Browsing by Author "Cai, Jing"

Purpose: To investigate the inter-patient and inter-sequence variation in liver tumor contrast in MRI and the feasibility of improving the liver tumor contrast by using an in-house developed multi-source adaptive fusion method for use in MRI-based treatment planning.

Methods and Materials: MR-images from 29 patients were retrospectively reviewed in this study. The imaging sequences acquired by a 1.5T GE and 3T Siemens MR scanner consisted of T1-w, T1-w, Post C, T2-w, T2/T1-w, and DWI. Using an in-house developed MSAF algorithm, we created fused images for a smaller subset of 12 patients using T1-w, T2-w, T2/T1-w, and DWI as inputs. Two fusion-images were obtained for each patient by implementing either an input-driven or output-driven fusion optimization method. Once a fusion-image was obtained an analysis was performed on each original image, and the fusion-image for each patient to calculate the tumor-to-tissue contrast-to-noise ratio(CNR) by contouring the tumor and a liver background-region(BG) in a homogeneous region of the liver using this in-house algorithm. CNR was calculated by (Itum-IBG)/SDBG, where Itum and IBG are the mean values of the tumor and the BG respectively, and SDBG is the standard deviation of the BG. To assess variation in tumor to tissue CNR for each image type an inter-patient coefficient-of-variation(CV) was calculated across all patients, as well as an inter-sequence CV. CV was calculated using the following: CV = σ/µ, where σ and µ are the standard deviation, and mean CNR for a single image sequence, respectively. These values were calculated for the original sequence types and fusion-images and compared.

Results: Our results from the 29 patients showed large inter-patient and inter-sequence variability, ranging from 86.90% to 67.03%, and 134.67% to 1.22% respectively. The T1-w, T1-w, Post Contrast, T2-w, T2/T1-w, DWI, and CT CV was 85.25%, 84.11%, 67.03%, 81.78%, 86.90%, and 74.30% respectively. Tumor CNR ranged from 0.95 to 4.47 with mean (± SD) CNR for T1-w, T1-w, Post Contrast, T2-w, T2/T1-w, DWI, and CT of 1.90 (±1.60), 2.12 (±1.42), 3.59 (±2.94), 1.95 (±1.70), 4.47 (±3.32), and 0.95 (±0.81) respectively. In the smaller subset of 12 patients, our results show a reduction in the inter-patient CV when using the in-house algorithm to obtain a tumor enhanced – fusion image. The inter-patient CV for T1-w, T2-w, T2/T1-w, DWI, Balanced Anatomy – Fusion, and Tumor Enhanced – Fusion was 94.16%, 112.73%, 105.69%, 124.23%, and 67.94% respectively. Tumor-CNR was significantly enhanced for each patient when using the in-house algorithm to obtain a tumor-enhanced image. The mean (± SD) CNR for T1-w, T2-w, T2/T1-w, Balanced Anatomy – Fusion, and Tumor Enhanced – Fusion was 2.11 (±1.99), 3.89 (±4.38), 3.71 (±3.92), 5.73 (±7.12), and 17.01 (±11.55) respectively.

Conclusion: The in-house multi-source adaptive fusion algorithm has the potential to increase the liver tumor contrast, as well as, improve the consistency for use in MRI based radiation therapy treatment planning.

A tenet of modern radiotherapy (RT) is to identify the treatment target accurately, following which the high-dose treatment volume may be expanded into the surrounding tissues in order to create the clinical and planning target volumes. Respiratory motion can induce errors in target volume delineation and dose delivery in radiation therapy for thoracic and abdominal cancers. Historically, radiotherapy treatment planning in the thoracic and abdominal regions has used 2D or 3D images acquired under uncoached free-breathing conditions, irrespective of whether the target tumor is moving or not. Once the gross target volume has been delineated, standard margins are commonly added in order to account for motion. However, the generic margins do not usually take the target motion trajectory into consideration. That may lead to under- or over-estimate motion with subsequent risk of missing the target during treatment or irradiating excessive normal tissue. That introduces systematic errors into treatment planning and delivery. In clinical practice, four-dimensional (4D) imaging has been popular in For RT motion management. It provides temporal information about tumor and organ at risk motion, and it permits patient-specific treatment planning. The most common contemporary imaging technique for identifying tumor motion is 4D computed tomography (4D-CT). However, CT has poor soft tissue contrast and it induce ionizing radiation hazard. In the last decade, 4D magnetic resonance imaging (4D-MRI) has become an emerging tool to image respiratory motion, especially in the abdomen, because of the superior soft-tissue contrast. Recently, several 4D-MRI techniques have been proposed, including prospective and retrospective approaches. Nevertheless, 4D-MRI techniques are faced with several challenges: 1) suboptimal and inconsistent tumor contrast with large inter-patient variation; 2) relatively low temporal-spatial resolution; 3) it lacks a reliable respiratory surrogate. In this research work, novel 4D-MRI techniques applying MRI weightings that was not used in existing 4D-MRI techniques, including T2/T1-weighted, T2-weighted and Diffusion-weighted MRI were investigated. A result-driven phase retrospective sorting method was proposed, and it was applied to image space as well as k-space of MR imaging. Novel image-based respiratory surrogates were developed, improved and evaluated.

Purpose: MR images have better soft tissue contrast and introduce no ionizing radiation dose to patient compared with CT images. MRI-based treatment planning for liver SBRT has the potential to contour a more precise target volume and reduce radiation dose to normal tissues. Considering the tumor motion, in this study we investigated the motion management strategy workflow. We also evaluated the dose error in MRI-based treatment planning for liver SBRT and the feasibility of slice-stacking method to generate internal target volume (ITV) in the workflow.

Methods and Materials: Five liver SBRT treatment plans were used to evaluate the dose errors caused by CT number assignment and MR geometric distortion. A bulk CT number was assigned to different structures manually contoured on CT images and MR geometric distortion was simulated on CT images to evaluate the dose errors. In addition, we used 4D XCAT digital phantom with regular and patient breathing motions to test the slice-stacking method to generate maximum intensity projection (MIP). The slice-stacking method is to generate MIP by determining the maximum pixel intensity throughout all scanning images instead of throughout 10 phases from Four-dimensional magnetic resonance imaging (4D-MRI). A simulation patient study was designed to test the workflow, and the dose error was calculated between MRI-based treatment plan and CT-based treatment plan.

Results: The dose error caused by CT number assignment uncertainties and MR geometric distortion was generally small (< 1Gy), except for large geometric distortion (> 3mm). Using slice-stacking method, the DSC of ITV was > 0.99 and the relative ITV volume difference was < 1.5% when using 5 repeated scanning images with regular breathing motion to reconstruct MIP. For patient’s breathing motion, when using 5 repeated scanning images to reconstruct MIP, the mean DSC of ITV is larger than 0.97 and the relative ITV volume difference is nearly 4%. In the patient study, for the patient with larger tumor motion, the PTV maximal dose error, minimal dose error and mean dose error was -0.75Gy, 4.90Gy and 0.00Gy. For the patient with smaller tumor motion, the PTV maximal dose error, minimal dose error and mean dose error was 0.36Gy, -2.25Gy and -0.09Gy, respectively.

Conclusions: The workflow of motion management strategy for liver SBRT has been developed based on a simulation patient study. In this workflow, the dose error is generally small and it is feasible to generate a fast clinically acceptable ITV using slice-stacking method.

Elastin-Like Polypeptide (ELP) nanoparticles present a promising mechanism for delivering brachytherapy for cancer treatment. These organic, polymer-based nanoparticles are injectable, biodegradable, and genetically tunable. Presented as the motivation of this thesis is a genetically encoded polymer-solution, composed of novel radiolabeled-ELP nanoparticles that are custom-designed to self-assemble into a local source upon intratumoral injection1. While preliminary results from a small animal study demonstrate 100% tumor response, effective radionuclide retention-rates, strong in vivo stability, and no polymer-induced toxicities, the current workflow lacks a dosimetry framework. The purpose of this thesis research was to provide such an infrastructure. We have developed a robust software framework that provides image-guided dosimetric-planning capabilities for ELP brachytherapy. This has resulted in several novel applications. First, the development of a point-dose-kernel-convolution-based dose calculation algorithm has invited the possibility of more quantitative ELP brachytherapy outcomes. Likewise, the ability to graphically pre-determine ELP injection sites under μCT image-guidance has introduced a new technical advantage into the current workflow. The planning system has also been integrated into a Monte Carlo environment, where SPECT imaging information can be exported and converted into a simulated source, allowing realistic, injection specific simulations to be performed. In addition to these technical developments, ELP steady state distributions have been experimentally measured via μSPECT acquisition, and the dose calculation algorithm has been validated against Monte Carlo simulation. The planning system was ultimately used to perform an internal dosimetry calculation of an in vivo ELP solution. Prior to this thesis work, this type of calculation had yet to be performed.

Accurate delineation of tumor target and organs at risk (OARs) is critically important in modern radiation therapy for optimal treatment planning and precise treatment delivery. Conventionally, radiotherapy treatment planning uses 2D X-ray or 3D CT images to assist the delineation of tumor volumes and OARs, often under free-breathing condition. In modern radiation therapy, four-dimensional (4D) imaging is the state-of-art technique for imaging respiratory motion of thoracic and abdominal cancers. However, its application for abdominal cancers has been limited due to the low soft-tissue contrast of CT. Recently, a number of 4D magnetic resonance imaging (4D-MRI) techniques have been developed to overcome the limitations of 4D-CT in abdominal cancer applications. In contrast to 4D-CT, 4D-MRI imposes no ionizing radiation dose to the patient and offers superior soft-tissue contrast.

However, current 4D-MRI techniques also have limitations that prevent them from being widely adapted in routine clinical practices. For example, current techniques are all developed based on one particular MR sequence with a particular imaging contrast, which may not be optimal or consistent across patients. Furthermore, current 4D-MRI techniques are often affected by patients’ breathing variations, leading to significant motion artifacts in 4D-MRI images and adversely affects its image quality and thus clinical applicability.

The overall goal of this dissertation is to develop a novel 5D-MRI technique to overcome current limitations of 4D-MRI. The 5D-MRI technique is developed by synergizing three main technical components: multi-source MRI fusion (MSMF), 4D-MRI, and deformable image registration (DIR). 5D-MRI is an extension of 4D-MRI, embedding all the characterizations of 4D-MRI with an additional dimension of “image contrast”. The MSMF method consists of five key components: input MR images, image pre-processing, fusion algorithm, fusion adaptation, and output fused MR images. A linear-weighting fusion algorithm was implemented for MSMF in this study to demonstrate the proof of concept. Fusion options (weighting parameters and image features) are pre-determined with given input MR images and saved in a database for fast fusion adaption. 5D-MRI images were generated by applying the 4D displacement vector fields (4D-DVF) determined from the original 4D-MRI via DIR onto each of the fused MR images.

The 5D-MRI technique was tested on a digital human phantom (XCAT) and a liver tumor patient. 5D-MRI images were qualitatively evaluated for image contrast versatility, and quantitatively evaluated for image quality and motion accuracy. For the latter, motion trajectories of the liver tumor in the superior-inferior (SI) direction were determined from each of the 4D-MRI image sets of 5D-MRI and compared with those tracked on the single-slice sagittal cine MR images. Mean difference in displacement (D) and correlation coefficient (CC) were determined for each comparison.

For both the XCAT phantom and the liver tumor patient, the MSMF method produced more than a large number of fused MR images with versatile image contrasts and improved image quality, each presenting a unique set of anatomical and image features. Among twenty-four liver cancer patients, the average tumor CNR was significantly improved from a range of -1.80 to 8.05 in the four single contrast source MR images, to 22.51 in the fused MR images. The standard deviation of tumor CNR among the 24 patients was 0.87, which suggested high level of consistency. 5D-MRI images clearly demonstrated the respiratory motion during breathing in each 4D-MRI set. Liver tumor motion trajectories measured from 5D-MRI images closely matched with that from the reference cine MR images: D ranged from 0.402 to 0.561 mm (mean=0.463 mm) and CC ranged from 0.980 to 0.987 (mean=0.986).

In this dissertation, beyond the developmental work for the 5D-MRI technique, a novel organ segmentation method, which is an extension of the multi-source MRI fusion technique, and an image mutual-information (MI) based 4D-MRI improvement technique are also presented.

In summary, a novel 5D-MRI technique was developed and its feasibility on digital human phantom and liver tumor patients is demonstrated. Two exploratory projects extending and improving the 5D-MRI technique are also presented. The developed 5D-MRI technique is capable of producing a large number of synthetic 4D-MRI images with versatile image contrasts and improved image quality, holding great promises in enhancing MRI applications for radiation therapy.

Purpose: Hyperpolarized gas (HP) tagging MRI is a novel imaging technique for direct measurement of lung motion during breathing. This study aims to quantitatively evaluate the accuracy of deformable image registration (DIR) in lung motion estimation using HP tagging MRI as references.

Method: Three healthy subjects were imaged using the HP MR tagging, as well as a high-resolution 3D proton MR sequence (TrueFISP) at the end-of-inhalation (EOI) and the end-of-exhalation (EOE). Ground truth of lung motion and corresponding displacement vector field (tDVF) was derived from HP tagging MRI by manually tracking the displacement of tagging grids between EOI and EOE. Seven different DIR methods were applied to the high-resolution TrueFISP MR images (EOI and EOE) to generate the DIR-based DVFs (dDVF). The DIR methods include Velocity (VEL), MIM, Mirada, multi-grid B-spline from Elastix (MGB) and 3 other algorithms from DIRART toolbox (Double Force Demons (DFD), Improved Lucas-Kanade (ILK), and Iterative Optical Flow (IOF)). All registrations were performed by independent experts. Target registration error (TRE) was calculated as tDVF - dDVF. Analysis was performed for the entire lungs, and separately for the upper and lower lungs.

Results: Significant differences between tDVF and dDVF were observed. Besides the DFD and IOF algorithms, all other dDVFs showed similarity in deformation magnitude distribution but away from the ground truth. The average TRE for entire lung ranged 2.5-23.7mm (mean=8.8mm), depending on the DIR method and subject's breathing amplitude. Larger TRE (13.3-23.7mm) was found in subject with larger breathing amplitude of 45.6mm. TRE was greater in lower lung (2.5-33.9 mm, mean=12.4mm) than that in upper lung (2.5-11.9 mm, mean=5.8mm).

Conclusion: Significant differences were observed in lung motion estimation between the HP gas tagging MRI method and the DIR methods, especially when lung motion is large. Large variation among different DIR methods was also observed.

Purpose: It is of clinical interest to study liver deformation during breathing by applying deformable image registration (DIR) on respiratory correlated four-dimensional (4D) images. This study aims to evaluate and compare the accuracy of DIR-derived liver deformation based on 4D computed tomography (CT) and 4D magnetic resonance imaging (MRI).

Methods: 4D CT, 4D MRI and cine magnetic resonance (MR) images of liver region were acquired from 5 patients with liver cancer under an IRB-approved protocol. ROIs containing tumors in each patient were tracked multiple times (3~5) in cine MR images. The trajectories from tracking, covering several breathing cycles, were converted to trajectories in one breathing cycle through phase sorting. The average phase sorted trajectories for each patient were used as reference trajectories after manual verification. Deformation vector fields (DVFs) from 4D CT and 4D MRI were generated via DIR implemented in Velocity AI. To enable comparison between DVFs and reference tumor trajectories, deformation vectors from each frame were linked together, forming DVF-based trajectories at each voxel. All DVF-based trajectories within each ROI were averaged to represent tumor motion. The single-phase difference, the trajectory difference and the correlation coefficient between each pair of DVF-based trajectory and reference trajectory were calculated. Wilcoxon signed-rank tests were conducted to determine whether there was significant difference between the single-phase differences, the trajectory differences and the correlation coefficients for 4D CT and 4D MRI.

Results: In the superior-inferior (SI) direction, 4D CT trajectories exhibit smaller trajectory differences (traj. diff.) in millimeters on average (traj. diff. (mm)= 2.09±0.75mm) but larger trajectory differences in number of voxels (traj. diff. (voxels)= 0.87±0.29) and smaller correlation coefficients (c. c. = 0.89±0.09) than 4D MRI trajectories (traj. diff. (mm)= 2.23±1.46mm, traj. diff. (voxels)= 0.45±0.29, c. c. = 0.93±0.06) whereas 4D MRI (traj. diff. = 1.09±1.23mm, traj. diff. (voxels)= 0.60±0.65, c. c. = 0.59±0.30) surpasses 4D CT (traj. diff. = 1.30±1.36mm, traj. diff. (voxels)= 1.02±1.07, c. c. = 0.15±0.64) in every metric in the right-left (RL) direction. In the anterior-posterior (AP) direction, 4D MRI trajectories have smaller trajectory differences in millimeters (traj. diff. (mm) = 1.11±0.70mm) and smaller trajectory differences in voxels (traj. diff. (voxels) = 0.61±0.36) but slightly smaller correlation coefficients (c. c. = 0.72±0.26) than 4D CT trajectories (traj. diff. (mm) = 1.47±0.63mm, traj. diff. (voxels) = 1.15±0.50, c. c. = 0.77±0.26). A trend that the trajectory differences in voxels for 4D MRI are smaller than those for 4D CT in every direction has been observed, though the results of Wilcoxon signed-rank tests do not support there is any significant difference between the accuracy of DVFs from 4D CT and 4D MRI.

Conclusion: We have implemented a novel approach for evaluating accuracy of DVFs based on 4D imaging for studying liver deformation. Current results indicate that the accuracy of DVFs from 4D CT and 4D MRI are comparable. Trends suggesting that the DVF from 4D MRI can be potentially more accurate than the DVF from 4D CT have been observed. Further study on more patients is warranted to determine whether there is significant difference between 4D CT and 4D MRI and to what degree the accuracy of the DVF from 4D MRI can be improved.

Purpose: Direct measurement of regional lung ventilation is often more favorable than the calculated ones using deformable image registration (DIR), which has been found susceptible to many uncertainties. This study aims to investigate the possible implementation of a novel imaging technique, 3D Hyperpolarized (HP) gas tagging MRI, to evaluate the regional ventilation of lung calculated with DIR-based algorithms, and to model the respiratory motion of lung towards 4D digital phantom application, respectively.

Method: Three healthy volunteers involved in this study underwent both 3D HP gas tagging MRI (t-MRI) and 3D proton true fast imaging with steady state precession (TrueFISP) MRI (p-MRI) during two consecutive breath-holds at the inhalation and the exhalation, respectively.

A ground truth of displacement vector field (DVF) was obtained by tracking the location of each tagged grid between the exhalation and the inhalation t-MRI images. Meanwhile, p-MRI images were registered with two commercial DIR algorithms, Velocity AI and MIM, to generate the corresponding DVFs. The regional ventilations were calculated as the Jacobian of each DVF. The cross-correlation and the mutual information were computed between either the DIR-based and the ground truth ventilations for comparison.

Regional ventilation of lung, as a physiological property, should remain invariable during the image acquisition. Therefore, in order to develop a realistic model for the respiratory motion of lung, the optimization of the DIR-based DVF was performed by imposing a regularization on the regional ventilation of lung. The robustness of the proposed model was evaluated by comparing the optimized DVF to the original DIR-based DVF in cross-correlation and mutual information.

Results: Compared to the ground truth result of the 3D HP gas tagging MRI, the regional ventilation maps calculated using the commercial DIR algorithms varied considerably for all the subjects of different respiration amplitudes. The ventilation derived from Velocity AI was preferable for the better spatial homogeneity and the larger accuracy, given by the higher average cross correlation (0.328 v.s. 0.262) and average mutual information (0.528 v.s. 0.323).

The optimized model represents a more realistic lung motion with the average deviation from the ground truth DVF reduced from 6.4 mm to 5.2 mm (p<0.05, student t-test) compared to the original DIR-based DVF. Furthermore, the accuracy of regional ventilation was also improved with larger cross correlation (0.98 v.s. 0.37) and mutual information values (5.84 v.s. 0.45).

Conclusion:

The results suggest that direct measurement of lung motion using 3D hyperpolarized gas tagging MRI may have the potential to assess, validate, and improve the DIR-based application. The proposed 4D motion model of lung also holds great promises for the implementation in a digital phantom, which can be applied in versatile researches for 4D radiation therapy of lung cancer.

Radiation-induced pulmonary toxicity poses a serious challenge and limiting factor in delivering a sufficient amount of dose to eradicate thoracic tumors without compromising lung function. Functional avoidance radiation therapy (RT) using lung ventilation mapping techniques would allow for preferential avoidance of functional lung tissue during radiotherapy and potentially reduce RT-induced lung injuries. Additionally, lung ventilation is also a key metric to assess lung function in patients with pulmonary diseases such as asthma, pulmonary embolism, cystic fibrosis, and chronic obstructive pulmonary disease (COPD). In contrast to global pulmonary function tests such as spirometry, ventilation images provide a regional measure of pulmonary function. Conventional methods for lung ventilation imaging include gamma camera scintigraphy and positron emission tomography scan after inhaling a gaseous radionuclide, as well as hyperpolarized (HP) gas magnetic resonance imaging (MRI) using Helium-3 and Xenon-129 as imaging contrast. Recently, a new method has been proposed in which deformable image registration (DIR) is performed on a pair of anatomical lung images at different respiratory phases to obtain the displacement vector field (DVF) between both phases, and generate a lung ventilation map from the Jacobian Determinant of the DVF. This DIR-based method is advantageous in its high image resolutions and simpler imaging procedures making it a more feasible option for implementation into the clinical workflow. However, current DIR-based lung ventilation methods have been largely hampered due to two major deficiencies: 1) current DIR algorithms are morphologically based, lacking of sufficient physiological realism and thus resulting in erroneous calculations of lung ventilation; and 2) there is a lack of validation of the DIR-based lung ventilation calculation against clinical ground truth, as well as large uncertainties and variations among different DIR algorithms. The long-term goal of this proposal is to develop the necessary tools and metrics for validation and testing of DIR-based lung ventilation mapping techniques to contribute to their clinical implementation in advanced radiotherapy of lung cancer and diagnosis of obstructive pulmonary diseases. The objective of the proposed research is to develop digital thoracic phantoms from physiologically-plausible lung motion models as a valuable tool for validation of DIR algorithms, and evaluate deformation-based lung ventilation mapping techniques against reference HP gas MRI ventilation images. The specific aims of this dissertation are the following. (1) Develop digital thoracic phantoms based on physiological modeling of respiratory motion from hyperpolarized gas tagging MRI. (2) Investigate the differences between HP gas tagging-based, DIR-based, and HP MRI ventilation mapping methods. (3) Evaluate and compare the sensitivity to deformation changes of ventilation and strain as lung functionality metrics.

This study investigates a unique dataset which includes three types of MR images acquired using a novel hybrid technique in a single breath-hold maneuver including a HP Helium-3 ventilation image, a pair of proton MR images, and a pair of HP Helium-3 tagging images at end of inhalation (EOI) and end of exhalation (EOE).

In order to create a physiologically plausible lung motion model, we used the novel HP gas tagging MRI technique. The tagged elements in the 3-dimensional (3D) tagged grid pattern are essentially ~500 evenly distributed landmarks throughout the entire lung area. Therefore, the displacement vector field calculated by tracking their motion from the EOI to the EOE phases provided us with a true lung deformation model which is physiologically-based.

The respiratory motion model was utilized to evaluate DIR-based displacement vector fields. The mean absolute DVF differences were found to be 8.2 mm for Subject 1, 7.5 mm for Subject 2, 5.6 mm for Subject 3, and 8.8 mm for Subject 4. These results show that there can be significant differences in DVF when performing a DIR compared to the respiratory motion models created from the tagged elements’ displacement.

The thoracic motion model was then created through a combination of the DIR-based DVF to model the deformation outside the lungs from the registration of proton MR images, and the tagging-based DVF to model deformation inside the lungs using the manually measured DVF from the tagging MR images.

The next part of this dissertation focused on investigating a DIR- based lung ventilation mapping technique using proton MR images by evaluating its correlation with hyperpolarized Helium-3 gas ventilation MRI reference images which provide a ground-truth measure of lung ventilation. Correlation between the reference ventilation images and ventilation maps computed from HP gas tagging MRIs, which provide ground-truth lung deformation, was also investigated. This is the first study, to our knowledge, to investigate three types of ventilation maps that are all MR-based. Furthermore, all images/data used in our evaluation are acquired during one same breath hold maneuver, eliminating the uncertainties associated with reproducibility of the respiratory cycle, patient positioning, and finding the spatial correspondence between the ventilation maps being evaluated.

The results of the spatial comparison between the DIR-based and reference ventilation images showed moderate to strong spearman correlation coefficients which are higher than many previous ventilation evaluation studies in the literature This may also be due to the fact that the images in this study were acquired during the same breath-hold and therefore inherently co-registered. The tagging-based ventilation maps, which are independent of the accuracy of any DIR algorithms, showed very similar spatial correlations to the reference images compared to the DIR-based ventilation maps. This proves the potential of the Jacobian ventilation calculation method which assumes that local volume changes are an appropriate lung ventilation surrogate. As more RT clinics incorporate MR imaging for patient simulation, and contouring for treatment planning, this study shows the feasibility of utilizing MR images for DIR-based ventilation calculations.

In the final part of this dissertation, we investigate lung strain as an additional metric to assess respiratory mechanics. We evaluation the sensitivity to deformation changes of both ventilation and strain as lung functionality metrics by comparing both metrics’ sensitivity to changes in displacement vector fields using Hyperpolarized He-3 Tagging MRI data. This study utilized physiologically-based respiratory motion models from three subjects to assess the sensitivity of lung strain and lung ventilation by introducing a number of modifications to the DVF, generating new lung function maps, and investigating how much each of these lung function metrics were affected. Lung strain was computed voxel-wise from the gradient of the tagging-based displacement. Through this algorithm, we obtained a 3x3 tensor that directly measures both the magnitude and direction of the lung deformation and then determines the three principal strains. For the lung ventilation calculation, we used the previously described voxel-by-voxel algorithm which was based on computing the Jacobian Determinant of the tagging-based DVF to determine the local volume changes. These results show much larger mean absolute percent differences between original and modified ventilation maps compared to the principal strain maps for all tests performed in this study; ranging from an average of 49.5 to 2743.7% for ventilation and 30.6 to 650.0% for strain among the 3 subjects. The principal strain maps showed much smaller average standard deviations between subjects. We found that Tagging-based ventilation maps calculated through the Jacobian of the DVF might be more sensitive to deformation changes compared to the lung strain maps, showing much larger mean absolute percent differences between the original and modified maps. This could indicate two things, while ventilation might be more sensitive to smaller deformation changes which could be an advantage, this could also indicate that it is more sensitive to small errors or uncertainties in the DVF which could make the calculation more unstable compared to strain.

In conclusion, this dissertation utilized a unique hybrid MR image acquisition method to present: the development of valuable physiologically-based respiratory motion models and DIR validation tools using novel tagging MR images; the first all MR-based evaluation of deformation-based ventilation mapping techniques, against reference HP gas He-3 MR ventilation images with improved spatial correspondence; and the investigation of lung strain as an additional metric to assess lung function.

Purpose: It has been shown that patients' irregular breathing can cause variation in the delineation of the internal target volume (ITV) and affect the accuracy of four dimensional computed tomography (4DCT) and cone-beam computed tomography (CBCT) images. Therefore, it is expected that the variations induced by irregular breathing will also affect image registration between the two images. This study aims to test a new method of ITV delineation, which involves using the gross tumor volume (GTV) in conjunction with the maximum intensity projection (MIP) generated from the 4DCT, rather than just the MIP itself. Additionally, this study aims to quantitatively assess breathing irregularity induced error in CBCT-based patient positioning in lung SBRT and correlate the error with a measure of breathing variability.

Methods and Materials: For testing the new method of ITV delineation, the Computerized Imaging Reference Systems (CIRS) Dynamic Thorax Phantom Model 008A (CIRS, Norfolk, VA) with CIRS motion control software was used to model 4 irregular patient respiratory profiles and one regular respiratory profile (sine wave) with a 3 cm tumor insert. A 3D-CT and repeated 4D-CT scans were performed on a 4-slice clinical scanner (Lightspeed, GE, WI). The RPM system (Varian, Palo Alto, CA) was used to track the respiratory profiles. GTV was contoured on 3D-CT, and ITV was contoured on each MIP (ITVMIP) using a consistent lung window by the same person. The new method of creating ITV was to combine the GTV and ITVMIP, namely ITVCOMB. To evaluate which ITV is more accurate, ITVCOMB and ITVMIP were compared to a "ground truth" ITV (ITVGT) which was generated by combining the three ITVMIPs. To investigate the error in image registration between the CBCT and 4DCT, the 4D extended cardiac-torso (XCAT) digital phantom was used to generate 10-phase 4DCTand CBCT images using in-house developed simulation programs. Images were generated using the same clinical-based parameters for various respiratory profiles (one regular sinusoidal and 10 irregular) and tumor sizes (1 cm, 2 cm, 3 cm). Maximum intensity projection (MIP) and average intensity projection (AIP) images were generated from 4DCT images The internal target volumes (ITVs) were contoured by the same user with the same window/level in Eclipse. Image registrations were performed between CBCT and AIP images by matching the target as in the clinic, for each respiratory profile and tumor size. Error of registration was determined as the difference between the manual CBCT-to-AIP registration and the known registration between the two. Variability of the respiratory profiles was measured, and a correlation between the error and breathing irregularity was investigated. Additionally, variation in ITV volumes among AIP, MIP, and CBCT images were examined.

Results: When examining the volumes for the ITV delineation study, for the regular profile, both ITVMIP (27.25 cm3) and ITVCOMB (28.12cm3) were comparable to ITVGT (27.25 cm3). For irregular profiles, the mean absolute difference between ITVCOMB and ITVGT (6.3%±4.9) was significantly (p-value=0.0078) smaller than that between ITVMIP and ITVGT (18.1%±12.3). A total of 33 registrations were performed to investigate error in image registration. As expected, negligible errors of registration were found for the regular respiratory profile at all tumor sizes: the median (± SD) error was 0.50 (± 0.73) mm, 0.20 (± 0.17) mm, and 0.40 (± 0.22) mm in the medial-lateral (ML), anterior-posterior (AP), and superior-inferior (SI) direction, respectively. For the irregular respiratory profiles and all tumor sizes combined, maximum error of registration was 1.2 mm, 2.6 mm, and 7.4 mm in the ML, AP, and SI direction, respectively. Median errors were found small in ML and AP directions (the median (± SD) error was 0.50 (± 0.21) mm and 0.50 (± 0.71) mm respectively), primarily due to small motion in these two directions. Median error in the SI direction was found non-trivial (the median (± SD) error was 1.90 (± 1.55) mm).

Conclusions: The results suggest that combining GTV of the 3D-CT with the ITV of the MIP is more accurate than the ITV of the MIP alone, and thus would be a simple method to reduce breathing irregularity induced errors in ITV delineation for treatment planning of lung cancer. Errors could occur during CBCT-to-AIP registration in lung SBRT when patient's breathing is irregular, especially in the SI direction. The error is largely induced by breathing irregularity and could not be overcome by perfecting manual matching, and it should be considered when determining the ITV to PTV margin. Differences in ITV volumes for AIP-MIP were seen to be minimal. However, significant differences in ITV volumes for MIP-CBCT were observed. Further studies of clinically minimizing such uncertainties are desirable.

Purpose: Current 4D-MRI techniques are prone to motion artifacts caused by irregular breathing. This study aims to develop and evaluate a novel, motion-robust multi-cycle 4D-MRI technique to overcome this deficiency.

Materials/Methods: The breathing signal was first analyzed to determine the main breathing cycles, providing tumor motion probability information for 4D-MRI reconstruction. 4D-MRI was reconstructed for each main breathing cycle using an in-house developed result-driven k-space reordering method. The new method was tested on the 4D-XCAT phantom. For comparison, conventional phase sorting method is also applied to generate a single-cycle 4D-MRI. Tumor and liver SNRs, tumor volume consistency, and AIP accuracy were determined and compared between the two methods. The original XCAT images were used as reference for the evaluations.

Results: Three-cycle 4D-MRI images were generated using the new method, presenting less noise and higher tumor and liver SNRs (30.41 and 15.28, 30.07 and 15.17, 28.63 and 15.25 for cycle 1, 2, and 3 respectively) than those of 4D-MRI images generated using phase sorting (17.33 and 12.04). These images have reduced motion artifacts, reflected by the improved inter-phase tumor volume consistency: the coefficients of variation in tumor volume were lower in the new method (0.027, 0.033 and 0.042 for cycle 1, 2, 3 respective) than that of the phase-sorting method (0.072). In addition, the AIP generated from the new method was more similar to the reference AIP than that from the phase sorting method; both the image intensity difference (0.21) and standard deviation of the difference map (6.4296e-8) were lower than those from the phase sorting method (0.46 and 1.1562e-7, respectively).

Conclusion: These results demonstrated the feasibility of the motion-robust, multi-cycle 4D-MRI technique through probability-driven k-space reordering. This new technique holds great promises to improve the image quality of 4D-MRI and the accuracy of its clinical applications.

Purpose: Motion artifacts induced by breathing variations are common in 4D-MRI

images. This study aims to reduce the motion artifacts by developing a novel, robust 4DMRI

sorting method based on anatomic feature matching, which is applicable in both

cine and sequential acquisition.

Method: The proposed method uses the diaphragm as the anatomic feature to guide the

sorting of 4D-MRI images. Initially, both abdominal 2D sagittal cine MRI images and

axial MRI images (in both axial cine and sequential scanning modes) were acquired. The

sagittal cine MRI images were divided into 10 phases as ground truth. Next, the phase of

each axial MRI image is determined by matching the diaphragm position in the

intersection plane between the axial MRI and the ground truth cine MRI. Then, those

matched phases axial MRI images were sorted into 10-phase bins identical to the ground

truth cine images. Finally, 10-phase 4D-MRI were reconstructed from these sorted axial

MRI images. The accuracy of reconstructed 4D-MRI data was evaluated in a simulation

study using the 4D eXtended Cardiac Torso (XCAT) digital phantom with a sphere

tumor in the liver. The effects of breathing signal, including both regular (cosine

function) and irregular (patient data), on reconstruction accuracy were investigated by

calculating total relative error (TRE) of the 4D volumes, Volume-Percent-Difference

(VPD) and Center-of-Mass-Shift(COMS) of the simulated tumor between the

reconstructed and the ground truth images.

Results: In both scanning modes, reconstructed 4D-MRI images matched well with the

ground truth except minimal motion artifacts. The averaged TRE of the 4D volume, VPD

and COMS of the EOE phase in both scanning modes were 0.32%/1.20%/±0.05𝑚𝑚 for

regular breathing, and 1.13%/4.26%/±0.21𝑚𝑚 for patient irregular breathing,

respectively.

Conclusion: The preliminary results illustrated the robustness of the new 4D-MRI

sorting method based on anatomic feature matching. This method improved image

quality with reduced motion artifacts in the resulting reconstructed 4D MRI is applicable

for axial MR images acquired using both cine and sequential scanning modes.

Purpose: To develop methods towards physiological-based respiratory motion management, through the enhancement of image quality of four dimensional magnetic resonance imaging (4D-MRI), as well as the deformable image registration (DIR) accuracy utilizing the data commonly available in radiotherapy treatment.

Methods: First part of the work is on the development of a probability density function (PDF) based method to improve the image quality of 4D-MRI. The overall idea is to identify a few main breathing cycles (and their corresponding weightings) that can best represent the main breathing patterns of the patient and then reconstruct a 4D image set for each of the identified main breathing cycles. This method is implemented in three steps: (1) The breathing signal is decomposed into individual breathing cycles, characterized by amplitude, A, and period, T; (2) Individual breathing cycles are grouped based on values of A and T to determine the main breathing cycles. If a group contains more than 10% of all breathing cycles in a breathing signal, it is determined as a main breathing pattern group and is represented by the average of individual breathing cycles in the group; (3) For each main breathing cycle, a set of 4D images is reconstructed using a result-driven sorting method adapted from our previous study. The probability-based sorting method was first tested on 26 patients’ breathing signals to evaluate its feasibility of improving tumor motion displacement PDF. Breathing motion PDFs were calculated for the original breathing signal (reference PDF), the average single-cycle breathing cycle (PDFsingle), and the main cycles determined by the probability-based sorting method (PDFprob). Similarity of PDFsingle/PDFprob to the reference PDF was evaluated using the Dice Similarity Coefficient (DSC) between the areas under the PDFsingle/PDFprob and under the reference PDF, noted as DSCsingle/DSCprob. The new method was subsequently tested for a sequential image acquisition scheme using the 4D digital eXtended Cardiac Torso (XCAT) phantom. Performance of the sorting methods was evaluated in terms of “tumor” volume precision and accuracy as measured by the 4D images, and also the accuracy of AIP of the 4D images.

The second part of the work is on the development of a radial basis function (RBF)-based hybrid DIR framework utilizing sparsely available measured motion information. The framework is carried out in three steps: (1) A base, intensity-based displacement vector field (DVF) is converted to an intensity-based coefficient matrix comprising expansion coefficients for the Wendland’s RBF; (2) The intensity-based coefficient matrix is modified under the guidance of sparely distributed measured motion information to generate the hybrid coefficient matrix; (3) The hybrid coefficient matrix is converted to the hybrid DVF. The hybrid DIR framework was tested on an in-house built lung motion phantom and compared with five different DIR methods including Velocity, MIM, ILK, OHS, and Elastix. Intensity-based and hybrid DVFs were compared to the known DVFs of the phantom. Synthesized EOI (primary) images were generated by deforming the phantom EOE (secondary) images with the intensity-based/hybrid DVFs. The intensity-based/hybrid synthesized EOI images were compared with the phantom EOI images. The effects of the number of landmarks were also studied by implementing the hybrid DIR process with 60-600 landmarks in the lungs.

The third part of the work is on the development of a method to automatically extract physiological landmarks (lung vessel bifurcations) using a novel random walking strategy. The lung images were first pre-processed with segmentation, binarizaion, and skeletonization. After the pre-processing, a vessel tree skeleton was acquired. For bifurcation extraction, first, end voxels were detected. Then a tracer would start from an end voxel and randomly move to one of its neighbors until reaching another end voxel. Numbers of being passed by the tracer were recorded for each voxel and a voxel was determined as a bifurcating voxel if it was visited more than all of its neighbors. The method was evaluated on a 7-layer simulated bifurcating tree, generated based on a modified Holton’s method for generation of botanical trees.

Results: For the first part, improved similarity of breathing motion PDF to reference PDF was observed as compared to PDFsingle, indicated by the significant increase in DSC (DSCprob = 0.89±0.03, and DSCsingle = 0.83±0.05, p-value <0.001). Based on the simulation study on XCAT, the probability-based method outperforms the conventional phase-based methods in qualitative evaluation on motion artifacts and quantitative evaluation on tumor volume precision and accuracy and accuracy of AIP of the 4D images.

In the second part, DIR errors were reduced in all tested cases after applying the hybrid DIR framework. Intensity-based only vs hybrid 3D DIR errors per voxel are 1.27 mm vs 1.07 mm, 1.56 mm vs 1.55 mm, 6.33 mm vs 4.81 mm, 3.53 mm vs 2.94 mm, and 2.00 mm vs 1.67 mm for for Velocity, MIM, ILK, OHS, and Elastix respectively. Reduction in intensity difference between the phantom EOI images and the synthesized EOI images are also observed in the hybrid synthesized EOI images. Study on the impacts of the number of landmarks confirms that a larger amount of landmarks can improve the accuracy of the hybrid DVF, and also indicates that the hybrid DIR framework may result in a larger improvement to intensity-based DVFs of poorer accuracy.

In the third part, an algorithm based on modified Holton’s model has been implemented to generate bifurcating vessel trees for testing the automatic bifurcation extraction method. Based on the results on a 7-layer bifurcating tree, the extraction method is able to detect more than 85% of the ground-truth bifurcations with 95% of the detected bifurcations located within 2 voxels from the nearest ground-truth bifurcation. The detection rate rises to 90% of the maximum detection rate in 4 iterations. The percentage of correctly detected bifurcations remains stable for different number of iterations ranging from 1 to 100. However, the variation of the percentage increases as the number of iterations lowers.

Conclusion: In this thesis, we developed a number of novel methods on the improvement in 4D image quality and hybrid deformable image registration, creating a more solid ground toward physiological plausible respiratory motion modeling. In particular, we have developed a probability-based multi-cycle sorting method for improved 4D image reconstruction and demonstrated the feasibility of a novel probability-based multi-cycle 4D image reconstruction method, presenting potential advantages over the conventional phase-based reconstruction method for radiation therapy motion management. In addition, we developed a hybrid DIR framework which is capable of potentially reducing registration errors by utilizing sparsely distributed measured motion information. The flexibility of the framework is expected to enhance the DIR accuracy in radiotherapy research. Furthermore, we have developed an automatic method to extract vessel bifurcations based on the random walking strategy and demonstrated its high accuracy and efficiency in a preliminary evaluation.