Extracting respiratory signals from thoracic cone beam CT projections

Patient respiratory signal associated with the cone beam CT (CBCT) projections is important for lung cancer radiotherapy. In contrast to monitoring an external surrogate of respiration, such signal can be extracted directly from the CBCT projections. In this paper, we propose a novel local principle component analysis (LPCA) method to extract the respiratory signal by distinguishing the respiration motion-induced content change from the gantry rotation-induced content change in the CBCT projections. The LPCA method is evaluated by comparing with three state-of-the-art projection-based methods, namely, the Amsterdam Shroud (AS) method, the intensity analysis (IA) method, and the Fourier-transform based phase analysis (FT-p) method. The clinical CBCT projection data of eight patients, acquired under various clinical scenarios, were used to investigate the performance of each method. We found that the proposed LPCA method has demonstrated the best overall performance for cases tested and thus is a promising technique for extracting respiratory signal. We also identified the applicability of each existing method.Comment: 21 pages, 11 figures, submitted to Phys. Med. Bio

A biomechanical approach for real-time tracking of lung tumors during External Beam Radiation Therapy (EBRT)

Lung cancer is the most common cause of cancer related death in both men and women. Radiation therapy is widely used for lung cancer treatment. However, this method can be challenging due to respiratory motion. Motion modeling is a popular method for respiratory motion compensation, while biomechanics-based motion models are believed to be more robust and accurate as they are based on the physics of motion. In this study, we aim to develop a biomechanics-based lung tumor tracking algorithm which can be used during External Beam Radiation Therapy (EBRT). An accelerated lung biomechanical model can be used during EBRT only if its boundary conditions (BCs) are defined in a way that they can be updated in real-time. As such, we have developed a lung finite element (FE) model in conjunction with a Neural Networks (NNs) based method for predicting the BCs of the lung model from chest surface motion data. To develop the lung FE model for tumor motion prediction, thoracic 4D CT images of lung cancer patients were processed to capture the lung and diaphragm geometry, trans-pulmonary pressure, and diaphragm motion. Next, the chest surface motion was obtained through tracking the motion of the ribcage in 4D CT images. This was performed to simulate surface motion data that can be acquired using optical tracking systems. Finally, two feedforward NNs were developed, one for estimating the trans-pulmonary pressure and another for estimating the diaphragm motion from chest surface motion data. The algorithm development consists of four steps of: 1) Automatic segmentation of the lungs and diaphragm, 2) diaphragm motion modelling using Principal Component Analysis (PCA), 3) Developing the lung FE model, and 4) Using two NNs to estimate the trans-pulmonary pressure values and diaphragm motion from chest surface motion data. The results indicate that the Dice similarity coefficient between actual and simulated tumor volumes ranges from 0.76±0.04 to 0.91±0.01, which is favorable. As such, real-time lung tumor tracking during EBRT using the proposed algorithm is feasible. Hence, further clinical studies involving lung cancer patients to assess the algorithm performance are justified

A phantom based evaluation on the effects of patient breathing motion on Stereotactic Body Radiotherapy treatment volumes

Aim: The aim of the study was to design an upper body phantom to mimic the movement of the lesion inside the lungs during a breathing cycle. Phantom design included an assessment of the motion observed for lung lesions, identification of suitable phantom materials as well as design of a motorized arm to mimic the movements observed inside the lung area of the phantom. Introduction: Expansion margins are added to clinical target volumes contoured by Oncologists in order to safeguard against under- or over-treatment of the target volume. They are designed to account for errors during setup, inaccuracies on the linear accelerator, and movement of targets inside the patient. If the margins are too small, there is a risk that the lesion/target may not receive the necessary dose, due to being partially missed. On the other hand, if the margins are too wide, the lesion will be covered, but normal tissue may receive unnecessary dose, resulting in additional side effects to the patient. Assessment of the impact of these margins is not possible in a static phantom and the availability of a low-cost motorized phantom would assist in the validation of these margins. Method: Previously treated patients' 4D CT scanning data were used to quantify the amount of movement seen for lesions within the lung. A phantom was then designed and built in an attempt to mimic both patient anatomy and movement. Materials were identified to replicate anatomical shape and densities of various organs in the thorax, as seen on CT scan data. Two treatment planning systems (Monaco, (Elekta) and Eclipse (Varian)) were used to determine the dosimetric characteristics of the materials. This was compared to actual dose as delivered by a linear accelerator (Elekta Synergy). Results: Paths were calculated from the breathing cycles during the 4D-CT scan sets and templates designed to mimic these movements. A thorax phantom was built with the appropriate materials suitable and matched densities to replicate a human thorax. Comparing transmission for these materials on a linear accelerator for 6MV and 10MV energy, the deviation from planned versus measured dose varied between 1.67% to 3.32% and 0.45% to 2.30%, respectively for the silicon material and between 0.77% to 3.22% and 0.17% to 2.57% for the 3D printed bone for 6MV and 10MV. iv Conclusion: The measurements done on the linear accelerator matched closely with the calculated values on the treatment planning system for transmission through the materials in the customised phantom. Various proposals were put forward to mimic the movement of the targets within the lung regions. However, it was not possible to manufacture a mechanically based working model due to the small movements observed (<5mm). It is recommended that a robotic solution be investigated as alternative to mimic these small movements

Surrogate-driven respiratory motion models for MRI-guided lung radiotherapy treatments

An MR-Linac integrates an MR scanner with a radiotherapy delivery system, providing non-ionizing real-time imaging of the internal anatomy before, during and after radiotherapy treatments. Due to spatio-temporal limitations of MR imaging, only high-resolution 2D cine-MR images can be acquired in real-time during MRI-guided radiotherapy (MRIgRT) to monitor the respiratory-induced motion of lung tumours and organs-at-risk. However, temporally-resolved 3D anatomical information is essential for accurate MR guidance of beam delivery and dose estimation of the actually delivered dose. Surrogate-driven respiratory motion models can estimate the 3D motion of the internal anatomy from surrogate signals, producing the required information. The overall aim of this thesis was to tailor a generalized respiratory motion modelling framework for lung MRIgRT. This framework can fit the model directly to unsorted 2D MR images sampling the 3D motion, and to surrogate signals extracted from the 2D cine-MR images acquired on an MR-Linac. It can model breath-to-breath variability and produce a motion compensated super-resolution reconstruction (MCSR) 3D image that can be deformed using the estimated motion. In this work novel MRI-derived surrogate signals were generated from 2D cine-MR images to model respiratory motion for lung cancer patients, by applying principal component analysis to the control point displacements obtained from the registration of the cine-MR images. An MR multi-slice interleaved acquisition potentially suitable for the MR-Linac was developed to generate MRI-derived surrogate signals and build accurate respiratory motion models with the generalized framework for lung cancer patients. The developed models and the MCSR images were thoroughly evaluated for lung cancer patients scanned on an MR-Linac. The results showed that respiratory motion models built with the generalized framework and minimal training data generally produced median errors within the MCSR voxel size of 2 mm, throughout the whole 3D thoracic field-of-view and over the expected lung MRIgRT treatment times

Dosimetry and 4D Modelling for Advanced Radiotherapy Treatments: Towards MRI-Guided Lung SBRT

A major problem for radiation therapy of lung cancer is respiration-induced motion, which causes both the tumour and surrounding normal tissue to move during treatment. This motion often results in inadequate target coverage and increases the likelihood of additional healthy tissue exposure; therefore detracting from the therapeutic benefits and increasing the risk of radiation induced toxicity. Some motion-management techniques include additional treatment margins to encompass the range of tumour motion, monitoring the respiratory cycle and treating only when in a particular phase i.e. respiratory gating, or imaging the tumour during treatment and adapting the radiation beam aperture to follow the tumour i.e. image guidance and tracking. Magnetic-Resonance-Imaging (MRI)-linacs are a form of image guided radiotherapy, these systems offer high soft-tissue contrast imaging (with MRI) while simultaneously treating with a therapeutic radiation beam (linear accelerator or linac). The effects of the magnetic field on dose deposition and detector response should be well understood to safely translate this technology to clinical treatments. For MRI-linacs where the magnetic field is inline with respect to the beam, the effects of the magnetic field on electron trajectories in lung can be significant and therefore it is important to study the impacts of this on dose distribution in order to treat lung SBRT on these systems. In this thesis, a 4D Monte Carlo dose calculation tool is developed and implemented for assessing current radiotherapy techniques for lung Stereotactic Body Radiotherapy (SBRT). In recent years there has been an increasing interest in MRI-guided radiotherapy and its potential to be used for lung SBRT. With the higher doses per fraction used for SBRT there is an increased need for highly accurate dose calculations and localised delivery; particularly for MRI-linac lung treatments, where the magnetic field strongly influences lung tissue and tumour dose distributions. This thesis also presents work towards translating the 4D Monte Carlo method for inline MRI-linacs

Real-time intrafraction motion monitoring in external beam radiotherapy

© 2019 Institute of Physics and Engineering in Medicine. Radiotherapy (RT) aims to deliver a spatially conformal dose of radiation to tumours while maximizing the dose sparing to healthy tissues. However, the internal patient anatomy is constantly moving due to respiratory, cardiac, gastrointestinal and urinary activity. The long term goal of the RT community to 'see what we treat, as we treat' and to act on this information instantaneously has resulted in rapid technological innovation. Specialized treatment machines, such as robotic or gimbal-steered linear accelerators (linac) with in-room imaging suites, have been developed specifically for real-time treatment adaptation. Additional equipment, such as stereoscopic kilovoltage (kV) imaging, ultrasound transducers and electromagnetic transponders, has been developed for intrafraction motion monitoring on conventional linacs. Magnetic resonance imaging (MRI) has been integrated with cobalt treatment units and more recently with linacs. In addition to hardware innovation, software development has played a substantial role in the development of motion monitoring methods based on respiratory motion surrogates and planar kV or Megavoltage (MV) imaging that is available on standard equipped linacs. In this paper, we review and compare the different intrafraction motion monitoring methods proposed in the literature and demonstrated in real-time on clinical data as well as their possible future developments. We then discuss general considerations on validation and quality assurance for clinical implementation. Besides photon RT, particle therapy is increasingly used to treat moving targets. However, transferring motion monitoring technologies from linacs to particle beam lines presents substantial challenges. Lessons learned from the implementation of real-time intrafraction monitoring for photon RT will be used as a basis to discuss the implementation of these methods for particle RT

On the investigation of a novel x-ray imaging techniques in radiation oncology

Radiation therapy is indicated for nearly 50% of cancer patients in Australia. Radiation therapy requires accurate delivery of ionising radiation to the neoplastic tissue and pre-treatment in situ x-ray imaging plays an important role in meeting treatment accuracy requirements. Four dimensional cone-beam computed tomography (4D CBCT) is one such pre-treatment imaging technique that can help to visualise tumour target motion due to breathing at the time of radiation treatment delivery. Measuring and characterising the target motion can help to ensure highly accurate therapeutic x-ray beam delivery. In this thesis, a novel pre-treatment x-ray imaging technique, called Respiratory Triggered 4D cone-beam Computed Tomography (RT 4D CBCT), is conceived and investigated. Specifically, the aim of this work is to progress the 4D CBCT imaging technology by investigating the use of a patient’s breathing signal to improve and optimise the use of imaging radiation in 4D CBCT to facilitate the accurate delivery of radiation therapy. These investigations are presented in three main studies: 1. Introduction to the concept of respiratory triggered four dimensional conebeam computed tomography. 2. A simulation study exploring the behaviour of RT 4D CBCT using patientmeasured respiratory data. 3. The experimental realisation of RT 4D CBCT working in a real-time acquisitions setting. The major finding from this work is that RT 4D CBCT can provide target motion information with a 50% reduction in the x-ray imaging dose applied to the patient

Assessing and Improving 4D-CT Imaging for Radiotherapy Applications

Lung cancer has both a high incidence and death rate. A contributing factor to these high rates comes from the difficulty of treating lung cancers due to the inherent mobility of the lung tissue and the tumour. 4D-CT imaging has been developed to image lung tumours as they move during respiration. Most 4D-CT imaging methods rely on data from an external respiratory surrogate to sort the images according to respiratory phase. However, it has been shown that respiratory surrogate 4D-CT methods can suffer from imaging artifacts that degrade the image quality of the 4D-CT volumes that are used to plan a patient\u27s radiation therapy. In Chapter 2 of this thesis a method to investigate the correlation between an external respiratory surrogate and the internal anatomy was developed. The studies were performed on ventilated pigs with an induced inconsistent amplitude of breathing. The effect of inconsistent breathing on the correlation between the external marker and the internal anatomy was tested using a linear regression. It was found in 10 of the 12 studies performed that there were significant changes in the slope of the regression line as a result of inconsistent breathing. From this study we conclude that the relationship between an external marker and the internal anatomy is not stable and can be perturbed by inconsistent breathing amplitudes. Chapter 3 describes the development of a image based 4D-CT imaging algorithm based on the concept of normalized cross correlation (NCC) between images. The volumes produced by the image based algorithm were compared to volumes produced using a clinical external marker 4D-CT algorithm. The image based method produced 4D-CT volumes that had a reduced number of imaging artifacts when compared to the external marker produced volumes. It was shown that an image based 4D-CT method could be developed and perform as well or better than external marker methods that are currently in clinical use. In Chapter 4 a method was developed to assess the uncertainties of the locations of anatomical structures in the volumes produced by the image based 4D-CT algorithm developed in Chapter 3. The uncertainties introduced by using NCC to match a pair of images according to respiratory phase were modeled and experimentally determined. Additionally, the assumption that two subvolumes could be matched in respiratory phase using a single pair of 2D overlapping images was experimentally validated. It was shown that when the image based 4D-CT algorithm developed in Chapter 3 was applied to data acquired from a ventilated pig with induced inconsistent breathing the displacement uncertainties were on the order of 1.0 millimeter. The results of this thesis show that there exists the possibility of a miscorrelation between the motion of a respiratory surrogate (marker) and the internal anatomy under inconsistent breathing amplitude. Additionally, it was shown that an image based 4D-CT method that operates without the need of one or more external respiratory surrogate(s) could produce artifact free volumes synchronous with respiratory phase. The spatial uncertainties of the volumes produced by the image based 4D-CT method were quantified and shown to be small (~ 1mm) which is an acceptable accuracy for radiation treatment planning. The elimination of the external respiratory surrogates simplifies the implementation and increases the throughput of the image based 4D-CT method as well

Multivariate Statistical Techniques for Accurately and Noninvasively Localizing Tumors Subject to Respiration-Induced Motion

Tumors in the lung, liver, and pancreas can move considerably with normal respiration. The tumor motion extent, path, and baseline position change over time. This creates a complex "moving target" for external beam radiation and is a major obstacle to treating cancer. Real-time tumor motion compensation systems have emerged, but device performance is limited by tumor localization accuracy. Direct tumor tracking is not feasible for these tumors, but tumor displacement can be predicted from surrogate measurements of respiration. In this dissertation, we have developed a series of multivariate statistical techniques for reliably and accurately localizing tumors from respiratory surrogate markers affixed to the torso surface. Our studies utilized radiographic tumor localizations measured concurrently with optically tracked respiratory surrogates during 176 lung, liver, and pancreas radiation treatment and dynamic MR imaging sessions. We identified measurement precision, tumor-surrogate correlation, training data selection, inter-patient variations, and algorithm design as factors impacting localization accuracy. Training data timing was particularly important, as tumor localization errors increased over time in 63% of 30-min treatments. This was a result of the changing relationship between surrogate signals and tumor motion. To account for these changes, we developed a method for detecting and correcting large localization errors. By monitoring the surrogate-to-surrogate and surrogate-to-model relationships, tumor localization errors exceeding 3 mm could be detected at a sensitivity of 95%. The method that we have proposed and validated in this dissertation leads to 69% fewer treatment interruptions than conventional respiratory surrogate model monitoring techniques. Finally, we extended respiratory surrogate-based tumor motion prediction to the otherwise time-consuming process of contouring respiratory-correlated computed tomography scans. This dissertation clarifies the scope and significance of problems underlying existing surrogate-based tumor localization models. Furthermore, it presents novel solutions that make it possible to improve radiation delivery to tumors without increasing the time required to plan and deliver radiation treatments