This study introduces a novel zero-shot scan-specific self-supervised reconstruction method for magnetic resonance imaging (MRI) to reduce scan times. Conventional supervised reconstruction methods require large amounts of fully-sampled reference data, which is often impractical to obtain and can lead to artifacts by overly emphasizing learned patterns. Existing zero-shot scan-specific methods have attempted to overcome this data dependency but show limited performance due to insufficient utilization of k-space information and constraints derived from MRI forward model. To address these limitations, we introduce a framework utilizing all acquired k-space measurements for both network inputs and training targets. While this framework suffers from training instability, we resolve these challenges through three key components: an Attention-guided K-space Selective Mechanism (AKSM) that provides indirect constraints for non-sampled k-space points, Iteration-wise K-space Masking (IKM) that enhances training stability, and a robust sensitivity map estimation model utilizing cross-channel constraint that performs effectively even at high reduction factors. Experimental results on the FastMRI knee and brain datasets with reduction factors of 4 and 8 demonstrate that the proposed method achieves superior reconstruction quality and faster convergence compared to existing zero-shot scan-specific methods, making it suitable for practical clinical applications. The implementation of our proposed method is publicly available at https://github.com/joojinho97/AeSPa.git.

Radiofrequency ablation (RFA) is a minimally invasive technique that is widely used to ablate solid tumors. Achieving precise personalized treatment requires feedback information on in situ thermal effects induced by RFA. Although computer simulations facilitate the prediction of electrical and thermal phenomena associated with RFA, their practical implementation in clinical settings is hindered by their high computational demands. In this paper, we propose a physics-guided radiofrequency ablation neural network (PhysRFANet) to enable real-time prediction of thermal effect during RFA treatment. Three networks, an encoder–decoder based convolutional neural network (EDCNN), U-Net, and attention U-Net, designed for predicting the temperature distribution and the corresponding ablation lesion, were trained using biophysical computational models that integrated electrostatics, bioheat transfer, and cell necrosis, along with magnetic resonance (MR) images of breast cancer patients. The computational model was validated through experiments using ex vivo bovine liver tissue. Our model demonstrated a Dice score of 96.3% in predicting lesion volume and a root mean squared error (RMSE) of 0.5624 for temperature distribution when tested with foreseen tumor images. Notably, even with unforeseen images, it achieved a Dice score of 93.8% for the ablation lesion and an RMSE of 0.7078 for the temperature distribution. All networks were capable of inferring results within 10 ms. The proposed technique, applied to optimize the placement of the electrode for a specific target region, holds significant promise for enhancing the safety and efficacy of RFA. • Real-time prediction of thermal effect during RFA. • Experimental validation using bovine liver tissue. • Incorporating multiphysics simulations.

Design optimization and fabrication of concentric tube robots are time consuming because of the complexity of their workspaces and the characteristics of the superelastic materials used to make them. This paper presents a procedure for the expeditious design and fabrication of a concentric tube robot for applications that require rapid tube preparation but have less complex design constraints. This procedure reduces a 3D workspace optimization problem to a 2D problem. The continuum robot includes a heat-shrink tube to reduce fabrication time and to give it a small radius of curvature. Experimental results illustrate the feasibility of the proposed procedure.

We propose a non-invasive ultrasound theragnos tic system that tracks movement in an affected area (kidney stones, in the present study) by irradiating the area with high intensity focused ultrasound (HIFU). In the present paper, the concept behind a novel medical support system that integrates therapy and diagnostics (theragnostics) is illustrated. The re quired functions for the proposed system are discussed and an overview of the constructed system configuration is illustrated. The problems associated with kidney stone motion tracking by ultrasonography are described. In order to overcome these problems, we consider two approaches. The first approach is to minimize the servoing error so as to enhance both the efficiency of the therapy and the safety of the patient. The second approach is to reduce the effect of the servoing error. With respect to the first approach, we propose a robust detection method of the stone position based on shape information. With respect to the second approach, we propose a solution for controlling the HIFU irradiation power in accordance with the servoing error, primarily in order to enhance the safety of the patient.

This paper presents a visual-servoing method for compensating motion of soft tissue structures using 4D ultra-sound. The motion of soft tissue structures caused by physiological and external motion makes it difficult to investigate them for diagnostic and therapeutic purposes. The main goal is to track non-rigidly moving soft tissue structures and compensate the motion in order to keep a lesion on its target position during a treatment. We define a 3D non-rigid motion model by extending the Thin-Plate Spline (TPS) algorithm. The motion parameters are estimated with intensity-value changes of a points set in a tracking soft tissue structure. Finally, the global rigid motion is compensated with a 6-DOF robot according to the motion parameters of the tracking structure. Simulation experiments are performed with recorded 3D US images of in-vivo soft tissue structures and validate the effectiveness of the non-rigid motion tracking method. Robotic experiments demonstrated the success of our method with a deformable phantom.

We propose a robust mesh-to-mesh collision detection algorithm using a ray tracing method. The algorithm checks all vertices of a geometrical object based on the proposed criteria, and then the colliding vertices are detected. In order to realize a real-time calculation, acceleration by spatial subdivision is performed. Since the proposed ray-traced collision detection method can directly calculate the reacting forces between the colliding objects, this method is apt for a real-time medical simulation dealing with deformable organs. Our method addresses the limitation of the previous ray-traced approach as it can detect collisions between all arbitrarily shaped objects, including non-convex or sharp objects. Moreover, deeply penetrated collisions can be detected effectively.

The non-invasive ultrasound theragnostic system, we propose, tracks and follows movement in an affected area -kidney stones here-, while High-Intensity Focused Ultrasound (HIFU) is irradiated onto the area. In this paper, the concept of the novel medical support system, which integrates the therapy and diagnostics, is illustrated at first. Secondly, structuring the required functions for the proposed system is discussed. Third, the overview of the constructed system configuration is illustrated. Fourth, the problem of the stone motion tracking by ultrasonography is clarified. To cope with this problem, the respiratory motion of a human kidney is analyzed and a controller, by utilizing the quasi-periodical motion of the respiratory kidney motion, is proposed. Finally, the result of the servoing and HIFU irradiation experiments of the model stone, which moves based on the real human kidney motion data, is reported to confirm the effectiveness of the proposed controller and the constructed system.

Recently, lithotripsy (kidney stone treatment) using HIFU (high intensity focused ultrasound) was developed by researchers in therapeutic ultrasound field. The lithotripsy crushes kidney stones powder, therefore, do not harm to the surrounding tissues of the kidney stones. However, it is necessary to continuously emit high intensity ultrasound waves on a target kidney stone during treatment. Therefore, HIFU transducers should follow a target kidney stone which moves due to respiration and heartbeat. In this paper, ultrasound-based visual servoing system is described. Two ultrasound probes and a HIFU transducer are mounted on the end effector of a xyz stage machine. The two ultrasound probes visually keep track of the target kidney stone within the body, and servo the xyz stage machine. The required techniques, such as conversion of a frame of radio frequency (RF) echo signals into an ultrasound bright mode image, visual tracking, pose estimation and control, are explained.