Effect of deformable registration uncertainty on lung SBRT dose accumulation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134767/1/mp8412.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134767/2/mp8412_am.pd

Improving Dose-Response Correlations for Locally Advanced NSCLC Patients Treated with IMRT or PSPT

The standard of care for locally advanced non-small cell lung cancer (NSCLC) is concurrent chemo-radiotherapy. Despite recent advancements in radiation delivery methods, the median survival time of NSCLC patients remains below 28 months. Higher tumor dose has been found to increase survival but also a higher rate of radiation pneumonitis (RP) that affects breathing capability. In fear of such toxicity, less-aggressive treatment plans are often clinically preferred, leading to metastasis and recurrence. Therefore, accurate RP prediction is crucial to ensure tumor coverage to improve treatment outcome. Current models have associated RP with increased dose but with limited accuracy as they lack spatial correlation between accurate dose representation and quantitative RP representation. These models represent lung tissue damage with radiation dose distribution planned pre-treatment, which assumes a fixed patient geometry and inevitably renders imprecise dose delivery due to intra-fractional breathing motion and inter-fractional anatomy response. Additionally, current models employ whole-lung dose metrics as the contributing factor to RP as a qualitative, binary outcome but these global dose metrics discard microscopic, voxel-(3D pixel)-level information and prevent spatial correlations with quantitative RP representation. To tackle these limitations, we developed advanced deformable image registration (DIR) techniques that registered corresponding anatomical voxels between images for tracking and accumulating dose throughout treatment. DIR also enabled voxel-level dose-response correlation when CT image density change (IDC) was used to quantify RP. We hypothesized that more accurate estimates of biologically effective dose distributions actually delivered, achieved through (a) dose accumulation using deformable registration of weekly 4DCT images acquired over the course or radiotherapy and (b) the incorporation of variable relative biological effectiveness (RBE), would lead to statistically and clinically significant improvement in the correlation of RP with biologically effective dose distributions. Our work resulted in a robust intra-4DCT and inter-4DCT DIR workflow, with the accuracy meeting AAPM TG-132 recommendations for clinical implementation of DIR. The automated DIR workflow allowed us to develop a fully automated 4DCT-based dose accumulation pipeline in RayStation (RaySearch Laboratories, Stockholm, Sweden). With a sample of 67 IMRT patients, our results showed that the accumulated dose was statistically different than the planned dose across the entire cohort with an average MLD increase of ~1 Gy and clinically different for individual patients where 16% resulted in difference in the score of the normal tissue complication probability (NTCP) using an established, clinically used model, which could qualify the patients for treatment planning re-evaluation. Lastly, we associated dose difference with accuracy difference by establishing and comparing voxel-level dose-IDC correlations and concluded that the accumulated dose better described the localized damage, thereby a closer representation of the delivered dose. Using the same dose-response correlation strategy, we plotted the dose-IDC relationships for both photon patients (N = 51) and proton patients (N = 67), we measured the variable proton RBE values to be 3.07–1.27 from 9–52 Gy proton voxels. With the measured RBE values, we fitted an established variable proton RBE model with pseudo-R2 of 0.98. Therefore, our results led to statistically and clinically significant improvement in the correlation of RP with accumulated and biologically effective dose distributions and demonstrated the potential of incorporating the effect of anatomical change and biological damage in RP prediction models

A Heterogeneous Patient-Specific Biomechanical Model of the Lung for Tumor Motion Compensation and Effective Lung Radiation Therapy Planning

Radiation therapy is a main component of treatment for many lung cancer patients. However, the respiratory motion can cause inaccuracies in radiation delivery that can lead to treatment complications. In addition, the radiation-induced damage to healthy tissue limits the effectiveness of radiation treatment. Motion management methods have been developed to increase the accuracy of radiation delivery, and functional avoidance treatment planning has emerged to help reduce the chances of radiation-induced toxicity. In this work, we have developed biomechanical model-based techniques for tumor motion estimation, as well as lung functional imaging. The proposed biomechanical model accurately estimates lung and tumor motion/deformation by mimicking the physiology of respiration, while accounting for heterogeneous changes in the lung mechanics caused by COPD, a common lung cancer comorbidity. A biomechanics-based image registration algorithm is developed and is combined with an air segmentation algorithm to develop a 4DCT-based ventilation imaging technique, with potential applications in functional avoidance therapies

Towards an optimal clinical protocol for the treatment of moving targets with pencil beam scanned proton therapy

At the University Medical Center Groningen (UMCG) Proton Therapy Center (GPTC), a novel and advantageous form of radiotherapy for the treatment of cancer is available. This treatment technique, referred as proton therapy, uses charged particles (protons) to irradiate, and consequently, damage the tumour. Due to the physical properties of protons, proton therapy allows to successfully kill the tumour, while sparing the adjacent healthy tissue from any damaging radiation. The tumours within liver, lung, and oesophageal cancer patients move due to the patients’ respiration. These moving tumours are surrounded by organs (heart, lung, spinal cord, and oesophagus), which need to be spared from radiation. This is why proton therapy is particularly valuable for these patients. However, the interference of the movement of these tumours with the highly precise proton therapy machine, brings a challenge for the execution of their treatments at the GPTC, and other proton centres. The work performed throughout this thesis focuses on achieving optimal and safe proton treatments for moving tumours. To complete this, we performed comprehensive and representative analysis with clinical patient data and proton machine specific information of our department. The tools developed and the results obtained contributed to the start of the treatment of moving tumours at the GPTC, and therefore we believe they will play an important role for the proton therapy community in general. We aim to extend our gained experience to all proton centres worldwide, so that moving tumours can take advantage of the clinical benefits expected from proton therapy

Inverse-Consistent Determination of Young\u27s Modulus of Human Lung

Human lung undergoes respiration-induced deformation due to sequential inhalation and exhalation. Accurate determination of lung deformation is crucial for tumor localization and targeted radiotherapy in patients with lung cancer. Numerical modeling of human lung dynamics based on underlying physics and physiology enables simulation and virtual visualization of lung deformation. Dynamical modeling is numerically complicated by the lack of information on lung elastic behavior, structural heterogeneity as well as boundary constrains. This study integrates physics-based modeling and image-based data acquisition to develop the patient-specific biomechanical model and consequently establish the first consistent Young\u27s modulus (YM) of human lung. This dissertation has four major components: (i) develop biomechanical model for computation of the flow and deformation characteristics that can utilize subject-specific, spatially-dependent lung material property; (ii) develop a fusion algorithm to integrate deformation results from a deformable image registration (DIR) and physics-based modeling using the theory of Tikhonov regularization; (iii) utilize fusion algorithm to establish unique and consistent patient specific Young\u27s modulus and; (iv) validate biomechanical model utilizing established patient-specific elastic property with imaging data. The simulation is performed on three dimensional lung geometry reconstructed from four-dimensional computed tomography (4DCT) dataset of human subjects. The heterogeneous Young\u27s modulus is estimated from a linear elastic deformation model with the same lung geometry and 4D lung DIR. The biomechanical model adequately predicts the spatio-temporal lung deformation, consistent with data obtained from imaging. The accuracy of the numerical solution is enhanced through fusion with the imaging data beyond the classical comparison of the two sets of data. Finally, the fused displacement results are used to establish unique and consistent patient-specific elastic property of the lung

Assessment of dosimetric errors induced by deformable image registration methods in 4D pencil beam scanned proton treatment planning for liver tumours

PURPOSE: Respiratory impacts in pencil beam scanned proton therapy (PBS-PT) are accounted by extensive 4D dose calculations, where deformable image registration (DIR) is necessary for estimating deformation vector fields (DVFs). We aim here to evaluate the dosimetric errors induced by different DIR algorithms in their resulting 4D dose calculations by using ground truth(GT)-DVFs from 4DMRI. MATERIALS AND METHODS: Six DIR methods: ANACONDA, Morfeus, B-splines, Demons, CT Deformable, and Total Variation, were respectively applied to nine 4DCT-MRI liver data sets. The derived DVFs were then used as input for 4D dose calculation. The DIR induced dosimetric error was assessed by individually comparing the resultant 4D dose distributions to those obtained with GT-DVFs. Both single-/three-field plans and single/rescanned strategies were investigated. RESULTS: Differences in 4D dose distributions among different DIR algorithms, and compared to the results using GT-DVFs, were pronounced. Up to 40 % of clinically relevant dose calculation points showed dose differences of 10 % or more between the GT. Differences in V95(CTV) reached up to 11.34 ± 12.57 %. The dosimetric errors became in general less substantial when applying multiple-field plans or using rescanning. CONCLUSION: Intrinsic geometric errors by DIR can influence the clinical evaluation of liver 4D PBS-PT plans. We recommend the use of an error bar for correctly interpreting individual 4D dose distributions

Optimization of Decision Making in Personalized Radiation Therapy using Deformable Image Registration

Cancer has become one of the dominant diseases worldwide, especially in western countries, and radiation therapy is one of the primary treatment options for 50% of all patients diagnosed. Radiation therapy involves the radiation delivery and planning based on radiobiological models derived primarily from clinical trials. Since 2015 improvements in information technologies and data storage allowed new models to be created using the large volumes of treatment data already available and correlate the actually delivered treatment with outcomes. The goals of this thesis are to 1) construct models of patient outcomes after receiving radiation therapy using available treatment and patient parameters and 2) provide a method to determine real accumulated radiation dose including the impact of registration uncertainties. In Chapter 2, a model was developed predicting overall survival for patients with hepatocellular carcinoma or liver metastasis receiving radiation therapy. These models show which patients benefit from curative radiation therapy based on liver function, and the survival benefit of increased radiation dose on survival. In Chapter 3, a method was developed to routinely evaluate deformable image registration (DIR) with computer-generated landmark pairs using the scale-invariant feature transform. The method presented in this chapter created landmark sets for comparing lung 4DCT images and provided the same evaluation of DIR as manual landmark sets. In Chapter 4, an investigation was performed on the impact of DIR error on dose accumulation using landmarked 4DCT images as the ground truth. The study demonstrated the relationship between dose gradient, DIR error and dose accumulation error, and presented a method to determine error bars on the dose accumulation process. In Chapter 5, a method was presented to determine quantitatively when to update a treatment plan during the course of a multi-fraction radiation treatment of head and neck cancer. This method investigated the ability to use only the planned dose with deformable image registration to predict dose changes caused by anatomical deformations. This thesis presents the fundamental elements of a decision support system including patient pre-treatment parameters and the actual delivered dose using DIR while considering registration uncertainties

A Morphing Technique Applied to Lung Motions in Radiotherapy: Preliminary Results

Organ motion leads to dosimetric uncertainties during a patient’s treatment. Much work has been done to quantify the dosimetric effects of lung movement during radiation treatment. There is a particular need for a good description and prediction of organ motion. To describe lung motion more precisely, we have examined the possibility of using a computer technique: a morphing algorithm. Morphing is an iterative method which consists of blending one image into another image. To evaluate the use of morphing, Four Dimensions Computed Tomography (4DCT) acquisition of a patient was performed. The lungs were automatically segmented for different phases, and morphing was performed using the end-inspiration and the end-expiration phase scans only. Intermediate morphing files were compared with 4DCT intermediate images. The results showed good agreement between morphing images and 4DCT images: fewer than 2 % of the 512 by 256 voxels were wrongly classified as belonging/not belonging to a lung section. This paper presents preliminary results, and our morphing algorithm needs improvement. We can infer that morphing offers considerable advantages in terms of radiation protection of the patient during the diagnosis phase, handling of artifacts, definition of organ contours and description of organ motion

Dynamic CBCT Imaging using Prior Model-Free Spatiotemporal Implicit Neural Representation (PMF-STINR)

Dynamic cone-beam computed tomography (CBCT) can capture high-spatial-resolution, time-varying images for motion monitoring, patient setup, and adaptive planning of radiotherapy. However, dynamic CBCT reconstruction is an extremely ill-posed spatiotemporal inverse problem, as each CBCT volume in the dynamic sequence is only captured by one or a few X-ray projections. We developed a machine learning-based technique, prior-model-free spatiotemporal implicit neural representation (PMF-STINR), to reconstruct dynamic CBCTs from sequentially acquired X-ray projections. PMF-STINR employs a joint image reconstruction and registration approach to address the under-sampling challenge. Specifically, PMF-STINR uses spatial implicit neural representation to reconstruct a reference CBCT volume, and it applies temporal INR to represent the intra-scan dynamic motion with respect to the reference CBCT to yield dynamic CBCTs. PMF-STINR couples the temporal INR with a learning-based B-spline motion model to capture time-varying deformable motion during the reconstruction. Compared with previous methods, the spatial INR, the temporal INR, and the B-spline model of PMF-STINR are all learned on the fly during reconstruction in a one-shot fashion, without using any patient-specific prior knowledge or motion sorting/binning. PMF-STINR was evaluated via digital phantom simulations, physical phantom measurements, and a multi-institutional patient dataset featuring various imaging protocols (half-fan/full-fan, full sampling/sparse sampling, different energy and mAs settings, etc.). The results showed that the one-shot learning-based PMF-STINR can accurately and robustly reconstruct dynamic CBCTs and capture highly irregular motion with high temporal (~0.1s) resolution and sub-millimeter accuracy. It can be a promising tool for motion management by offering richer motion information than traditional 4D-CBCTs