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

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

Evaluating and Improving 4D-CT Image Segmentation for Lung Cancer Radiotherapy

Lung cancer is a high-incidence disease with low survival despite surgical advances and concurrent chemo-radiotherapy strategies. Image-guided radiotherapy provides for treatment measures, however, significant challenges exist for imaging, treatment planning, and delivery of radiation due to the influence of respiratory motion. 4D-CT imaging is capable of improving image quality of thoracic target volumes influenced by respiratory motion. 4D-CT-based treatment planning strategies requires highly accurate anatomical segmentation of tumour volumes for radiotherapy treatment plan optimization. Variable segmentation of tumour volumes significantly contributes to uncertainty in radiotherapy planning due to a lack of knowledge regarding the exact shape of the lesion and difficulty in quantifying variability. As image-segmentation is one of the earliest tasks in the radiotherapy process, inherent geometric uncertainties affect subsequent stages, potentially jeopardizing patient outcomes. Thus, this work assesses and suggests strategies for mitigation of segmentation-related geometric uncertainties in 4D-CT-based lung cancer radiotherapy at pre- and post-treatment planning stages

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

Clinical practice vs. state-of-the-art research and future visions:Report on the 4D treatment planning workshop for particle therapy - Edition 2018 and 2019

The 4D Treatment Planning Workshop for Particle Therapy, a workshop dedicated to the treatment of moving targets with scanned particle beams, started in 2009 and since then has been organized annually. The mission of the workshop is to create an informal ground for clinical medical physicists, medical physics researchers and medical doctors interested in the development of the 4D technology, protocols and their translation into clinical practice. The 10th and 11th editions of the workshop took place in Sapporo, Japan in 2018 and Krakow, Poland in 2019, respectively. This review report from the Sapporo and Krakow workshops is structured in two parts, according to the workshop programs. The first part comprises clinicians and physicists review of the status of 4D clinical implementations. Corresponding talks were given by speakers from five centers around the world: Maastro Clinic (The Netherlands), University Medical Center Groningen (The Netherlands), MD Anderson Cancer Center (United States), University of Pennsylvania (United States) and The Proton Beam Therapy Center of Hokkaido University Hospital (Japan). The second part is dedicated to novelties in 4D research, i.e. motion modelling, artificial intelligence and new technologies which are currently being investigated in the radiotherapy field

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