Quantifying variability of intrafractional target motion in stereotactic body radiotherapy for lung cancers

published_or_final_versio

Doctor of Philosophy

dissertationThe statistical study of anatomy is one of the primary focuses of medical image analysis. It is well-established that the appropriate mathematical settings for such analyses are Riemannian manifolds and Lie group actions. Statistically defined atlases, in which a mean anatomical image is computed from a collection of static three-dimensional (3D) scans, have become commonplace. Within the past few decades, these efforts, which constitute the field of computational anatomy, have seen great success in enabling quantitative analysis. However, most of the analysis within computational anatomy has focused on collections of static images in population studies. The recent emergence of large-scale longitudinal imaging studies and four-dimensional (4D) imaging technology presents new opportunities for studying dynamic anatomical processes such as motion, growth, and degeneration. In order to make use of this new data, it is imperative that computational anatomy be extended with methods for the statistical analysis of longitudinal and dynamic medical imaging. In this dissertation, the deformable template framework is used for the development of 4D statistical shape analysis, with applications in motion analysis for individualized medicine and the study of growth and disease progression. A new method for estimating organ motion directly from raw imaging data is introduced and tested extensively. Polynomial regression, the staple of curve regression in Euclidean spaces, is extended to the setting of Riemannian manifolds. This polynomial regression framework enables rigorous statistical analysis of longitudinal imaging data. Finally, a new diffeomorphic model of irrotational shape change is presented. This new model presents striking practical advantages over standard diffeomorphic methods, while the study of this new space promises to illuminate aspects of the structure of the diffeomorphism group

Optimization of Radiation Therapy in Time-Dependent Anatomy

The objective of this dissertation is to develop treatment planning techniques that have the potential to improve radiation therapy of time-dependent (4D) anatomy. Specifically, this study examines dose estimation, dose evaluation, and decision making in the context of optimizing lung cancer radiation therapy. Two methods of dose estimation are compared in patients with locally advanced and early stage lung cancer: dose computed on a single image (3D-dose) and deformably registered, accumulated dose (or 4D-dose). The results indicate that differences between 3D- and 4D- dose are not significant in organs at risk (OARs), however, 4D-dose to a moving lung cancer target can deviate from 3D-dose. These differences imply that optimization of the 4D-dose through multiple-anatomy optimization (MAO) can improve radiation therapy in 4D-anatomy. MAO incorporates time-dependent target and OAR geometry while enabling a simple, clinically realizable delivery. MAO has the potential to enhance the therapeutic ratio in terms of target coverage and OAR sparing in 4D-anatomy. In dose evaluation within 4D-anatomy; dose-to-mass is a more intuitive and precise metric in estimating the effects of radiation in tissues. Assuming physical density is proportional to functional tissue density, dose-to-mass has a 1-1 correspondence with radiation damage. Dose-to-mass optimization boosts dose in massive regions of lung cancer targets and can reduce integral dose to lung by preferentially treating through regions of low-density lung tissue. Finally, multi-criteria optimization (MCO) is implemented in order to clarify decision making during plan design for lung cancer treatment. An MCO basis set establishes a patient-specific decision space which reveals trade-offs in OAR-dose at a fixed, constrained target dose. By interpolating the MCO basis set and evaluating the plan on 4D-anatomy, patient- and organ- specific conservatism in plan design can be expressed in real time. Through improved methods of dose estimation, dose evaluation, and decision making, this dissertation will positively impact radiation therapy of time-dependent anatomy

The management of imaging dose during imageâ guided radiotherapy: Report of the AAPM Task Group 75

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134823/1/mp5667.pd

Investigation of time-resolved volumetric MRI to enhance MR-guided radiotherapy of moving lung tumors

In photon radiotherapy of lung cancer, respiratory-induced motion introduces systematic and statistical uncertainties in treatment planning and dose delivery. By integrating magnetic resonance imaging (MRI) in the treatment planning process in MR-guided radiotherapy (MRgRT), uncertainties in target volume definition can be reduced with respect to state-of-the-art X-ray-based approaches. Furthermore, MR-guided linear accelerators (MR-Linacs) offer dose delivery with enhanced accuracy and precision through daily treatment plan adaptation and gated beam delivery based on real-time MRI. Today, the potential of MRgRT of moving targets is, however, not fully exploited due to the lack of time-resolved four-dimensional MRI (4D-MRI) in clinical practice. Therefore, the aim of this thesis was to develop and experimentally validate new methods for motion characterization and estimation with 4D-MRI for MRgRT of lung cancer. Different concepts were investigated for all phases of the clinical workflow - treatment planning, beam delivery, and post-treatment analysis. Firstly, a novel internal target volume (ITV) definition method based on the probability-of-presence of moving tumors derived from real-time 4D-MRI was developed. The ability of the ITVs to prospectively account for changes occurring over the course of several weeks was assessed in retrospective geometric analyses of lung cancer patient data. Higher robustness of the probabilistic 4D-MRI-based ITVs against interfractional changes was observed compared to conventional target volumes defined with four-dimensional computed tomography (4D-CT). The study demonstrated that motion characterization over extended times enabled by real-time 4D-MRI can reduce systematic and statistical uncertainties associated with today’s standard workflow. Secondly, experimental validation of a published motion estimation method - the propagation method - was conducted with a porcine lung phantom under realistic patient-like conditions. Estimated 4D-MRIs with a temporal resolution of 3.65 Hz were created based on orthogonal 2D cine MRI acquired at the scanner unit of an MR-Linac. A comparison of these datasets with ground truth respiratory-correlated 4D-MRIs in geometric analyses showed that the propagation method can generate geometrically accurate estimated 4D-MRIs. These could decrease target localization errors and enable 3D motion monitoring during beam delivery at the MR-Linac in the future. Lastly, the propagation method was extended to create continuous time-resolved estimated synthetic CTs (tresCTs). The proposed method was experimentally tested with the porcine lung phantom, successively imaged at a CT scanner and an MR-Linac. A high agreement of the images and corresponding dose distributions of the tresCTs and measured ground truth 4D-CTs was found in geometric and dosimetric analyses. The tresCTs could be used for post-treatment time-resolved reconstruction of the delivered dose to guide treatment adaptations in the future. These studies represent important steps towards a clinical application of time-resolved 4D-MRI methods for enhanced MRgRT of lung tumors in the near future

Incorporating Cardiac Substructures Into Radiation Therapy For Improved Cardiac Sparing

Growing evidence suggests that radiation therapy (RT) doses to the heart and cardiac substructures (CS) are strongly linked to cardiac toxicities, though only the heart is considered clinically. This work aimed to utilize the superior soft-tissue contrast of magnetic resonance (MR) to segment CS, quantify uncertainties in their position, assess their effect on treatment planning and an MR-guided environment. Automatic substructure segmentation of 12 CS was completed using a novel hybrid MR/computed tomography (CT) atlas method and was improved upon using a 3-dimensional neural network (U-Net) from deep learning. Intra-fraction motion due to respiration was then quantified. The inter-fraction setup uncertainties utilizing a novel MR-linear accelerator were also quantified. Treatment planning comparisons were performed with and without substructure inclusions and methods to reduce radiation dose to sensitive CS were evaluated. Lastly, these described technologies (deep learning U-Net) were translated to an MR-linear accelerator and a segmentation pipeline was created. Automatic segmentations from the hybrid MR/CT atlas was able to generate accurate segmentations for the chambers and great vessels (Dice similarity coefficient (DSC) \u3e 0.75) but coronary artery segmentations were unsuccessful (DSC\u3c0.3). After implementing deep learning, DSC for the chambers and great vessels was ≥0.85 along with an improvement in the coronary arteries (DSC\u3e0.5). Similar accuracy was achieved when implementing deep learning for MR-guided RT. On average, automatic segmentations required ~10 minutes to generate per patient and deep learning only required 14 seconds. The inclusion of CS in the treatment planning process did not yield statistically significant changes in plan complexity, PTV, or OAR dose. Automatic segmentation results from deep learning pose major efficiency and accuracy gains for CS segmentation offering high potential for rapid implementation into radiation therapy planning for improved cardiac sparing. Introducing CS into RT planning for MR-guided RT presented an opportunity for more effective sparing with limited increase in plan complexity

Incorporating Cardiac Substructures Into Radiation Therapy For Improved Cardiac Sparing

Growing evidence suggests that radiation therapy (RT) doses to the heart and cardiac substructures (CS) are strongly linked to cardiac toxicities, though only the heart is considered clinically. This work aimed to utilize the superior soft-tissue contrast of magnetic resonance (MR) to segment CS, quantify uncertainties in their position, assess their effect on treatment planning and an MR-guided environment. Automatic substructure segmentation of 12 CS was completed using a novel hybrid MR/computed tomography (CT) atlas method and was improved upon using a 3-dimensional neural network (U-Net) from deep learning. Intra-fraction motion due to respiration was then quantified. The inter-fraction setup uncertainties utilizing a novel MR-linear accelerator were also quantified. Treatment planning comparisons were performed with and without substructure inclusions and methods to reduce radiation dose to sensitive CS were evaluated. Lastly, these described technologies (deep learning U-Net) were translated to an MR-linear accelerator and a segmentation pipeline was created. Automatic segmentations from the hybrid MR/CT atlas was able to generate accurate segmentations for the chambers and great vessels (Dice similarity coefficient (DSC) \u3e 0.75) but coronary artery segmentations were unsuccessful (DSC\u3c0.3). After implementing deep learning, DSC for the chambers and great vessels was ≥0.85 along with an improvement in the coronary arteries (DSC\u3e0.5). Similar accuracy was achieved when implementing deep learning for MR-guided RT. On average, automatic segmentations required ~10 minutes to generate per patient and deep learning only required 14 seconds. The inclusion of CS in the treatment planning process did not yield statistically significant changes in plan complexity, PTV, or OAR dose. Automatic segmentation results from deep learning pose major efficiency and accuracy gains for CS segmentation offering high potential for rapid implementation into radiation therapy planning for improved cardiac sparing. Introducing CS into RT planning for MR-guided RT presented an opportunity for more effective sparing with limited increase in plan complexity

Three Essays on Radiotherapy Treatment Planning Optimization

Radiation therapy is one of the most common and effective methods of treating cancer. There are two main types: external and internal. External to the patient, a linear accelerator aims beams of radiation toward the patient; internal to the patient, radioactive sources are placed temporarily or permanently at the treatment site to deposit dose locally. Both methods of treatment can deliver complex dose distributions to a patient. The radiation damages both tumorous tissue and nearby healthy organs; treatment planning optimization determines how to deliver a dose distribution that maximizes tumor kill while sparing nearby healthy organs as much as possible. This thesis studies three treatment planning problems: the first two are in the context of external radiation therapy and the third is in the context of internal radiation therapy. Conventional planning is based on only the physical geometry of the patient anatomy. In chapter II, we propose two models that incorporate (additional) liver function information for planning liver cancer treatment to preserve as much post-treatment liver function as possible and compare this to a conventional approach that ignores liver function information. Conventional plans assume the patient geometry does not change between the time of patient imaging and later treatments. Although the patient geometry can be updated at treatment for plan adaptation, current practice may lead to plans that result in significantly worse quality than originally intended due to its myopic nature. In chapter III, we propose a model that produces a plan that caters to each potential patient geometry while considering both day-of and cumulative impact. In high-dose rate brachytherapy, the patient undergoes anesthesia due to the need to implant catheters for radiation source placement before planning. Consideration of multiple conflicting criteria in treatment planning results in challenging optimization problems. Current commercial systems require iterative guess-and-checking of optimization input parameters to make trade-offs among criteria, but a plan must be finalized quickly to minimize anesthesia administration. In chapter IV, we develop a practical optimization engine that generates a trade-off surface and feeds into a graphical user interface that provides the clinician more control to make trade-offs without trial-and-error optimizations.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/140827/1/vwwu_1.pd

Assessing the impact of motion on treatment planning during stereotactic body radiotherapy of lung cancer

Cancer is a leading cause of death in Australia and approximately 52% of cancer patients will require radiotherapy at some stage in their treatment. In recent years, stereotactic radiotherapy has emerged as an increasingly common treatment modality for small lesions in various sites of the human body.  To facilitate the investigation into the effects of imaging small mobile lesions, a see-saw 4D-CT phantom was developed. This phantom was used to investigate phase-binning artifacts that can be present when assigning an insufficient number of phases to 4D-CT data. The interplay between a lesion’s size and its amplitude, and the effects this relationship has on 4D-CT data was also investigated. An upgrade to a commercially available respiratory motion phantom was also pursued in order to replicate patient motion recorded with the Varian RPM system. Monte Carlo methods were used to determine the impact of motion on PET data by incorporating a computational moving phantom (XCAT) with a full Monte Carlo model of a commercially available PET scanner. To assess the impact of motion on treatment planning and dose calculation, two treatment planning scenarios were simulated using Monte Carlo. The traditional method of calculating dose on an average intensity projection from 4D-CT was compared to 4D dose calculation, in which tumour motion data from 4D-CT is explicitly incorporated into the treatment plan. Monte Carlo methods are also employed to evaluate the degree of underdosage at the periphery of lung lesions arising from electronic disequilibrium associated with density changes.  It was found that small lesions typically seen in SBRT of lung cancer require extra care when considering treatment planning, motion mitigation, and treatment delivery. The upgraded QUASAR phantom allows for patient specific verification of SBRT/SABR treatment plans to be conducted and was found to replicate patient motion accurately. Respiratory analysis software presented in this work enables detailed statistics of a patient’s respiratory characteristics to be evaluated. The number of phase-bins required to mitigate banding artifacts in 4D-CT projections is quantified in a simple equation for sinusoidal motion. It was also found that for lesion with diameters greater than 2.0 cm and amplitudes less than 4.0 cm, ten phase-bins are adequate to negate all banding artifacts in projection images.  Experimental and Monte Carlo simulations of PET and 4D-PET revealed that motion greater than 1.0 cm resulted in a reduction in apparent activity that increased with motion amplitude. A Dose Reduction Factor (DRF) metric was developed using Monte Carlo simulation which is defined as the ratio of the average dose to the periphery of the lesion to the dose in the central portion. The mean DRF was found to be 0.97 and 0.92 for 6 MV and 15 MV photon beams respectively, for lesion sizes ranging from 10 – 50 mm. The dynamic scenario was simulated with 4D dose calculation methods of registering and adding the dose distributions in each phase-bin from 4D-CT. The dose-volume distributions compared well with 3D (AIP) methods if multiple beams were used and the amplitude of motion was less than 3.0 cm.  

2012 Activity Report of the Regional Research Programme on Hadrontherapy for the ETOILE Center

2012 is the penultimate year of financial support by the CPER 2007-2013 for ETOILE's research program, sustained by the PRRH at the University Claude Bernard. As with each edition we make the annual review of the research in this group, so active for over 12 years now. Over the difficulties in the decision-making process for the implementation of the ETOILE Center, towards which all our efforts are focussed, some "themes" (work packages) were strengthened, others have progressed, or have been dropped. This is the case of the eighth theme (technological developments), centered around the technology for rotative beam distribution heads (gantries) and, after being synchronized with the developments of ULICE's WP6, remained so by ceasing its activities, coinciding also with the retirement of its historic leader at IPNL, Marcel Bajard. Topic number 5 ("In silico simulations") has suffered the departure of its leader, Benjamin Ribba, although the work has still been provided by Branka Bernard, a former postdoctoral fellow in Lyon Sud, and now back home in Croatia, still in contract with UCBL for the ULICE project. Aside from these two issues (and the fact that the theme "Medico-economical simulations" is now directly linked to the first one ("Medical Project"), the rest of the teams are growing, as evidenced by the publication statistics at the beginning of this report. This is obviously due to the financial support of our always faithful regional institutions, but also to the synergy that the previous years, the European projects, the arrival of the PRIMES LabEx, and the national France Hadron infrastructure have managed to impulse. The Rhone-Alpes hadron team, which naturally includes the researchers of LPC at Clermont, should also see its influence result in a strong presence in France Hadron's regional node, which is being organized. The future of this regional research is not yet fully guaranteed, especially in the still uncertain context of ETOILE, but the tracks are beginning to emerge to allow past and present efforts translate into a long future that we all want to see established. Each of the researchers in PRRH is aware that 2013 will be (and already is) the year of great challenge : for ETOILE, for the PRRH, for hadron therapy in France, for French hadrontherapy in Europe (after the opening and beginning of treatments in the German [HIT Heidelberg, Marburg], Italian [CNAO, Pavia] and Austrian [MedAustron, Wien Neuerstadt]) centers. Let us meet again in early 2014 for a comprehensive review of the past and a perspective for the future ..