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Peng W, Wu W, Zhang J, Xie H, Zhang S, Gu L. An automatic framework for estimating the pose of the catheter distal section using a coarse-to-fine network. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 225:107036. [PMID: 35905696 DOI: 10.1016/j.cmpb.2022.107036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 06/22/2022] [Accepted: 07/20/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE During percutaneous coronary intervention procedures, generally only 2D X-ray images are provided. The consequent lack of depth perception makes it difficult for interventionists to visually estimate the pose of medical tools inside the vasculature, especially for novices. Although some automatic methods have been developed to aid interventionists, it is still a challenging task to obtain stable and accurate pose estimation. In this paper, we describe a learning-based framework for estimating the pose of the catheter distal section (CDS). The main innovation of this framework is the proposal of a coarse-to-fine fusion network (CFF-Net) which can achieve the shape and orientation estimation for the CDS. METHODS By adopting a two-step fusion, CFF-Net progressively solves the shape and orientation ambiguities. The first step is the early fusion where the 2D projection image fuses with the shape prior before input, which makes the estimated result own a specific catheter distal shape. The second step is the late fusion where CFF-Net fuse feature maps and the orientation data from Electromagnetic (EM) sensors to confirm the overall orientation of the CDS. Finally, the estimated pose in the EM space will be obtained after we combine the estimated shape and orientation from CFF-Net with the position information from the EM sensor. RESULTS The effectiveness of CFF-Net has been verified in a simulated environment where RMSE of CFF-Net is 0.706 ± 0.121 mm. This approach was further transferred from simulation to reality using the real-world data, where RMSE of CFF-Net is 1.121 ± 0.124 mm and RMSE of the whole proposed framework is 1.577 ± 0.144 mm. CONCLUSION In simulated and real-world experiments, our proposed approach has been proven to achieve high accuracy while ensuring real-time processing for estimating the pose of the CDS.
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Affiliation(s)
- Wenjia Peng
- School of Biomedical Engineering and Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Wei Wu
- School of Biomedical Engineering and Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Jingyang Zhang
- School of Biomedical Engineering and Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Hongzhi Xie
- Department of Cardiology, Peking Union Medical College Hospital, Peking, China.
| | - Shuyang Zhang
- Department of Cardiology, Peking Union Medical College Hospital, Peking, China
| | - Lixu Gu
- School of Biomedical Engineering and Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China.
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Bertsche D, Rasche V, Rottbauer W, Vernikouskaya I. 3D localization from 2D X-ray projection. Int J Comput Assist Radiol Surg 2022; 17:1553-1558. [PMID: 35819654 PMCID: PMC9463320 DOI: 10.1007/s11548-022-02709-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 06/21/2022] [Indexed: 11/28/2022]
Abstract
PURPOSE Most cardiology procedures are guided using X-ray (XR) fluoroscopy. However, the projective nature of the XR fluoroscopy does not allow for true depth perception as required for safe and efficient intervention guidance in structural heart diseases. For improving guidance, different methods have been proposed often being radiation-intensive, time-consuming, or expensive. We propose a simple 3D localization method based on a single monoplane XR projection using a co-registered centerline model. METHODS The method is based on 3D anatomic surface models and corresponding centerlines generated from preprocedural imaging. After initial co-registration, 2D working points identified in monoplane XR projections are localized in 3D by minimizing the angle between the projection lines of the centerline points and the working points. The accuracy and reliability of the located 3D positions were assessed in 3D using phantom data and in patient data projected to 2D obtained during placement of embolic protection system in interventional procedures. RESULTS With the proposed methods, 2D working points identified in monoplane XR could be successfully located in the 3D phantom and in the patient-specific 3D anatomy. Accuracy in the phantom (3D) resulted in 1.6 mm (± 0.8 mm) on average, and 2.7 mm (± 1.3 mm) on average in the patient data (2D). CONCLUSION The use of co-registered centerline models allows reliable and accurate 3D localization of devices from a single monoplane XR projection during placement of the embolic protection system in TAVR. The extension to different vascular interventions and combination with automatic methods for device detection and registration might be promising.
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Affiliation(s)
- Dagmar Bertsche
- Department of Internal Medicine II, Ulm University Medical Center, Ulm, Germany
| | - Volker Rasche
- Department of Internal Medicine II, Ulm University Medical Center, Ulm, Germany
| | - Wolfgang Rottbauer
- Department of Internal Medicine II, Ulm University Medical Center, Ulm, Germany
| | - Ina Vernikouskaya
- Department of Internal Medicine II, Ulm University Medical Center, Ulm, Germany.
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Schulmann N, Soltani-Sarvestani MA, De Landro M, Korganbayev S, Cotin S, Saccomandi P. Model-Based Thermometry for Laser Ablation Procedure Using Kalman Filters and Sparse Temperature Measurements. IEEE Trans Biomed Eng 2022; 69:2839-2849. [PMID: 35230944 DOI: 10.1109/tbme.2022.3155574] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
In this work, we implement a data-assimilation Bayesian framework for the reconstruction of the spatiotemporal profile of the tissue temperature during laser irradiation. The predictions of a physical model simulating the heat transfer in the tissue are associated with sparse temperature measurements, using an Unscented Kalman Filter. We compare a standard state-estimation filtering procedure with a joint-estimation (state and parameters) approach: whereas in the state-estimation only the temperature is evaluated, in the joint-estimation the filter corrects also uncertain model parameters (i.e., the medium thermal diffusivity, and laser beam properties). We have tested the method on synthetic temperature data, and on the temperature measured on agar-gel phantom and porcine liver with fiber optic sensors. The joint-estimation allows retrieving an accurate estimate of the temperature distribution with a maximal error < 1.5 C in both synthetic and liver 1D data, and < 2 C in phantom 2D data. Our approach allows also suggesting a strategy for optimizing the temperature estimation based on the positions of the sensors. Under the constraint of using only two sensors, optimal temperature estimations are obtained when one sensor is placed in proximity of the source, and the other one is in a non-symmetrical position.
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Sahu SK, Sozer C, Rosa B, Tamadon I, Renaud P, Menciassi A. Shape Reconstruction Processes for Interventional Application Devices: State of the Art, Progress, and Future Directions. Front Robot AI 2021; 8:758411. [PMID: 34869615 PMCID: PMC8640970 DOI: 10.3389/frobt.2021.758411] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 10/11/2021] [Indexed: 01/02/2023] Open
Abstract
Soft and continuum robots are transforming medical interventions thanks to their flexibility, miniaturization, and multidirectional movement abilities. Although flexibility enables reaching targets in unstructured and dynamic environments, it also creates challenges for control, especially due to interactions with the anatomy. Thus, in recent years lots of efforts have been devoted for the development of shape reconstruction methods, with the advancement of different kinematic models, sensors, and imaging techniques. These methods can increase the performance of the control action as well as provide the tip position of robotic manipulators relative to the anatomy. Each method, however, has its advantages and disadvantages and can be worthwhile in different situations. For example, electromagnetic (EM) and Fiber Bragg Grating (FBG) sensor-based shape reconstruction methods can be used in small-scale robots due to their advantages thanks to miniaturization, fast response, and high sensitivity. Yet, the problem of electromagnetic interference in the case of EM sensors, and poor response to high strains in the case of FBG sensors need to be considered. To help the reader make a suitable choice, this paper presents a review of recent progress on shape reconstruction methods, based on a systematic literature search, excluding pure kinematic models. Methods are classified into two categories. First, sensor-based techniques are presented that discuss the use of various sensors such as FBG, EM, and passive stretchable sensors for reconstructing the shape of the robots. Second, imaging-based methods are discussed that utilize images from different imaging systems such as fluoroscopy, endoscopy cameras, and ultrasound for the shape reconstruction process. The applicability, benefits, and limitations of each method are discussed. Finally, the paper draws some future promising directions for the enhancement of the shape reconstruction methods by discussing open questions and alternative methods.
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Affiliation(s)
- Sujit Kumar Sahu
- The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy
- Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, Pisa, Italy
- ICube, CNRS, INSA Strasbourg, University of Strasbourg, Strasbourg, France
| | - Canberk Sozer
- The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy
- Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, Pisa, Italy
| | - Benoit Rosa
- ICube, CNRS, INSA Strasbourg, University of Strasbourg, Strasbourg, France
| | - Izadyar Tamadon
- The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy
- Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, Pisa, Italy
| | - Pierre Renaud
- ICube, CNRS, INSA Strasbourg, University of Strasbourg, Strasbourg, France
| | - Arianna Menciassi
- The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy
- Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, Pisa, Italy
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