Browsing by Author "Yin, FangFang"

Purpose: Ensuring that tumor motion is within the radiation field for high-dose and high-precision radiosurgery in areas greatly influenced by respiratory motion. Therefore tracking the target or gating the radiation beam by using real-time imaging and surrogate motion monitoring methods are employed. However, these methods cannot be used to depict the effect of respiratory motion on tumor deviation. Therefore, an investigation of parameters for method predicting the tumor motion induced by respiratory motion multiple steps ahead of real time is performed. Currently, algorithms exist to make predictions about future real-time events, however these methods are tedious or unable to predict far enough in advance.

Methods and Materials: The algorithm takes data collected from the Varian RPM$ System, which is a one-dimensional (1D) surrogate signal of amplitude versus time. After the 1D surrogate signal is obtained, the algorithm determines on average what an approximate respiratory cycle is over the entire signal using a rising edge function. The signal is further dividing it into three components: (a) training component is the core portion of the data set which is further divided into subcomponents of length equal to the input component; (b) input component serves as the parameter searched for throughout the training component, (c) analysis component used as a validation against the prediction. The prediction algorithm consists of three major steps: (1) extracting top-ranked subcomponents from training component which best-match the input component; (2) calculating weighting factors from these best-matched subcomponents; (3) collecting the proceeding optimal subcomponent and fusing them with assigned weighting factors to form prediction. The prediction algorithm was examined for several patients, and its performance is assessed based on the correlation and root mean square error (RMSE) between prediction and known output.

Results: Respiratory motion data was simulated for 30 cases and 555 patients and phantoms using the RPM system. Simulations were used to optimize prediction algorithm parameters. The simulation cases were used to determine optimal filters for smoothing and number of top-ranked subcomponents to determine optimal subcomponents for prediction. Summed difference results in a value of 0.4770 for the 15 Point Savitzky-Golay filter.

After determining the proper filter for data preprocessing the number of required top-ranked subcomponents for each method was determine. Equal Weighting has a maximum average correlation, c=0.997 when using 1 Subcomponent, Relative Weighting has a maximum average correlation, c=0.997 when using 2 Subcomponents, Pattern Weighting has a maximum average correlation c=0.915 when using 1 subcomponent, Derivative Equal Weighting has a maximum average correlation c=0.976 when using 2 Subcomponents, and Derivative Relative Weighting has a maximum average correlation of c=0.976 when using 5 Subcomponents.

The correlation coefficient and RMSE of prediction versus analysis component distributions demonstrate an improvement during optimization for simulations. This is true for both the full and half cycle prediction. However, when moving to the clinical data the distribution of prediction data, both correlation coefficient and RMSE, there is not an improvement as the optimization occurs. Therefore, a comparison of the clinical data using the 5 Pt moving filter and arbitrarily chosen number of subcomponents was performed. In the clinical data, average correlation coefficient between prediction and analysis component 0.721+/-0.390, 0.727+/-0.383, 0.535+/-0.454, 0.725+/-0.397, and 0.725+/-0.398 for full respiratory cycle prediction and 0.789+/-0.398, 0.800+/-0.385, 0.426+/-0.562, 0.784+/-0.389, and 0.784+/-0.389 for half respiratory cycle prediction for equal weighting, relative weighting, pattern, derivative equal and derivative relative weighting methods, respectively. Additionally, the clinical data average RMSE between prediction and analysis component 0.196+/-0.174, 0.189+/-0.161, 0.302+/-0.162, 0.200+/-0.169, and 0.202+/-0.181 for full respiratory cycle prediction and 0.155+/-0.171, 0.149+/-0.138, 0.528+/-0.179, 0.174+/-0.150, and 0.173+/-0.149 for half respiratory cycle prediction for equal weighting, relative weighting, pattern, derivative equal and derivative relative weighting methods, respectively. The half cycle prediction displays higher accuracy over the full cycle prediction. Wilcoxon signed-rank test reveals statistically highly significant values (p<0.1%) for 4 out of 5 algorithms favoring the half cycle prediction (Equal, Relative, Derivative Equal, and Derivative Relative Weighting Methods). In this method, the relative weighting method has the most correlations coefficients with values greater than 0.9 and also yields the largest number of highest correlations over other prediction methods.

Conclusions: In conclusion, the number of subcomponents used for prediction may be better determined based on individual breathing pattern. The prediction accuracy using patient data is better using half cycle prediction over full cycle prediction for all algorithms for the majority of methods tested. Finally, relative weighting method performed better than other methods.

With the advent of new, more efficient, rotational therapy techniques such as volumetric modulated arc therapy (VMAT), radiation therapy treatment precision requires evolving quality assurance. Two dimensional (2D) detector arrays have shown angular dependence that must be compensated for by the creation of angular correction factor tables. Currently available correction factor tables have several underlying assumptions that leave room for improvement: first, these correction factors assume that the response of all ion chambers is identical for each angle; second, that the ion chamber array response from gantry angles 0°-180° are equivalent to the response from 180°-360° and, third, that the response is independent of the direction of rotation.

Measurements were acquired using a 2D ion chamber array (MatriXX®, IBA Dosimetry) for static open fields delivered every 5° around the MatriXX while dose was calculated using Eclipse v8.6 (analytic anisotropic algorithm, Varian Medical Systems). Customized correction factors were created by dividing the calculated dose by the measured dose for each ion chamber. Two measurement positions were used in the creation of the custom correction factors: a coronal position in which the couch was included, and two sagittal orientations in which the couch was not included.

The correction factors were verified using open field arcs and VMAT patient plans, where measured doses were compared to calculated doses using gamma analysis (3%, 3 mm). Narrow fields were also delivered clockwise and counterclockwise in order to investigate the effect of the internal structure of the ion chamber array.

The angular response of the individual ion chambers appears to vary significantly (1 &sigma &le 4.6%). The response from 0°-180° vs. 180°-360° is significantly different (paired t-test yields p<0.0001). Custom correction factors do enhance the agreement between measured and calculated doses for open field arcs and VMAT patient plans compared to the default correction factors. The direction of rotation appears to affect the dose to the penumbra region of narrow fields, which could affect VMAT patient specific quality assurance.

The custom correction factor tables, using measurements for individual ion chambers over a full 0°-360° range, allows for improved accuracy in measurements by the 2D ion chamber array. However, even the corrected measurements still showed discrepancies with the calculated doses for VMAT plans.

Since the publication of TG119 in 2009, new techniques have emerged in the field of radiation therapy including VMAT (Volumetric Arc Therapy) and the FFF (Flattening Filter Free) mode in Varian linear accelerators. Our goal in this work is to verify the feasibility of using TG119 to test the commissioning of VMAT and FFF systems and to set a benchmark for other institutions to use.

We created 48 plans of the five sites given in TG119 in addition to a "real" HN case. For each site, we planned IMRT and VMAT using 6MV and 10MV, FF and FFF modes (6*2*2*2 = 48 plans). All our plans were created on the Eclipse treatment planning system (Varian Medical Systems, Palo Alto, CA) and delivered on three beam-matched TrueBeam linear accelerators (Varian) at Duke University Medical Center.

Measurements were taken using ion chamber, film, and a pseudo-3D diode array (Delta4), and compared to the planned doses. Confidence limits were determined using the approach of TG119 (CL = average mean deviation + 1.96 * standard deviation). We used the student's paired t-test to determine any statistically significant differences between IMRT and VMAT, FF and FFF for 6MV and 10MV.

The majority of the ion chamber measurements (94%) agreed with the planned doses within 3%. The majority of errors > 3% involved the HN IMRT plans, either TG119 or "real". For film measurements, we used gamma parameters of 3%/3mm with a 20% threshold. All films met Duke's acceptability criteria of <= 10% of pixels failing gamma. As for Delta4, gamma parameters of 3%/3mm with a 5% threshold were used. All plans met Duke's acceptability criteria of 90% of pixels passing (average 99.7% +/- 0.8%). A second analysis was performed using 2%/2mm gamma parameters, where almost all plans met the 90% passing rate criteria (average 98.9% +/- 2.5%).

Confidence limits were established for ion chamber (3.1%), film (6%), and Delta4 (3.1%) measurements. All the confidence limits were comparable to TG119 institutions. We recommend that non-clinical plans (e.g. 10MV HN plans) not be included in TG119 evaluations. We also recommend that film continue to be used as the gold standard of multi-dimensional measurements, rather than be replaced by diode-based technology.

Accurate delivery of external beam radiation therapy relies on localization of the treatment target. For this work, the accuracy of an electromagnetic localization system (ELS) was first verified. Next, prostate deformations and rotations occurring throughout therapy were analyzed. Motion studies were also conducted to investigate the use of the ELS during intensity modulated arc therapy (IMAT) for prostate cancer. Lastly, appropriate margins were determined and new plans utilizing smaller margins were tested for two patients who exhibited large transponder displacements. Electromagnetic alignments were accurate to within 1mm as compared to x-ray imaging. All patients fell within the default geometric residual limit (2mm) and most fell within the default rotation limit (10°). The ELS appeared to be suitable for use during IMAT with a 5mm margin. A 3mm margin was tested and was adequate when the main displacements were translational shifts; however, it was not adequate when large rotational displacements occurred.

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.

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.

Purpose: Computed Tomography (CT) is one of the standard diagnostic imaging modalities for the evaluation of a patient’s medical condition. In comparison to other imaging modalities such as Magnetic Resonance Imaging (MRI), CT is a fast acquisition imaging device with higher spatial resolution and higher contrast-to-noise ratio (CNR) for bony structures. CT images are presented through a gray scale of independent values in Hounsfield units (HU). High HU-valued materials represent higher density. High density materials, such as metal, tend to erroneously increase the HU values around it due to reconstruction software limitations. This problem of increased HU values due to metal presence is referred to as metal artefacts. Hip prostheses, dental fillings, aneurysm clips, and spinal clips are a few examples of metal objects that are of clinical relevance. These implants create artefacts such as beam hardening and photon starvation that distort CT images and degrade image quality. This is of great significance because the distortions may cause improper evaluation of images and inaccurate dose calculation in the treatment planning system. Different algorithms are being developed to reduce these artefacts for better image quality for both diagnostic and therapeutic purposes. However, very limited information is available about the effect of artefact correction on dose calculation accuracy. This research study evaluates the dosimetric effect of metal artefact reduction algorithms on severe artefacts on CT images. This study uses Gemstone Spectral Imaging (GSI)-based MAR algorithm, projection-based Metal Artefact Reduction (MAR) algorithm, and the Dual-Energy method.

Materials and Methods: The Gemstone Spectral Imaging (GSI)-based and SMART Metal Artefact Reduction (MAR) algorithms are metal artefact reduction protocols embedded in two different CT scanner models by General Electric (GE), and the Dual-Energy Imaging Method was developed at Duke University. All three approaches were applied in this research for dosimetric evaluation on CT images with severe metal artefacts. The first part of the research used a water phantom with four iodine syringes. Two sets of plans, multi-arc plans and single-arc plans, using the Volumetric Modulated Arc therapy (VMAT) technique were designed to avoid or minimize influences from high-density objects. The second part of the research used projection-based MAR Algorithm and the Dual-Energy Method. Calculated Doses (Mean, Minimum, and Maximum Doses) to the planning treatment volume (PTV) were compared and homogeneity index (HI) calculated.

Results: (1) Without the GSI-based MAR application, a percent error between mean dose and the absolute dose ranging from 3.4-5.7% per fraction was observed. In contrast, the error was decreased to a range of 0.09-2.3% per fraction with the GSI-based MAR algorithm. There was a percent difference ranging from 1.7-4.2% per fraction between with and without using the GSI-based MAR algorithm. (2) A range of 0.1-3.2% difference was observed for the maximum dose values, 1.5-10.4% for minimum dose difference, and 1.4-1.7% difference on the mean doses. Homogeneity indexes (HI) ranging from 0.068-0.065 for dual-energy method and 0.063-0.141 with projection-based MAR algorithm were also calculated.

Conclusion: (1) Percent error without using the GSI-based MAR algorithm may deviate as high as 5.7%. This error invalidates the goal of Radiation Therapy to provide a more precise treatment. Thus, GSI-based MAR algorithm was desirable due to its better dose calculation accuracy. (2) Based on direct numerical observation, there was no apparent deviation between the mean doses of different techniques but deviation was evident on the maximum and minimum doses. The HI for the dual-energy method almost achieved the desirable null values. In conclusion, the Dual-Energy method gave better dose calculation accuracy to the planning treatment volume (PTV) for images with metal artefacts than with or without GE MAR Algorithm.

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.

A bench-top dual cone-beam computed tomography (CBCT) system was developed consisting of two orthogonally placed 40x30 cm2 flat-panel detectors and two conventional X-ray tubes with two individual high-voltage generators sharing the same rotational axis. The X-ray source to detector distance is 150 cm and X-ray source to rotational axis distance is 100 cm for both subsystems. The objects are scanned through 200° of rotation. The dual CBCT (DCBCT) system utilized 110° of projection data from one detector and 90° from the other while the two individual single CBCTs utilized 200° data from each detector. The system performance was characterized in terms of uniformity, contrast, spatial resolution, noise power spectrum and CT number linearity. The uniformity, within the axial slice and along the longitudinal direction, and noise power spectrum were assessed by scanning a water bucket; the contrast and CT number linearity were measured using the Catphan phantom; and the spatial resolution was evaluated using a tungsten wire phantom. A skull phantom and a ham were also scanned to provide qualitative evaluation of high- and low-contrast resolution. Each measurement was compared between dual and single CBCT systems.

Compared with single CBCT, the DCBCT presented: 1) a decrease in uniformity by 1.9% in axial view and 1.1% in the longitudinal view, as averaged for four energies (80, 100, 125 and 150 kVp); 2) comparable or slightly better contrast to noise ratio (CNR) for low-contrast objects and comparable contrast for high-contrast objects; 3) comparable spatial resolution; 4) comparable CT number linearity with R2 ≥ 0.99 for all four tested energies; 5) lower noise power spectrum in magnitude. DCBCT images of the skull phantom and the ham demonstrated both high-contrast resolution and good soft-tissue contrast.

One of the major challenges for clinical implementation of four-dimensional (4D) CBCT is the long scan time. To investigate the 4D imaging capabilities of the DCBCT system, motion phantom studies were conducted to validate the efficiency by comparing 4D images generated from 4D-DCBCT and 4D-CBCT. First, a simple sinusoidal profile was used to confirm the scan time reduction. Next, both irregular sinusoidal and patient-derived profiles were used to investigate the advantage of temporally correlated orthogonal projections due to a reduced scan time. Normalized mutual information (NMI) between 4D-DCBCT and 4D-CBCT was used for quantitative evaluation.

For the simple sinusoidal profile, the average NMI for ten phases between two single 4D-CBCTs was 0.336, indicating the maximum NMI that can be achieved for this study. The average NMIs between 4D-DCBCT and each single 4D-CBCT were 0.331 and 0.320. For both irregular sinusoidal and patient-derived profiles, 4D-DCBCT generated phase images with less motion blurring when compared with single 4D-CBCT.

For dual kV energy imaging, we acquired 80kVp projections and 150 kVp projections, with an additional 0.8 mm tin filtration. The virtual monochromatic (VM) technique was implemented, by first decomposing these projections into acrylic and aluminum basis material projections to synthesize VM projections, which were then used to reconstruct VM CBCTs. The effect of the VM CBCT on metal artifact reduction was evaluated with an in-house titanium-BB phantom. The optimal VM energy to maximize CNR for iodine contrast and minimize beam hardening in VM CBCT was determined using a water phantom containing two iodine concentrations. The linearly-mixed (LM) technique was implemented by linearly combining the low- (80kVp) and high-energy (150kVp) CBCTs. The dose partitioning between low- and high-energy CBCTs was varied (20%, 40%, 60% and 80% for low-energy) while keeping total dose approximately equal to single-energy CBCTs, measured using an ion chamber. Noise levels and CNRs for four tissue types were investigated for dual-energy LM CBCTs in comparison with single-energy CBCTs at 80, 100, 125 and 150kVp.

The VM technique showed a substantial reduction of metal artifacts at 100 keV with a 40% reduction in the background standard deviation compared with a 125 kVp single-energy scan of equal dose. The VM energy to maximize CNR for both iodine concentrations and minimize beam hardening in the metal-free object was 50 keV and 60 keV, respectively. The difference in average noise levels measured in the phantom background was 1.2% for dual-energy LM CBCTs and equivalent-dose single-energy CBCTs. CNR values in the LM CBCTs of any dose partitioning were better than those of 150 kVp single-energy CBCTs. The average CNRs for four tissue types with 80% dose fraction at low-energy showed 9.0% and 4.1% improvement relative to 100 kVp and 125 kVp single-energy CBCTs, respectively. CNRs for low contrast objects improved as dose partitioning was more heavily weighted towards low-energy (80kVp) for LM CBCTs.

For application of the dual-energy technique in the kilovoltage (kV) and megavoltage (MV) range, we acquired both MV projections (from gantry angle of 0° to 100°) and kV projections (90° to 200°) with the current orthogonal kV/MV imaging hardware equipped in modern linear accelerators, as gantry rotated a total of 110°. A selected range of overlap projections between 90° to 100° were then decomposed into two material projections using experimentally determined parameters from orthogonally stacked aluminum and acrylic step-wedges. Given attenuation coefficients of aluminum and acrylic at a predetermined energy, one set of VM projections could be synthesized from two corresponding sets of decomposed projections. Two linear functions were generated using projection information at overlap angles to convert kV and MV projections at non-overlap angles to approximate VM projections for CBCT reconstruction. The CNRs were calculated for different inserts in VM CBCTs of a CatPhan phantom with various selected energies and compared with those in kV and MV CBCTs. The effect of overlap projection number on CNR was evaluated. Additionally, the effect of beam orientation was studied by scanning the CatPhan sandwiched with two 5 cm solid-water phantoms on both lateral sides and an electronic density phantom with two metal bolt inserts.

Proper selection of VM energy (30keV and 40keV for low-density polyethylene (LDPE), polymethylpentene (PMP), 2MeV for Delrin) provided comparable or even better CNR results as compared with kV or MV CBCT. An increased number of overlap between kV and MV projections demonstrated only marginal improvements of CNR for different inserts (with the exception of LDPE) and therefore one projection overlap was found to be sufficient for the CatPhan study. It was also evident that the optimal CBCT image quality was achieved when MV beams penetrated through the heavy attenuation direction of the object.

In conclusion, the performance of a bench-top DCBCT imaging system has been characterized and is comparable to that of a single CBCT. The 4D-DCBCT provides an efficient 4D imaging technique for motion management. The scan time is reduced by approximately a factor of two. The temporally correlated orthogonal projections improved the image blur across 4D phase images. Dual-energy CBCT imaging techniques were implemented to synthesize VM CBCT and LM CBCTs. VM CBCT was effective at achieving metal artifact reduction. Depending on the dose-partitioning scheme, LM CBCT demonstrated the potential to improve CNR for low contrast objects compared with single-energy CBCT acquired with equivalent dose. A novel technique was developed to generate VM CBCTs from kV/MV projections. This technique has the potential to improve CNR at selected VM energies and to suppress artifacts at appropriate beam orientations.

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.

To date, image localization of mobile tumors prior to radiation delivery has primarily been confined to 2D and 3D technologies, such as fluoroscopy and 3D cone-beam CT (3D-CBCT). Due to the limited information from these images, larger volumes of healthy tissue are often irradiated in order to ensure the radiation field encompasses the entirety of the target motion. Since the overarching goal of radiation therapy is to deliver maximum dose to cancerous cells and simultaneously minimize the radiation delivered to healthy surrounding tissues, it would be ideal to use 4D imaging to obtain time-resolved volume images of the tumor motion during respiration.

4D-CBCT imaging has been previously investigated, but has not yet seen large clinical translation due to the obstacles of long acquisition time and large image radiation dose. Furthermore, 4D-CBCT currently requires the use of external surrogates to correlate the patient's respiration with the image acquisition process. This correlation has been under question by a multitude of studies demonstrating the uncertainties that exist between the surrogate and the actual motion of the internal anatomy. Errors in the correlation process may result in image artifacts, which could potentially lead to reconstructions with inaccurate target volumes, thereby defeating the purpose of even using 4D-CBCT.

It is therefore the aim of this dissertation to initially highlight an additional limitation of using 3D-CBCT for imaging respiratory motion and thereby reiterate the need for 4D-CBCT imaging in the treatment room, develop a simple and efficient technique to achieve markerless, self-sorted 4D-CBCT and finally to comprehensively evaluate its robustness across a variety of potential clinical scenarios with a digital human phantom.

People often spend a longer period of time exhaling as compared with inhaling, and some do so in an extremely disproportionate manner. To demonstrate the disadvantage of using 3D-CBCT in such instances, a dynamic thorax phantom was imaged with a large variety of simulated and patient-derived respiratory traces of ratios of time spent in the inspiration phase versus time spent in the expiration phase (I/E ratio). Canny edge detection and contrast measures were employed to compare the internal target volumes (ITVs) generated per profile. The results revealed that an I/E ratio of less than one can lead to potential underestimation of the ITV with the severity increasing as the inspiration becomes more disproportionate to the expiration. This occurs because of the loss of contrast in the inspiration phase, due to the fewer number of projections acquired there. The measured contrast reduction was as high as 94% for small targets (0.5 cm) moving large amplitudes (2.0 cm) and still as much as 22.3% for large targets (3.0 cm) moving small amplitudes (0.5 cm). This is alarming because the degraded visibility of the target in the inspiration phase may inaccurately impact the alignment of the planning ITV with that of the FB-CBCT and thereby affect the accuracy of the localization and consequent radiation delivery. These potential errors can be avoided with the use of 4D-CBCT instead, to form the composite volume and serve as the verification ITV for alignment.

In order to delineate accurate target volumes from 4D-CBCT phase images, it is crucial that the projections be properly associated with the patient's respiration. Thus, in order to improve previously developed 4D-CBCT techniques, the basics of Fourier Transform (FT) theory were utilized to extract the respiratory signal directly from the acquired projection data. Markerless, self-sorted 4D-CBCT reconstruction was achieved by developing methods based on the phase and magnitude information of the Fourier Transform. Their performance was subsequently compared to the gold standard of visual identification of peak-inspiration projections. Slow-gantry acquired projections of two sets of physical phantom data with sinusoidal respiratory cycles of 3 and 6 seconds as well as three patients were used as initial evaluation of the feasibility of the Fourier technique. Quantitative criteria consisted of average difference in respiratory phase (ADRP) and percentage of projections assigned within 10% respiratory phase of the gold standard (PP10). For all five projection datasets, the results supported feasibility of both FT-Phase and FT-Magnitude methods with ADRP values less than 5.3% and PP10 values of 87.3% and above.

Because the technique proved to be promising in the initial feasibility study, a more comprehensive evaluation was necessary in order to assess the robustness of the technique across a larger set of possibilities that may be encountered in the clinic. A 4D digital XCAT phantom was used to generate an array of respiratory and anatomical variables that affect the performance of the technique. The respiratory variables studied included: inspiration to expiration ratio, respiratory cycle length, diaphragmatic motion amplitude, AP chest wall expansion amplitude, breathing irregularities such as baseline shift and inconsistent peak-inspiration amplitude, as well as six breathing profiles derived from cine-MRI images of three healthy volunteers and three lung cancer patients. The anatomical variables studied included: male and female patient size (physical dimension and adipose content), body-mass-index (BMI) category, tumor location, and percentage of the lung in the field-of-view (FOV) of the projection data. CBCT projections of each XCAT phantom were then generated. Additional external imaging factors such as image noise and detector wobble were added to select cases with different percentages of lung in the projection FOV to investigate any effects on the robustness. FT-Phase and FT-Magnitude were each applied and quantitatively compared to the gold standard. Both methods proved to be robust across the studied scenarios with ADRP<10% and PP10>90%, when incorporating minor modifications to region-of-interest (ROI) selection and/or low-frequency location to certain cases of diaphragm amplitude and lung percentage in the FOV of the projection (for which a method may have previously struggled). Nevertheless, in the instance where one method initially faltered, the other method prevailed and successfully identified peak-inspiration projections. This is promising because it suggests that the two methods provide complementary information to each other. To ensure appropriate clinical adaptation of markerless, self-sorted 4D-CBCT, perhaps an optimal integration of the two methods can be developed.

The developments of highly conformal and precise radiation therapy techniques promote the necessity of more accurate treatment target localization and tracking. On-board imaging techniques, especially the x-ray based techniques, have found a great popularity nowadays for on-board target localization and tracking. With an objective to improve the accuracy of on-board imaging for lung cancer patients, the dissertation work focuses on the investigations of using limited-angle on-board x-ray projections for image guidance. The limited-angle acquisition enables scan time and imaging dose reduction and improves the mechanical clearance of imaging.

First of all, the dissertation developed a phase-matched digital tomosynthesis (DTS) technique using limited-angle (<=30 deg) projections for lung tumor localization. This technique acquires the same traditional motion-blurred on-board DTS image as the 3D-DTS technique, but uses the planning 4D computed tomography (CT) to synthesize a phase-matched reference DTS to register with the on-board DTS for tumor localization. Of the 324 different scenarios simulated using the extended cardiac torso (XCAT) digital phantom, the phase-matched DTS technique localizes the 3D target position with an localization error of 1.07 mm (± 0.57 mm) (average ± standard deviation (S.D.)). Similarly, for the total 60 scenarios evaluated using the computerized imaging reference system (CIRS) 008A physical phantom, the phase-matched DTS technique localizes the 3D target position with an average localization error of 1.24 mm (± 0.87 mm). In addition to the phantom studies, preliminary clinical cases were also studied using imaging data from three lung cancer patients. Using the localization results of 4D cone beam computed tomography (CBCT) as `gold-standard', the phase-matched DTS techniques localized the tumor to an average localization error of 1.5 mm (± 0.5 mm).

The phantom and patient study results show that the phase-matched DTS technique substantially improved the accuracy of moving lung target localization, as compared to the 3D-DTS technique. The phase-matched DTS technique can provide accurate lung target localizations like 4D-DTS, but with much reduced imaging dose and scan time. The phase-matched DTS technique is also found more robust, being minimally affected by variations of respiratory cycle lengths, fractions of respiration cycle contained within the DTS scan and the scan directions, which potentially enables quasi-instantaneous (within a sub-breathing cycle) moving target verification during radiation therapy, preferably arc therapy.

Though the phase-matched DTS technique can provide accurate target localization under normal scenarios, its accuracy is limited when the patient on-board breathing experiences large variations in motion amplitudes. In addition, the limited-angle based acquisition leads to severe structural distortions in DTS images reconstructed by the current clinical gold-standard Feldkamp-Davis-Kress (FDK) reconstruction algorithm, which prohibit accurate target deformation tracking, delineation and dose calculation.

To solve the above issues, the dissertation further developed a prior knowledge based image estimation technique to fundamentally change the landscape of limited-angle based imaging. The developed motion modeling and free-form deformation (MM-FD) method estimates high quality on-board 4D-CBCT images through applying deformation field maps to existing prior planning 4D-CT images. The deformation field maps are solved using two steps: first, a principal component analysis based motion model is built using the planning 4D-CT (motion modeling). The deformation field map is constructed as an optimized linear combination of the extracted motion modes. Second, with the coarse deformation field maps obtained from motion modeling, a further fine-tuning process called free-form deformation is applied to further correct the residual errors from motion modeling. Using the XCAT phantom, a lung patient with a 30 mm diameter tumor was simulated to have various anatomical and respirational variations from the planning 4D-CT to on-board 4D-CBCTs, including respiration amplitude variations, tumor size variations, tumor average position variations, and phase shift between tumor and body respiratory cycles. The tumors were contoured in both the estimated and the `ground-truth' on-board 4D-CBCTs for comparison. 3D volume percentage error (VPE) and center-of-mass error (COME) were calculated to evaluate the estimation accuracy of the MM-FD technique. For all simulated patient scenarios, the average (± S.D.) VPE / COME of the tumor in the prior image without image estimation was 136.11% (± 42.76%) / 15.5 mm (± 3.9 mm). Using orthogonal-view 30 deg scan angle, the average VPE/COME of the tumors in the MM-FD estimated on-board images was substantially reduced to 5.22% (± 2.12%) / 0.5 mm (± 0.4 mm).

In addition to XCAT simulation, CIRS phantom measurements and actual patient studies were also performed. For these clinical studies, we used the normalized cross-correlation (NCC) as a new similarity metric and developed an updated MMFD-NCC method, to improve the robustness of the image estimation technique to the intensity mismatches between CT and CBCT imaging systems. Using 4D-CBCT reconstructed from fully-sampled on-board projections as `gold-standard', for the CIRS phantom study, the average (± S.D.) VPE / COME of the tumor in the prior image and the tumors in the MMFD-NCC estimated images was 257.1% (± 60.2%) / 10.1 mm (± 4.5 mm) and 7.7% (± 1.2%) / 1.2 mm (± 0.2mm), respectively. For three patient cases, the average (± S.D.) VPE / COME of tumors in the prior images and tumors in the MMFD-NCC estimated images was 55.6% (± 45.9%) / 3.8 mm (± 1.9 mm) and 9.6% (± 6.1%) / 1.1 mm (± 0.5 mm), respectively. With the combined benefits of motion modeling and free-form deformation, the MMFD-NCC method has achieved highly accurate image estimation under different scenarios.

Another potential benefit of on-board 4D-CBCT imaging is the on-board dose calculation and verification. Since the MMFD-NCC estimates the on-board 4D-CBCT through deforming prior 4D-CT images, the 4D-CBCT inherently has the same image quality and Hounsfield unit (HU) accuracy as 4D-CT and therefore can potentially improve the accuracy of on-board dose verification. Both XCAT and CIRS phantom studies were performed for the dosimetric study. Various inter-fractional variations featuring patient motion pattern change, tumor size change and tumor average position change were simulated from planning CT to on-board images. The doses calculated on the on-board CBCTs estimated by MMFD-NCC (MMFD-NCC doses) were compared to the doses calculated on the `gold-standard' on-board images (gold-standard doses). The absolute deviations of minimum dose (DDmin), maximum dose (DDmax), mean dose (DDmean) and prescription dose coverage (DV100%) of the planning target volume (PTV) were evaluated. In addition, 4D on-board treatment dose accumulations were performed using 4D-CBCT images estimated by MMFD-NCC in the CIRS phantom study. The accumulated doses were compared to those measured using optically stimulated luminescence (OSL) detectors and radiochromic films.

The MMFD-NCC doses matched very well with the gold-standard doses. For the XCAT phantom study, the average (± S.D.) DDmin, DDmax, DDmean and DV100% (values normalized by the prescription dose or the total PTV volume) between the MMFD-NCC PTV doses and the gold-standard PTV doses were 0.3% (± 0.2%), 0.9% (± 0.6%), 0.6% (± 0.4%) and 1.0% (± 0.8%), respectively. Similarly, for the CIRS phantom study, the corresponding values between the MMFD-NCC PTV doses and the gold-standard PTV doses were 0.4% (± 0.8%), 0.8% (± 1.0%), 0.5% (± 0.4%) and 0.8% (± 0.8%), respectively. For the 4D dose accumulation study, the average (± S.D.) absolute dose deviation (normalized by local doses) between the accumulated doses and the OSL measured doses was 3.0% (± 2.4%). The average gamma index (3%/3mm) between the accumulated doses and the radiochromic film measured doses was 96.1%. The MMFD-NCC estimated 4D-CBCT enables accurate on-board dose calculation and accumulation for lung radiation therapy under different scenarios. It can potentially be valuable for treatment quality assessment and adaptive radiation therapy.

However, a major limitation of the estimated 4D-CBCTs above is that they can only capture inter-fractional patient variations as they were acquired prior to each treatment. The intra-treatment patient variations cannot be captured, which can also affect the treatment accuracy. In light of this issue, an aggregated kilo-voltage (kV) and mega-voltage (MV) imaging scheme was developed to enable intra-treatment imaging. Through using the simultaneously acquired kV and MV projections during the treatment, the MMFD-NCC method enabled 4D-CBCT estimation using combined kV and MV projections.

For all XCAT-simulated patient scenarios, the average (± S.D.) VPE / COME of the tumor in the prior image and tumors in the MMFD-NCC estimated images (using kV + open field MV) was 136.11% (± 42.76%) / 15.5 mm (± 3.9 mm) and 4.5% (± 1.9%) / 0.3 mm (± 0.4 mm), respectively. In contrast, the MMFD-NCC estimation using kV + beam's eye view (BEV) MV projections yielded results of 4.3% (± 1.5%) / 0.3 mm (± 0.3 mm). The kV + BEV MV aggregation can estimate the target as accurately as the kV + open field MV aggregation. The impact of this study is threefold: 1. the kV and MV projections can be acquired at the same time. The imaging time will be cut to half as compared to the cases which use kV projections only. 2. The kV and MV aggregation enables intra-treatment imaging and target tracking, since the MV projections can be the side products of the treatment beams (BEV MV). 3. As the BEV MV projections originate from the treatment beams, there will be no extra MV imaging dose to the patient.

The above introduced 4D-CBCT estimation techniques were all based on limited-angle acquisition. Though limited-angle acquisition enables substantial scan time and dose reduction as compared to the full-angle scan, it is still not real-time and cannot provide `cine' imaging, which refers to the instantaneous imaging with negligible scan time and imaging dose. Cine imaging is important in image guided radiation therapy practice, considering the respirational variations may occur quickly and frequently during the treatment. For instance, the patient may experience a breathing baseline shift after every respiratory cycle. The limited-angle 4D-CBCT approach still requires a scan time of multiple respiratory cycles, which will not be able to capture the baseline shift in a timely manner.

In light of this issue, based on the previously developed MMFD-NCC method, an AI-FD-NCC method was further developed to enable quasi-cine CBCT imaging using extremely limited-angle (<=6 deg) projections. Using pre-treatment 4D-CBCTs acquired just before the treatment as prior information, AI-FD-NCC enforces an additional prior adaptive constraint to estimate high quality `quasi-cine' CBCT images. Two on-board patient scenarios: tumor baseline shift and continuous motion amplitude change were simulated through the XCAT phantom. Using orthogonal-view 6 deg projections, for the baseline shift scenario, the average (± S.D.) VPE / COME of the tumors in the AI-FD-NCC estimated images was 1.3% (± 0.5%) / 0.4 mm (± 0.1 mm). For the amplitude variation scenario, the average (± S.D.) VPE / COME of the tumors in the AI-FD-NCC estimated images was 1.9% (± 1.1%) / 0.5 mm (± 0.2 mm). The impact of this study is three-fold: first, the quasi-cine CBCT technique enables actual real-time volumetric tracking of tumor and normal tissues. Second, the method enables real-time tumor and normal tissues dose calculation and accumulation. Third, the high-quality volumetric images obtained can potentially be used for real-time adaptive radiation therapy.

In summary, the dissertation work uses limited-angle on-board x-ray projections to reconstruct/estimate volumetric images for lung tumor localization, delineation and dose calculation. Limited-angle acquisition reduces imaging dose, scan time and improves imaging mechanical clearance. Using limited-angle projections enables continuous, sub respiratory-cycle tumor localization, as validated in the phase-matched DTS study. The combination of prior information, motion modeling, free-form deformation and limited-angle on-board projections enables high-quality on-board 4D-CBCT estimation, as validated by the MM-FD / MMFD-NCC techniques. The high-quality 4D-CBCT not only can be applied for accurate target localization and delineation, but also can be used for accurate treatment dose verification, as validated in the dosimetric study. Through aggregating the kV and MV projections for image estimation, intra-treatment 4D-CBCT imaging was also proposed and validated for its feasibility. At last, the introduction of more accurate prior information and additional adaptive prior knowledge constraints also enables quasi-cine CBCT imaging using extremely-limited angle projections. The dissertation work contributes to lung on-board imaging in many aspects with various approaches, which can be beneficial to the future lung image guided radiation therapy practice.

Abstract

The goal of modern radiotherapy is to precisely deliver a prescribed radiation dose to delineated target volumes that contain a significant amount of tumor cells while sparing the surrounding healthy tissues/organs. Precise delineation of treatment and avoidance volumes is the key for the precision radiation therapy. In recent years, considerable clinical and research efforts have been devoted to integrate MRI into radiotherapy workflow motivated by the superior soft tissue contrast and functional imaging possibility. Dynamic contrast-enhanced MRI (DCE-MRI) is a noninvasive technique that measures properties of tissue microvasculature. Its sensitivity to radiation-induced vascular pharmacokinetic (PK) changes has been preliminary demonstrated. In spite of its great potential, two major challenges have limited DCE-MRI’s clinical application in radiotherapy assessment: the technical limitations of accurate DCE-MRI imaging implementation and the need of novel DCE-MRI data analysis methods for richer functional heterogeneity information.

This study aims at improving current DCE-MRI techniques and developing new DCE-MRI analysis methods for particular radiotherapy assessment. Thus, the study is naturally divided into two parts. The first part focuses on DCE-MRI temporal resolution as one of the key DCE-MRI technical factors, and some improvements regarding DCE-MRI temporal resolution are proposed; the second part explores the potential value of image heterogeneity analysis and multiple PK model combination for therapeutic response assessment, and several novel DCE-MRI data analysis methods are developed.

I. Improvement of DCE-MRI temporal resolution. First, the feasibility of improving DCE-MRI temporal resolution via image undersampling was studied. Specifically, a novel MR image iterative reconstruction algorithm was studied for DCE-MRI reconstruction. This algorithm was built on the recently developed compress sensing (CS) theory. By utilizing a limited k-space acquisition with shorter imaging time, images can be reconstructed in an iterative fashion under the regularization of a newly proposed total generalized variation (TGV) penalty term. In the retrospective study of brain radiosurgery patient DCE-MRI scans under IRB-approval, the clinically obtained image data was selected as reference data, and the simulated accelerated k-space acquisition was generated via undersampling the reference image full k-space with designed sampling grids. Two undersampling strategies were proposed: 1) a radial multi-ray grid with a special angular distribution was adopted to sample each slice of the full k-space; 2) a Cartesian random sampling grid series with spatiotemporal constraints from adjacent frames was adopted to sample the dynamic k-space series at a slice location. Two sets of PK parameters’ maps were generated from the undersampled data and from the fully-sampled data, respectively. Multiple quantitative measurements and statistical studies were performed to evaluate the accuracy of PK maps generated from the undersampled data in reference to the PK maps generated from the fully-sampled data. Results showed that at a simulated acceleration factor of four, PK maps could be faithfully calculated from the DCE images that were reconstructed using undersampled data, and no statistically significant differences were found between the regional PK mean values from undersampled and fully-sampled data sets. DCE-MRI acceleration using the investigated image reconstruction method has been suggested as feasible and promising.

Second, for high temporal resolution DCE-MRI, a new PK model fitting method was developed to solve PK parameters for better calculation accuracy and efficiency. This method is based on a derivative-based deformation of the commonly used Tofts PK model, which is presented as an integrative expression. This method also includes an advanced Kolmogorov-Zurbenko (KZ) filter to remove the potential noise effect in data and solve the PK parameter as a linear problem in matrix format. In the computer simulation study, PK parameters representing typical intracranial values were selected as references to simulated DCE-MRI data for different temporal resolution and different data noise level. Results showed that at both high temporal resolutions (<1s) and clinically feasible temporal resolution (~5s), this new method was able to calculate PK parameters more accurate than the current calculation methods at clinically relevant noise levels; at high temporal resolutions, the calculation efficiency of this new method was superior to current methods in an order of 102. In a retrospective of clinical brain DCE-MRI scans, the PK maps derived from the proposed method were comparable with the results from current methods. Based on these results, it can be concluded that this new method can be used for accurate and efficient PK model fitting for high temporal resolution DCE-MRI.

II. Development of DCE-MRI analysis methods for therapeutic response assessment. This part aims at methodology developments in two approaches. The first one is to develop model-free analysis method for DCE-MRI functional heterogeneity evaluation. This approach is inspired by the rationale that radiotherapy-induced functional change could be heterogeneous across the treatment area. The first effort was spent on a translational investigation of classic fractal dimension theory for DCE-MRI therapeutic response assessment. In a small-animal anti-angiogenesis drug therapy experiment, the randomly assigned treatment/control groups received multiple fraction treatments with one pre-treatment and multiple post-treatment high spatiotemporal DCE-MRI scans. In the post-treatment scan two weeks after the start, the investigated Rényi dimensions of the classic PK rate constant map demonstrated significant differences between the treatment and the control groups; when Rényi dimensions were adopted for treatment/control group classification, the achieved accuracy was higher than the accuracy from using conventional PK parameter statistics. Following this pilot work, two novel texture analysis methods were proposed. First, a new technique called Gray Level Local Power Matrix (GLLPM) was developed. It intends to solve the lack of temporal information and poor calculation efficiency of the commonly used Gray Level Co-Occurrence Matrix (GLCOM) techniques. In the same small animal experiment, the dynamic curves of Haralick texture features derived from the GLLPM had an overall better performance than the corresponding curves derived from current GLCOM techniques in treatment/control separation and classification. The second developed method is dynamic Fractal Signature Dissimilarity (FSD) analysis. Inspired by the classic fractal dimension theory, this method measures the dynamics of tumor heterogeneity during the contrast agent uptake in a quantitative fashion on DCE images. In the small animal experiment mentioned before, the selected parameters from dynamic FSD analysis showed significant differences between treatment/control groups as early as after 1 treatment fraction; in contrast, metrics from conventional PK analysis showed significant differences only after 3 treatment fractions. When using dynamic FSD parameters, the treatment/control group classification after 1st treatment fraction was improved than using conventional PK statistics. These results suggest the promising application of this novel method for capturing early therapeutic response.

The second approach of developing novel DCE-MRI methods is to combine PK information from multiple PK models. Currently, the classic Tofts model or its alternative version has been widely adopted for DCE-MRI analysis as a gold-standard approach for therapeutic response assessment. Previously, a shutter-speed (SS) model was proposed to incorporate transcytolemmal water exchange effect into contrast agent concentration quantification. In spite of richer biological assumption, its application in therapeutic response assessment is limited. It might be intriguing to combine the information from the SS model and from the classic Tofts model to explore potential new biological information for treatment assessment. The feasibility of this idea was investigated in the same small animal experiment. The SS model was compared against the Tofts model for therapeutic response assessment using PK parameter regional mean value comparison. Based on the modeled transcytolemmal water exchange rate, a biological subvolume was proposed and was automatically identified using histogram analysis. Within the biological subvolume, the PK rate constant derived from the SS model were proved to be superior to the one from Tofts model in treatment/control separation and classification. Furthermore, novel biomarkers were designed to integrate PK rate constants from these two models. When being evaluated in the biological subvolume, this biomarker was able to reflect significant treatment/control difference in both post-treatment evaluation. These results confirm the potential value of SS model as well as its combination with Tofts model for therapeutic response assessment.

In summary, this study addressed two problems of DCE-MRI application in radiotherapy assessment. In the first part, a method of accelerating DCE-MRI acquisition for better temporal resolution was investigated, and a novel PK model fitting algorithm was proposed for high temporal resolution DCE-MRI. In the second part, two model-free texture analysis methods and a multiple-model analysis method were developed for DCE-MRI therapeutic response assessment. The presented works could benefit the future DCE-MRI routine clinical application in radiotherapy assessment.

Probabilistic planning is an evolving approach for tumor motion management in which reproducibility of probability distribution function (PDF) of tumor motion is critical yet unclear. Aim of the first study is to evaluate the reproducibility of tumor motion PDF in stereotactic body radiation therapy (SBRT) using cine megavoltage (MV) images. External surrogate is used clinically in 4DCT imaging and radiation treatments for respiratory motion monitoring. However, studies have shown questionable correlation between external surrogate motion and internal tumor motion. Thus, Aim of the second study is to evaluate the correlation of external surrogate motion and internal tumor motion from a statistical point of view.

20 lung cancer patients who underwent SBRT treatment using 3D conformal technique were included in our study. During simulation, 4DCT scan assisted with RPM system was done. Cine MV images acquired during treatments were collected to extract tumor motion trajectories. For each patient, tumor motion PDFn was generated using 3 "usable" beams for each fraction. Patients without at least 3 "usable" beams were excluded. PDFn reproducibility (Rn) was calculated using the Dice Coefficient between PDFn to a "ground-truth" PDF (PDFg). The mean of Rn (Rm) was calculated for each patient and correlated to mean tumor motion rang (Am). Change of Rm during the course of SBRT treatments was also evaluated.

Thirteen patients were kept for further analysis. The tumor motion PDF during the treatments can be determined using cine MV images. The reproducibility of lung tumor motion PDF decreased exponentially as the tumor motion range increased and also decreased slightly throughout the course of treatments.

For each of thirteen patients, tumor motion range, tumor motion "ground-truth" PDFg (PDF_MV), and tumor motion variability VMV were calculated using the cine-MV images. Similarly, surrogate motion range (R_MV), surrogate PDF (PDF_RPM), and surrogate variability (V_RPM) were calculated using motion trajectory of the reflective marker. Correlation between and RRPM, and between VMV and VRPM, and between similarity of PDF_MV and PDF_RPM and R_{MV were determined.}

No correlations were found in motion range and variability between the external surrogate (RPM) and the internal lung tumor motion. High PDF similarity, with a mean (±standard deviation) of 0.83(±0.1) was found between RPM and internal lung tumor motion, but no correlation exists between this PDF similarity and tumor motion range.