Anatomical changes can have significant clinical impact during head and neck radiotherapy. Adaptive radiotherapy (ART) may be applied to account for such changes. Implementation of ART to alter dose delivery requires deformable image registration (DIR) to assess 3D deformations. This study evaluates the performance and accuracy of a commercial DIR system for clinical applications. The investigations in this project were carried out using images of induced changes in two standard radiotherapy phantoms (RANDO® and CIRS®) and one in-house built phantom. CT image data before and after deformation of the phantoms were processed using Eclipse / SmartAdapt® v.10 system employing a Demons-based algorithm. A DIR protocol was designed, and algorithm performance was assessed quantitatively, using volume analysis and the Dice Similarity Index (DSI), and also evaluated qualitatively. In addition, algorithm performance was assessed for 5 head and neck cancer patients using clinical CT images. Each original planning CT image containing contours of 10 volumes of interest including treatment target volumes and organs at risk was deformed to match a second CT image acquired during the course of the treatment. The original structures were deformed, copied onto the target image and compared to reference contours drawn by 3 radiation oncologists. Phantom investigations gave varied results with average DSI scores ranging from 0.69 to 0.93, with an overall average of 0.86 ± 0.08. These quantitative results were reflected qualitatively, with generally accurate matching between reference and DIR-generated structures. Although air gaps in the phantoms compromised algorithm performance and gave rise to physically aberrant results. Clinical results were generally better with a DSI range of 0.75-0.99 and an overall average of 0.89 ± 0.05, suggesting high DIR accuracy. Qualitatively, some minor contour deformations were noted, as well as artefacts in the axial direction that were due to the CT slice resolution (3 mm) that was used to scan the patients. In addition, contour propagation between images using DIR reduced the time required by physicians to contour the images of head and neck cancer patients by ~47%. This study demonstrated that deformable image registration using a Modified Demons algorithm yields clinically acceptable results and time-saving benefits in contouring that improve clinical workflow. The study also showed that it is feasible to incorporate deformable image registration as part of an adaptive radiotherapy strategy for head and neck cancer, provided further studies are designed to carry out accurate and verifiable dose deformation.

Inaccurate dose calculation in radiotherapy can lead to errors in treatment delivery and evaluation of treatment efficacy. Respiration can cause of intra-fractional motions, leading to uncertainties in tumor targeting. These motions should therefore be included in dose calculation. The finite element method-based deformable registration platform MORFEUS is able to accurately quantify organ deformations. The dose accumulation algorithm included in MORFEUS takes organ deformation and tumor movement into account. This study has experimentally validated this dose accumulation algorithm by combining 3D gel dosimetry, respiratory motion-mimicking actuation mechanism, and finite element analysis. Results have shown that within the intrinsic measurement uncertainties of gel dosimetry, under normal conformal dose distribution conditions, more than 90% of the voxels in MORFEUS generated dose grids have met the criterion analogous to the gamma test. The average (SD) distance between selected pairs of isodose surfaces on the gel and MORFEUS dose distributions is 0.12 (0.08) cm.

Inaccurate dose calculation in radiotherapy can lead to errors in treatment delivery and evaluation of treatment efficacy. Respiration can cause of intra-fractional motions, leading to uncertainties in tumor targeting. These motions should therefore be included in dose calculation. The finite element method-based deformable registration platform MORFEUS is able to accurately quantify organ deformations. The dose accumulation algorithm included in MORFEUS takes organ deformation and tumor movement into account. This study has experimentally validated this dose accumulation algorithm by combining 3D gel dosimetry, respiratory motion-mimicking actuation mechanism, and finite element analysis. Results have shown that within the intrinsic measurement uncertainties of gel dosimetry, under normal conformal dose distribution conditions, more than 90% of the voxels in MORFEUS generated dose grids have met the criterion analogous to the gamma test. The average (SD) distance between selected pairs of isodose surfaces on the gel and MORFEUS dose distributions is 0.12 (0.08) cm.

<p>Quality assurance (QA) is a critical component of radiation oncology medical physics for both effective treatment and patient safety, particularly as innovations in technology allow movement toward advanced treatment techniques that require increasingly higher accuracy in delivery. Comprehensive 3D dosimetry with PRESAGE® 3D dosimeters read out via optical CT has the potential to detect errors that would be missed by current systems of measurement, and thereby improve the rigor of current QA techniques through providing high-resolution, full 3D verification for a wide range of clinical applications. The broad objective of this dissertation research is to advance and strengthen the standards of QA for radiation therapy, both by driving the development and optimization of PRESAGE® 3D dosimeters for specific clinical and research applications and by applying the technique of high resolution 3D dosimetry toward addressing clinical needs in the current practice of radiation therapy. The specific applications that this dissertation focuses on address several topical concerns: (1) increasing the quality, consistency, and rigor of radiation therapy delivery through comprehensive 3D verification in remote credentialing evaluations, (2) investigating a reusable 3D dosimeter that could potentially facilitate wider implementation of 3D dosimetry through improving cost-effectiveness, and (3) validating deformable image registration (DIR) algorithms prior to clinical implementation in dose deformation and accumulation calculations.</p><p>3D Remote Dosimetry: The feasibility of remote high-resolution 3D dosimetry with the PRESAGE®/Optical-CT system was investigated using two nominally identical optical-CT scanners for 3D dosimetry were constructed and placed at the base (Duke University) and remote (IROC Houston) institutions. Two formulations of PRESAGE® (SS1, SS2) were investigated with four unirradiated PRESAGE® dosimeters imaged at the base institution, then shipped to the remote institution for planning and irradiation. After each dosimeter was irradiated with the same treatment plan and subsequently read out by optical CT at the remote institution, the dosimeters were shipped back to the base institution for remote dosimetry readout 3 days post-irradiation. Measured on-site and remote relative 3D dose distributions were registered to the Pinnacle dose calculation, which served as the reference distribution for 3D gamma calculations with passing criteria of 5%/2mm, 3%/3mm, and 3%/2mm with a 10% dose threshold. Gamma passing rates, dose profiles, and dose maps were used to assess and compare the performance of both PRESAGE® formulations for remote dosimetry. Both PRESAGE® formulations under study maintained high linearity of dose response (R2>0.996) over 14 days with response slope consistency within 4.9% (SS1) and 6.6% (SS2). Better agreements between the Pinnacle plan and dosimeter readout were observed in PRESAGE® formulation SS2, which had higher passing rates and consistency between immediate and remote results at all metrics. This formulation also demonstrated a relative dose distribution that remained stable over time. These results provide a foundation for future investigations using remote dosimetry to study the accuracy of advanced radiation treatments.</p><p>A Reusable 3D Dosimeter: New Presage-RU formulations made using a lower durometer polyurethane matrix (Shore hardness 30-50A) exhibit a response that optically clears following irradiation and opens up the potential for reirradiation and dosimeter reusability. This would have the practical benefit of improving cost-effectiveness and thereby facilitating the wider implementation of comprehensive, high resolution 3D dosimetry. Three formulations (RU-3050-1.7, RU-3050-1.5, and RU-50-1.5) were assessed with multiple irradiations of both small volume samples and larger volume dosimeters, then characterized and evaluated for dose response sensitivity, optical clearing, dose-rate independence, dosimetric accuracy, and the effects of reirradiation on dose measurement. The primary shortcoming of these dosimeters was the discovery of age-dependent gradients in dose response sensitivity, which varied dose response by as much as 30% and prevented accurate measurement. This is unprecedented in the standard formulations and presumably caused by diffusion of a desensitizing agent into the lower durometer polyurethane. The effect of prior irradiation on the dosimeters would also be a concern as it was seen that the relative amount of dose delivered to any given region of the dosimeter will affect subsequent sensitivity in that area, which would in effect create spatially-dependent variable dose sensitivities throughout the dosimeter based on the distributions of prior irradiations. While a successful reusable dosimeter may not have been realized from this work, these studies nonetheless contributed useful information that will affect future development, including in the area of deformable dosimetry, and provide a framework for future reusable dosimeter testing.</p><p>Validating Deformable Image Registration Algorithms: Deformable image registration (DIR) algorithms are used for multi-fraction dose accumulation and treatment response assessment for adaptive radiation therapy, but the accuracy of these methods must be investigated prior to clinical implementation. 12 novel deformable PRESAGE® 3D dosimeter formulations were introduced and characterized for potential use in validating DIR algorithms by providing accurate, ground-truth deformed dose measurement for comparison to DIR-predicted deformed dose distributions. Two commercial clinical DIR software algorithms were evaluated for dose deformation accuracy by comparison against a measured deformed dosimeter dose distribution. This measured distribution was obtained by irradiating a dosimeter under lateral compression, then releasing it from compression so that it could return to its original geometry. The dose distribution within the dosimeter deformed along with the dosimeter volume as it regained to its original shape, thus providing a measurable ground truth deformed dose distribution. Results showed that intensity-based DIR algorithms produce high levels of error and physically unrealistic deformations when deforming a homogeneous structure; this is expected as lack of internal structure is challenging for intensity-based DIR algorithms to deform accurately as they rely on matching fairly closely spaced heterogeneous intensity features. A biomechanical, intensity-independent DIR algorithm demonstrated substantially closer agreement to the measured deformed dose distribution with 3D gamma passing rates (3%/3mm) in the range of 90-91%. These results underscore the necessity and importance of validating DIR algorithms for specific clinical scenarios prior to clinical implementation.</p> / Dissertation

Anatomic changes occur throughout head and neck radiotherapy, and a new treatment plan is often required to mitigate the resulting changes in delivered dose to key structures. This process is known as adaptive radiotherapy (ART), and can be labour-intensive. The aim of this thesis is to optimise ART, addressing some of the technical and clinical challenges facing its routine clinical implementation. Optimising the frequency and timing of adaptive replanning is important, and it has been shown here that intensity modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT) are equally robust to weight loss during head and neck radiotherapy. Plan adaptation strategies that have previously been developed for IMRT are therefore applicable to VMAT.Contour propagation is an important component of ART, and it is essential to ensure that propagated contours are accurate. A method for assessing the suitability of a metric for measuring automatic segmentation accuracy has been developed and applied to the head and neck. For the parotids and larynx, metrics based on surface agreement were better than the commonly used Dice similarity coefficient. By establishing a consensus on which metrics should be used to assess segmentation accuracy, comparison of different algorithms is more objective and should lead to more accurate automatic segmentation. A novel method of assessing contour propagation accuracy on a patient-specific basis has also been developed. This was demonstrated on a cohort of head and neck patients and shows potential as a tool for identifying propagated contours that are subject to a high degree of uncertainty. This is a novel tool that will increase the efficiency of automatic segmentation and, therefore, ART.Optimum ART requires consideration of different radiotherapy-related toxicities, and image-based data mining is a powerful technique for spatially localising dose-response relationships. Correction for multiple comparisons through permutation testing is essential, but has so far only been applied to categorical data. A novel method has been developed for performing permutation testing and image-based data mining with a continuously variable clinical endpoint. Application to trismus for head and neck radiotherapy identified a region with a dose-response relationship in the ipsilateral masseter. Sparing this structure during radiotherapy should reduce the severity of radiation-induced trismus. ART mitigates the dosimetric effects of anatomic changes, and this thesis has addressed technical and clinical challenges that have so far limited its clinical implementation. Detailed knowledge of dose-response relationships will enable selection of patients for ART based on potential clinical benefit, and accurate contour propagation will make ART more efficient, facilitating its routine implementation.

A common co-pathology of large lung tumors located near the central airways is collapse of portions of lung due to blockage of airflow by the tumor. Not only does the lung volume decrease as collapse occurs, but fluid from capillaries also fills the space no longer occupied by air, greatly altering tissue appearance. During radiotherapy, typically administered to the patient over multiple weeks, the tumor can dramatically shrink in response to the treatment, restoring airflow to the lung sections which were collapsed when therapy began. While return of normal lung function is a positive development, the change in anatomy presents problems for future radiation sessions since the treatment was planned on lung geometry which is no longer accurate. The treatment must be adapted to the new lung state so that the radiation continues to accurately target the tumor while safely avoiding healthy tissue. However, to account for the dose delivered previously, correspondences of anatomy between the former image when the lung was collapsed and the re-expanded lung in a current image must be obtained. This process, known as deformable image registration, is performed by registration software. Most registration algorithms assume that identical anatomy is contained in the images and that intensities of corresponding image elements are similar; both assumptions are untrue when collapsed lung re-expands. This work was to develop an algorithm which accurately registers images in the presence of lung expansion. The lung registration method matched CT images of patients aided by vessel enhancement and information of individual lobe boundaries. The algorithm was tested on eighteen patients with lung collapse using physician-specified correspondences to measure registration error. The image registration algorithm developed in this work which was designed for challenging lung patients resulted in accuracy comparable to that of other methods when large lung changes are absent.

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第22642号 / 医博第4625号 / 新制||医||1044(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 武田 俊一, 教授 増永 慎一郎, 教授 鈴木 実 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

Deformable image registration (DIR) is an essential tool in medical image processing. It provides a means to combine image datasets, allowing for intra-subject, inter-subject, multi-modality, and multi-instance analysis, as well as motion detection and compensation. One of the most popular DIR algorithms models the displacement vector field (DVF) as B-splines, a sum of piecewise polynomials with coefficients that enable local shape control. B-splines have many advantageous properties in the context of DIR, but they often struggle to adequately model steep local gradients and discontinuities. This dissertation addresses that limitation by proposing the replacement of conventional B-splines with a generalized formulation known as a Non-Uniform Rational B-Splines (NURBS). Beginning with the 1D fitting, heuristic rules are developed to determine the values of the additional free parameters introduced by NURBS. These rules are subsequently modified and extended to the 2D and 3D fitting of anonymized and publicly available patient DVFs. Based on the lessons learned from these increasingly complex test cases, a 2D DIR scheme is developed and tested on slices from a thoracic computed tomography (CT) scan. Finally, an automatic, non-uniform scheme is presented, and its registration performance is compared to the conventional uniform methods.

Ventilation imaging using 4D-CT is a convenient and cost effective functional imaging methodology which might be of value in radiotherapy treatment planning to spare functional lung volumes. To calculate ventilation imaging from 4D-CT we must use deformable image registration (DIR). This study validates the DIR methods and investigates the dependence of calculated ventilation on DIR methods and ventilation algorithms. The first hypothesis is if ventilation algorithms are robust then they will be insensitive to the precise DIR used provided the DIR is accurate. The second hypothesis is that the change in Houndsfield Unit (HU) method is less dependent on the DIR used and depends more on the CT image quality due to the inherent noise of HUs in normal CT imaging. DIR of the normal end expiration and inspiration phases of the 4D-CT images was used to correlate the voxels between the two respiratory phases. All DIR algorithms were validated using a 4D pixel-based and point-validated breathing thorax model, consisting of a 4D-CT image data set along with associated landmarks. Three different DIR algorithms, Optical Flow (OF), Diffeomorphic Demons (DD) and Diffeomorphic Morphons (DM), were retrospectively applied to the same group of 10 esophagus and 10 lung cancer cases all of which had associated 4D-CT image sets that encompassed the entire lung volume. Three different ventilation calculation algorithms were compared (Jacobian, ΔV, and HU) using the Dice similarity coefficient comparison. In the validation of the DIR algorithms, the average target registration errors with one standard deviation for the DIR algorithms were 1.6 ± 0.7 mm, maximum 3.1 mm for OF, 1.3 ± 0.6 mm, maximum 3.3 mm for DM, 1.3 ± 0.6 mm, maximum 2.8 mm for DD, indicating registration errors were within 2 voxels. Dependence of ventilation images on the DIR was greater for the ΔV and the Jacobian methods than for the HU method. The Dice similarity coefficient for 20% of low ventilation volume for ΔV was 0.33 ± 0.03 between OF and DM, 0.44 ± 0.05 between OF and DD and 0.51 ± 0.04 between DM and DD. The similarity comparisons for Jacobian was 0.32 ± 0.03, 0.44 ± 0.05 and 0.51 ± 0.04 respectively, and for HU 0.53 ± 0.03, 0.56 ± 0.03 and 0.76 ± 0.04 respectively. Dependence of ventilation images on the ventilation method used showed good agreement between the ΔV and Jacobian methods but differences between these two and the HU method were significantly greater. Dice similarity coefficient for using OF as DIR was 0.86 ± 0.01 between ΔV and Jacobian, 0.28 ± 0.04 between ΔV and HU and 0.28 ± 0.04 between Jacobian and HU respectively. When using DM or DD as DIR, similar values were obtained when comparing the different ventilation calculation methods. The similarity values for 20% of the high ventilation volume were close to those found for the 20% low ventilation volume. Mean target registration error for all three DIR methods was within one voxel suggesting that the registration done by either of the methods is quite accurate. Ventilation calculation from 4D-CT demonstrates some degree of dependency on the DIR algorithm employed. Similarities between ΔV and Jacobian are higher than between ΔV and HU and Jacobian and HU. This shows that ΔV and Jacobian are very similar, but HU is a very different ventilation calculation method.

Deformable image registration (DIR) is a type of registration that calculates a deformable vector field (DVF) between two image data sets and permits contour and dose propagation. However the calculation of a DVF is considered an ill-posed problem, as there is no exact solution to a deformation problem, therefore all DVFs calculated contain errors. As a result it is important to evaluate and assess the accuracy and limitations of any DIR algorithm intended for clinical use. The influence of image quality on the DIR algorithms performance was also evaluated. The hybrid DIR algorithm in RayStation 4.0.1.4 was assessed using a number of evaluation methods and data. The evaluation methods were point of interest (POI) propagation, contour propagation and dose measurements. The data types used were phantom and patient data. A number of metrics were used for quantitative analysis and visual inspection was used for qualitative analysis. The quantitative and qualitative results indicated that all DVFs calculated by the DIR algorithm contained errors which translated into errors in the propagated contours and propagated dose. The results showed that the errors were largest for small contour volumes (<20cm3) and for large anatomical volume changes between the image sets, which pushes the algorithms ability to deform, a significant decrease in accuracy was observed for anatomical volume changes of greater than 10%. When the propagated contours in the head and neck were used for planning the errors in the DVF were found to cause under dosing to the target tumour by up to 32% and over dosing to the organs at risk (OAR) by up to 12% which is clinically significant. The results also indicated that the image quality does not have a significant effect on the DIR algorithms calculations. Dose measurements indicated errors in the DVF calculations that could potentially be clinically significant. The results indicate that contour propagation and dose propagation must be used with caution if clinical use is intended. For clinical use contour propagation requires evaluation of every propagated contour by an expert user and dose propagation requires thorough evaluation of the DVF.