Respiratory Motion Tracking Research Articles

Respiratory motion tracking of the thoracoabdominal surface based on defect-aware point cloud registration.

The performance of conventional lung puncture surgery is a complex undertaking due to the surgeon's reliance on visual assessment of respiratory conditions and the manual execution of the technique while the patient maintains breath-holding. However, the failure to correctly perform a puncture technique can lead to negative outcomes, such as the development of sores and pneumothorax. In this work, we proposed a novel approach for monitoring respiratory motion by utilizing defect-aware point cloud registration and descriptor computation. Through a thorough examination of the attributes of the inputs, we suggest the incorporation of a defect detection branch into the registration network. Additionally, we developed two modules with the aim of augmenting the quality of the extracted features. A coarse-to-fine respiratory phase recognition approach based on descriptor computation is devised for the respiratory motion tracking. The efficacy of the suggested registration method is demonstrated through experimental findings conducted on both publicly accessible datasets and thoracoabdominal point cloud datasets. We obtained state-of-the-art registration results on ModelNet40 datasets, with 1.584∘ on rotation mean absolute error and 0.016 mm on translation mean absolute error, respectively. The experimental findings conducted on a thoracoabdominal point cloud dataset indicate that our method exhibits efficacy and efficiency, achieving a frame matching rate of 2 frames per second and a phase recognition accuracy of 96.3%. This allows identifying matching frames from template point clouds that display different parts of a patient's thoracoabdominal surface while breathing regularly to distinguish breathing stages and track breathing.

Deep match: A zero-shot framework for improved fiducial-free respiratory motion tracking

Background and purposeMotion management is essential to reduce normal tissue exposure and maintain adequate tumor dose in lung stereotactic body radiation therapy (SBRT). Lung SBRT using an articulated robotic arm allows dynamic tracking during radiation dose delivery. Two stereoscopic X-ray tracking modes are available – fiducial-based and fiducial-free tracking. Although X-ray detection of implanted fiducials is robust, the implantation procedure is invasive and inapplicable to some patients and tumor locations. Fiducial-free tracking relies on tumor contrast, which challenges the existing tracking algorithms for small (e.g., <15 mm) and/or tumors obscured by overlapping anatomies. To markedly improve the performance of fiducial-free tracking, we proposed a deep learning-based template matching algorithm – Deep Match. MethodDeep Match consists of four self-definable stages – training-free feature extractor, similarity measurements for location proposal, local refinements, and uncertainty level prediction for constructing a more trustworthy and versatile pipeline. Deep Match was validated on a 10 (38 fractions; 2661 images) patient cohort whose lung tumor was trackable on one X-ray view, while the second view did not offer sufficient conspicuity for tumor tracking using existing methods. The patient cohort was stratified into subgroups based on tumor sizes (<10 mm, 10–15 mm, and >15 mm) and tumor locations (with/without thoracic anatomy overlapping). ResultsOn X-ray views that conventional methods failed to track the lung tumor, Deep Match achieved robust performance as evidenced by >80 % 3 mm-Hit (detection within 3 mm superior/inferior margin from ground truth) for 70 % of patients and <3 mm superior/inferior distance (SID) ∼1 mm standard deviation for all the patients. ConclusionDeep Match is a zero-shot learning network that explores the intrinsic neural network benefits without training on patient data. With Deep Match, fiducial-free tracking can be extended to more patients with small tumors and with tumors obscured by overlapping anatomy.

Neural signals-based respiratory motion tracking: a proof-of-concept study

Objective. Respiratory motion tracking techniques can provide optimal treatment accuracy for thoracoabdominal radiotherapy and robotic surgery. However, conventional imaging-based respiratory motion tracking techniques are time-lagged owing to the system latency of medical linear accelerators and surgical robots. This study aims to investigate the precursor time of respiratory-related neural signals and analyze the potential of neural signals-based respiratory motion tracking. Approach. The neural signals and respiratory motion from eighteen healthy volunteers were acquired simultaneously using a 256-channel scalp electroencephalography (EEG) system. The neural signals were preprocessed using the MNE python package to extract respiratory-related EEG neural signals. Cross-correlation analysis was performed to assess the precursor time and cross-correlation coefficient between respiratory-related EEG neural signals and respiratory motion. Main results. Respiratory-related neural signals that precede the emergence of respiratory motion are detectable via non-invasive EEG. On average, the precursor time of respiratory-related EEG neural signals was 0.68 s. The representative cross-correlation coefficients between EEG neural signals and respiratory motion of the eighteen healthy subjects varied from 0.22 to 0.87. Significance. Our findings suggest that neural signals have the potential to compensate for the system latency of medical linear accelerators and surgical robots. This indicates that neural signals-based respiratory motion tracking is a potential promising solution to respiratory motion and could be useful in thoracoabdominal radiotherapy and robotic surgery.

Live-view 4D GRASP MRI: A framework for robust real-time respiratory motion tracking with a sub-second imaging latency.

To propose a framework called live-view golden-angle radial sparse parallel (GRASP) MRI for low-latency and high-fidelity real-time volumetric MRI. Live-view GRASP MRI has two stages. The first one is called an off-view stage and the second one is called a live-view stage. In the off-view stage, 3D k-space data and 2D navigators are acquired alternatively using a new navi-stack-of-stars sampling scheme. A 4D motion database is then generated that contains time-resolved MR images at a sub-second temporal resolution, and each image is linked to a 2D navigator. In the live-view stage, only 2D navigators are acquired. At each time point, a live-view 2D navigator is matched to all the off-view 2D navigators. A 3D image that is linked to the best-matched off-view 2D navigator is then selected for this time point. This framework places the typical acquisition and reconstruction burden of MRI in the off-view stage, enabling low-latency real-time 3D imaging in the live-view stage. The accuracy of live-view GRASP MRI and the robustness of 2D navigators for characterizing respiratory variations and/or body movements were assessed. Live-view GRASP MRI can efficiently generate real-time volumetric images that match well with the ground-truth references, with an imaging latency below 500 ms. Compared to 1D navigators, 2D navigators enable more reliable characterization of respiratory variations and/or body movements that may occur throughout the two imaging stages. Live-view GRASP MRI represents a novel, accurate, and robust framework for real-time volumetric imaging, which can potentially be applied for motion adaptive radiotherapy on MRI-Linac.

Respiratory motion tracking of spine stereotactic radiotherapy in prone position.

The CyberKnife system is a specialized device for non-coplanar irradiation; however, it possesses the geometric restriction that the beam cannot be irradiated from under the treatment couch. Prone positioning is expected to reduce the dose to normal lung tissue in spinal stereotactic body radiotherapy (SBRT) owing to the efficiency of beam arrangement; however, respiratory motion occurs. Therefore, the Xsight spine prone tracking (XSPT) system is used to reduce the effects of respiratory motion. The purpose of this study was to evaluate the motion-tracking error of the spine in the prone position. Data from all 25 patients who underwent spinal SBRT at our institution between April 2020 and February 2022 using CyberKnife (VSI, version 11.1.0) with the XSPT tracking system were retrospectively analyzed using log files. The tumor motion, correlation, and prediction errors for each patient were examined. Furthermore, to assess the potential relationships between the parameters, the relationships between the tumor-motion amplitudes and correlation or prediction errors were investigated using linear regression. The tumor-motion amplitudes in each direction were as follows: superior-inferior (SI), 0.51±0.39mm; left-right (LR), 0.37±0.29mm; and anterior-posterior (AP), 3.43±1.63mm. The overall mean correlation and prediction errors were 0.66±0.48mm and 0.06±0.07mm, respectively. The prediction errors were strongly correlated with the tumor-motion amplitudes, whereas the correlation errors were not. This study demonstrated that the correlation error of spinal SBRT in the prone position is sufficiently small to be independent of the tumor-motion amplitude. Furthermore, the prediction error is small, contributing only slightly to the tracking error. These findings will improve the understanding of how to compensate for respiratory-motion uncertainty in the prone position.

Real-time prediction of stomach motions based upon gastric contraction and breathing models

Objective. Precision radiation therapy requires managing motions of organs at risk that occur during treatment. While methods have been developed for real-time respiratory motion tracking, non-breathing intra-fractional variations (including gastric contractile motion) have seen little attention to date. The purpose of this study is to develop a cyclic gastric contractile motion prediction model to support real-time management during radiotherapy. Approach. The observed short-term reproducibility of gastric contractile motion permitted development of a prediction model that (1) extracts gastric contraction motion phases from few minutes of golden angle stack of stars scanning (at patient positioning), (2) estimate gastric phase of real-time sampled data acquired during treatment delivery to these reconstructed phases and (3) predicting future gastric phase by linear extrapolation using estimation results from step 2 to account for processing and system latency times. Model was evaluated on three parameters including training time window for step 1, number of spokes for real-time sampling data in step 2 and future prediction time. Main results. The model was tested on a population of 20 min data samples from 25 scans from 15 patients. The mean prediction error with 10 spokes and 2 min training was 0.3 ± 0.1 mm (0.1–0.7 mm) with 5.1 s future time, slowly rising to 0.6 ± 0.2 mm (0.2–1.1 mm) for 6.8 s future time and then increasing rapidly for longer forward predictions, for an average 3.6 ± 0.5 mm (2.8–4.7 mm) HD95 of gastric motion. Results showed that reducing of train time window (5–2 min) does not influence the prediction performance, while using 5 spokes increased prediction errors. Significance. The proposed gastric motion prediction model has sufficiently accurate prediction performance to allow for sub-millimeter accuracy while allowing sufficient time for data processing and machine interaction and shows the potential for clinical implementation to support stomach motion tracking during radiotherapy.

PO-1589 Gating Device L: an external surrogate for respiratory motion tracking on TrueBeam

PO-1589 Gating Device L: an external surrogate for respiratory motion tracking on TrueBeam

Fiducial markers for stereotactic lung radiation therapy: review of the transthoracic, endovascular and endobronchial approaches.

Stereotactic body radiation therapy is an alternative to surgery for early-stage, inoperable peripheral non-small cell lung cancer. As opposed to linear accelerator (linac)-based (e.g. gating) and free-breathing techniques, CyberKnife® with Synchrony® technology allows accurate radiation delivery by means of a real-time respiratory motion tracking system using, in most cases, metal fiducial markers (FMs) placed in the vicinity of the target. The aims of this review are as follows. First, to describe the safety and efficacy of the transthoracic, endovascular and endobronchial FM insertion techniques for peripheral pulmonary lesions (PPLs). Second, to analyse performance in terms of the migration and tracking rates of different FM types. Recent developments in FM tracking for central lesions will also be reviewed. In conclusion, for PPLs, the endobronchial approach provides a low rate of pneumothorax, offers the possibility of concurrent diagnostic sampling for both the PPL and the lymph nodes, and, finally, reduces the intervention time compared to other techniques. In this context, coil-tailed and coil-spring FMs have shown the lowest migration rate with a consequently high tracking rate.

Data-driven respiratory signal estimation from temporally finely sampled projection data in conventional cardiac perfusion SPECT imaging.

The aim of this work was to revisit the data-driven approach of axial center-of-mass (COM) measurements to recover a surrogate respiratory signal from finely sampled (100ms) single photon emission computed tomography (SPECT) projection data derived from list-mode acquisitions. For our initial evaluation, we acquired list-mode projection data from an anthropomorphic cardiac phantom mounted on a Quasar respiratory motion platform simulating 15mm amplitude respiratory motion. We also selected 302 consecutive patients (138 males, 164 females) with list-mode acquisitions, external respiratory motion tracking, and written consent to evaluate the clinical efficacy of our data-driven approach. Linear regression, Pearson's correlation coefficient (r), and standard error of the estimates (SEE) between the respiratory signals obtained with a visual tracking system (VTS) and COM measurements were calculated for individual projection data sets and for the patient group as a whole. Both the VTS- and COM-derived respiratory signals were used to estimate and correct respiratory motion. The reconstruction for six-degree of freedom rigid-body motion estimation was done in two ways: (1) using three iterations of ordered-subsets expectation-maximization (OSEM) with four subsets (16 projection angles per subset), or 12 iterations of maximum-likelihood expectation-maximization (MLEM). Respiratory motion compensation was done employing either OSEM with 16 subsets (four projection angles per subset) and five iterations or MLEM and 80 iterations, using the two respiratory estimates, respectively. Polar map quantification was also performed, calculating the percentage count difference (%Diff) between polar maps without and with respiratory motion included. Average % Diff was calculated in 17 segments (defined according to ASNC Guidelines). Paired t-tests were used to determine significance (p-values). The r-value calculated when comparing the VTS and COM respiratory signals varied widely between -0.01 and 0.96 with an average of 0.70, while the SEE varied between 0.80 and 6.48mm with an average of 2.05mm for our patient set, while the same values for the one anthropomorphic phantom acquisition are 0.91 and 1.11mm, respectively. A comparison between the respiratory motion estimates for VTS and COM in the S-I direction yielded an r=0.90 (0.94), and an SEE of 1.56mm (1.20mm) for OSEM (MLEM), respectively. Bland-Altman plots and calculated intraclass correlation coefficients also showed excellent agreement between the VTS and COM respiratory motion estimates. Average S-I respiratory estimates for the VTS (COM) were 9.04 (9.2mm) and 9.01mm (9.14mm) for the OSEM and MLEM, respectively. The paired t-test approached significance when comparing VTS and COM estimated respiratory signals with p-values of 0.069 and 0.051 for OSEM and MLEM. The respiratory estimates from the anthropomorphic cardiac phantom experiment using the VTS (COM) were 12.62 (14.10mm) and 12.55mm (14.29mm) for OSEM and MLEM, respectively. Polar map quantification yielded average % Diff consistently better when employing VTS-derived respiratory estimates to correct for respiration compared to the COM-derived estimates. The results indicate that our COM method has the potential to provide an automated data-driven correction of cardiac respiratory motion without the drawbacks of our VTS methodology. However, it is not generally equivalent to the VTS method in extent of correction.

Research on multi-layer perception motion prediction of breathing on thoracic and abdominal surface under non-therapy

When a nano-level radiotherapy robot with stereotactic technology and nano-therapy technology treats thoracic and abdominal tumours, the interference caused by the precise positioning of the tumour by the breathing motion of the human body cannot be ignored. In order to compensate for the breathing movement and improve the treatment accuracy of the nano-level radiotherapy robot, the main method adopted is the real-time breathing tracking technology, that is, by establishing the correlation model between the tumour in the body and the marker points on the body surface, the movement of the marker points is used to predict the position of the tumour, thereby adjusting the beam of the robotic arm for precise radiotherapy. Aiming at the problem of tumour respiratory motion tracking modelling, a three-dimensional tumour respiratory motion simulator was designed. This simulator can stably simulate the nonlinear and time-lag motion relationship between tumour and markers, and is the tumour breathing of nano-level radiotherapy robots. The motion tracking model provides a data source and verification platform.

A data-driven respiratory motion estimation approach for PET based on time-of-flight weighted positron emission particle tracking.

PurposeRespiratory motion of patients during positron emission tomography (PET)/computed tomography (CT) imaging affects both image quality and quantitative accuracy. Hardware‐based motion estimation, which is the current clinical standard, requires initial setup, maintenance, and calibration of the equipment, and can be associated with patient discomfort. Data‐driven techniques are an active area of research with limited exploration into lesion‐specific motion estimation. This paper introduces a time‐of‐flight (TOF)‐weighted positron emission particle tracking (PEPT) algorithm that facilitates lesion‐specific respiratory motion estimation from raw listmode PET data.MethodsThe TOF‐PEPT algorithm was implemented and investigated under different scenarios: (a) a phantom study with a point source and an Anzai band for respiratory motion tracking; (b) a phantom study with a point source only, no Anzai band; (c) two clinical studies with point sources and the Anzai band; (d) two clinical studies with point sources only, no Anzai band; and (e) two clinical studies using lesions/internal regions instead of point sources and no Anzai band. For studies with radioactive point sources, they were placed on patients during PET/CT imaging. The motion tracking was performed using a preselected region of interest (ROI), manually drawn around point sources or lesions on reconstructed images. The extracted motion signals were compared with the Anzai band when applicable. For the purposes of additional comparison, a center‐of‐mass (COM) algorithm was implemented both with and without the use of TOF information. Using the motion estimate from each method, amplitude‐based gating was applied, and gated images were reconstructed.ResultsThe TOF‐PEPT algorithm is shown to successfully determine the respiratory motion for both phantom and clinical studies. The derived motion signals correlated well with the Anzai band; correlation coefficients of 0.99 and 0.94‐0.97 were obtained for the phantom study and the clinical studies, respectively. TOF‐PEPT was found to be 13–38% better correlated with the Anzai results than the COM methods. Maximum Standardized Uptake Values (SUVs) were used to quantitatively compare the reconstructed‐gated images. In comparison with the ungated image, a 14–39% increase in the max SUV across several lesion areas and an 8.7% increase in the max SUV on the tracked lesion area were observed in the gated images based on TOF‐PEPT. The distinct presence of lesions with reduced blurring effect and generally sharper images were readily apparent in all clinical studies. In addition, max SUVs were found to be 4–10% higher in the TOF‐PEPT‐based gated images than in those based on Anzai and COM methods.ConclusionA PEPT‐ based algorithm has been presented for determining movement due to respiratory motion during PET/CT imaging. Gating based on the motion estimate is shown to quantifiably improve the image quality in both a controlled point source phantom study and in clinical data patient studies. The algorithm has the potential to facilitate true motion correction where the reconstruction algorithm can use all data available.

Respiratory Motion Prediction with Random Vector Functional Link (RVFL) Based Neural Networks

Respiratory motion exhibits non-linear and non-stationary behavior in nature and this has been a great hindrance to the accurate prediction of tumor in motion adaptive radiotherapy. Accurate prediction of respiratory motion and subsequent tracking of tumor has been a challenge due to the irregularities and intra-trace variabilities. In order to overcome this issue, prediction models can be trained by using neural networks. However due to the burden of large training data, computational efficacy of existing neural networks can be affected. Moreover, training of neural networks using conventional methods like back-propagation (BP) may result in local minima and it may slow down the learning rate and convergence respectively. As a solution, in this paper, we employed random vector function link (RVFL) based neural networks to train the model in a very efficient way to achieve high accuracy in respiratory motion prediction. In RVFL, the direct link from input features to output layer acts as regularization to prevent the network from overfitting. The proposed method is tested with real respiratory motion traces acquired from 31 patients. Results show that RVFL with the use of direct link performs quite better than without direct link.

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.

Accuracy of real-time respiratory motion tracking and time delay of gating radiotherapy based on optical surface imaging technique

BackgroundSurface-guided radiation therapy (SGRT) employs a non-invasive real-time optical surface imaging (OSI) technique for patient surface motion monitoring during radiotherapy. The main purpose of this study is to verify the real-time tracking accuracy of SGRT for respiratory motion and provide a fitting method to detect the time delay of gating.MethodsA respiratory motion phantom was utilized to simulate respiratory motion using 17 cosine breathing pattern curves with various periods and amplitudes. The motion tracking of the phantom was performed by the Catalyst™ system. The tracking accuracy of the system (with period and amplitude variations) was evaluated by analyzing the adjusted coefficient of determination (A_R2) and root mean square error (RMSE). Furthermore, 13 actual respiratory curves, which were categorized into regular and irregular patterns, were selected and then simulated by the phantom. The Fourier transform was applied to the respiratory curves, and tracking accuracy was compared through the quantitative analyses of curve similarity using the Pearson correlation coefficient (PCC). In addition, the time delay of amplitude-based respiratory-gating radiotherapy based on the OSI system with various beam hold times was tested using film dosimetry for the Elekta Versa-HD and Varian Edge linacs. A dose convolution-fitting method was provided to accurately measure the beam-on and beam-off time delays.ResultsA_R2 and RMSE for the cosine curves were 0.9990–0.9996 and 0.110–0.241 mm for periods ranging from 1 s to 10 s and 0.9990–0.9994 and 0.059–0.175 mm for amplitudes ranging from 3 mm to 15 mm. The PCC for the actual respiratory curves ranged from 0.9955 to 0.9994, which was not significantly affected by breathing patterns. For gating radiotherapy, the average beam-on and beam-off time delays were 1664 ± 72 and 25 ± 30 ms for Versa-HD and 303 ± 45 and 34 ± 25 ms for Edge, respectively. The time delay was relatively stable as the beam hold time increased.ConclusionsThe OSI technique provides high accuracy for respiratory motion tracking. The proposed dose convolution-fitting method can accurately measure the time delay of respiratory-gating radiotherapy. When the OSI technique is used for respiratory-gating radiotherapy, the time delay for the beam-on is considerably longer than the beam-off.

MRSIGMA: Magnetic Resonance SIGnature MAtching for real-time volumetric imaging.

To propose a real-time 3D MRI technique called MR SIGnature MAtching (MRSIGMA) for high-resolution volumetric imaging and motion tracking with very low imaging latency. MRSIGMA consists of two steps: (1) offline learning of a database of possible 3D motion states and corresponding motion signature ranges and (2) online matching of new motion signatures acquired in real time with prelearned motion states. Specifically, the offline learning step (non-real-time) reconstructs motion-resolved 4D images representing different motion states and assigns a unique motion range to each state. The online matching step (real-time) acquires motion signatures only and selects one of the prelearned 3D motion states for each newly acquired signature, which generates 3D images efficiently in real time. The MRSIGMA technique was evaluated on 15 golden-angle stack-of-stars liver data sets, and the performance of respiratory motion tracking with the online-generated real-time 3D MRI was compared with the corresponding 2D projections acquired in real time. The total latency of generating each 3D image during online matching was about 300ms, including acquisition of the motion signature data (~138ms) and corresponding matching process (~150ms). Linear correlation assessment suggested excellent correlation (R2 =0.948) between motion displacement measured from the online-generated real-time 3D images and the 2D real-time projections. This proof-of-concept study demonstrates the feasibility of MRSIGMA for high-resolution real-time volumetric imaging, which shifts the acquisition and reconstruction burden to an offline learning step and leaves fast online matching for online imaging with very low imaging latency. The MRSIGMA technique can potentially be used for real-time motion tracking in MRI-guided radiation therapy.

Rapid Prediction of Respiratory Motion Based on Bidirectional Gated Recurrent Unit Network

In chest and abdomen robotic radiosurgery, due to the motion delay of the robotic manipulator, the tumor position tracking process has a period of delay. This delay ultimately affects the accuracy of radiosurgery treatment. To address the influence of the delay in robotic radiosurgery, a Long-and-Short-Term Memory (LSTM) network as a deep Recurrent Neural Network (RNN) has been applied in a prediction network model for respiratory motion tracking in recent years. However, patients' respiratory state may change in the process of treatment, which may influence the accuracy of prediction. Therefore, it is necessary to update the prediction network through additional data, such as the actual position of the tumor obtained by X-ray imaging. However, the LSTM network has a long update time, and it may not be able to complete the prediction model update in a cycle of X-ray acquisition. To solve this problem, a fast prediction model based on Bidirectional Gated Recurrent Unit (Bi-GRU), is proposed in this paper. This method can reduce the average updating time of the network model by 30%.

Respiratory Motion Detection and Correction for MR Using the Pilot Tone

The aim of this study was to develop a method for tracking respiratory motion throughout full MR or PET/MR studies that requires only minimal additional hardware and no modifications to the sequences. Patient motion that is caused by respiration affects the quality of the signal of the individual radiofrequency receive coil elements. This effect can be detected as a modulation of a monofrequent signal that is emitted by a small portable transmitter placed inside the bore (Pilot Tone). The frequency is selected such that it is located outside of the frequency band of the actual MR readout experiment but well within the bandwidth of the radiofrequency receiver, that is, the oversampling area. Temporal variations of the detected signal indicate motion. After extraction of the signal from the raw data, principal component analysis was used to identify respiratory motion. The approach and potential applications during MR and PET/MR examinations that rely on a continuous respiratory signal were validated with an anthropomorphic, PET/MR-compatible motion phantom as well as in a volunteer study. Respiratory motion detection and correction were presented for MR and PET data in phantom and volunteer studies. The Pilot Tone successfully recovered the ground-truth respiratory signal provided by the phantom. The presented method provides reliable respiratory motion tracking during arbitrary imaging sequences throughout a full PET/MR study. All results can directly be transferred to MR-only applications as well.

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.

Single community-based institutional series of stereotactic body radiation therapy (SBRT) for treatment of liver metastases.

Stereotactic body radiation therapy (SBRT) is a safe and effective option for treatment of liver metastases. However, existing data are mostly reported by high-volume centers. There have been reports that advanced radiotherapy techniques performed at low-volume centers result in inferior outcomes. Our goal was to assess the implementation of SBRT for the treatment of liver metastases at a low-volume center by studying the efficacy and toxicity of this technology through retrospective database review at a single, community-based institution. We performed an IRB approved patient registry study. Patients had a median age of 65, KPS of at least 70 (median 90) and primary tumor controlled. All patients underwent fiducial marker placement under CT-guidance 1-2 weeks prior to planning scans. Gross tumor volume (GTV) was delineated using contrast enhanced CT scans, as well as fusion with PET and/or MRI scans. GTV was expanded by 5 mm to create the planning target volume (PTV). Treatment was delivered by image guided stereotactic robotic radiosurgery with respiratory motion tracking. Lesions were treated with 3 fractions to a median total dose of 54 Gy. Overall survival, progression-free survival (PFS) and local failure-free survival were estimated using the Kaplan-Meier method. Log-rank statistic was used to compare local control based on GTV volume. Between 2006 and 2016, 42 consecutively treated patients with 81 metastatic liver lesions were treated with SBRT. Median follow-up was 25 months. Major primary tumor sites were colon (n=18) and lung (n=7). Synchronous extrahepatic disease was present in 15% of the treated lesions and 46% had prior local treatment of liver metastases. The number of lesions treated concurrently ranged from 1 to 4. Lesions had a median maximum diameter of 2.5 cm (range, 0.5-9.5 cm), and a mean volume of 53 cc (range, 0.5-363.0 cc). Kaplan-Meier estimated 1- and 2-year overall survival was 72% and 62%. Estimated 1- and 2-year progression free survival was 32% and 23%. Estimated 1- and 2-year local control was 86% and 80%. Two-year local control was worse for lesions >50 cc compared to lesions ≤50 cc (62% vs. 84%, P=0.04). Toxicity occurred in 26% of treatment courses and included grade 1 (n=12) and grade 2 toxicity (n=3). These results are comparable to available published data regarding the safety and efficacy of liver metastasis SBRT on trial at high volume institutions. Our findings, therefore, demonstrate the successful implementation of a liver metastasis SBRT program in the low-volume, community-hospital setting. These findings suggest that low-volume and high-volume centers are both options for liver metastasis SBRT.

Sorted Golden-step phase encoding: an improved Golden-step imaging technique for cardiac and respiratory self-gated cine cardiovascular magnetic resonance imaging

BackgroundNumerous self-gated cardiac imaging techniques have been reported in the literature. Most can track either cardiac or respiratory motion, and many incur some overhead to imaging data acquisition. We previously described a Cartesian cine imaging technique, pseudo-projection motion tracking with golden-step phase encoding, capable of tracking both cardiac and respiratory motion at no cost to imaging data acquisition. In this work, we describe improvements to the technique by dramatically reducing its vulnerability to eddy current and flow artifacts and demonstrating its effectiveness in expanded cardiovascular applications.MethodsAs with our previous golden-step technique, the Cartesian phase encodes over time were arranged based on the integer golden step, and readouts near ky = 0 (pseudo-projections) were used to derive motion. In this work, however, the readouts were divided into equal and consecutive temporal segments, within which the readouts were sorted according to ky. The sorting reduces the phase encode jump between consecutive readouts while maintaining the pseudo-randomness of ky to sample both cardiac and respiratory motion without comprising the ability to retrospectively set the temporal resolution of the original technique. On human volunteers, free-breathing, electrocardiographic (ECG)-free cine scans were acquired for all slices of the short axis stack and the 4-chamber view of the long axis. Retrospectively, cardiac motion and respiratory motion were automatically extracted from the pseudo-projections to guide cine reconstruction. The resultant image quality in terms of sharpness and cardiac functional metrics was compared against breath-hold ECG-gated reference cines.ResultsWith sorting, motion tracking of both cardiac and respiratory motion was effective for all slices orientations imaged, and artifact occurrence due to eddy current and flow was efficiently eliminated. The image sharpness derived from the self-gated cines was found to be comparable to the reference cines (mean difference less than 0.05 mm− 1 for short-axis images and 0.075 mm− 1 for long-axis images), and the functional metrics (mean difference < 4 ml) were found not to be statistically different from those from the reference.ConclusionsThis technique dramatically reduced the eddy current and flow artifacts while preserving the ability of cost-free motion tracking and the flexibility of choosing arbitrary navigator zone width, number of cardiac phases, and duration of scanning. With the restriction of the artifacts removed, the Cartesian golden-step cine imaging can now be applied to cardiac imaging slices of more diverse orientation and anatomy at greater reliability.