Quantifying the reproducibility of lung ventilation images between 4-Dimensional Cone Beam CT and 4-Dimensional CT.

PURPOSE: Computed tomography ventilation imaging derived from four-dimensional cone beam CT (CTVI4DCBCT ) can complement existing 4DCT-based methods (CTVI4DCT ) to track lung function changes over a course of lung cancer radiation therapy. However, the accuracy of CTVI4DCBCT needs to be assessed since anatomic 4DCBCT has demonstrably poor image quality and small field of view (FOV) compared to treatment planning 4DCT. We perform a direct comparison between short interval CTVI4DCBCT and CTVI4DCT pairs to understand the patient specific image quality factors affecting the intermodality CTVI reproducibility in the clinic. METHODS AND MATERIALS: We analysed 51 pairs of 4DCBCT and 4DCT scans acquired within 1 day of each other for nine lung cancer patients. To assess the impact of image quality, CTVIs were derived from 4DCBCT scans reconstructed using both standard Feldkamp-Davis-Kress backprojection (CTVIFDK4DCBCT) and an iterative McKinnon-Bates Simultaneous Algebraic Reconstruction Technique (CTVIMKBSART4DCBCT). Also, the influence of FOV was assessed by deriving CTVIs from 4DCT scans that were cropped to a similar FOV as the 4DCBCT scans (CTVIcrop4DCT), or uncropped (CTVIuncrop4DCT). All CTVIs were derived by performing deformable image registration (DIR) between the exhale and inhale phases and evaluating the Jacobian determinant of deformation. Reproducibility between corresponding CTVI4DCBCT and CTVI4DCT pairs was quantified using the voxel-wise Spearman rank correlation and the Dice similarity coefficient (DSC) for ventilation defect regions (identified as the lower quartile of ventilation values). Mann-Whitney U-tests were applied to determine statistical significance of each reconstruction and cropping condition. RESULTS: The (mean ± SD) Spearman correlation between CTVIFDK4DCBCT and CTVIuncrop4DCT was 0.60 ± 0.23 (range -0.03-0.88) and the DSC was 0.64 ± 0.12 (0.34-0.83). By comparison, correlations between CTVIMKBSART4DCBCT and CTVIuncrop4DCT showed a small but statistically significant improvement with = 0.64 ± 0.20 (range 0.06-0.90, P = 0.03) and DSC = 0.66 ± 0.13 (0.31-0.87, P = 0.02). Intermodal correlations were noted to decrease with an increasing fraction of lung truncation in 4DCBCT relative to 4DCT, albeit not significantly (Pearson correlation R = 0.58, P = 0.002). CONCLUSIONS: This study demonstrates that DIR based CTVIs derived from 4DCBCT can exhibit reasonable to good voxel-level agreement with CTVIs derived from 4DCT. These correlations outperform previous cross-modality comparisons between 4DCT-based ventilation and nuclear medicine. The use of 4DCBCT scans with iterative reconstruction and minimal lung truncation is recommended to ensure better reproducibility between 4DCBCT- and 4DCT-based CTVIs

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

Toward adaptive radiotherapy for head and neck patients: Uncertainties in dose warping due to the choice of deformable registration algorithm.

The aims of this work were to evaluate the performance of several deformable image registration (DIR) algorithms implemented in our in-house software (NiftyReg) and the uncertainties inherent to using different algorithms for dose warping

INVESTIGATION OF RADIATION INJURY IN THE ESOPHAGUS FROM DEFINITIVE CHEMORADIATION THERAPY USING NOVEL IMAGING BIOMARKERS

Radiation injury in the esophagus occurs with high frequency from the treatment of non-small cell lung cancer (NSCLC). Radiation esophagitis is an acute normal tissue toxicity that negatively affects treatment efficacy by limiting dose and potentially interrupting radiation therapy. Clinical quantification of this toxicity is typically achieved by utilizing physician grading scales, assigning complication severity on an ordinal scale of symptom presentation and/or physician chosen interventions. These criteria are subjective in nature, both from the physician assigning the grade and the patient reporting the symptom. Furthermore, radiation therapy planning guidelines for the esophagus are derived from toxicity prediction models utilizing these subjective grading scores as complication endpoints. Not only does this schema of toxicity analysis leads to a lack of consistency between models from different study populations, and thereby radiation therapy planning recommendations for the esophagus, but inherent patient radiosensitivity is not considered, leading to suboptimal treatment regimens. The purpose of this work was to investigate radiation injury in the esophagus by first developing in-vivo imaging biomarkers of radiation-response in the esophagus using 4-dimensional computed tomography (4DCT) and 18fluorodeoxyglucose positron emission tomography (FDG-PET), separately. These imaging biomarkers were then compare with radiation esophagitis grade, using traditional and machine learning techniques, and shown to objectively quantify esophageal radiation toxicity. Metrics describing the esophageal radiation response from either imaging modality were strong classifiers of radiation esophagitis grade. Multivariate models to predict maximum esophagitis treatment grade (4DCT), and esophagitis symptom progression (FDG-PET) were developed and had strong performance for both scenarios. These imaging biomarkers were then used to comprehensively investigate the influence of dose-geometry and radiation type (photon or proton) on esophageal response. Using these radiation-response biomarkers in esophageal dose-response analysis, dose metrics with spatial information of esophageal dose coverage, (e.g. dose to a subregion of the esophagus with specific percent cross-sectional area coverage), as well as without spatial information, (traditional dose-volume histogram), was analyzed separately using machine learning methods. No detectable difference in response was observed when comparing dose metrics with and without spatial information. Statistical analysis showed no significant difference (p Inherent patient radiation sensitivity was investigated using esophageal expansion and delivered dose to the corresponding esophageal subregion. Cluster analysis was used to group patient patients based on their maximum expansion and delivered dose to the analyzed subregion of the esophagus. Patients clustered with proportionally higher expansion per delivered dose were considered radiosensitive. These results were then applied to NTCP toxicity modelling by using patient radiosensitivity cluster membership as a predictor variable. Models with the radiosensitive predictor outperformed models not including the cluster membership variable for prediction of grade 3 esophagitis

INVESTIGATION OF RADIATION INJURY IN THE ESOPHAGUS FROM DEFINITIVE CHEMORADIATION THERAPY USING NOVEL IMAGING BIOMARKERS

Radiation injury in the esophagus occurs with high frequency from the treatment of non-small cell lung cancer (NSCLC). Radiation esophagitis is an acute normal tissue toxicity that negatively affects treatment efficacy by limiting dose and potentially interrupting radiation therapy. Clinical quantification of this toxicity is typically achieved by utilizing physician grading scales, assigning complication severity on an ordinal scale of symptom presentation and/or physician chosen interventions. These criteria are subjective in nature, both from the physician assigning the grade and the patient reporting the symptom. Furthermore, radiation therapy planning guidelines for the esophagus are derived from toxicity prediction models utilizing these subjective grading scores as complication endpoints. Not only does this schema of toxicity analysis leads to a lack of consistency between models from different study populations, and thereby radiation therapy planning recommendations for the esophagus, but inherent patient radiosensitivity is not considered, leading to suboptimal treatment regimens. The purpose of this work was to investigate radiation injury in the esophagus by first developing in-vivo imaging biomarkers of radiation-response in the esophagus using 4-dimensional computed tomography (4DCT) and 18fluorodeoxyglucose positron emission tomography (FDG-PET), separately. These imaging biomarkers were then compare with radiation esophagitis grade, using traditional and machine learning techniques, and shown to objectively quantify esophageal radiation toxicity. Metrics describing the esophageal radiation response from either imaging modality were strong classifiers of radiation esophagitis grade. Multivariate models to predict maximum esophagitis treatment grade (4DCT), and esophagitis symptom progression (FDG-PET) were developed and had strong performance for both scenarios. These imaging biomarkers were then used to comprehensively investigate the influence of dose-geometry and radiation type (photon or proton) on esophageal response. Using these radiation-response biomarkers in esophageal dose-response analysis, dose metrics with spatial information of esophageal dose coverage, (e.g. dose to a subregion of the esophagus with specific percent cross-sectional area coverage), as well as without spatial information, (traditional dose-volume histogram), was analyzed separately using machine learning methods. No detectable difference in response was observed when comparing dose metrics with and without spatial information. Statistical analysis showed no significant difference (p Inherent patient radiation sensitivity was investigated using esophageal expansion and delivered dose to the corresponding esophageal subregion. Cluster analysis was used to group patient patients based on their maximum expansion and delivered dose to the analyzed subregion of the esophagus. Patients clustered with proportionally higher expansion per delivered dose were considered radiosensitive. These results were then applied to NTCP toxicity modelling by using patient radiosensitivity cluster membership as a predictor variable. Models with the radiosensitive predictor outperformed models not including the cluster membership variable for prediction of grade 3 esophagitis

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

A Heterogeneous and Multi-Range Soft-Tissue Deformation Model for Applications in Adaptive Radiotherapy

During fractionated radiotherapy, anatomical changes result in uncertainties in the applied dose distribution. With increasing steepness of applied dose gradients, the relevance of patient deformations increases. Especially in proton therapy, small anatomical changes in the order of millimeters can result in large range uncertainties and therefore in substantial deviations from the planned dose. To quantify the anatomical changes, deformation models are required. With upcoming MR-guidance, the soft-tissue deformations gain visibility, but so far only few soft-tissue models meeting the requirements of high-precision radiotherapy exist. Most state-of-the-art models either lack anatomical detail or exhibit long computation times. In this work, a fast soft-tissue deformation model is developed which is capable of considering tissue properties of heterogeneous tissue. The model is based on the chainmail (CM)-concept, which is improved by three basic features. For the first time, rotational degrees of freedom are introduced into the CM-concept to improve the characteristic deformation behavior. A novel concept for handling multiple deformation initiators is developed to cope with global deformation input. And finally, a concept for handling various shapes of deformation input is proposed to provide a high flexibility concerning the design of deformation input. To demonstrate the model flexibility, it was coupled to a kinematic skeleton model for the head and neck region, which provides anatomically correct deformation input for the bones. For exemplary patient CTs, the combined model was shown to be capable of generating artificially deformed CT images with realistic appearance. This was achieved for small-range deformations in the order of interfractional deformations, as well as for large-range deformations like an arms-up to arms-down deformation, as can occur between images of different modalities. The deformation results showed a strong improvement in biofidelity, compared to the original chainmail-concept, as well as compared to clinically used image-based deformation methods. The computation times for the model are in the order of 30 min for single-threaded calculations, by simple code parallelization times in the order of 1 min can be achieved. Applications that require realistic forward deformations of CT images will benefit from the improved biofidelity of the developed model. Envisioned applications are the generation of plan libraries and virtual phantoms, as well as data augmentation for deep learning approaches. Due to the low computation times, the model is also well suited for image registration applications. In this context, it will contribute to an improved calculation of accumulated dose, as is required in high-precision adaptive radiotherapy