Ultrasound image based visual servoing for moving target ablation by high intensity focused ultrasound

Respiration-Induced Organ Motion Compensation: A Review

PURPOSE

Motion of organs in the abdominal and thoracic cavity caused by respiration is a major issue that affects a wide range of clinical diagnoses or treatment outcomes, including radiotherapy, high-intensity focused ultrasound ablation, and many generalized percutaneous needle interventions. These motions pose significant challenges in accurately reaching the target even for the experienced clinician.

METHODS

This review was conducted through comprehensive search on IEEE Explore, Google Scholar, and PubMed.

RESULTS

Diverse methods have been proposed to compensate for this motion effect to enable effective surgical operations. This review paper aims to examine the current respiratory motion compensation techniques used across the clinical procedures of radiotherapy, high-intensity focused ultrasound, and percutaneous needle procedures.

CONCLUSION

The complexity of respiratory-induced organ motion and diversity of areas for which compensation can be applied allows for a variety of methods to be implemented. This review aims to serve as inspiration for the future development of new systems to achieve clinical relevance.

In vivo evaluation of focused ultrasound ablation surgery (FUAS)-induced coagulation using echo amplitudes of the therapeutic focused ultrasound transducer

OBJECTIVE

Monitoring sensitivity of sonography in focused ultrasound ablation surgery (FUAS) is limited (no hyperechoes in ∼50% of successful coagulation in uterine fibroids). A more accurate and sensitive approach is required.

METHOD

The echo amplitudes of the focused ultrasound (FUS) transducer in a testing mode (short pulse duration and low power) were found to correlate with the ex vivo coagulation. To further evaluate its coagulation prediction capabilities, in vivo experiments were carried out. The liver, kidney, and leg muscles of three adult goats were treated using clinical FUAS settings, and the echo amplitude of the FUS transducer and grayscale in sonography before and after FUAS were collected. On day 7, animals were sacrificed humanely, and the treated tissues were dissected to expose the lesion. Echo amplitude changes and lesion areas were analyzed statistically, as were the coagulation prediction metrics.

RESULTS

The echo amplitude changes of the FUS transducer correlate well with the lesion areas in the liver (R = 0.682). Its prediction in accuracy (94.4% vs. 50%), sensitivity (92.9% vs. 35.7%), and negative prediction (80% vs. 30.8%) is better than sonography, but similar in specificity (80% vs. 100%) and positive prediction (100% vs. 100%). In addition, the correlation between tissue depth and the lesion area is not good (|R| < 0.2). Prediction performances in kidney and leg muscles are similar.

CONCLUSION

The FUS echo amplitudes are sensitive to the tissue properties and their changes after FUAS. They are sensitive and reliable in evaluating and predicting FUAS outcomes.

Ultrasound Monitoring of Simultaneous high-intensity focused ultrasound (HIFU) therapy using minimum-peak-sidelobe coded excitation

Bipolar sequences can be readily transmitted by ultrasound (US) pulser hardware with the full driving voltage to boost the echo magnitude in B-mode monitoring of HIFU treatment. In this study, a novel single-transmit bipolar sequence with minimum-peak-sidelobe (MPS) level is developed not only to restore the image quality of US monitoring but also remove acoustic interference from simultaneous HIFU transmission. The proposed MPS code is designed with an equal number of positive and negative bits and the bit duration should be an integer multiple of the period of the HIFU waveform. In addition, different permutations of code sequence are searched in order to obtain the optimal encoding. The received imaging echo is firstly decoded by matched filtering to cancel HIFU interference and to enhance the echo magnitude of US monitoring. Then, Wiener filtering is applied as the second-stage pulse compression to improve the final image quality. Simulations and phantom experiments are performed to compare the single-transmit MPS decoding with conventional two-transmit methods such as pulse-inversion subtraction (PIS) and Golay decoding for their performance in simultaneous US monitoring of HIFU treatment. Results show that the MPS decoding effectively removes HIFU interference even in the presence of tissue motion. The image quality of PIS and Golay decoding, on the other hand, is compromised by the uncancelled HIFU components due to tissue motion. Simultaneous US monitoring of tissue ablation using the proposed MPS decoding has also demonstrated to be feasible in ex-vivo experiments. Compared to the notch filtering that also allows single-transmit HIFU elimination, the MPS decoding is preferrable because it does not suffer from the tradeoff between residual HIFU and speckle deterioration in US monitoring images.

Modeling for Quantitative Analysis of Nakagami Imaging in Accurate Detection and Monitoring of Therapeutic Lesions by High-Intensity Focused Ultrasound

OBJECTIVE

Nakagami imaging is an appealing monitoring and evaluation technique for high-intensity focused ultrasound treatment when bubbles are present in ultrasound images. This study aimed to investigate the accuracy of thermal lesion detection using Nakagami imaging.

METHODS

Simulations were conducted to explore and quantify the influence of the bubbles and the subresolvable effect at the boundary of the thermal lesion on thermal lesion detection. The thermal ablation experiments were conducted in phantom and porcine liver ex vivo.

RESULTS

In the simulation, the estimated lateral and axial size of the thermal lesion in the Nakagami image was 4.91 and 4.79 mm, close to the actual size (5 × 5 mm). The simulation results indicated that the subresolvable region in high-intensity focused ultrasound treatment thermal ablation mainly happened at the boundary between bubbles and the untreated region and does not affect the accuracy of thermal lesion detection. The accurate detection of the thermal lesion using Nakagami imaging mainly depends on bubbles and thermal lesion characterization. Our thermal ablation experiments confirmed that Nakagami imaging has the ability to accurately identify thermal lesions from bubbles.

CONCLUSION

The subresolvable effect is helpful for thermal lesion identification, and precision is related to the Nakagami values chosen for boundary division in Nakagami imaging. Therefore, Nakagami imaging is a promising method for accurately evaluating thermal lesions. Further studies in vivo and in clinical settings will be needed to explore its potential applications.

Probabilistic 4D predictive model from in-room surrogates using conditional generative networks for image-guided radiotherapy

Shape and location organ variability induced by respiration constitutes one of the main challenges during dose delivery in radiotherapy. Providing up-to-date volumetric information during treatment can improve tumor tracking, thereby increasing treatment efficiency and reducing damage to healthy tissue. We propose a novel probabilistic model to address the problem of volumetric estimation with scalable predictive horizon from image-based surrogates during radiotherapy treatments, thus enabling out-of-plane tracking of targets. This problem is formulated as a conditional learning task, where the predictive variables are the 2D surrogate images and a pre-operative static 3D volume. The model learns a distribution of realistic motion fields over a population dataset. Simultaneously, a seq-2-seq inspired temporal mechanism acts over the surrogate images yielding extrapolated-in-time representations. The phase-specific motion distributions are associated with the predicted temporal representations, allowing the recovery of dense organ deformation in multiple times. Due to its generative nature, this model enables uncertainty estimations by sampling the latent space multiple times. Furthermore, it can be readily personalized to a new subject via fine-tuning, and does not require inter-subject correspondences. The proposed model was evaluated on free-breathing 4D MRI and ultrasound datasets from 25 healthy volunteers, as well as on 11 cancer patients. A navigator-based data augmentation strategy was used during the slice reordering process to increase model robustness against inter-cycle variability. The patient data was used as a hold-out test set. Our approach yields volumetric prediction from image surrogates with a mean error of 1.67 ± 1.68 mm and 2.17 ± 0.82 mm in unseen cases of the patient MRI and US datasets, respectively. Moreover, model personalization yields a mean landmark error of 1.4 ± 1.1 mm compared to ground truth annotations in the volunteer MRI dataset, with statistically significant improvements over state-of-the-art.

Partial Respiratory Motion Compensation for Abdominal Extracorporeal Boiling Histotripsy Treatments With a Robotic Arm

Extracorporeal boiling histotripsy (BH), a noninvasive method for mechanical tissue disintegration, is getting closer to clinical applications. However, the motion of the targeted organs, mostly resulting from the respiratory motion, reduces the efficiency of the treatment. Here, a practical and affordable unidirectional respiratory motion compensation method for BH is proposed and evaluated in ex vivo tissues. The BH transducer is fixed on a robotic arm following the motion of the skin, which is tracked using an inline ultrasound imaging probe. In order to compensate for system lags and obtain a more accurate compensation, an autoregressive motion prediction model is implemented. BH pulse gating is also implemented to ensure targeting accuracy. The system is then evaluated with ex vivo BH treatments of tissue samples undergoing motion simulating breathing with the movement of amplitudes between 5 and 10 mm, the frequency between 16 and 18 breaths/min, and a maximum speed of 14.2 mm/s. Results show a reduction of at least 89% of the value of the targeting error during treatment while only increasing the treatment time by no more than 1%. The lesions obtained by treating with the motion compensation were close in size and affected area to the no-motion case, whereas lesions obtained without the compensation were often incomplete and had larger affected areas. This approach to motion compensation could benefit extracorporeal BH and other histotripsy methods in clinical translation.

Medical Robotics for Ultrasound Imaging: Current Systems and Future Trends

Purpose of Review

This review provides an overview of the most recent robotic ultrasound systems that have contemporary emerged over the past five years, highlighting their status and future directions. The systems are categorized based on their level of robot autonomy (LORA).

Recent Findings

Teleoperating systems show the highest level of technical maturity. Collaborative assisting and autonomous systems are still in the research phase, with a focus on ultrasound image processing and force adaptation strategies. However, missing key factors are clinical studies and appropriate safety strategies. Future research will likely focus on artificial intelligence and virtual/augmented reality to improve image understanding and ergonomics.

Summary

A review on robotic ultrasound systems is presented in which first technical specifications are outlined. Hereafter, the literature of the past five years is subdivided into teleoperation, collaborative assistance, or autonomous systems based on LORA. Finally, future trends for robotic ultrasound systems are reviewed with a focus on artificial intelligence and virtual/augmented reality.

Real-time and multimodality image-guided intelligent HIFU therapy for uterine fibroid

Rationale: High-intensity focused ultrasound (HIFU) therapy represents a noninvasive surgical approach to treat uterine fibroids. The operation of HIFU therapy relies on the information provided by medical images. In current HIFU therapy, all operations such as positioning of the lesion in magnetic resonance (MR) and ultrasound (US) images are manually performed by specifically trained doctors. Manual processing is an important limitation of the efficiency of HIFU therapy. In this paper, we aim to provide an automatic and accurate image guidance system, intelligent diagnosis, and treatment strategy for HIFU therapy by combining multimodality information.

Methods: In intelligent HIFU therapy, medical information and treatment strategy are automatically processed and generated by a real-time image guidance system. The system comprises a novel multistage deep convolutional neural network for preoperative diagnosis and a nonrigid US lesion tracking procedure for HIFU intraoperative image-assisted treatment. In the process of intelligent therapy, the treatment area is determined from the autogenerated lesion area. Based on the autodetected treatment area, the HIFU foci are distributed automatically according to the treatment strategy. Moreover, an image-based unexpected movement warning and other physiological monitoring are used during the intelligent treatment procedure for safety assurance.

Results: In the experiment, we integrated the intelligent treatment system on a commercial HIFU treatment device, and eight clinical experiments were performed. In the clinical validation, eight randomly selected clinical cases were used to verify the feasibility of the system. The results of the quantitative experiment indicated that our intelligent system met the HIFU clinical tracking accuracy and speed requirements. Moreover, the results of simulated repeated experiments confirmed that the autodistributed HIFU focus reached the level of intermediate clinical doctors. Operations performed by junior- or middle-level operators with the assistance of the proposed system can reach the level of operation performed by senior doctors. Various experiments prove that our proposed intelligent HIFU therapy process is feasible for treating common uterine fibroid cases.

Conclusion: We propose an intelligent HIFU therapy for uterine fibroid which integrates multiple medical information processing procedures. The experiment results demonstrated that the proposed procedures and methods can achieve monitored and automatic HIFU diagnosis and treatment. This research provides a possibility for intelligent and automatic noninvasive therapy for uterine fibroid.

Anatomical Workspace Study of Endonasal Endoscopic Transsphenoidal Approach

Purpose

To determine the workspace through an anatomical dimensional study of the skull base to further facilitate the design of the robot for endonasal endoscopic transsphenoidal (EET) surgery.

Methods

There were 120 cases having a paranasal sinus CT scan in the database. The internal volumes of the nasal cavities (NC), the volumes of the sphenoid sinuses (SS), and the distance between the anterior nasal spine and base of the sellar (d-ANS-BS) were measured.

Results

The Pearson correlation coefficient (PCC) between the relevant distances and the volumes of the right NC was 0.32; between the relevant distances and the volumes of the left NC was 0.43; and between the relevant distances and volumes of NC was 0.41; with a statistically significant difference (p < 0.001). All PCCs had a statistically significant meaningful difference (p < 0.05).

Conclusion

The volume of NCs were significantly correlated with distances (p < 0.05). The safest and shortest distance to guide the robotic arm length in the EET approach could be represented by d-ANS-BS. This result was also used as primary information for further robotic design.

Servoing Performance Enhancement via a Respiratory Organ Motion Prediction Model for a Non-Invasive Ultrasound Theragnostic System

[abstFig src='/00290002/15.jpg' width='300' text='Proposed method for tracking and following respiratory organ motion' ] High intensity focused ultrasound (HIFU) is potentially useful for treating stones and/or tumors. With respect to HIFU therapy, it is difficult to focus HIFU on the focal lesion due to respiratory organ motion, and this increases the risk of damaging the surrounding healthy tissues around the target focal lesion. Thus, this study proposes a method to cope with the fore-mentioned problem involving tracking and following the respiratory organ motion via a visual feedback and a prediction model for respiratory organ motion to realize highly accurate servoing performance for focal lesions. The prediction model is continuously updated based on the latest organ motion data. The results indicate that respiratory kidney motion of two healthy subjects is successfully tracked and followed with an accuracy of 0.88 mm by the proposed method and the constructed system.

Ultrasound image based visual servoing for moving target ablation by high intensity focused ultrasound

Background

Although high intensity focused ultrasound (HIFU) is a promising technology for tumor treatment, a moving abdominal target is still a challenge in current HIFU systems. In particular, respiratory‐induced organ motion can reduce the treatment efficiency and negatively influence the treatment result. In this research, we present: (1) a methodology for integration of ultrasound (US) image based visual servoing in a HIFU system; and (2) the experimental results obtained using the developed system.

Materials and methods

In the visual servoing system, target motion is monitored by biplane US imaging and tracked in real time (40 Hz) by registration with a preoperative 3D model. The distance between the target and the current HIFU focal position is calculated in every US frame and a three‐axis robot physically compensates for differences. Because simultaneous HIFU irradiation disturbs US target imaging, a sophisticated interlacing strategy was constructed.

Results

In the experiments, respiratory‐induced organ motion was simulated in a water tank with a linear actuator and kidney‐shaped phantom model. Motion compensation with HIFU irradiation was applied to the moving phantom model. Based on the experimental results, visual servoing exhibited a motion compensation accuracy of 1.7 mm (RMS) on average. Moreover, the integrated system could make a spherical HIFU‐ablated lesion in the desired position of the respiratory‐moving phantom model.

Conclusions

We have demonstrated the feasibility of our US image based visual servoing technique in a HIFU system for moving target treatment.