A block matching based approach with multiple simultaneous templates for the real-time 2D ultrasound tracking of liver vessels

Self-supervised context-aware correlation filter for robust landmark tracking in liver ultrasound sequences

OBJECTIVES

Respiratory motion-induced displacement of internal organs poses a significant challenge in image-guided radiation therapy, particularly affecting liver landmark tracking accuracy.

METHODS

Addressing this concern, we propose a self-supervised method for robust landmark tracking in long liver ultrasound sequences. Our approach leverages a Siamese-based context-aware correlation filter network, trained by using the consistency loss between forward tracking and back verification. By effectively utilizing both labeled and unlabeled liver ultrasound images, our model, Siam-CCF , mitigates the impact of speckle noise and artifacts on ultrasonic image tracking by a context-aware correlation filter. Additionally, a fusion strategy for template patch feature helps the tracker to obtain rich appearance information around the point-landmark.

RESULTS

Siam-CCF achieves a mean tracking error of 0.79 ± 0.83 mm at a frame rate of 118.6 fps, exhibiting a superior speed-accuracy trade-off on the public MICCAI 2015 Challenge on Liver Ultrasound Tracking (CLUST2015) 2D dataset. This performance won the 5th place on the CLUST2015 2D point-landmark tracking task.

CONCLUSIONS

Extensive experiments validate the effectiveness of our proposed approach, establishing it as one of the top-performing techniques on the CLUST2015 online leaderboard at the time of this submission.

Real-Time Motion Analysis With 4D Deep Learning for Ultrasound-Guided Radiotherapy

Motion compensation in radiation therapy is a challenging scenario that requires estimating and forecasting motion of tissue structures to deliver the target dose. Ultrasound offers direct imaging of tissue in real-time and is considered for image guidance in radiation therapy. Recently, fast volumetric ultrasound has gained traction, but motion analysis with such high-dimensional data remains difficult. While deep learning could bring many advantages, such as fast data processing and high performance, it remains unclear how to process sequences of hundreds of image volumes efficiently and effectively. We present a 4D deep learning approach for real-time motion estimation and forecasting using long-term 4D ultrasound data. Using motion traces acquired during radiation therapy combined with various tissue types, our results demonstrate that long-term motion estimation can be performed markerless with a tracking error of 0.35±0.2 mm and with an inference time of less than 5 ms. Also, we demonstrate forecasting directly from the image data up to 900 ms into the future. Overall, our findings highlight that 4D deep learning is a promising approach for motion analysis during radiotherapy.

Experimental Validation of a Real-Time Adaptive 4D-Optimized Particle Radiotherapy Approach to Treat Irregularly Moving Tumors

PURPOSE

Treatment of locally advanced lung cancer is limited by toxicity and insufficient local control. Particle therapy could enable more conformal treatment than intensity modulated photon therapy but is challenged by irregular tumor motion, associated range changes, and tumor deformations. We propose a new strategy for robust, online adaptive particle therapy, synergizing 4-dimensional optimization with real-time adaptive beam tracking. The strategy was tested and the required motion monitoring precision was determined.

METHODS AND MATERIALS

In multiphase 4-dimensional dose delivery (MP4D), a dedicated quasistatic treatment plan is delivered to each motion phase of periodic 4-dimensional computed tomography (4DCT). In the new extension, "MP4D with residual tracking" (MP4DRT), lateral beam tracking compensates for the displacement of the tumor center-of-mass relative to the current phase in the planning 4DCT. We implemented this method in the dose delivery system of a clinical carbon facility and tested it experimentally for a lung cancer plan based on a periodic subset of a virtual lung 4DCT (planned motion amplitude 20 mm). Treatments were delivered in a quality assurance-like setting to a moving ionization chamber array. We considered variable motion amplitudes and baseline drifts. The required motion monitoring precision was evaluated by adding noise to the motion signal. Log-file-based dose reconstructions were performed in silico on the entire 4DCT phantom data set capable of simulating nonperiodic motion. MP4DRT was compared with MP4D, rescanned beam tracking, and internal target volume plans. Treatment quality was assessed in terms of target coverage (D95), dose homogeneity (D5-D95), conformity number, and dose to heart and lung.

RESULTS

For all considered motion scenarios and metrics, MP4DRT produced the most favorable metrics among the tested motion mitigation strategies and delivered high-quality treatments. The conformity was similar to static treatments. The motion monitoring precision required for D95 >95% was 1.9 mm.

CONCLUSIONS

With clinically feasible motion monitoring, MP4DRT can deliver highly conformal dose distributions to irregularly moving targets.

Multi3: multi-templates siamese network with multi-peaks detection and multi-features refinement for target tracking in ultrasound image sequences

Objective. Radiation therapy requires a precise target location. However, respiratory motion increases the uncertainties of the target location. Accurate and robust tracking is significant for improving operation accuracy. Approach. In this work, we propose a tracking framework Multi3, including a multi-templates Siamese network, multi-peaks detection and multi-features refinement, for target tracking in ultrasound sequences. Specifically, we use two templates to provide the location and deformation of the target for robust tracking. Multi-peaks detection is applied to extend the set of potential target locations, and multi-features refinement is designed to select an appropriate location as the tracking result by quality assessment. Main results. The proposed Multi3 is evaluated on a public dataset, i.e. MICCAI 2015 challenge on liver ultrasound tracking (CLUST), and our clinical dataset provided by the Chinese People’s Liberation Army General Hospital. Experimental results show that Multi3 achieves accurate and robust tracking in ultrasound sequences (0.75 ± 0.62 mm and 0.51 ± 0.32 mm tracking errors in two datasets). Significance. The proposed Multi3 is the most robust method on the CLUST 2D benchmark set, exhibiting potential in clinical practice.

An integrated ultrasound imaging and abdominal compression device for respiratory motion management in radiation therapy

BACKGROUND

Radiotherapy to tumors in the abdomen is challenging because of the significant organ movement and tissue deformation caused by respiration.

PURPOSE

A motion management strategy that integrated ultrasound (US) imaging with abdominal compression was developed and evaluated, where US was used to real-time monitor organ motion after abdominal compression.

METHODS

A device that combined a US imaging system and an abdominal compression plate (ACP) was developed. Twenty-one healthy volunteers were involved to evaluate the motion management efficacy. Each volunteer was immobilized on a flat bench by the device. Abdominal US data were successively collected with and without ACP compression and experiments were repeated three times to verify the imaging reproducibility. A template matching algorithm based on normalized cross correlation (NCC) was implemented to track the targets (vessels in the liver, pancreas and stomach) automatically. The matching algorithm was validated by comparing with the manual references. Automatic tracking was judged as failed if the center of mass difference from manual tracking was beyond a failure threshold. Based on the locations obtained through the template matching algorithm, the motion correlation between liver and pancreas/stomach was investigated using Pearson correlation test. Paired Student's t-test was used to analyze the difference between the results without and with ACP compression.

RESULTS

The liver motion amplitude over all 21 volunteers was significantly (p<0.001) reduced from 14.9 ± 5.5/3.4 ± 1.8 mm in superior-inferior (SI)/anterior-posterior (AP) direction before ACP compression to 7.3 ± 1.5/1.6 ± 0.7 mm after ACP compression. The mean liver motion standard deviation before compression was on average 2.8/1.4 mm in SI/AP direction, and was significantly (p<0.001) reduced to 0.9/0.4 mm after compression. The failure rates of automatic tracking for liver, pancreas and stomach were reduced for failure thresholds of 1-5 mm after applying ACP. The Pearson correlation coefficients between liver and pancreas/stomach were 0.98/0.97 without ACP and 0.96/0.94 with ACP in SI direction, and were 0.68/0.68 and 0.43/0.42 in AP direction. The motion prediction errors for pancreas/stomach with ACP have significantly (p<0.001) reduced to 0.45 ± 0.36/0.52 ± 0.43 mm from 0.69 ± 0.56/0.71 ± 0.66 mm without ACP in SI direction, and to 0.38 ± 0.33/0.39 ± 0.27 mm from 0.44 ± 0.35/0.61 ± 0.59 mm in AP direction.

CONCLUSIONS

The proposed strategy that combines real-time US imaging and abdominal compression has the potential to reduce the abdominal organ motion while improving both target tracking reliability and motion reproducibility. Furthermore, the observed correlation between liver and pancreas/stomach motion indicates the possibility of indirect pancreas/stomach tracking using liver markers as tracking surrogates. The strategy is expected to provide an alternative for respiratory motion management in the radiation treatment of abdominal tumors. This article is protected by copyright. All rights reserved.

Fusion Siamese network with drift correction for target tracking in ultrasound sequences

Motion tracking techniques can revise the bias arising from respiration-caused motion in radiation therapy. Tracking key structures accurately and at a real-time speed is necessary for effective motion tracking. In this work, we propose a fusion Siamese network with drift correction for target tracking in ultrasound sequences. Specifically, the network fuses four response maps generated by the cross-correlation between convolution layers at different resolutions to reduce up-sampling error. A correction strategy combining local structural similarity and target trajectory is proposed to revise the target drift predicted by the network. Moreover, a coarse-to-fine strategy is proposed to train the network with a limited number of annotated images, in which an augmented dataset is generated by corner points to learn network features with high generalizability. The proposed method is evaluated on the basis of the public dataset of the MICCAI 2015 Challenge on Liver UltraSound Tracking (CLUST) and our ultrasound image dataset, which is provided by the Chinese People’s Liberation Army General Hospital (CPLAGH). A tracking error of 0.80 ± 1.16 mm is observed for 85 targets across 39 ultrasound sequences in the CLUST dataset. A tracking error of 0.61 ± 0.36 mm is observed for 20 targets across 10 ultrasound sequences in the CPLAGH dataset. The effectiveness of the proposed fusion and correction strategies is verified via two ablation experiments. Overall, the experimental results demonstrate the effectiveness of the proposed fusion Siamese network with drift correction and reveal its potential in clinical practice.

An Upgraded Siamese Neural Network for Motion Tracking in Ultrasound Image Sequences

Deep learning is heavily being borrowed to solve problems in medical imaging applications, and Siamese neural networks are the front runners of motion tracking. In this article, we propose to upgrade one such Siamese architecture-based neural network for robust and accurate landmark tracking in ultrasound images to improve the quality of image-guided radiation therapy. Although several researchers have improved the Siamese architecture-based networks with sophisticated detection modules and by incorporating transfer learning, the inherent assumptions of the constant position model and the missing motion model remain unaddressed limitations. In our proposed model, we overcome these limitations by introducing two modules into the original architecture. We employ a reference template update to resolve the constant position model and a linear Kalman filter (LKF) to address the missing motion model. Moreover, we demonstrate that the proposed architecture provides promising results without transfer learning. The proposed model was submitted to an open challenge organized by MICCAI and was evaluated exhaustively on the Liver US Tracking (CLUST) 2D dataset. Experimental results proved that the proposed model tracked the landmarks with promising accuracy. Furthermore, we also induced synthetic occlusions to perform a qualitative analysis of the proposed approach. The evaluations were performed on the training set of the CLUST 2D dataset. The proposed method outperformed the original Siamese architecture by a significant margin.

Deep learning-based motion tracking using ultrasound images

PURPOSE

Ultrasound (US) imaging is an established imaging modality capable of offering video-rate volumetric images without ionizing radiation. It has the potential for intra-fraction motion tracking in radiation therapy. In this study, a deep learning-based method has been developed to tackle the challenges in motion tracking using US imaging.

METHODS

We present a Markov-like network, which is implemented via generative adversarial networks, to extract features from sequential US frames (one tracked frame followed by untracked frames) and thereby estimate a set of deformation vector fields (DVFs) through the registration of the tracked frame and the untracked frames. The positions of the landmarks in the untracked frames are finally determined by shifting landmarks in the tracked frame according to the estimated DVFs. The performance of the proposed method was evaluated on the testing dataset by calculating the tracking error (TE) between the predicted and ground truth landmarks on each frame.

RESULTS

The proposed method was evaluated using the MICCAI CLUST 2015 dataset which was collected using seven US scanners with eight types of transducers and the Cardiac Acquisitions for Multi-structure Ultrasound Segmentation (CAMUS) dataset which was acquired using GE Vivid E95 ultrasound scanners. The CLUST dataset contains 63 2D and 22 3D US image sequences respectively from 42 and 18 subjects, and the CAMUS dataset includes 2D US images from 450 patients. On CLUST dataset, our proposed method achieved a mean tracking error of 0.70 ± 0.38 mm for the 2D sequences and 1.71 ± 0.84 mm for the 3D sequences for those public available annotations. And on CAMUS dataset, a mean tracking error of 0.54 ± 1.24 mm for the landmarks in the left atrium was achieved.

CONCLUSIONS

A novel motion tracking algorithm using US images based on modern deep learning techniques has been demonstrated in this study. The proposed method can offer millimeter-level tumor motion prediction in real time, which has the potential to be adopted into routine tumor motion management in radiation therapy.

Deep Learning for Ultrasound Beamforming in Flexible Array Transducer

Ultrasound imaging has been developed for image-guided radiotherapy for tumor tracking, and the flexible array transducer is a promising tool for this task. It can reduce the user dependence and anatomical changes caused by the traditional ultrasound transducer. However, due to its flexible geometry, the conventional delay-and-sum (DAS) beamformer may apply incorrect time delay to the radio-frequency (RF) data and produce B-mode images with considerable defocusing and distortion. To address this problem, we propose a novel end-to-end deep learning approach that may alternate the conventional DAS beamformer when the transducer geometry is unknown. Different deep neural networks (DNNs) were designed to learn the proper time delays for each channel, and they were expected to reconstruct the undistorted high-quality B-mode images directly from RF channel data. We compared the DNN results to the standard DAS beamformed results using simulation and flexible array transducer scan data. With the proposed DNN approach, the averaged full-width-at-half-maximum (FWHM) of point scatters is 1.80 mm and 1.31 mm lower in simulation and scan results, respectively; the contrast-to-noise ratio (CNR) of the anechoic cyst in simulation and phantom scan is improved by 0.79 dB and 1.69 dB, respectively; and the aspect ratios of all the cysts are closer to 1. The evaluation results show that the proposed approach can effectively reduce the distortion and improve the lateral resolution and contrast of the reconstructed B-mode images.

Population-based 3D respiratory motion modelling from convolutional autoencoders for 2D ultrasound-guided radiotherapy

Radiotherapy is a widely used treatment modality for various types of cancers. A challenge for precise delivery of radiation to the treatment site is the management of internal motion caused by the patient's breathing, especially around abdominal organs such as the liver. Current image-guided radiation therapy (IGRT) solutions rely on ionising imaging modalities such as X-ray or CBCT, which do not allow real-time target tracking. Ultrasound imaging (US) on the other hand is relatively inexpensive, portable and non-ionising. Although 2D US can be acquired at a sufficient temporal frequency, it doesn't allow for target tracking in multiple planes, while 3D US acquisitions are not adapted for real-time. In this work, a novel deep learning-based motion modelling framework is presented for ultrasound IGRT. Our solution includes an image similarity-based rigid alignment module combined with a deep deformable motion model. Leveraging the representational capabilities of convolutional autoencoders, our deformable motion model associates complex 3D deformations with 2D surrogate US images through a common learned low dimensional representation. The model is trained on a variety of deformations and anatomies which enables it to generate the 3D motion experienced by the liver of a previously unseen subject. During inference, our framework only requires two pre-treatment 3D volumes of the liver at extreme breathing phases and a live 2D surrogate image representing the current state of the organ. In this study, the presented model is evaluated on a 3D+t US data set of 20 volunteers based on image similarity as well as anatomical target tracking performance. We report results that surpass comparable methodologies in both metric categories with a mean tracking error of 3.5±2.4 mm, demonstrating the potential of this technique for IGRT.

First-in-human imaging using a MR-compatible e4D ultrasound probe for motion management of radiotherapy

PURPOSE

Respiration-induced tumor or organ positional changes can impact the accuracy of external beam radiotherapy. Motion management strategies are used to account for these changes during treatment. The authors report on the development, testing, and first-in-human evaluation of an electronic 4D (e4D) MR-compatible ultrasound probe that was designed for hands-free operation in a MR and linear accelerator (LINAC) environment.

METHODS

Ultrasound components were evaluated for MR compatibility. Electromagnetic interference (EMI) shielding was used to enclose the entire probe and a factory-fabricated cable shielded with copper braids was integrated into the probe. A series of simultaneous ultrasound and MR scans were acquired and analyzed in five healthy volunteers.

RESULTS

The ultrasound probe led to minor susceptibility artifacts in the MR images immediately proximal to the ultrasound probe at a depth of <10 mm. Ultrasound and MR-based motion traces that were derived by tracking the salient motion of endogenous target structures in the superior-inferior (SI) direction demonstrated good concordance (Pearson correlation coefficients of 0.95-0.98) between the ultrasound and MRI datasets.

CONCLUSION

We have demonstrated that our hands-free, e4D probe can acquire ultrasound images during a MR acquisition at frame rates of approximately 4 frames per second (fps) without impacting either the MR or ultrasound image quality. This use of this technology for interventional procedures (e.g. biopsies and drug delivery) and motion compensation during imaging are also being explored.

3D dosimetric validation of ultrasound-guided radiotherapy with a dynamically deformable abdominal phantom

OBJECTIVES

The purpose of this study was to dosimetrically benchmark gel dosimetry measurements in a dynamically deformable abdominal phantom for intrafraction image guidance through a multi-dosimeter comparison. Once benchmarked, the study aimed to perform a proof-of-principle study for validation measurements of an ultrasound image-guided radiotherapy delivery system.

METHODS

The phantom was dosimetrically benchmarked by delivering a liver VMAT plan and measuring the 3D dose distribution with DEFGEL dosimeters. Measured doses were compared to the treatment planning system and measurements acquired with radiochromic film and an ion chamber. The ultrasound image guidance validation was performed for a hands-free ultrasound transducer for the tracking of liver motion during treatment.

RESULTS

Gel dosimeters were compared to the TPS and film measurements, showing good qualitative dose distribution matches, low γ values through most of the high dose region, and average 3%/5 mm γ-analysis pass rates of 99.2%(0.8%) and 90.1%(0.8%), respectively. Gel dosimeter measurements matched ion chamber measurements within 3%. The image guidance validation study showed the measurement of the treatment delivery improvements due to the inclusion of the ultrasound image guidance system. Good qualitative matching of dose distributions and improvements of the γ-analysis results were observed for the ultrasound-gated dosimeter compared to the ungated dosimeter.

CONCLUSIONS

DEFGEL dosimeters in phantom showed good agreement with the planned dose and other dosimeters for dosimetric benchmarking. Ultrasound image guidance validation measurements showed good proof-of-principle of the utility of the phantom system as a method of validating ultrasound-based image guidance systems and potentially other image guidance methods.

Cascaded one-shot deformable convolutional neural networks: Developing a deep learning model for respiratory motion estimation in ultrasound sequences

Improving the quality of image-guided radiation therapy requires the tracking of respiratory motion in ultrasound sequences. However, the low signal-to-noise ratio and the artifacts in ultrasound images make it difficult to track targets accurately and robustly. In this study, we propose a novel deep learning model, called a Cascaded One-shot Deformable Convolutional Neural Network (COSD-CNN), to track landmarks in real time in long ultrasound sequences. Specifically, we design a cascaded Siamese network structure to improve the tracking performance of CNN-based methods. We propose a one-shot deformable convolution module to enhance the robustness of the COSD-CNN to appearance variation in a meta-learning manner. Moreover, we design a simple and efficient unsupervised strategy to facilitate the network's training with a limited number of medical images, in which many corner points are selected from raw ultrasound images to learn network features with high generalizability. The proposed COSD-CNN has been extensively evaluated on the public Challenge on Liver UltraSound Tracking (CLUST) 2D dataset and on our own ultrasound image dataset from the First Affiliated Hospital of Sun Yat-sen University (FSYSU). Experiment results show that the proposed model can track a target through an ultrasound sequence with high accuracy and robustness. Our method achieves new state-of-the-art performance on the CLUST 2D benchmark set, indicating its strong potential for application in clinical practice.

An improved abdominal phantom for intrafraction image guidance validation

A dynamically compressible phantom of the human abdomen that simulates organ motion with breathing is being developed for possible testing of image-gated beam delivery in radiotherapy. The polyvinyl chloride plastisol (PVCP) phantom features a cavity that can contain a deformable normoxic polyacrylamide gel (nPAG) dosimeter that is intended for use with MRI to provide dosimetric data. The phantom has been improved by the inclusion of new components that are more realistic anatomically and exhibit CT values similar to those of the tissues they mimic. Component organs were made from 3D-printed molds developed from CT contours of a real patient and their radiodensities adjusted by varying the mass ratios of the PVCP hardener and softener during manufacture. To make the phantom more compatible with ultrasound imaging a graphite scatterer was mixed into some of the phantom components to produce a background speckle pattern. This provided contrast between the body and a moving anatomical target intended for motion tracking. Phantom insert motion magnitude and repeatibility was assessed using CT by imaging two phantom inserts, one containing fiducial markers and the other containing iodinated gelatin, at the same position after repeated cycles of deformation. The maximum motion of a phantom fiducial at the position of the phantom treatment target was found to be 12.2 mm. The phantom design resulted in dosimeter motion with a point-to-point repatability within 0.3 mm on average and contour repeatability resulting in Dice coefficients exceeding 0.98 on average.

Investigation of tumor and vessel motion correlation in the liver

Intrafraction imaging‐based motion management systems for external beam radiotherapy can rely on internal surrogate structures when the target is not easily visualized. This work evaluated the validity of using liver vessels as internal surrogates for the estimation of liver tumor motion. Vessel and tumor motion were assessed using ten two‐dimensional sagittal MR cine datasets collected on the ViewRay MRIdian. For each case, a liver tumor and at least one vessel were tracked for 175 s. A tracking approach utilizing block matching and multiple simultaneous templates was applied. Accuracy of the tracked motion was calculated from the error between the tracked centroid position and manually defined ground truth annotations. The patient’s abdomen surface and diaphragm were manually annotated in all frames. The Pearson correlation coefficient (CC) was used to compare the motion of the features and tumor in the anterior–posterior (AP) and superior–inferior (SI) directions. The distance between the centroids of the features and the tumors was calculated to assess if feature proximity affects relative correlation, and the tumor range of motion was determined. Intra‐ and interfraction motion amplitude variabilities were evaluated to further assess the relationship between tumor and feature motion. The mean CC between the motion of the vessel and the tumor were 0.85 ± 0.11 (AP) and 0.92 ± 0.04 (SI), 0.83 ± 0.11 (AP) and −0.89 ± 0.06 (SI) for the surface and tumor, and 0.80 ± 0.17 (AP) and 0.94 ± 0.03 (SI) for the diaphragm and tumor. For intrafraction analysis, the average amplitude variability was 2.47 ± 0.77 mm (AP) and 3.14 ± 1.49 mm (SI) for the vessels, 2.70 ± 1.08 mm (AP) and 3.43 ± 1.73 mm (SI) for the surface, and 2.76 ± 1.41 mm (AP) and 2.91 ± 1.38 mm (SI) for the diaphragm. No relationship between distance and motion correlation was observed. The motion of liver tumors and liver vessels was well correlated, making vessels a suitable surrogate for tumor motion in the liver.

2D ultrasound imaging based intra-fraction respiratory motion tracking for abdominal radiation therapy using machine learning

We have previously developed a robotic ultrasound imaging system for motion monitoring in abdominal radiation therapy. Owing to the slow speed of ultrasound image processing, our previous system could only track abdominal motions under breath-hold. To overcome this limitation, a novel 2D-based image processing method for tracking intra-fraction respiratory motion is proposed. Fifty-seven different anatomical features acquired from 27 sets of 2D ultrasound sequences were used in this study. Three 2D ultrasound sequences were acquired with the robotic ultrasound system from three healthy volunteers. The remaining datasets were provided by the 2015 MICCAI Challenge on Liver Ultrasound Tracking. All datasets were preprocessed to extract the feature point, and a patient-specific motion pattern was extracted by principal component analysis and slow feature analysis (SFA). The tracking finds the most similar frame (or indexed frame) by a k-dimensional-tree-based nearest neighbor search for estimating the tracked object location. A template image was updated dynamically through the indexed frame to perform a fast template matching (TM) within a learned smaller search region on the incoming frame. The mean tracking error between manually annotated landmarks and the location extracted from the indexed training frame is 1.80  ±  1.42 mm. Adding a fast TM procedure within a small search region reduces the mean tracking error to 1.14  ±  1.16 mm. The tracking time per frame is 15 ms, which is well below the frame acquisition time. Furthermore, the anatomical reproducibility was measured by analyzing the location's anatomical landmark relative to the probe; the position-controlled probe has better reproducibility and yields a smaller mean error across all three volunteer cases, compared to the force-controlled probe (2.69 versus 11.20 mm in the superior-inferior direction and 1.19 versus 8.21 mm in the anterior-posterior direction). Our method reduces the processing time for tracking respiratory motion significantly, which can reduce the delivery uncertainty.

Evaluation of 2D and 3D ultrasound tracking algorithms and impact on ultrasound-guided liver radiotherapy margins

PURPOSE

Compensation for respiratory motion is important during abdominal cancer treatments. In this work we report the results of the 2015 MICCAI Challenge on Liver Ultrasound Tracking and extend the 2D results to relate them to clinical relevance in form of reducing treatment margins and hence sparing healthy tissues, while maintaining full duty cycle.

METHODS

We describe methodologies for estimating and temporally predicting respiratory liver motion from continuous ultrasound imaging, used during ultrasound-guided radiation therapy. Furthermore, we investigated the trade-off between tracking accuracy and runtime in combination with temporal prediction strategies and their impact on treatment margins.

RESULTS

Based on 2D ultrasound sequences from 39 volunteers, a mean tracking accuracy of 0.9 mm was achieved when combining the results from the 4 challenge submissions (1.2 to 3.3 mm). The two submissions for the 3D sequences from 14 volunteers provided mean accuracies of 1.7 and 1.8 mm. In combination with temporal prediction, using the faster (41 vs 228 ms) but less accurate (1.4 vs 0.9 mm) tracking method resulted in substantially reduced treatment margins (70% vs 39%) in contrast to mid-ventilation margins, as it avoided non-linear temporal prediction by keeping the treatment system latency low (150 vs 400 ms). Acceleration of the best tracking method would improve the margin reduction to 75%.

CONCLUSIONS

Liver motion estimation and prediction during free-breathing from 2D ultrasound images can substantially reduce the in-plane motion uncertainty and hence treatment margins. Employing an accurate tracking method while avoiding non-linear temporal prediction would be favorable. This approach has the potential to shorten treatment time compared to breath-hold and gated approaches, and increase treatment efficiency and safety.