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Purpose: Many interventional procedures require the precise placement of needles or therapy applicators (tools) to correctly achieve planned targets for optimal diagnosis or treatment of cancer, typically leveraging the temporal resolution of ultrasound (US) to provide real‐time feedback. Identifying tools in two‐dimensional (2D) images can often be time‐consuming with the precise position difficult to distinguish. We have developed and implemented a deep learning method to segment tools in 2D US images in near real‐time for multiple anatomical sites, despite the widely varying appearances across interventional applications. Methods: A U‐Net architecture with a Dice similarity coefficient (DSC) loss function was used to perform segmentation on input images resized to 256 × 256 pixels. The U‐Net was modified by adding 50% dropouts and the use of transpose convolutions in the decoder section of the network. The proposed approach was trained with 917 images and manual segmentations from prostate/gynecologic brachytherapy, liver ablation, and kidney biopsy/ablation procedures, as well as phantom experiments. Real‐time data augmentation was applied to improve generalizability and doubled the dataset for each epoch. Postprocessing to identify the tool tip and trajectory was performed using two different approaches, comparing the largest island with a linear fit to random sample consensus (RANSAC) fitting. Results: Comparing predictions from 315 unseen test images to manual segmentations, the overall median [first quartile, third quartile] tip error, angular error, and DSC were 3.5 [1.3, 13.5] mm, 0.8 [0.3, 1.7]°, and 73.3 [56.2, 82.3]%, respectively, following RANSAC postprocessing. The predictions with the lowest median tip and angular errors were observed in the gynecologic images (median tip error: 0.3 mm; median angular error: 0.4°) with the highest errors in the kidney images (median tip error: 10.1 mm; median angular error: 2.9°). The performance on the kidney images was likely due to a reduction in acoustic signal associated with oblique insertions relative to the US probe and the increased number of anatomical interfaces with similar echogenicity. Unprocessed segmentations were performed with a mean time of approximately 50 ms per image. Conclusions: We have demonstrated that our proposed approach can accurately segment tools in 2D US images from multiple anatomical locations and a variety of clinical interventional procedures in near real‐time, providing the potential to improve image guidance during a broad range of diagnostic and therapeutic cancer interventions. [ABSTRACT FROM AUTHOR]

Purpose: Image‐guided focal ablation procedures are first‐line therapy options in the treatment of liver cancer tumors that provide advantageous reductions in patient recovery times and complication rates relative to open surgery. However, extensive physician training is required and image guidance variabilities during freehand therapy applicator placement limit the sufficiency of ablation volumes and the overall potential of these procedures. We propose the use of three‐dimensional ultrasound (3D US) to provide guidance and localization of therapy applicators, augmenting current ablation therapies without the need for specialized procedure suites. We have developed a novel scanning mechanism for geometrically variable 3D US images, a mechanical tracking system, and a needle applicator insertion workflow using a custom needle applicator guide for targeted image‐guided procedures. Methods: A three‐motor scanner was designed to use any commercially available US probe to generate accurate, consistent, and geometrically variable 3D US images. The designed scanner was mounted on a counterbalanced stabilizing and mechanical tracking system for determining the US probe orientation, which was assessed using optical tracking. Further exploiting the utility of the motorized scanner, an image‐guidance workflow was developed that moved the probe to any identified target within an acquired 3D US image. The complete 3D US guidance system was used to perform mock targeted interventional procedures on a phantom by selecting a target in a 3D US image, navigating to the target, and performing needle insertion using a custom 3D‐printed needle applicator guide. Registered postinsertion 3D US images and cone‐beam computed tomography (CBCT) images were used to evaluate tip targeting errors when using the motors, tracking system, or mixed navigation approaches. Two 3D US image geometries were investigated to assess the accuracy of a small‐footprint tilt approach and a large field‐of‐view hybrid approach for a total of 48 targeted needle insertions. 3D US image quality was evaluated in a healthy volunteer and compared to a commercially available matrix array US probe. Results: A mean positioning error of 1.85 ± 1.33 mm was observed when performing compound joint manipulations with the mechanical tracking system. A combined approach for navigation that incorporated the motorized movement and the in‐plane tracking system corrections performed the best with a mean tip error of 3.77 ± 2.27 mm and 4.27 ± 2.47 mm based on 3D US and CBCT images, respectively. No significant differences were observed between hybrid and tilt image acquisition geometries with all mean registration errors ≤1.2 mm. 3D US volunteer images resulted in clear reconstruction of clinically relevant anatomy. Conclusions: A mechanically tracked system with geometrically variable 3D US provides a utility that enables enhanced applicator guidance, placement verification, and improved clinical workflow during focal liver tumor ablation procedures. Evaluations of the tracking accuracy, targeting capabilities, and clinical imaging feasibility of the proposed 3D US system, provided evidence for clinical translation. This system could provide a workflow for improving applicator placement and reducing local cancer recurrence during interventional procedures treating liver cancer and has the potential to be expanded to other abdominal interventions and procedures. [ABSTRACT FROM AUTHOR]

Radiofrequency ablation (RFA) is now a widely used minimally invasive treatment method for hepatic tumors. Preoperative planning plays a vital role in RFA therapy. With increasing tumor size, multiple overlapping ablations are needed, which are challenging to optimize while considering clinical constraints. In this paper, we present a new automatic RFA planning method. First, a 2-steps set cover-based model is formulated, which can integrate multiple clinical constraints for optimization of overlapping ablations. To ensure that the planning model can be solved in a reasonable time, a search space reducing strategy is then proposed. We also developed an algorithm for automatic RFA electrode selection, which provides a proper electrode ablation zone for the planning model. The proposed method was evaluated with 20 tumors of varying sizes (0.92 cm3 to 28.4 cm3). Results showed that the proposed method can generate clinical feasible RFA plans with a minimum number of RFA electrodes and ablations, complete tumor coverage and minimized ablation of normal tissue. [ABSTRACT FROM AUTHOR]

Purpose: Minimally invasive procedures, such as microwave ablation, are becoming first‐line treatment options for early‐stage liver cancer due to lower complication rates and shorter recovery times than conventional surgical techniques. Although these procedures are promising, one reason preventing widespread adoption is inadequate local tumor ablation leading to observations of higher local cancer recurrence compared to conventional procedures. Poor ablation coverage has been associated with two‐dimensional (2D) ultrasound (US) guidance of the therapy needle applicators and has stimulated investigation into the use of three‐dimensional (3D) US imaging for these procedures. We have developed a supervised 3D US needle applicator segmentation algorithm using a single user input to augment the addition of 3D US to the current focal liver tumor ablation workflow with the goals of identifying and improving needle applicator localization efficiency. Methods: The algorithm is initialized by creating a spherical search space of line segments around a manually chosen seed point that is selected by a user on the needle applicator visualized in a 3D US image. The most probable trajectory is chosen by maximizing the count and intensity of threshold voxels along a line segment and is filtered using the Otsu method to determine the tip location. Homogeneous tissue mimicking phantom images containing needle applicators were used to optimize the parameters of the algorithm prior to a four‐user investigation on retrospective 3D US images of patients who underwent microwave ablation for liver cancer. Trajectory, axis localization, and tip errors were computed based on comparisons to manual segmentations in 3D US images. Results: Segmentation of needle applicators in ten phantom 3D US images was optimized to median (Q1, Q3) trajectory, axis, and tip errors of 2.1 (1.1, 3.6)°, 1.3 (0.8, 2.1) mm, and 1.3 (0.7, 2.5) mm, respectively, with a mean ± SD segmentation computation time of 0.246 ± 0.007 s. Use of the segmentation method with a 16 in vivo 3D US patient dataset resulted in median (Q1, Q3) trajectory, axis, and tip errors of 4.5 (2.4, 5.2)°, 1.9 (1.7, 2.1) mm, and 5.1 (2.2, 5.9) mm based on all users. Conclusions: Segmentation of needle applicators in 3D US images during minimally invasive liver cancer therapeutic procedures could provide a utility that enables enhanced needle applicator guidance, placement verification, and improved clinical workflow. A semi‐automated 3D US needle applicator segmentation algorithm used in vivo demonstrated localization of the visualized trajectory and tip with less than 5° and 5.2 mm errors, respectively, in less than 0.31 s. This offers the ability to assess and adjust needle applicator placements intraoperatively to potentially decrease the observed liver cancer recurrence rates associated with current ablation procedures. Although optimized for deep and oblique angle needle applicator insertions, this proposed workflow has the potential to be altered for a variety of image‐guided minimally invasive procedures to improve localization and verification of therapy needle applicators intraoperatively. [ABSTRACT FROM AUTHOR]

Purpose: Two-dimensional ultrasound (2D US) imaging is commonly used for diagnostic and intraoperative guidance of interventional liver procedures; however, 2D US lacks volumetric information that may benefit interventional procedures. Over the past decade, three-dimensional ultrasound (3D US) has been developed to provide the missing spatial information. 3D US image acquisition is mainly based on mechanical, electromagnetic, and freehand tracking of conventional 2D US transducers, or 2D array transducers available on high-end machines. These approaches share many problems during clinical use for interventional liver imaging due to lack of flexibility and compatibility with interventional equipment, limited field-of-view (FOV), and significant capital cost compared to the benefits they introduce. In this paper, a novel system for mechanical 3D US scanning is introduced to address these issues. Methods: The authors have developed a handheld mechanical 3D US system that incorporates mechanical translation and tilt sector sweeping of any standard 2D US transducer to acquire 3D images. Each mechanical scanning function can be operated independently or may be combined to allow for a hybrid wide FOV acquisition. The hybrid motion mode facilitates registration of other modalities (e.g., CT or MRI) to the intraoperative 3D US images by providing a larger FOV in which to acquire anatomical information. The tilting mechanism of the developed mover allows image acquisition in the intercostal rib space to avoid acoustic shadowing from bone. The geometric and volumetric scanning validity of the 3D US system was evaluated on tissue mimicking US phantoms for different modes of operation. Identical experiments were performed on a commercially available 3D US system for direct comparison. To replicate a clinical scenario, the authors evaluated their 3D US system by comparing it to CT for measurement of angle and distance between interventional needles in different configurations, similar to those used for percutaneous ablation of liver tumors. Results: The mean geometrical hybrid 3D reconstruction error measured from scanning of a known string phantom was less than 1 mm in two directions and 2.5 mm in the scanning direction, which was comparable or better than the same measurements obtained from a commercially available 3D US system. The error in volume measurements of spherical phantom models depended on depth of the object. For a 20 cm3 model at a depth of 15 cm, a standard depth for liver imaging, the mean error was 3.6% ± 4.5% comparable to the 2.3% ± 1.8% error for the 3D US commercial system. The error in 3D US measurement of the tip distance and angle between two microwave ablation antennas inserted into the phantom was 0.9 ± 0.5 mm and 1.1° ± 0.7°, respectively. Conclusions: A 3D US system with hybrid scanning motions for large field-of-view 3D abdominal imaging has been developed and validated. The superior spatial information provided by 3D US might enhance image-guidance for percutaneous interventional treatment of liver malignancies. The system has potential to be integrated with other liver procedures and has application in other abdominal organs such as kidneys, spleen, or adrenals. [ABSTRACT FROM AUTHOR]

Ultrasound imaging is used extensively in diagnosis and image-guidance for interventions of human diseases. However, conventional 2D ultrasound suffers from limitations since it can only provide 2D images of 3-dimensional structures in the body. Thus, measurement of organ size is variable, and guidance of interventions is limited, as the physician is required to mentally reconstruct the 3-dimensional anatomy using 2D views. Over the past 20 years, a number of 3-dimensional ultrasound imaging approaches have been developed. We have developed an approach that is based on a mechanical mechanism to move any conventional ultrasound transducer while 2D images are collected rapidly and reconstructed into a 3D image. In this presentation, 3D ultrasound imaging approaches will be described for use in image-guided interventions.

We present an unusual case of extensive avascular malformations (AVMs) causing non-cirrhotic portal hypertension. This phenomenon, though previously described, is a rare clinical entity which, in the setting of life threatening portal hypertension, may require vascular decompression either by surgery or a transjugular intrahepatic portosystemic shunt.