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Matsuda RH, Souza VH, Kirsten PN, Ilmoniemi RJ, Baffa O. MarLe: Markerless estimation of head pose for navigated transcranial magnetic stimulation. Phys Eng Sci Med 2023; 46:887-896. [PMID: 37166586 DOI: 10.1007/s13246-023-01263-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 04/16/2023] [Indexed: 05/12/2023]
Abstract
Navigated transcranial magnetic stimulation (nTMS) is a valuable tool for non-invasive brain stimulation. Currently, nTMS requires fixing of markers on the patient's head. Head marker displacements lead to changes in coil placement and brain stimulation inaccuracy. A markerless neuronavigation method is needed to increase the reliability of nTMS and simplify the nTMS protocol. In this study, we introduce and release MarLe, a Python markerless head tracker neuronavigation software for TMS. This novel software uses computer-vision techniques combined with low-cost cameras to estimate the head pose for neuronavigation. A coregistration algorithm, based on a closed-form solution, was designed to track the patient's head and the TMS coil referenced to the individual's brain image. We show that MarLe can estimate head pose based on real-time video processing. An intuitive pipeline was developed to connect the MarLe and nTMS neuronavigation software. MarLe achieved acceptable accuracy and stability in a mockup nTMS experiment. MarLe allows real-time tracking of the patient's head without any markers. The combination of face detection and a coregistration algorithm can overcome nTMS head marker displacement concerns. MarLe can improve reliability, simplify, and reduce the protocol time of brain intervention techniques such as nTMS.
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Affiliation(s)
- Renan H Matsuda
- Department of Physics, Faculty of Philosophy Sciences and Letters of Ribeirão Preto, University of São Paulo, Av. Bandeirantes, Ribeirão Preto, 3900, 14040-901, SP, Brazil.
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Rakentajanaukio 2, Espoo, 02150, Finland.
| | - Victor H Souza
- Department of Physics, Faculty of Philosophy Sciences and Letters of Ribeirão Preto, University of São Paulo, Av. Bandeirantes, Ribeirão Preto, 3900, 14040-901, SP, Brazil
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Rakentajanaukio 2, Espoo, 02150, Finland
- School of Physiotherapy, Federal University of Juiz de Fora, Juiz de Fora - MG, Cascatinha, Brazil
| | - Petrus N Kirsten
- Department of Physics, Faculty of Philosophy Sciences and Letters of Ribeirão Preto, University of São Paulo, Av. Bandeirantes, Ribeirão Preto, 3900, 14040-901, SP, Brazil
| | - Risto J Ilmoniemi
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Rakentajanaukio 2, Espoo, 02150, Finland
| | - Oswaldo Baffa
- Department of Physics, Faculty of Philosophy Sciences and Letters of Ribeirão Preto, University of São Paulo, Av. Bandeirantes, Ribeirão Preto, 3900, 14040-901, SP, Brazil
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Miura H. [5. Robust Techniques for Radiotherapy Treatment Plan]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2022; 78:882-888. [PMID: 35989258 DOI: 10.6009/jjrt.2022-2072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
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Feature Point Extraction and Motion Tracking of Cardiac Color Ultrasound under Improved Lucas-Kanade Algorithm. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:4959727. [PMID: 34394892 PMCID: PMC8357506 DOI: 10.1155/2021/4959727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Revised: 07/13/2021] [Accepted: 07/26/2021] [Indexed: 11/17/2022]
Abstract
The purpose of this research is to study the application effect of Lucas–Kanade algorithm in right ventricular color Doppler ultrasound feature point extraction and motion tracking under the condition of scale invariant feature transform (SIFT). This study took the right ventricle as an example to analyze the extraction effect and calculation rate of SIFT algorithm and improved Lucas–Kanade algorithm. It was found that the calculation time before and after noise removal by the SIFT algorithm was 0.49 s and 0.46 s, respectively, and the number of extracted feature points was 703 and 698, respectively. The number of feature points extracted by the SIFT algorithm and the calculation time were significantly better than those of other algorithms (P < 0.01). The mean logarithm of the matching points of the SIFT algorithm for order matching and reverse order matching was 20.54 and 20.46, respectively. The calculation time and the number of feature points for the SIFT speckle tracking method were 1198.85 s and 81, respectively, and those of the optical flow method were 3274.19 s and 80, respectively. The calculation time of the SIFT speckle tracking method was significantly lower than that of the optical flow method (P < 0.05), and there was no statistical difference in the number of feature points between the SIFT speckle tracking method and the optical flow method (P > 0.05). In conclusion, the improved Lucas–Kanade algorithm based on SIFT significantly improves the accuracy of feature extraction and motion tracking of color Doppler ultrasound, which shows the value of the algorithm in the clinical application of color Doppler ultrasound.
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