Real-time profiling of respiratory motion: baseline drift, frequency variation and fundamental pattern change

Explicitly encoding the cyclic nature of breathing signal allows for accurate breathing motion prediction in radiotherapy with minimal training data

Background and purpose

Active breathing motion management in radiotherapy consists of motion monitoring, quantification and mitigation. It is impacted by associated latencies of a few 100 ms. Artificial neural networks can successfully predict breathing motion and eliminate latencies. However, they require usually a large dataset for training. The objective of this work was to demonstrate that explicitly encoding the cyclic nature of the breathing signal into the training data enables significant reduction of training datasets which can be obtained from healthy volunteers.

Material and methods

Seventy surface scanner breathing signals from 25 healthy volunteers in anterior-posterior direction were used for training and validation (ratio 4:1) of long short-term memory models. The model performance was compared to a model using decomposition into phase, amplitude and a time-dependent baseline. Testing of the models was performed on 55 independent breathing signals in anterior-posterior direction from surface scanner (35 lung, 20 liver) of 30 patients with a mean breathing amplitude of (5.9 ± 6.7) mm.

Results

Using the decomposed breathing signal allowed for a reduction of the absolute root-mean square error (RMSE) from 0.34 mm to 0.12 mm during validation. Testing using patient data yielded an average absolute RMSE of the breathing signal of (0.16 ± 0.11) mm with a prediction horizon of 500 ms.

Conclusion

It was demonstrated that a motion prediction model can be trained with less than 100 datasets of healthy volunteers if breathing cycle parameters are considered. Applied to 55 patients, the model predicted breathing motion with a high accuracy.

Analysis of emerging trends and hot spots in respiratory biomechanics from 2003 to 2022 based on CiteSpace

Introduction: With the global prevalence of coronavirus disease 2019 (COVID-19), an increasing number of people are experiencing respiratory discomfort. Respiratory biomechanics can monitor breathing patterns and respiratory movements and it is easier to prevent, diagnose, treat or rehabilitate. However, there is still a lack of global knowledge structure in the field of respiratory biomechanics. With the help of CiteSpace software, we aim to help researchers identify potential collaborators and collaborating institutions, hotspots and research frontiers in respiratory biomechanics. Methods: Articles on respiratory biomechanics from 2003 to 2022 were retrieved from the Web of Science Core Collection by using a specific strategy, resulting a total of 2,850 publications. We used CiteSpace 6.1.R6 to analyze the year of publication, journal/journals cited, country, institution, author/authors cited, references, keywords and research trends. Co-citation maps were created to visually observe research hot spots and knowledge structures. Results and discussion: The number of annual publications gradually increased over the past 20 years. Medical Physics published the most articles and had the most citations in this study. The United States was the most influential country, with the highest number and centrality of publications. The most productive and influential institution was Harvard University in the United States. Keall PJ was the most productive author and MCCLELLAND JR was the most cited authors The article by Keall PJ (2006) article (cocitation counts: 55) and the article by McClelland JR (2013) were the most representative and symbolic references, with the highest cocitation number and centrality, respectively. The top keywords were "radiotherapy", "volume", and "ventilation". The top Frontier keywords were "organ motion," "deep inspiration," and "deep learning". The keywords were clustered to form seven labels. Currently, the main area of research in respiratory biomechanics is respiratory motion related to imaging techniques. Future research may focus on respiratory assistance techniques and respiratory detection techniques. At the same time, in the future, we will pay attention to personalized medicine and precision medicine, so that people can monitor their health status anytime and anywhere.

CArdiac and REspiratory adaptive Computed Tomography (CARE-CT): a proof-of-concept digital phantom study

Current respiratory 4DCT imaging for high-dose rate thoracic radiotherapy treatments are negatively affected by the complex interaction of cardiac and respiratory motion. We propose an imaging method to reduce artifacts caused by thoracic motion, CArdiac and REspiratory adaptive CT (CARE-CT), that monitors respiratory motion and ECG signals in real-time, triggering CT acquisition during combined cardiac and respiratory bins. Using a digital phantom, conventional 4DCT and CARE-CT acquisitions for nineteen patient-measured physiological traces were simulated. Ten respiratory bins were acquired for conventional 4DCT scans and ten respiratory bins during cardiac diastole were acquired for CARE-CT scans. Image artifacts were quantified for 10 common thoracic organs at risk (OAR) substructures using the differential normalized cross correlation between axial slices (ΔNCC), mean squared error (MSE) and sensitivity. For all images, on average, CARE-CT improved the ΔNCC for 18/19 and the MSE and sensitivity for all patient traces. The ΔNCC was reduced for all cardiac OARs (mean reduction 21%). The MSE was reduced for all OARs (mean reduction 36%). In the digital phantom study, the average scan time was increased from 1.8 ± 0.4 min to 7.5 ± 2.2 min with a reduction in average beam on time from 98 ± 28 s to 45 s using CARE-CT compared to conventional 4DCT. The proof-of-concept study indicates the potential for CARE-CT to image the thorax in real-time during the cardiac and respiratory cycle simultaneously, to reduce image artifacts for common thoracic OARs.

A Teenager Physical Fitness Evaluation Model Based on 1D-CNN with LSTM and Wearable Running PPG Recordings

People attach greater importance to the physical health of teenagers because adolescence is a critical period for the healthy development of the human body. With the progress of biosensing technologies and artificial intelligence, it is feasible to apply wearable devices to continuously record teenagers’ physiological signals and make analyses based on modern advanced methods. To solve the challenge that traditional methods of monitoring teenagers’ physical fitness lack accurate computational models and in-depth data analyses, we propose a novel evaluation model for predicting the physical fitness of teenagers. First, we collected 1024 teenagers’ PPGs under the guidance of the proposed three-stage running paradigm. Next, we applied the median filter and wavelet transform to denoise the original signals and obtain HR and SpO₂. Then, we used the Pearson correlation coefficient method to finalize the feature set, based on the extracted nine physical features. Finally, we built a 1D-CNN with LSTM model to classify teenagers’ physical fitness condition into four levels: excellent, good, medium, and poor, with an accuracy of 98.27% for boys’ physical fitness prediction, and 99.26% for girls’ physical fitness prediction. The experimental results provide evidence supporting the feasibility of predicting teenagers’ physical fitness levels by their running PPG recordings.

The first-in-human implementation of adaptive 4D cone beam CT for lung cancer radiotherapy: 4DCBCT in less time with less dose

BACKGROUND AND PURPOSE

We present the first implementation of Adaptive 4D cone beam CT (4DCBCT) that adapts the image hardware (gantry rotation speed and kV projections) in response to the patient's real-time respiratory signal. Adaptive 4DCBCT was applied on lung cancer patients to reduce the scan time and imaging dose in the ADaptive CT Acquisition for Personalised Thoracic imaging (ADAPT) trial.

MATERIALS AND METHODS

The ADAPT technology measures the patient's real-time respiratory signal and uses mathematical optimisation and external circuitry attached to the linear accelerator to modulate the gantry rotation speed and kV projection rate to reduce scan times and imaging dose. For each patient, ADAPT scans were acquired on two treatment fractions and reconstructed with a motion compensated reconstruction algorithm and compared to the current state-of-the-art four-minute 4DCBCT acquisition (conventional 4DCBCT). We report on the scan time, imaging dose and image quality for the first four adaptive 4DCBCT patients.

RESULTS

The ADAPT imaging dose was reduced by 85% and scan times were 73 ± 12 s representing a 70% reduction compared to the 240 s conventional 4DCBCT scan. The contrast-to-noise ratio was improved from 9.2 ± 3.9 with conventional 4DCBCT to 11.7 ± 4.1 with ADAPT.

DISCUSSION

The ADAPT trial represents the first time that gantry rotation speed and projection acquisition have been adapted and optimised in real-time in response to changes in the patient's breathing. ADAPT demonstrates substantially reduced scan times and imaging dose for clinical 4DCBCT imaging that could enable more efficient and optimised lung cancer radiotherapy.

Effect of spontaneous movement on respiration in preterm infants

NEW FINDINGS

What is the central question of this study? The respiratory centres in the brainstem that control respiration receive inputs from various sources, including proprioceptors in muscles and joints and suprapontine centres, which all affect limb movements. What is the effect of spontaneous movement on respiration in preterm infants? What is the main finding and its importance? Apnoeic events tend to be preceded by movements. These activity bursts can cause respiratory instability that leads to an apnoeic event. These findings show promise that infant movements might serve as potential predictors of life-threatening apnoeic episodes, but more research is required.

ABSTRACT

A common condition in preterm infants (<37 weeks' gestational age) is apnoea resulting from immaturity and instability of the respiratory system. As apnoeas are implicated in several acute and long-term complications, prediction of apnoeas may preempt their onset and subsequent complications. This study tests the hypothesis that infant movements are a predictive marker for apnoeic episodes and examines the relation between movement and respiration. Movement was detected using a wavelet algorithm applied to the photoplethysmographic signal. Respiratory activity was measured in nine infants using respiratory inductance plethysmography; in an additional eight infants, respiration and partial pressure of airway carbon dioxide ( P C O 2 ) were measured by a nasal cannula with side-stream capnometry. In the first cohort, the distribution of movements before and after the onset of 370 apnoeic events was compared. Results showed that apnoeic events were associated with longer movement duration occurring before apnoea onsets compared to after. In the second cohort, respiration was analysed in relation to movement, comparing standard deviation of inter-breath intervals (IBI) before and after apnoeas. Poincaré maps of the respiratory activity quantified variability of airway P C O 2 in phase space. Movement significantly increased the variability of IBI and P C O 2 . Moreover, destabilization of respiration was dependent on the duration of movement. These findings support that bodily movements of the infants precede respiratory instability. Further research is warranted to explore the predictive value of movement for life-threatening events, useful for clinical management and risk stratification.

Gated Radiotherapy Development and its Expansion

One of the most important challenges in treatment of patients with cancerous tumors of chest and abdominal areas is organ movement. The delivery of treatment radiation doses to tumor tissue is a challenging matter while protecting healthy and radio sensitive tissues. Since the movement of organs due to respiration causes a discrepancy in the middle of planned and delivered dose distributions. The moderation in the fatalistic effect of intra-fractional target travel on the radiation therapy correctness is necessary for cutting-edge methods of motion remote monitoring and cancerous growth irradiancy. Tracking respiratory milling and implementation of breath-hold techniques by respiratory gating systems have been used for compensation of respiratory motion negative effects. Therefore, these systems help us to deliver precise treatments and also protect healthy and critical organs. It seems aspiration should be kept under observation all over treatment period employing tracking seed markers (e.g. fiducials), skin surface scanners (e.g. camera and laser monitoring systems) and aspiration detectors (e.g. spirometers). However, these systems are not readily available for most radiotherapy centers around the word. It is believed that providing and expanding the required equipment, gated radiotherapy will be a routine technique for treatment of chest and abdominal tumors in all clinical radiotherapy centers in the world by considering benefits of respiratory gating techniques in increasing efficiency of patient treatment in the near future. This review explains the different technologies and systems as well as some strategies available for motion management in radiotherapy centers.

Variations of MRI-assessed peristaltic motions during radiation therapy

PURPOSE

Understanding complex abdominal organ motion is essential for motion management in radiation therapy (RT) of abdominal tumors. This study investigates abdominal motion induced by respiration and peristalsis, during various time durations relevant to RT, using various CT and MRI techniques acquired under free breathing (FB) and breath hold (BH).

METHODS

A series of CT and MRI images acquired with various techniques under free breathing and/or breath hold from 37 randomly-selected pancreatic or liver cancer patients were analyzed to assess the motion in various time frames. These data include FB 4DCT from 15 patients (for motion in time duration of 5 sec), FB 2D cine-MRI from 4 patients (time duration of 1.7 min, 1 second acquisition time per slice), FB cine-MRI acquired using MR-Linac from 6 patients in various fractions (acquisition time is less than 0.6 seconds per slice), FB 4DMRI from 2 patients (time duration of 2 min), respiration-gated T2 with gating at the end expiration (time duration of 3-5 min), and BH T1 with multiphase dynamic contrast in acquisition times of 17 seconds for each of five phases (pre-contrast, arterial, venous, portal venous and delayed post-contrast) from 10 patients. Motions of various organs including gallbladder (GB) and liver were measured based on these MRI data. The GB motion includes both respiration and peristalsis, while liver motion is primarily respiration. By subtracting liver motion (respiration) from GB motion (respiration and peristalsis), the peristaltic motion, along with small residual motion, was obtained.

RESULTS

From cine-MRI, the residual motion beyond the respiratory motion was found to be up to 0.6 cm in superior-inferior (SI) and 0.55 cm in anterior-posterior (AP) directions. From 2D cine-MRI acquired by the MR-Linac, different peristaltic motions were found from different fractions for each patient. The peristaltic motion was found to vary between 0.3-1 cm. From BH T1 phase images, the average motions that were primarily due to peristalsis movements were found to be 1.2 cm in SI, 0.7 cm in AP, and 0.9 cm in left-right (LR) directions. The average motions assessed from 4DCT were 1.0 cm in SI and 0.3 cm in AP directions, which were generally smaller than the motions assessed from cine-MRI, i.e., 1.8 cm in SI and 0.6 cm in AP directions, for the same patients. However, average motions from 4DMRI, which are coming from respiratory were measured to be 1.5, 0.5, and 0.4 cm in SI, AP, and LR directions, respectively.

CONCLUSION

The abdominal motion due to peristalsis can be similar in magnitude to respiratory motion as assessed. These motions can be irregular and persistent throughout the imaging and RT delivery procedures, and should be considered together with respiratory motion during RT for abdominal tumors.

Predicting tumour motion during the whole radiotherapy treatment: a systematic approach for thoracic and abdominal lesions based on real time MR

INTRODUCTION

Aim of this study was to investigate the ability of pre-treatment four dimensional computed tomography (4DCT) to capture respiratory-motion observed in thoracic and abdominal lesions during treatment. Treatment motion was acquired using full-treatment cine-MR acquisitions. Results of this analysis were compared to the ability of 30 seconds (s) cine Magnetic Resonance (MR) to estimate the same parameters.

METHODS

A 4DCT and 30 s cine-MR (ViewRay, USA) were acquired on the simulation day for 7 thoracic and 13 abdominal lesions. Mean amplitude, intra- and inter-fraction amplitude variability, and baseline drift were extracted from the full treatment data acquired by 2D cine-MR, and correlated to the motion on pre-treatment 30 s cine-MR and 4DCT. Using the full treatment data, safety margins on the ITV, necessary to account for all motion variability from 4DCT observed during treatment, were calculated. Mean treatment amplitudes were 2 ± 1 mm and 5 ± 3 mm in the anteroposterior (AP) and craniocaudal (CC) direction, respectively. Differences between mean amplitude during treatment and amplitude on 4DCT or during 30 s cine-MR were not significant, but 30 s cine-MR was more accurate than 4DCT. Intra-fraction amplitude variability was positively correlated with both 30 s cine-MR and 4DCT amplitude. Inter-fraction amplitude variability was minimal.

RESULTS

Mean baseline drift over all fractions and patients equalled 1 ± 1 mm in both CC and AP direction, but drifts per fraction up to 16 mm (CC) and 12 mm (AP) were observed. Margins necessary on the ITV ranged from 0 to 8 mm in CC and 0 to 5 mm in AP direction. Neither amplitude on 4DCT nor during 30 s cine MR is correlated to the magnitude of drift or the necessary margins in both directions.

CONCLUSION

Lesions moving with small amplitude show limited amplitude variability throughout treatment, making passive motion management strategies seem adequate. However, other variations such as baseline drifts and shifts still cause significant geometrical uncertainty, favouring real-time monitoring and an active approach for all lesions influenced by respiratory motion.

Evaluation of the combined use of two different respiratory monitoring systems for 4D CT simulation and gated treatment

Purpose

Two different respiratory monitoring systems (Varian's Real‐Time Position Management (RPM) System and Siemens’ ANZAI belt Respiratory Gating System) are compared in the context of respiratory signals and 4D CT images that are accordingly reconstructed. This study aims to evaluate the feasibility of combined use of RPM and ANZAI systems for 4DCT simulation and gated radiotherapy treatment, respectively.

Methods

The RPM infrared reflecting marker and the ANZAI belt pressure sensor were both placed on the patient's abdomen during 4DCT scans. The respiratory signal collected by the two systems was synchronized. Fifteen patients were enrolled for respiratory signal collection and analysis. The discrepancies between the RPM and ANZAI traces can be characterized by phase shift and shape distortion. To reveal the impact of the changes in respiratory signals on 4D images, two sets of 4D images based on the same patient's raw data were reconstructed using the RPM and ANZAI data for phase sorting, respectively. The volume of whole lung and the position of diaphragm apex were measured and compared for each respiratory phase.

Results

The mean phase shift was measured as 0.2 ± 0.1 s averaged over 15 patients. The shape of the breathing trace was found to be in disagreement. For all the patients, the ANZAI trace had a steeper falloff in exhalation than RPM. The inhalation curve, however, was matched for nine patients, steeper in ANZAI for five patients and steeper in RPM for one patient. For 4D image comparison, the difference in whole‐lung volume was about −4% to +4% and the difference in diaphragm position was about −5 mm to +4 mm, compared in each individual phase and averaged over seven patients.

Conclusions

Combined use of one system for 4D CT simulation and the other for gated treatment should be avoided as the resultant gating window would not fully match with each other due to the remarkable discrepancy in breathing traces acquired by the two different surrogate systems.

Optimization of training periods for the estimation model of three-dimensional target positions using an external respiratory surrogate

Background

During therapeutic beam irradiation, an unvisualized three-dimensional (3D) target position should be estimated using an external surrogate with an estimation model. Training periods for the developed model with no additional imaging during beam irradiation were optimized using clinical data.

Methods

Dual-source 4D-CBCT projection data for 20 lung cancer patients were used for validation. Each patient underwent one to three scans. The actual target positions of each scan were equally divided into two equal parts: one for the modeling and the other for the validating session. A quadratic target position estimation equation was constructed during the modeling session. Various training periods for the session—i.e., modeling periods (T_M)—were employed: T_M ∈ {5,10,15,25,35} [s]. First, the equation was used to estimate target positions in the validating session of the same scan (intra-scan estimations). Second, the equation was then used to estimate target positions in the validating session of another temporally different scan (inter-scan estimations). The baseline drift of the surrogate and target between scans was corrected. Various training periods for the baseline drift correction—i.e., correction periods (T_Cs)—were employed: T_C ∈ {5,10,15; T_C ≤ T_M} [s]. Evaluations were conducted with and without the correction. The difference between the actual and estimated target positions was evaluated by the root-mean-square error (RMSE).

Results

The range of mean respiratory period and 3D motion amplitude of the target was 2.4–13.0 s and 2.8–34.2 mm, respectively. On intra-scan estimation, the median 3D RMSE was within 1.5–2.1 mm, supported by previous studies. On inter-scan estimation, median elapsed time between scans was 10.1 min. All T_Ms exhibited 75th percentile 3D RMSEs of 5.0–6.4 mm due to baseline drift of the surrogate and the target. After the correction, those for each T_Ms fell by 1.4–2.3 mm. The median 3D RMSE for both the 10-s T_M and the T_C period was 2.4 mm, which plateaued when the two training periods exceeded 10 s.

Conclusions

A widely-applicable estimation model for the 3D target positions during beam irradiation was developed. The optimal T_M and T_C for the model were both 10 s, to allow for more than one respiratory cycle.

Trial registration

UMIN000014825. Registered: 11 August 2014.

Electronic supplementary material

The online version of this article (10.1186/s13014-018-1019-9) contains supplementary material, which is available to authorized users.

A comparison of gantry-mounted x-ray-based real-time target tracking methods

PURPOSE

Most modern radiotherapy machines are built with a 2D kV imaging system. Combining this imaging system with a 2D-3D inference method would allow for a ready-made option for real-time 3D tumor tracking. This work investigates and compares the accuracy of four existing 2D-3D inference methods using both motion traces inferred from external surrogates and measured internally from implanted beacons.

METHOD

Tumor motion data from 160 fractions (46 thoracic/abdominal patients) of Synchrony traces (inferred traces), and 28 fractions (7 lung patients) of Calypso traces (internal traces) from the LIGHT SABR trial (NCT02514512) were used in this study. The motion traces were used as the ground truth. The ground truth trajectories were used in silico to generate 2D positions projected on the kV detector. These 2D traces were then passed to the 2D-3D inference methods: interdimensional correlation, Gaussian probability density function (PDF), arbitrary-shape PDF, and the Kalman filter. The inferred 3D positions were compared with the ground truth to determine tracking errors. The relationships between tracking error and motion magnitude, interdimensional correlation, and breathing periodicity index (BPI) were also investigated.

RESULTS

Larger tracking errors were observed from the Calypso traces, with RMS and 95th percentile 3D errors of 0.84-1.25 mm and 1.72-2.64 mm, compared to 0.45-0.68 mm and 0.74-1.13 mm from the Synchrony traces. The Gaussian PDF method was found to be the most accurate, followed by the Kalman filter, the interdimensional correlation method, and the arbitrary-shape PDF method. Tracking error was found to strongly and positively correlate with motion magnitude for both the Synchrony and Calypso traces and for all four methods. Interdimensional correlation and BPI were found to negatively correlate with tracking error only for the Synchrony traces. The Synchrony traces exhibited higher interdimensional correlation than the Calypso traces especially in the anterior-posterior direction.

CONCLUSION

Inferred traces often exhibit higher interdimensional correlation, which are not true representation of thoracic/abdominal motion and may underestimate kV-based tracking errors. The use of internal traces acquired from systems such as Calypso is advised for future kV-based tracking studies. The Gaussian PDF method is the most accurate 2D-3D inference method for tracking thoracic/abdominal targets. Motion magnitude has significant impact on 2D-3D inference error, and should be considered when estimating kV-based tracking error.

A prospective gating method to acquire a diverse set of free-breathing CT images for model-based 4DCT

Breathing motion modeling requires observation of tissues at sufficiently distinct respiratory states for proper 4D characterization. This work proposes a method to improve sampling of the breathing cycle with limited imaging dose. We designed and tested a prospective free-breathing acquisition protocol with a simulation using datasets from five patients imaged with a model-based 4DCT technique. Each dataset contained 25 free-breathing fast helical CT scans with simultaneous breathing surrogate measurements. Tissue displacements were measured using deformable image registration. A correspondence model related tissue displacement to the surrogate. Model residual was computed by comparing predicted displacements to image registration results. To determine a stopping criteria for the prospective protocol, i.e. when the breathing cycle had been sufficiently sampled, subsets of N scans where 5  ⩽  N  ⩽  9 were used to fit reduced models for each patient. A previously published metric was employed to describe the phase coverage, or 'spread', of the respiratory trajectories of each subset. Minimum phase coverage necessary to achieve mean model residual within 0.5 mm of the full 25-scan model was determined and used as the stopping criteria. Using the patient breathing traces, a prospective acquisition protocol was simulated. In all patients, phase coverage greater than the threshold necessary for model accuracy within 0.5 mm of the 25 scan model was achieved in six or fewer scans. The prospectively selected respiratory trajectories ranked in the (97.5  ±  4.2)th percentile among subsets of the originally sampled scans on average. Simulation results suggest that the proposed prospective method provides an effective means to sample the breathing cycle with limited free-breathing scans. One application of the method is to reduce the imaging dose of a previously published model-based 4DCT protocol to 25% of its original value while achieving mean model residual within 0.5 mm.

Feasibility of predicting tumor motion using online data acquired during treatment and a generalized neural network optimized with offline patient tumor trajectories

PURPOSE

The accurate prediction of intrafraction lung tumor motion is required to compensate for system latency in image-guided adaptive radiotherapy systems. The goal of this study was to identify an optimal prediction model that has a short learning period so that prediction and adaptation can commence soon after treatment begins, and requires minimal reoptimization for individual patients. Specifically, the feasibility of predicting tumor position using a combination of a generalized (i.e., averaged) neural network, optimized using historical patient data (i.e., tumor trajectories) obtained offline, coupled with the use of real-time online tumor positions (obtained during treatment delivery) was examined.

METHODS

A 3-layer perceptron neural network was implemented to predict tumor motion for a prediction horizon of 650 ms. A backpropagation algorithm and batch gradient descent approach were used to train the model. Twenty-seven 1-min lung tumor motion samples (selected from a CyberKnife patient dataset) were sampled at a rate of 7.5 Hz (0.133 s) to emulate the frame rate of an electronic portal imaging device (EPID). A sliding temporal window was used to sample the data for learning. The sliding window length was set to be equivalent to the first breathing cycle detected from each trajectory. Performing a parametric sweep, an averaged error surface of mean square errors (MSE) was obtained from the prediction responses of seven trajectories used for the training of the model (Group 1). An optimal input data size and number of hidden neurons were selected to represent the generalized model. To evaluate the prediction performance of the generalized model on unseen data, twenty tumor traces (Group 2) that were not involved in the training of the model were used for the leave-one-out cross-validation purposes.

RESULTS

An input data size of 35 samples (4.6 s) and 20 hidden neurons were selected for the generalized neural network. An average sliding window length of 28 data samples was used. The average initial learning period prior to the availability of the first predicted tumor position was 8.53 ± 1.03 s. Average mean absolute error (MAE) of 0.59 ± 0.13 mm and 0.56 ± 0.18 mm were obtained from Groups 1 and 2, respectively, giving an overall MAE of 0.57 ± 0.17 mm. Average root-mean-square-error (RMSE) of 0.67 ± 0.36 for all the traces (0.76 ± 0.34 mm, Group 1 and 0.63 ± 0.36 mm, Group 2), is comparable to previously published results. Prediction errors are mainly due to the irregular periodicities between cycles. Since the errors from Groups 1 and 2 are within the same range, it demonstrates that this model can generalize and predict on unseen data.

CONCLUSIONS

This is a first attempt to use an averaged MSE error surface (obtained from the prediction of different patients' tumor trajectories) to determine the parameters of a generalized neural network. This network could be deployed as a plug-and-play predictor for tumor trajectory during treatment delivery, eliminating the need for optimizing individual networks with pretreatment patient data.

Comparison of lung tumor motion measured using a model-based 4DCT technique and a commercial protocol

PURPOSE

To compare lung tumor motion measured with a model-based technique to commercial 4-dimensional computed tomography (4DCT) scans and describe a workflow for using model-based 4DCT as a clinical simulation protocol.

METHODS AND MATERIALS

Twenty patients were imaged using a model-based technique and commercial 4DCT. Tumor motion was measured on each commercial 4DCT dataset and was calculated on model-based datasets for 3 breathing amplitude percentile intervals: 5th to 85th, 5th to 95th, and 0th to 100th. Internal target volumes (ITVs) were defined on the 4DCT and 5th to 85th interval datasets and compared using Dice similarity. Images were evaluated for noise and rated by 2 radiation oncologists for artifacts.

RESULTS

Mean differences in tumor motion magnitude between commercial and model-based images were 0.47 ± 3.0, 1.63 ± 3.17, and 5.16 ± 4.90 mm for the 5th to 85th, 5th to 95th, and 0th to 100th amplitude intervals, respectively. Dice coefficients between ITVs defined on commercial and 5th to 85th model-based images had a mean value of 0.77 ± 0.09. Single standard deviation image noise was 11.6 ± 9.6 HU in the liver and 6.8 ± 4.7 HU in the aorta for the model-based images compared with 57.7 ± 30 and 33.7 ± 15.4 for commercial 4DCT. Mean model error within the ITV regions was 1.71 ± 0.81 mm. Model-based images exhibited reduced presence of artifacts at the tumor compared with commercial images.

CONCLUSION

Tumor motion measured with the model-based technique using the 5th to 85th percentile breathing amplitude interval corresponded more closely to commercial 4DCT than the 5th to 95th or 0th to 100th intervals, which showed greater motion on average. The model-based technique tended to display increased tumor motion when breathing amplitude intervals wider than 5th to 85th were used because of the influence of unusually deep inhalations. These results suggest that care must be taken in selecting the appropriate interval during image generation when using model-based 4DCT methods.

Motion management strategies and technical issues associated with stereotactic body radiotherapy of thoracic and upper abdominal tumors: A review from NRG oncology

The efficacy of stereotactic body radiotherapy (SBRT) has been well demonstrated. However, it presents unique challenges for accurate planning and delivery especially in the lungs and upper abdomen where respiratory motion can be significantly confounding accurate targeting and avoidance of normal tissues. In this paper, we review the current literature on SBRT for lung and upper abdominal tumors with particular emphasis on addressing respiratory motion and its affects. We provide recommendations on strategies to manage motion for different, patient-specific situations. Some of the recommendations will potentially be adopted to guide clinical trial protocols.

A Bayesian approach for three-dimensional markerless tumor tracking using kV imaging during lung radiotherapy

The ability to monitor tumor motion without implanted markers can potentially enable broad access to more accurate and precise lung radiotherapy. A major challenge is that kilovoltage (kV) imaging based methods are rarely able to continuously track the tumor due to the inferior tumor visibility on 2D kV images. Another challenge is the estimation of 3D tumor position based on only 2D imaging information. The aim of this work is to address both challenges by proposing a Bayesian approach for markerless tumor tracking for the first time. The proposed approach adopts the framework of the extended Kalman filter, which combines a prediction and measurement steps to make the optimal tumor position update. For each imaging frame, the tumor position is first predicted by a respiratory-correlated model. The 2D tumor position on the kV image is then measured by template matching. Finally, the prediction and 2D measurement are combined based on the 3D distribution of tumor positions in the past 10 s and the estimated uncertainty of template matching. To investigate the clinical feasibility of the proposed method, a total of 13 lung cancer patient datasets were used for retrospective validation, including 11 cone-beam CT scan pairs and two stereotactic ablative body radiotherapy cases. The ground truths for tumor motion were generated from the the 3D trajectories of implanted markers or beacons. The mean, standard deviation, and 95th percentile of the 3D tracking error were found to range from 1.6-2.9 mm, 0.6-1.5 mm, and 2.6-5.8 mm, respectively. Markerless tumor tracking always resulted in smaller errors compared to the standard of care. The improvement was the most pronounced in the superior-inferior (SI) direction, with up to 9.5 mm reduction in the 95th-percentile SI error for patients with  >10 mm 5th-to-95th percentile SI tumor motion. The percentage of errors with 3D magnitude  <5 mm was 96.5% for markerless tumor tracking and 84.1% for the standard of care. The feasibility of 3D markerless tumor tracking has been demonstrated on realistic clinical scenarios for the first time. The clinical implementation of the proposed method will enable more accurate and precise lung radiotherapy using existing hardware and workflow. Future work is focused on the clinical and real-time implementation of this method.

Respiratory motion prediction and prospective correction for free-breathing arterial spin-labeled perfusion MRI of the kidneys

PURPOSE

Respiratory motion prediction using an artificial neural network (ANN) was integrated with pseudocontinuous arterial spin labeling (pCASL) MRI to allow free-breathing perfusion measurements in the kidney. In this study, we evaluated the performance of the ANN to accurately predict the location of the kidneys during image acquisition.

METHODS

A pencil-beam navigator was integrated with a pCASL sequence to measure lung/diaphragm motion during ANN training and the pCASL transit delay. The ANN algorithm ran concurrently in the background to predict organ location during the 0.7-s 15-slice acquisition based on the navigator data. The predictions were supplied to the pulse sequence to prospectively adjust the axial slice acquisition to match the predicted organ location. Additional navigators were acquired immediately after the multislice acquisition to assess the performance and accuracy of the ANN. The technique was tested in eight healthy volunteers.

RESULTS

The root-mean-square error (RMSE) and mean absolute error (MAE) for the eight volunteers were 1.91 ± 0.17 mm and 1.43 ± 0.17 mm, respectively, for the ANN. The RMSE increased with transit delay. The MAE typically increased from the first to last prediction in the image acquisition. The overshoot was 23.58% ± 3.05% using the target prediction accuracy of ± 1 mm.

CONCLUSION

Respiratory motion prediction with prospective motion correction was successfully demonstrated for free-breathing perfusion MRI of the kidney. The method serves as an alternative to multiple breathholds and requires minimal effort from the patient.

Ensemble framework based real-time respiratory motion prediction for adaptive radiotherapy applications

Successful treatment of tumors with motion-adaptive radiotherapy requires accurate prediction of respiratory motion, ideally with a prediction horizon larger than the latency in radiotherapy system. Accurate prediction of respiratory motion is however a non-trivial task due to the presence of irregularities and intra-trace variabilities, such as baseline drift and temporal changes in fundamental frequency pattern. In this paper, to enhance the accuracy of the respiratory motion prediction, we propose a stacked regression ensemble framework that integrates heterogeneous respiratory motion prediction algorithms. We further address two crucial issues for developing a successful ensemble framework: (1) selection of appropriate prediction methods to ensemble (level-0 methods) among the best existing prediction methods; and (2) finding a suitable generalization approach that can successfully exploit the relative advantages of the chosen level-0 methods. The efficacy of the developed ensemble framework is assessed with real respiratory motion traces acquired from 31 patients undergoing treatment. Results show that the developed ensemble framework improves the prediction performance significantly compared to the best existing methods.

Objective function to obtain multiple representative waveforms for a novel helical CT scan protocol

PURPOSE

To develop objective functions for selecting multiple representative respiratory waveforms. A specific application considered is to reduce the number of swiping scans in a novel helical CT scan protocol to harvest efficiency and dose reduction benefit.

METHODS

The authors consider a general class of potential objective functions consisting of weighted norms on pointwise profile differentials. The authors utilize the Lagrangian approach and derive proper conditions on the formulation based on first and second order optimality conditions. The derived objective functions are applied to clinically acquired respiratory trajectories for swipe subset selection to verify the validity and generality of the proposed rationale. An end-to-end 4DCT reconstruction comparison is performed using a swipe subset of data corresponding to 3 out of the full 25 waveforms to assess the consequence in image quality and dose.

RESULTS

Their results show that maximizing the proposed objective function with the suggested parameters yields maximal spread of trajectories among the selected subset. 4DCT Reconstruction using the chosen subset of data indicates the potential for further dose reduction by about 5 to 10 folds without significant sacrifice in image quality. Experimental results also support further generalization to include slice prioritization.

CONCLUSIONS

The authors have derived a formulation that is both simple and general as a metric to quantify the spread of a set of respiratory trajectories, which can be used for subset selection with potential computation and dose reduction benefit when applied to a newly developed helical 4DCT scan protocol.

Markerless tumor tracking using short kilovoltage imaging arcs for lung image-guided radiotherapy

The ability to monitor tumor motion without implanted markers is clinically advantageous for lung image-guided radiotherapy (IGRT). Existing markerless tracking methods often suffer from overlapping structures and low visibility of tumors on kV projection images. We introduce the short arc tumor tracking (SATT) method to overcome these issues. The proposed method utilizes multiple kV projection images selected from a nine-degree imaging arc to improve tumor localization, and respiratory-correlated 4D cone-beam CT (CBCT) prior knowledge to minimize the effects of overlapping anatomies. The 3D tumor position is solved as an optimization problem with prior knowledge incorporated via regularization. We retrospectively validated SATT on 11 clinical scans from four patients with central tumors. These patients represent challenging scenarios for markerless tumor tracking due to the inferior adjacent contrast. The 3D trajectories of implanted fiducial markers were used as the ground truth for tracking accuracy evaluation. In all cases, the tumors were successfully tracked at all gantry angles. Compared to standard pre-treatment CBCT guidance alone, trajectory errors were significantly smaller with tracking in all cases, and the improvements were the most prominent in the superior-inferior direction. The mean 3D tracking error ranged from 2.2-9.9 mm, which was 0.4-2.6 mm smaller compared to pre-treatment CBCT. In conclusion, we were able to directly track tumors with inferior visibility on kV projection images using SATT. Tumor localization accuracies are significantly better with tracking compared to the current standard of care of lung IGRT. Future work involves the prospective evaluation and clinical implementation of SATT.

Impact of scanning parameters and breathing patterns on image quality and accuracy of tumor motion reconstruction in 4D CBCT: a phantom study

Four-dimensional, cone-beam CT (4D CBCT) substantially reduces respiration-induced motion blurring artifacts in three-dimension (3D) CBCT. However, the image quality of 4D CBCT is significantly degraded which may affect its accuracy in localizing a mobile tumor for high-precision, image-guided radiation therapy (IGRT). The purpose of this study was to investigate the impact of scanning parameters hereinafter collectively referred to as scanning sequence) and breathing patterns on the image quality and the accuracy of computed tumor trajectory for a commercial 4D CBCT system, in preparation for its clinical implementation. We simulated a series of periodic and aperiodic sinusoidal breathing patterns with a respiratory motion phantom. The aperiodic pattern was created by varying the period or amplitude of individual sinusoidal breathing cycles. 4D CBCT scans of the phantom were acquired with a manufacturer-supplied scanning sequence (4D-S-slow) and two in-house modified scanning sequences (4D-M-slow and 4D-M-fast). While 4D-S-slow used small field of view (FOV), partial rotation (200°), and no imaging filter, 4D-M-slow and 4D-M-fast used medium FOV, full rotation, and the F1 filter. The scanning speed was doubled in 4D-M-fast (100°/min gantry rotation). The image quality of the 4D CBCT scans was evaluated using contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and motion blurring ratio (MBR). The trajectory of the moving target was reconstructed by registering each phase of the 4D CBCT with a reference CT. The root-mean-squared-error (RMSE) analysis was used to quantify its accuracy. Significant decrease in CNR and SNR from 3D CBCT to 4D CBCT was observed. The 4D-S-slow and 4D-M-fast scans had comparable image quality, while the 4D-M-slow scans had better performance due to doubled projections. Both CNR and SNR decreased slightly as the breathing period increased, while no dependence on the amplitude was observed. The difference of both CNR and SNR between periodic and aperiodic breathing patterns was insignificant (p > 0.48). At end-exhale phases, the motion blurring was negligible for both periodic and aperiodic breathing patterns; at mid-inhale phase, the motion blurring increased as the period, the amplitude or the amount of cycle-to-cycle variation on amplitude increased. Overall, the accuracy of localizing the moving target in 4D CBCT was within 2 mm under all studied cases. No difference in the RMSEs was noticed among the three scanning sequences. The 4D-M-fast scans, free of volume truncation artifacts, exhibited comparable image quality and accuracy in tumor motion reconstruction as the 4D-S-slow scans with reduced imaging dose (0.60 cGy vs. 0.99 cGy) due to the use of faster gantry rotation and the F1 filter, suggesting its suitability for clinical use.

Adaptive and robust radiation therapy in the presence of drift

Combining adaptive and robust optimization in radiation therapy has the potential to mitigate the negative effects of both intrafraction and interfraction uncertainty over a fractionated treatment course. A previously developed adaptive and robust radiation therapy (ARRT) method for lung cancer was demonstrated to be effective when the sequence of breathing patterns was well-behaved. In this paper, we examine the applicability of the ARRT method to less well-behaved breathing patterns. We develop a novel method to generate sequences of probability mass functions that represent different types of drift in the underlying breathing pattern. Computational results derived from applying the ARRT method to these sequences demonstrate that the ARRT method is effective for a much broader class of breathing patterns than previously demonstrated.

High-quality t2-weighted 4-dimensional magnetic resonance imaging for radiation therapy applications

PURPOSE

The purpose of this study was to improve triggering efficiency of the prospective respiratory amplitude-triggered 4-dimensional magnetic resonance imaging (4DMRI) method and to develop a 4DMRI imaging protocol that could offer T2 weighting for better tumor visualization, good spatial coverage and spatial resolution, and respiratory motion sampling within a reasonable amount of time for radiation therapy applications.

METHODS AND MATERIALS

The respiratory state splitting (RSS) and multi-shot acquisition (MSA) methods were analytically compared and validated in a simulation study by using the respiratory signals from 10 healthy human subjects. The RSS method was more effective in improving triggering efficiency. It was implemented in prospective respiratory amplitude-triggered 4DMRI. 4DMRI image datasets were acquired from 5 healthy human subjects. Liver motion was estimated using the acquired 4DMRI image datasets.

RESULTS

The simulation study showed the RSS method was more effective for improving triggering efficiency than the MSA method. The average reductions in 4DMRI acquisition times were 36% and 10% for the RSS and MSA methods, respectively. The human subject study showed that T2-weighted 4DMRI with 10 respiratory states, 60 slices at a spatial resolution of 1.5 × 1.5 × 3.0 mm(3) could be acquired in 9 to 18 minutes, depending on the individual's breath pattern. Based on the acquired 4DMRI image datasets, the ranges of peak-to-peak liver displacements among 5 human subjects were 9.0 to 12.9 mm, 2.5 to 3.9 mm, and 0.5 to 2.3 mm in superior-inferior, anterior-posterior, and left-right directions, respectively.

CONCLUSIONS

We demonstrated that with the RSS method, it was feasible to acquire high-quality T2-weighted 4DMRI within a reasonable amount of time for radiation therapy applications.

Effect of secondary particles on image quality of dynamic flat panels in carbon ion scanning beam treatment

OBJECTIVE

Real-time markerless tumour tracking using radiographic fluoroscopic imaging is one of the better solutions to improving respiratory-gated radiotherapy. However, particle beams cause secondary particles from patients, which could affect radiographs. Here, we evaluated the quality of radiographs during carbon ion pencil beam scanning (CPBS) irradiation for respiratory gating.

METHODS

A water phantom and chest phantom were used. The phantoms were irradiated with CPBS at 290 MeV n(-1) from orthogonal directions. Dose rates were 3.4 × 10(8), 1.14 × 10(8) and 3.79 × 10(7) particles per second. A dynamic flat panel detector (DFPD) was installed on the upstream (DFPD1) or downstream (DFPD2) side of the vertical irradiation port. DFPD images were acquired during CPBS at 15.00, 7.50 and 3.75 frames per second (fps). Charge on the DFPD was cleaned using fast readout technique every 30 fps. DFPD images were acquired during CPBS with radiographic exposure, and results with and without fast readout technique were compared.

RESULTS

Secondary particles were visualized as spots or streak-like shapes. Capture of secondary particles from the horizontal beam direction was lower with fast readout technique than without it. With regard to beam irradiation direction dependency, CPBS from the horizontal direction resulted in a greater magnitude of secondary particles reaching DFPD2 than reaching DFPD1. When CPBS was delivered from the vertical direction, however, the magnitude of secondary particles on both DFPDs was very similar.

CONCLUSION

Fast readout technique minimized the effect of secondary particles on DFPD images during CPBS.

ADVANCES IN KNOWLEDGE

This technique may be useful for markerless tumour tracking for respiratory gating.

Motion based X-ray imaging modality

A new X-ray imaging method (patent pending) was developed to visualize function-related motion information. We modify existing X-ray imaging methods to provide four images without increasing the necessary measurement time or radiation dose. The most important of these images is a new "kinetic" image that represents motions inside the object or living body. The motion-based contrast of the kinetic image can help visualize details that were not accessible before. The broad range of the movements and high sensitivity of the method are illustrated by imaging the mechanics of a working clock and the chest of a living African clawed frog (Xenopus laevis). The heart, valves, aorta, and lungs of the frog are clearly visualized in spite of the low soft tissue contrast of the animal. The new technology also reconstructs a "static" image similar to the existing conventional X-ray image. The static image shows practically the same information as the conventional image. The new technology presents two more images which show the point-wise errors of the static and kinetic images. This technique gives a better estimation of errors than present methods because it is based entirely on measured data. The new technology could be used in imaging cardiopulmonary movements, nondestructive testing, or port security screening.

A novel fast helical 4D-CT acquisition technique to generate low-noise sorting artifact-free images at user-selected breathing phases

PURPOSE

To develop a novel 4-dimensional computed tomography (4D-CT) technique that exploits standard fast helical acquisition, a simultaneous breathing surrogate measurement, deformable image registration, and a breathing motion model to remove sorting artifacts.

METHODS AND MATERIALS

Ten patients were imaged under free-breathing conditions 25 successive times in alternating directions with a 64-slice CT scanner using a low-dose fast helical protocol. An abdominal bellows was used as a breathing surrogate. Deformable registration was used to register the first image (defined as the reference image) to the subsequent 24 segmented images. Voxel-specific motion model parameters were determined using a breathing motion model. The tissue locations predicted by the motion model in the 25 images were compared against the deformably registered tissue locations, allowing a model prediction error to be evaluated. A low-noise image was created by averaging the 25 images deformed to the first image geometry, reducing statistical image noise by a factor of 5. The motion model was used to deform the low-noise reference image to any user-selected breathing phase. A voxel-specific correction was applied to correct the Hounsfield units for lung parenchyma density as a function of lung air filling.

RESULTS

Images produced using the model at user-selected breathing phases did not suffer from sorting artifacts common to conventional 4D-CT protocols. The mean prediction error across all patients between the breathing motion model predictions and the measured lung tissue positions was determined to be 1.19 ± 0.37 mm.

CONCLUSIONS

The proposed technique can be used as a clinical 4D-CT technique. It is robust in the presence of irregular breathing and allows the entire imaging dose to contribute to the resulting image quality, providing sorting artifact-free images at a patient dose similar to or less than current 4D-CT techniques.

Simultaneous motion estimation and image reconstruction (SMEIR) for 4D cone-beam CT

PURPOSE

Image reconstruction and motion model estimation in four-dimensional cone-beam CT (4D-CBCT) are conventionally handled as two sequential steps. Due to the limited number of projections at each phase, the image quality of 4D-CBCT is degraded by view aliasing artifacts, and the accuracy of subsequent motion modeling is decreased by the inferior 4D-CBCT. The objective of this work is to enhance both the image quality of 4D-CBCT and the accuracy of motion model estimation with a novel strategy enabling simultaneous motion estimation and image reconstruction (SMEIR).

METHODS

The proposed SMEIR algorithm consists of two alternating steps: (1) model-based iterative image reconstruction to obtain a motion-compensated primary CBCT (m-pCBCT) and (2) motion model estimation to obtain an optimal set of deformation vector fields (DVFs) between the m-pCBCT and other 4D-CBCT phases. The motion-compensated image reconstruction is based on the simultaneous algebraic reconstruction technique (SART) coupled with total variation minimization. During the forward- and backprojection of SART, measured projections from an entire set of 4D-CBCT are used for reconstruction of the m-pCBCT by utilizing the updated DVF. The DVF is estimated by matching the forward projection of the deformed m-pCBCT and measured projections of other phases of 4D-CBCT. The performance of the SMEIR algorithm is quantitatively evaluated on a 4D NCAT phantom. The quality of reconstructed 4D images and the accuracy of tumor motion trajectory are assessed by comparing with those resulting from conventional sequential 4D-CBCT reconstructions (FDK and total variation minimization) and motion estimation (demons algorithm). The performance of the SMEIR algorithm is further evaluated by reconstructing a lung cancer patient 4D-CBCT.

RESULTS

Image quality of 4D-CBCT is greatly improved by the SMEIR algorithm in both phantom and patient studies. When all projections are used to reconstruct a 3D-CBCT by FDK, motion-blurring artifacts are present, leading to a 24.4% relative reconstruction error in the NACT phantom. View aliasing artifacts are present in 4D-CBCT reconstructed by FDK from 20 projections, with a relative error of 32.1%. When total variation minimization is used to reconstruct 4D-CBCT, the relative error is 18.9%. Image quality of 4D-CBCT is substantially improved by using the SMEIR algorithm and relative error is reduced to 7.6%. The maximum error (MaxE) of tumor motion determined from the DVF obtained by demons registration on a FDK-reconstructed 4D-CBCT is 3.0, 2.3, and 7.1 mm along left-right (L-R), anterior-posterior (A-P), and superior-inferior (S-I) directions, respectively. From the DVF obtained by demons registration on 4D-CBCT reconstructed by total variation minimization, the MaxE of tumor motion is reduced to 1.5, 0.5, and 5.5 mm along L-R, A-P, and S-I directions. From the DVF estimated by SMEIR algorithm, the MaxE of tumor motion is further reduced to 0.8, 0.4, and 1.5 mm along L-R, A-P, and S-I directions, respectively.

CONCLUSIONS

The proposed SMEIR algorithm is able to estimate a motion model and reconstruct motion-compensated 4D-CBCT. The SMEIR algorithm improves image reconstruction accuracy of 4D-CBCT and tumor motion trajectory estimation accuracy as compared to conventional sequential 4D-CBCT reconstruction and motion estimation.

GateCT™ surface tracking system for respiratory signal reconstruction in 4DCT imaging

PURPOSE

To assess the temporal and spatial accuracy of the GateCT™ system (VisionRT, London, UK), a recently released respiratory tracking system for 4DCT, under both ideal and nonideal respiratory conditions.

METHODS

Three experiments were performed by benchmarking and comparing its results with the ground-truth input data and those generated by the widely used Varian RPM™ system (Real-time Position Management, Varian, Palo Alto, CA). The first experiment used 10 sinusoidal breathing patterns (constant amplitude and frequency using sin(6)ωt), 10 "consistent" patient breathing patterns, and 10 "sporadic" patient breathing patterns. Motion was simulated with the quasar™ Programmable Respiratory Motion Platform (MODUS, London, Canada) as the surrogate. The GateCT™ and RPM™ systems were used to track the breathing patterns. The data from both systems were then analyzed in the Fourier domain, to evaluate temporal/phase accuracy, using the Pearson's correlation coefficient (PCC). The analysis correlated the ground-truth input data against the GateCT™ and RPM™ tracking results, respectively. The second experiment used 10 ideal sinusoidal breathing patterns, five of period 2.0 s, and five of period 5.0 s, with varying abdominal amplitudes found in clinical cases (peak-to-peak range: 1.67-10 mm) to test the sensitivity of the system to reconstruct various range of motion. And, the third experiment used 12 consecutive clinical patients to track the abdominal motion simultaneously by the GateCT™ and RPM™ systems. The baseline of the tracking results from both the two systems was analyzed via the mean-position-estimate (MPE) calculations. All experiments were tracked for at least 120 s.

RESULTS

In the first experiment, the average PCC values (±SD) of all thirty breathing patterns were 0.9995 ± 0.00035 and 0.9994 ± 0.00041 for the GateCT™ and the RPM™ system, respectively. These nearly identical results demonstrated similar temporal/phase tracking accuracy for the two systems. The results in the second experiment, however, revealed a pattern for the GateCT™ system in which the uncertainty of its mean-position tracking increased as the amplitude of the breathing pattern decreased. For example, a non-negligible baseline drift of up to 29.3% with respect to the peak-to-peak amplitude of 1.67-mm was observed. On the contrary, the RPM™ system displayed a more consistent recording of amplitudes over time with the greatest drift being <7.7%. The third experiment confirmed these findings in the clinical setting. Consistent decrease in PCC values due to the increase in artificial amplitude drifts, as the breathing amplitude decreased, was found. The lowest PCC value was 0.7239 for a patient with 1.57-mm peak-to-peak amplitude.

CONCLUSIONS

The GateCT™ system revealed its consistency in temporal/phase tracking but had limitations in accurately tracking the absolute abdominal positions, thus suggesting its appropriateness for phase-sorting of 4DCT rather than amplitude-sorting. In contrast, the RPM™ system demonstrated stable respiratory signal tracking in all ranges and accurately both in phase and amplitude, and is a robust system to use for both phase-sorting and amplitude-sorting techniques. The impact of the observed mean-position drift in the GateCT™ system on the resulting 4DCT image quality, in amplitude-sorting, needs further investigation.

On a PCA-based lung motion model

Respiration-induced organ motion is one of the major uncertainties in lung cancer radiotherapy and is crucial to be able to accurately model the lung motion. Most work so far has focused on the study of the motion of a single point (usually the tumor center of mass), and much less work has been done to model the motion of the entire lung. Inspired by the work of Zhang et al (2007 Med. Phys. 34 4772-81), we believe that the spatiotemporal relationship of the entire lung motion can be accurately modeled based on principle component analysis (PCA) and then a sparse subset of the entire lung, such as an implanted marker, can be used to drive the motion of the entire lung (including the tumor). The goal of this work is twofold. First, we aim to understand the underlying reason why PCA is effective for modeling lung motion and find the optimal number of PCA coefficients for accurate lung motion modeling. We attempt to address the above important problems both in a theoretical framework and in the context of real clinical data. Second, we propose a new method to derive the entire lung motion using a single internal marker based on the PCA model. The main results of this work are as follows. We derived an important property which reveals the implicit regularization imposed by the PCA model. We then studied the model using two mathematical respiratory phantoms and 11 clinical 4DCT scans for eight lung cancer patients. For the mathematical phantoms with cosine and an even power (2n) of cosine motion, we proved that 2 and 2n PCA coefficients and eigenvectors will completely represent the lung motion, respectively. Moreover, for the cosine phantom, we derived the equivalence conditions for the PCA motion model and the physiological 5D lung motion model (Low et al 2005 Int. J. Radiat. Oncol. Biol. Phys. 63 921-9). For the clinical 4DCT data, we demonstrated the modeling power and generalization performance of the PCA model. The average 3D modeling error using PCA was within 1 mm (0.7 ± 0.1 mm). When a single artificial internal marker was used to derive the lung motion, the average 3D error was found to be within 2 mm (1.8 ± 0.3 mm) through comprehensive statistical analysis. The optimal number of PCA coefficients needs to be determined on a patient-by-patient basis and two PCA coefficients seem to be sufficient for accurate modeling of the lung motion for most patients. In conclusion, we have presented thorough theoretical analysis and clinical validation of the PCA lung motion model. The feasibility of deriving the entire lung motion using a single marker has also been demonstrated on clinical data using a simulation approach.

Real time 4D IMRT treatment planning based on a dynamic virtual patient model: proof of concept

PURPOSE

To develop a novel four-dimensional (4D) intensity modulated radiation therapy (IMRT) treatment planning methodology based on dynamic virtual patient models.

METHODS

The 4D model-based planning (4DMP) is a predictive tracking method which consists of two main steps: (1) predicting the 3D deformable motion of the target and critical structures as a function of time during treatment delivery; (2) adjusting the delivery beam apertures formed by the dynamic multi-leaf collimators (DMLC) to account for the motion. The key feature of 4DMP is the application of a dynamic virtual patient model in motion prediction, treatment beam adjustment, and dose calculation. A lung case was chosen to demonstrate the feasibility of the 4DMP. For the lung case, a dynamic virtual patient model (4D model) was first developed based on the patient's 4DCT images. The 4D model was capable of simulating respiratory motion of different patterns. A model-based registration method was then applied to convert the 4D model into a set of deformation maps and 4DCT images for dosimetric purposes. Based on the 4D model, 4DMP treatment plans with different respiratory motion scenarios were developed. The quality of 4DMP plans was then compared with two other commonly used 4D planning methods: maximum intensity projection (MIP) and planning on individual phases (IP).

RESULTS

Under regular periodic motion, 4DMP offered similar target coverage as MIP with much better normal tissue sparing. At breathing amplitude of 2 cm, the lung V20 was 23.9% for a MIP plan and 16.7% for a 4DMP plan. The plan quality was comparable between 4DMP and IP: PTV V97 was 93.8% for the IP plan and 93.6% for the 4DMP plan. Lung V20 of the 4DMP plan was 2.1% lower than that of the IP plan and Dmax to cord was 2.2 Gy higher. Under a real time irregular breathing pattern, 4DMP had the best plan quality. PTV V97 was 90.4% for a MIP plan, 88.6% for an IP plan and 94.1% for a 4DMP plan. Lung V20 was 20.1% for the MIP plan, 17.8% for the IP plan and 17.5% for the 4DMP plan. The deliverability of the real time 4DMP plan was proved by calculating the maximum leaf speed of the DMLC.

CONCLUSIONS

The 4D model-based planning, which applies dynamic virtual patient models in IMRT treatment planning, can account for the real time deformable motion of the tumor under different breathing conditions. Under regular motion, the quality of 4DMP plans was comparable with IP and superior to MIP. Under realistic motion in which breathing amplitude and period change, 4DMP gave the best plan quality of the three 4D treatment planning techniques.

Investigation of a breathing surrogate prediction algorithm for prospective pulmonary gating

PURPOSE

A major challenge of four dimensional computed tomography (4DCT) in treatment planning and delivery has been the lack of respiration amplitude and phase reproducibility during image acquisition. The implementation of a prospective gating algorithm would ensure that images would be acquired only during user-specified breathing phases. This study describes the development and testing of an autoregressive moving average (ARMA) model for human respiratory phase prediction under quiet respiration conditions.

METHODS

A total of 47 4DCT patient datasets and synchronized respiration records was utilized in this study. Three datasets were used in model development and were removed from further evaluation of the ARMA model. The remaining 44 patient datasets were evaluated with the ARMA model for prediction time steps from 50 to 1000 ms in increments of 50 and 100 ms. Thirty-five of these datasets were further used to provide a comparison between the proposed ARMA model and a commercial algorithm with a prediction time step of 240 ms.

RESULTS

The optimal number of parameters for the ARMA model was based on three datasets reserved for model development. Prediction error was found to increase as the prediction time step increased. The minimum prediction time step required for prospective gating was selected to be half of the gantry rotation period. The maximum prediction time step with a conservative 95% confidence criterion was found to be 0.3 s. The ARMA model predicted peak inhalation and peak exhalation phases significantly better than the commercial algorithm. Furthermore, the commercial algorithm had numerous instances of missed breath cycles and falsely predicted breath cycles, while the proposed model did not have these errors.

CONCLUSIONS

An ARMA model has been successfully applied to predict human respiratory phase occurrence. For a typical CT scanner gantry rotation period of 0.4 s (0.2 s prediction time step), the absolute error was relatively small, 0.06 +/- 0.02 s at peak inhalation and 0.05 +/- 0.04 s at peak exhalation. The application of the ARMA model for prospective pulmonary gating has been demonstrated.

Four-dimensional cone-beam computed tomography and digital tomosynthesis reconstructions using respiratory signals extracted from transcutaneously inserted metal markers for liver SBRT

PURPOSE

Respiration-induced intrafraction target motion is a concern in liver cancer radiotherapy, especially in stereotactic body radiotherapy (SBRT), and therefore, verification of its motion is necessary. An effective means to localize the liver cancer is to insert metal fiducial markers to or near the tumor with simultaneous imaging using cone-beam computed tomography (CBCT). Utilizing the fiducial markers, the authors have demonstrated a method to generate breath-induced motion signal of liver for reconstructing 4D digital tomosynthesis (4DDTS) and 4DCBCT images based on phasewise and/or amplitudewise sorting of projection data.

METHODS

The marker extraction algorithm is based on template matching of a prior known marker image and has been coded to optimally extract marker positions in CBCT projections from the On-Board Imager (Varian Medical Systems, Palo Alto, CA). To validate the algorithm, multiple projection images of moving thorax phantom and five patient cases were examined. Upon extraction of the motion signals from the markers, 4D image sorting and image reconstructions were subsequently performed. In the case of incomplete signals due to projections with missing markers, the authors have implemented signal profiling to replace the missing portion.

RESULTS

The proposed marker extraction algorithm was shown to be very robust and accurate in the phantom and patient cases examined. The maximum discrepancy of the algorithm predicted marker location versus operator selected location was < 1.2 mm, with the overall average of 0.51 +/- 0.15 mm, for 500 projections. The resulting 4DDTS and 4DCBCT images showed clear reduction in motion-induced blur of the markers and the anatomy for an effective image guidance. The signal profiling method was useful in replacing missing signals.

CONCLUSIONS

The authors have successfully demonstrated that motion tracking of fiducial markers and the subsequent 4D reconstruction of CBCT and DTS are possible. Due to the significant reduction in motion-induced image blur, it is anticipated that such technology will be useful in image-guided liver SBRT treatments.