25.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
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.
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Affiliation(s)
- Azaam Aziz
- Institute for Integrative Nanosciences, Leibniz IFW Dresden, Helmholtzstrasse 20, 01069 Dresden, Germany
| | - Stefano Pane
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa 56025, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, 56127 Pisa, Italy
| | - Veronica Iacovacci
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa 56025, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, 56127 Pisa, Italy
| | - Nektarios Koukourakis
- Chair of Measurement and Sensor System Technique, School of Engineering, TU Dresden, Helmholtzstrasse 18, 01069 Dresden, Germany
- Center for Biomedical Computational Laser Systems, TU Dresden, 01062 Dresden, Germany
| | - Jürgen Czarske
- Chair of Measurement and Sensor System Technique, School of Engineering, TU Dresden, Helmholtzstrasse 18, 01069 Dresden, Germany
- Cluster of Excellence Physics of Life, TU Dresden, 01307 Dresden, Germany
- Center for Biomedical Computational Laser Systems, TU Dresden, 01062 Dresden, Germany
| | - Arianna Menciassi
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa 56025, Italy
- Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, 56127 Pisa, Italy
| | - Mariana Medina-Sánchez
- Institute for Integrative Nanosciences, Leibniz IFW Dresden, Helmholtzstrasse 20, 01069 Dresden, Germany
| | - Oliver G Schmidt
- Institute for Integrative Nanosciences, Leibniz IFW Dresden, Helmholtzstrasse 20, 01069 Dresden, Germany
- Center for Materials, Architectures, and Integration of Nanomembranes (MAIN), TU Chemnitz, Reichenhainer Strasse 10, 09107 Chemnitz, Germany
- School of Science, TU Dresden, 01062 Dresden, Germany
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Dencks S, Piepenbrock M, Schmitz G. Assessing Vessel Reconstruction in Ultrasound Localization Microscopy by Maximum Likelihood Estimation of a Zero-Inflated Poisson Model. IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL 2020; 67:1603-1612. [PMID: 32167890 DOI: 10.1109/tuffc.2020.2980063] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
In clinical applications of super-resolution ultrasound imaging, it is often not possible to achieve a full reconstruction of the microvasculature within a limited measurement time. This makes the comparison of studies and quantitative parameters of vascular morphology and perfusion difficult. Therefore, saturation models were proposed to predict adequate measurement times and estimate the degree of vessel reconstruction. Here, we derive a statistical model for the microbubble counts in super-resolution voxels by a zero-inflated Poisson (ZIP) process. In this model, voxels either belong to vessels with probability Pv and count events with Poisson rate Λ , or they are empty and remain zero. In this model, Pv represents the vessel voxel density in the super-resolution image after infinite measurement time. For the parameters Pv and Λ , we give Cramér-Rao lower bounds (CRLBs) for the estimation variance and derive maximum likelihood estimators (MLEs) in a novel closed-form solution. These can be calculated with knowledge of only the counts at the end of the acquisition time. The estimators are applied to preclinical and clinical data and the MLE outperforms alternative estimators proposed before. The estimated degree of reconstruction lies between 38% and 74% after less than 90 s. Vessel probability Pv ranged from 4% to 20%. The rate parameter Λ was estimated in the range of 0.5-1.3 microbubbles/voxel. For these parameter ranges, the CRLB gives standard deviations of less than 2%, which supports that the parameters can be estimated with good precision already for limited acquisition times.
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