A multi-resolution strategy for a multi-objective deformable image registration framework that accommodates large anatomical differences

Currently, two major challenges dominate the field of deformable image registration. The first challenge is related to the tuning of the developed methods to specific problems (i.e. how to best combine different objectives such as similarity measure and transformation effort). This is one of the reasons why, despite significant progress, clinical implementation of such techniques has proven to be difficult. The second challenge is to account for large anatomical differences (e.g. large deformations, (dis)appearing structures) that occurred between image acquisitions. In this paper, we study a framework based on multi-objective optimization to improve registration robustness and to simplify tuning for specific applications. Within this framework we specifically consider the use of an advanced model-based evolutionary algorithm for optimization and a dual-dynamic transformation model (i.e. two "non-fixed" grids: one for the source- and one for the target image) to accommodate for large anatomical differences. The framework computes and presents multiple outcomes that represent efficient trade-offs between the different objectives (a so-called Pareto front). In image processing it is common practice, for reasons of robustness and accuracy, to use a multi-resolution strategy. This is, however, only well-established for single-objective registration methods. Here we describe how such a strategy can be realized for our multi-objective approach and compare its results with a single-resolution strategy. For this study we selected the case of prone-supine breast MRI registration. Results show that the well-known advantages of a multi-resolution strategy are successfully transferred to our multi-objective approach, resulting in superior (i.e. Pareto-dominating) outcomes

A first step toward uncovering the truth about weight tuning in deformable image registration

Deformable image registration is currently predominantly solved by optimizing a weighted linear combination of objectives. Successfully tuning the weights associated with these objectives is not trivial, leading to trial-and-error approaches. Such an approach assumes an intuitive interplay between weights, optimization objectives, and target registration errors. However, it is not known whether this always holds for existing registration methods. To investigate the interplay between weights, optimization objectives, and registration errors, we employ multi-objective optimization. Here, objectives of interest are optimized simultaneously, causing a set of multiple optimal solutions to exist, called the optimal Pareto front. Our medical application is in breast cancer and includes the challenging prone-supine registration problem. In total, we studied the interplay in three different ways. First, we ran many random linear combinations of objectives using the well-known registration software elastix. Second, since the optimization algorithms used in registration are typically of a local-search nature, final solutions may not always form a Pareto front. We therefore employed a multi-objective evolutionary algorithm that finds weights that correspond to registration outcomes that do form a Pareto front. Third, we examined how the interplay differs if a true multi-objective (i.e., weight-free) image registration method is used. Results indicate that a trial-and-error weight-adaptation approach can be successful for the easy prone to prone breast image registration case, due to the absence of many local optima. With increasing problem difficulty the use of more advanced approaches can be of value in finding and selecting the optimal registration outcomes

On the usefulness of gradient information in multi-objective deformable image registration using a B-spline-based dual-dynamic transformation model: comparison of three optimization algorithms

The use of gradient information is well-known to be highly useful in single-objective optimization-based image registration methods. However, its usefulness has not yet been investigated for deformable image registration from a multi-objective optimization perspective. To this end, within a previously introduced multi-objective optimization framework, we use a smooth B-spline-based dual-dynamic transformation model that allows us to derive gradient information analytically, while still being able to account for large deformations. Within the multi-objective framework, we previously employed a powerful evolutionary algorithm (EA) that computes and advances multiple outcomes at once, resulting in a set of solutions (a so-called Pareto front) that represents efficient trade-offs between the objectives. With the addition of the B-spline-based transformation model, we studied the usefulness of gradient information in multiobjective deformable image registration using three different optimization algorithms: the (gradient-less) EA, a gradientonly algorithm, and a hybridization of these two. We evaluated the algorithms to register highly deformed images: 2D MRI slices of the breast in prone and supine positions. Results demonstrate that gradient-based multi-objective optimization significantly speeds up optimization in the initial stages of optimization. However, allowing sufficient computational resources, better results could still be obtained with the EA. Ultimately, the hybrid EA found the best overall approximation of the optimal Pareto front, further indicating that adding gradient-based optimization for multiobjective optimization-based deformable image registration can indeed be beneficial

Diversifying Multi-Objective Gradient Techniques and their Role in Hybrid Multi-Objective Evolutionary Algorithms for Deformable Medical Image Registration

Gradient methods and their value in single-objective, real-valued optimization are well-established. As such, they play a key role in tackling real-world, hard optimization problems such as deformable image registration (DIR). A key question is to which extent gradient techniques can also play a role in a multi-objective approach to DIR. We therefore aim to exploit gradient information within an evolutionary-algorithm-based multi-objective optimization framework for DIR. Although an analytical description of the multi-objective gradient (the set of all Pareto-optimal improving directions) is available, it is nontrivial how to best choose the most appropriate direction per solution because these directions are not necessarily uniformly distributed in objective space. To address this, we employ a Monte-Carlo method to obtain a discrete, spatially-uniformly distributed approximation of the set of Pareto-optimal improving directions. We then apply a diversification technique in which each solution is associated with a unique direction from this set based on its multi- as well as single-objective rank. To assess its utility, we compare a state-of-the-art multi-objective evolutionary algorithm with three different hybrid versions thereof on several benchmark problems and two medical DIR problems. Results show that the diversification strategy successfully leads to unbiased improvement, helping an adaptive hybrid scheme solve all problems, but the evolutionary algorithm remains the most powerful optimization method, providing the best balance between proximity and diversity

Deformable image registration by multi-objective optimization using a dual-dynamic transformation model to account for large anatomical differences

Some of the hardest problems in deformable image registration are problems where large anatomical differences occur between image acquisitions (e.g. large deformations due to images acquired in prone and supine positions and (dis)appearing structures between image acquisitions due to surgery). In this work we developed and studied, within a previously introduced multi-objective optimization framework, a dual-dynamic transformation model to be able to tackle such hard problems. This model consists of two non-fixed grids: one for the source image and one for the target image. By not requiring a fixed, i.e. pre-determined, association of the grid with the source image, we can accommodate for both large deformations and (dis)appearing structures. To find the transformation that aligns the source with the target image we used an advanced, powerful model-based evolutionary algorithm that exploits features of a problem's structure in a principled manner via probabilistic modeling. The actual transformation is given by the association of coordinates with each point in the two grids. Linear interpolation inside a simplex was used to extend the correspondence (i.e. transformation) as found for the grid to the rest of the volume. As a proof of concept we performed tests on both artificial and real data with disappearing structures. Furthermore, the case of prone-supine image registration for 2D axial slices of breast MRI scans was evaluated. Results demonstrate strong potential of the proposed approach to account for large deformations and (dis)appearing structures in deformable image registration

Hybrid registration of prostate and seminal vesicles for image guided radiation therapy.

Fiducial markers are a good surrogate for the prostate but provide little information on the position and orientation of the seminal vesicles (SVs). Therefore, a more advanced localization method is warranted if the SVs are part of the target volume. The purpose of this study was to develop a hybrid registration technique for the localization of the prostate and SVs. Twenty prostate patients implanted with 2 or 3 elongated fiducial markers had cone beam computed tomography (CBCT) scans acquired at every fraction. The first step of the hybrid registration was to localize the prostate by CBCT-to-planning-CT alignment of the fiducial markers, allowing both translations and rotations. Using this marker registration as a starting point, the SVs were registered based on gray values, allowing only rotations around the lateral axis. We analyzed the differential rotation between the prostate and SVs and compared the required SV margins for 3 correction strategies. The SV registration had a precision of 2.7° (1 standard deviation) and was successful for 96% of the scans. Mean (M), systematic (Σ), and random (σ) differences between the orientation of the prostate and SV were M = -0.4°, Σ = 7.2°, and σ = 6.4°. Daily marker-based corrections required an SV margin of 11.4 mm (translations only) and 11.6 mm (translations + rotations). Rotation corrections of the SVs reduced the required margin to 8.2 mm. We found substantial differences between the orientation of the prostate and SVs. The hybrid registration technique can accurately detect these rotations during treatment. Rotation correction of the SVs allows for margin reduction for the SV

Replacing pretreatment verification with in vivo EPID dosimetry for prostate IMRT.

PURPOSE: To investigate the feasibility of replacing pretreatment verification with in vivo electronic portal imaging device (EPID) dosimetry for prostate intensity-modulated radiotherapy (IMRT). METHODS AND MATERIALS: Dose distributions were reconstructed from EPID images, inside a phantom (pretreatment) or the patient (five fractions in vivo) for 75 IMRT prostate plans. Planned and EPID dose values were compared at the isocenter and in two dimensions using the gamma index (3%/3 mm). The number of measured in vivo fractions required to achieve similar levels of agreement with the plan as pretreatment verification was determined. The time required to perform both methods was compared. RESULTS: Planned and EPID isocenter dose values agreed, on average, within +/-1% (1 SD) of the total plan for both pretreatment and in vivo verification. For two-dimensional field-by-field verification, an alert was raised for 10 pretreatment checks with clear but clinically irrelevant discrepancies. Multiple in vivo fractions were combined by assessing gamma images consisting of median, minimum and low (intermediate) pixel values of one to five fractions. The "low" gamma values of three fractions rendered similar results as pretreatment verification. Additional time for verification was approximately 2.5 h per plan for pretreatment verification, and 15 min +/- 10 min/fraction using in vivo dosimetry. CONCLUSIONS: In vivo EPID dosimetry is a viable alternative to pretreatment verification for prostate IMRT. For our patients, combining information from three fractions in vivo is the best way to distinguish systematic errors from non-clinically relevant discrepancies, save hours of quality assurance time per patient plan, and enable verification of the actual patient treatmen

Anatomy changes in radiotherapy detected using portal imaging.

BACKGROUND AND PURPOSE: Localisation images normally acquired to verify patient positioning also contain information about the patient's internal anatomy. The aim of this study was to investigate the anatomical changes observed in localisation images and examples of dosimetric consequences. PATIENTS AND METHODS: Localisation images were obtained weekly prior to radiotherapy with an electronic portal imaging device (EPID). A series of 'difference images' was created by subtracting the first localisation image from that of subsequent fractions. Images from 81 lung, 40 head and neck and 34 prostate cancer patients were classified according to the changes observed. Changes were considered relevant if the average pixel value over an area of at least 1cm(2) differed by more than 5%, to allow for variations in linac output and EPID signal. Two patients were selected to illustrate the dosimetric effects of relevant changes. Their plans were re-calculated with repeat CT scans acquired after 4 weeks of treatment and compared with the difference images of the corresponding days. RESULTS: Progressive changes were detected for 57% of lung and 37% of head and neck cancer patients studied. Random changes were observed in 37% of lung, 28% of head and neck and 82% of prostate cancer patients. For a lung case, an increase of 10.0% in EPID dose due to tumour shrinkage corresponded to an increase of 9.8% in mean lung dose. Gas pockets in the rectum region of the prostate case increased the EPID dose by 6.3%, and resulted in a decrease of the minimum dose to the planning target volume of 26.4%. CONCLUSIONS: Difference images are an efficient means of qualitatively detecting anatomical changes for various treatment sites in clinical practice. They can be used to identify changes for a particular patient, to indicate if the dose delivered to the patient would differ from planning and to detect if there is a need for re-plannin