Abstract:Purpose
The liver is a common site for metastatic disease, which is a challenging and life‐threatening condition with a grim prognosis and outcome. We propose a standardized workflow for the diagnosis of oligometastatic disease (OMD), as a gold standard workflow has not been established yet. The envisioned workflow comprises the acquisition of a multimodal image data set, novel image processing techniques, and cone beam computed tomography (CBCT)‐guided biopsy for subsequent molecular subtyping. By combining m… Show more
“…3 . The system was placed on the table of a CT scanner together with an abdominal phantom including liver lesions, further described in [ 15 ]. Fig.…”
Purpose
A robotic assistive device is developed for needle-based percutaneous interventions. The aim is a hybrid system using both manual and actuated robotic operation in order to obtain a device that has a large workspace but can still fit in the gantry opening of a CT scanner. This will enable physicians to perform precise and time-efficient CT-guided percutaneous interventions. The concept of the mechanics and software of the device is presented in this work.
Methods
The approach is a semi-automated robotic assistive device, which combines manual and robotic positioning to reduce the number and size of necessary motors. The system consists of a manual rough positioning unit, a robotic fine positioning unit and an optical needle tracking unit. The resulting system has eight degrees of freedom, of which four are manual, which comprise encoders to monitor the position of each axis. The remaining four axes are actuated axes for fine positioning of the needle. Cameras are attached to the mechanical structure for 3D tracking of the needle pose. The software is based on open-source software, mainly ROS2 as robotic middleware, Moveit2 for trajectory calculation and 3D Slicer for needle path planning.
Results
The communication between the components was successfully tested with a clinical CT scanner. In a first experiment, four needle insertions were planned and the deviation of the actual needle path from the planned path was measured. The mean deviation from the needle path to the target point was 21.9 mm, which is mainly caused both by translational deviation (15.4 mm) and angular deviation (6.8°) of the needle holder. The optical tracking system was able to detect the needle position with a mean deviation of 3.9 mm.
Conclusion
The first validation of the system was successful which proves that the proposed concept for both the hardware and software is feasible. In a next step, an automatic position correction based on the optical tracking system will be integrated, which is expected to significantly improve the system accuracy.
“…3 . The system was placed on the table of a CT scanner together with an abdominal phantom including liver lesions, further described in [ 15 ]. Fig.…”
Purpose
A robotic assistive device is developed for needle-based percutaneous interventions. The aim is a hybrid system using both manual and actuated robotic operation in order to obtain a device that has a large workspace but can still fit in the gantry opening of a CT scanner. This will enable physicians to perform precise and time-efficient CT-guided percutaneous interventions. The concept of the mechanics and software of the device is presented in this work.
Methods
The approach is a semi-automated robotic assistive device, which combines manual and robotic positioning to reduce the number and size of necessary motors. The system consists of a manual rough positioning unit, a robotic fine positioning unit and an optical needle tracking unit. The resulting system has eight degrees of freedom, of which four are manual, which comprise encoders to monitor the position of each axis. The remaining four axes are actuated axes for fine positioning of the needle. Cameras are attached to the mechanical structure for 3D tracking of the needle pose. The software is based on open-source software, mainly ROS2 as robotic middleware, Moveit2 for trajectory calculation and 3D Slicer for needle path planning.
Results
The communication between the components was successfully tested with a clinical CT scanner. In a first experiment, four needle insertions were planned and the deviation of the actual needle path from the planned path was measured. The mean deviation from the needle path to the target point was 21.9 mm, which is mainly caused both by translational deviation (15.4 mm) and angular deviation (6.8°) of the needle holder. The optical tracking system was able to detect the needle position with a mean deviation of 3.9 mm.
Conclusion
The first validation of the system was successful which proves that the proposed concept for both the hardware and software is feasible. In a next step, an automatic position correction based on the optical tracking system will be integrated, which is expected to significantly improve the system accuracy.
“…Both qualitative and quantitative MRI phantoms are useful for developing or updating body MRI protocols, because they provide a consistent and reproducible environment for testing and optimizing imaging parameters. For example, a recent study used an abdominal phantom to design a standardized workflow and multimodal imaging pipeline to detect liver lesions [70].…”
In this paper, we review the value of phantoms for body MRI in the context of their uses for quantitative MRI methods research, clinical trials, and clinical imaging. Certain uses of phantoms are common throughout the body MRI community, including measuring bias, assessing reproducibility, and training. In addition to these uses, phantoms in body MRI methods research are used for novel methods development and the design of motion compensation and mitigation techniques. For clinical trials, phantoms are an essential part of quality management strategies, facilitating the conduct of ethically sound, reliable, and regulatorily compliant clinical research of both novel MRI methods and therapeutic agents. In the clinic, phantoms are used for development of protocols, mitigation of cost, quality control, and radiotherapy. We briefly review phantoms developed for quantitative body MRI, and finally, we review open questions regarding the most effective use of a phantom for body MRI.
“…Personalized image-guided therapies that consider tumor heterogeneity have shown potential in improving survival rates [9,10]. Typically, CT imaging is used for these interventions, while MRI is utilized to characterize tumor heterogeneity, which is often invisible in CT scans [11]. Differences in patient positioning in the two scanners, as well as the patient's respiration, result in the organs being deformed differently in the corresponding CT and MR images.…”
Multimodal image registration is an important component of medical image processing, allowing the integration of complementary information from various imaging modalities to improve clinical applications like diagnosis and treatment planning. We proposed a novel multistage neural network for three-dimensional multimodal medical image registration, which addresses the challenge of larger rigid deformations commonly present in medical images due to variations in patient positioning in different scanners and rigid anatomical structures. This multistage network combines rigid, affine and deformable transformations in three stages. The network was trained unsupervised with Mutual Information and Gradient L2 loss. We compared the results of our proposed multistage network with a rigid-affine-deformable registration with the classical registration method NiftyReg as a baseline and a multistage network, which combines affine and deformable transformation, as a benchmark. To evaluate the performance of the proposed multistage network, we used four three-dimensional multimodal in vivo datasets: three renal MR datasets consisting of T1-weighted and T2-weighted MR scans and one liver dataset containing CT and T1-weighted MR scans. Experimental results showed that combining rigid, affine and deformable transformations in a multistage network leads to registration results with a high structural similarity, overlap of the corresponding structures (Dice: 76.7 ± 12.5, 61.1 ± 14.0, 64.8 ± 16.2, 68.1 ± 24.6 for the four datasets) and a low level of image folding (|J| ≤ 0: less than or equal to 1.1%), resulting in a medical plausible registration result.
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