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

3-D lung deformation and function from respiratory-gated 4-D x-ray CT images : application to radiation treatment planning.

Many lung diseases or injuries can cause biomechanical or material property changes that can alter lung function. While the mechanical changes associated with the change of the material properties originate at a regional level, they remain largely asymptomatic and are invisible to global measures of lung function until they have advanced significantly and have aggregated. In the realm of external beam radiation therapy of patients suffering from lung cancer, determination of patterns of pre- and post-treatment motion, and measures of regional and global lung elasticity and function are clinically relevant. In this dissertation, we demonstrate that 4-D CT derived ventilation images, including mechanical strain, provide an accurate and physiologically relevant assessment of regional pulmonary function which may be incorporated into the treatment planning process. Our contributions are as follows: (i) A new volumetric deformable image registration technique based on 3-D optical flow (MOFID) has been designed and implemented which permits the possibility of enforcing physical constraints on the numerical solutions for computing motion field from respiratory-gated 4-D CT thoracic images. The proposed optical flow framework is an accurate motion model for the thoracic CT registration problem. (ii) A large displacement landmark-base elastic registration method has been devised for thoracic CT volumetric image sets containing large deformations or changes, as encountered for example in registration of pre-treatment and post-treatment images or multi-modality registration. (iii) Based on deformation maps from MOFIO, a novel framework for regional quantification of mechanical strain as an index of lung functionality has been formulated for measurement of regional pulmonary function. (iv) In a cohort consisting of seven patients with non-small cell lung cancer, validation of physiologic accuracy of the 4-0 CT derived quantitative images including Jacobian metric of ventilation, Vjac, and principal strains, (V?1, V?2, V?3, has been performed through correlation of the derived measures with SPECT ventilation and perfusion scans. The statistical correlations with SPECT have shown that the maximum principal strain pulmonary function map derived from MOFIO, outperforms all previously established ventilation metrics from 40-CT. It is hypothesized that use of CT -derived ventilation images in the treatment planning process will help predict and prevent pulmonary toxicity due to radiation treatment. It is also hypothesized that measures of regional and global lung elasticity and function obtained during the course of treatment may be used to adapt radiation treatment. Having objective methods with which to assess pre-treatment global and regional lung function and biomechanical properties, the radiation treatment dose can potentially be escalated to improve tumor response and local control

Advanced H-1 Lung Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is one of the widely used medical imaging modality, since it can provide both structural and functional assessment in a single imaging session. However, two major challenges should be considered by using MRI for lung imaging. The first challenge is the intrinsic low SNR of H-1 lung MRI due to the low proton density as well as the fast decay of the lung parenchyma signal. And the second challenge is subject motion. To achieve high resolution structural image, MRI requires a long scan time, usually a few minutes or even longer, which make MRI sensitive to subject motion. To address the first challenge, ultra-short echo time (UTE) MRI sequence is used to capture the lung parenchyma signal before decay. As for subject motion, two major strategies are widely used. One strategy is fast breath-holding scan, the subjects are asked to hold their breaths for a short duration, and the fast 3D MR sequence would be used to acquire data within that duration. This dissertation proposes a new acquisition scheme based on the standard UTE sequence, which largely increases the encoding efficiency and improves the breath-holding scan images. The other is free breathing scan with motion correction. The subjects are allowed to breathe during the MR acquisition. After the acquisition, the motion corrupted data would go through the motion correction step to reconstruct the motion free images. In this dissertation, two novel motion corrected reconstruction strategies are proposed to incorporate the motion modeling and compensation into the reconstruction to get high SNR motion corrected 3D and 4D images. When translating the developed techniques to the clinical studies, specifically for pediatric and neonatal studies, more practical problems need to be considered, such as smaller but finer anatomy to image, the different respiratory patterns of the young subjects etc. This dissertation proposes a 5-minute free breathing UTE MRI strategy to achieve a 3D high resolution motion free lung image for pediatric and neonatal studies

Segmentation, tracking, and kinematics of lung parenchyma and lung tumors from 4D CT with application to radiation treatment planning.

This thesis is concerned with development of techniques for efficient computerized analysis of 4-D CT data. The goal is to have a highly automated approach to segmentation of the lung boundary and lung nodules inside the lung. The determination of exact lung tumor location over space and time by image segmentation is an essential step to track thoracic malignancies. Accurate image segmentation helps clinical experts examine the anatomy and structure and determine the disease progress. Since 4-D CT provides structural and anatomical information during tidal breathing, we use the same data to also measure mechanical properties related to deformation of the lung tissue including Jacobian and strain at high resolutions and as a function of time. Radiation Treatment of patients with lung cancer can benefit from knowledge of these measures of regional ventilation. Graph-cuts techniques have been popular for image segmentation since they are able to treat highly textured data via robust global optimization, avoiding local minima in graph based optimization. The graph-cuts methods have been used to extract globally optimal boundaries from images by s/t cut, with energy function based on model-specific visual cues, and useful topological constraints. The method makes N-dimensional globally optimal segmentation possible with good computational efficiency. Even though the graph-cuts method can extract objects where there is a clear intensity difference, segmentation of organs or tumors pose a challenge. For organ segmentation, many segmentation methods using a shape prior have been proposed. However, in the case of lung tumors, the shape varies from patient to patient, and with location. In this thesis, we use a shape prior for tumors through a training step and PCA analysis based on the Active Shape Model (ASM). The method has been tested on real patient data from the Brown Cancer Center at the University of Louisville. We performed temporal B-spline deformable registration of the 4-D CT data - this yielded 3-D deformation fields between successive respiratory phases from which measures of regional lung function were determined. During the respiratory cycle, the lung volume changes and five different lobes of the lung (two in the left and three in the right lung) show different deformation yielding different strain and Jacobian maps. In this thesis, we determine the regional lung mechanics in the Lagrangian frame of reference through different respiratory phases, for example, Phase10 to 20, Phase10 to 30, Phase10 to 40, and Phase10 to 50. Single photon emission computed tomography (SPECT) lung imaging using radioactive tracers with SPECT ventilation and SPECT perfusion imaging also provides functional information. As part of an IRB-approved study therefore, we registered the max-inhale CT volume to both VSPECT and QSPECT data sets using the Demon\u27s non-rigid registration algorithm in patient subjects. Subsequently, statistical correlation between CT ventilation images (Jacobian and strain values), with both VSPECT and QSPECT was undertaken. Through statistical analysis with the Spearman\u27s rank correlation coefficient, we found that Jacobian values have the highest correlation with both VSPECT and QSPECT

Comparison of hyperpolarised gas MRI and CT-based surrogates of ventilation

Background: Non-contrast CT-based surrogates of regional ventilation derived from pulmonary images acquired at multiple inflation levels have been proposed as alternatives to established modalities. However, their physiological accuracy has yet to be validated prior to clinical translation. Purpose: To address the hypothesis that these surrogates can provide information comparable to a direct measure of ventilation from hyperpolarised gas MRI ventilation via: i. development of a methodology for registering CT and gas MRI. ii. comparison of these surrogates with gas MRI at the lobar level. iii. evaluation of the impact of inflation levels when comparing gas MRI and ventilation CT. iv. development of an image acquisition and analysis framework to facilitate spatial correlations of both techniques. v. assessment of the effect of using different gases on the correlation. Methods: i. A method to indirectly register gas MRI to CT via same-breath 1H-structural MR images was developed and its accuracy was assessed. ii. A ventilation model based on expansion of lobar CT segmentations was compared with gas MRI lobar ventilation measurements. iii. The spatial overlap of ventilation CT was compared to gas MRI acquired at two different inflation levels. iv. An image acquisition protocol was designed to minimise differences in acquisition settings between scans such as posture and breathing manoeuvre and analysis methods were developed to enable direct regional and voxel level correlations. v. The effect of using two different noble gases, namely, 3He and 129Xe, on correlation with ventilation CT was assessed. Results: i. The indirect method of registration was more accurate than direct registration. ii. Despite subtle differences, lobar ventilation measurements derived from CT and hyperpolarised gas MRI were comparable. iii. Comparison of ventilation CT and gas MRI varied with inflation state. iv. The spatial correlation between ventilation CT and gas MRI increased at coarser levels. v. A marked improvement in correlation was observed for 3He and 129Xe MRI in contrast to when ventilation CT was compared with either 3He and 129Xe MRI. Conclusion: Although CT-based surrogates of ventilation show promise for replacing established ventilation modalities such as hyperpolarised gas MRI, particularly at coarser levels, they cannot be assumed to be equivalent to the techniques they purport to replace

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