Automated finite-element analysis for deformable registration of prostate images

Skeletons, Object Shape, Statistics

Objects and object complexes in 3D, as well as those in 2D, have many possible representations. Among them skeletal representations have special advantages and some limitations. For the special form of skeletal representation called "s-reps," these advantages include strong suitability for representing slabular object populations and statistical applications on these populations. Accomplishing these statistical applications is best if one recognizes that s-reps live on a curved shape space. Here we will lay out the definition of s-reps, their advantages and limitations, their mathematical properties, methods for fitting s-reps to single- and multi-object boundaries, methods for measuring the statistics of these object and multi-object representations, and examples of such applications involving statistics. While the basic theory, ideas, and programs for the methods are described in this paper and while many applications with evaluations have been produced, there remain many interesting open opportunities for research on comparisons to other shape representations, new areas of application and further methodological developments, many of which are explicitly discussed here.

Prostate brachytherapy intraoperative dosimetry using a combination of radiographic seed localization with a C-arm and deformed ultrasound prostate contours

PURPOSE

The purpose of the study was to assess the feasibility of performing intraoperative dosimetry for permanent prostate brachytherapy by combining transrectal ultrasound (TRUS) and fluoroscopy/cone beam CT [CBCT] images and accounting for the effect of prostate deformation.

METHODS AND MATERIALS

13 patients underwent TRUS and multiview two-dimensional fluoroscopic imaging partway through the implant, as well as repeat fluoroscopic imaging with the TRUS probe inserted and retracted, and finally three-dimensional CBCT imaging at the end of the implant. The locations of all the implanted seeds were obtained from the fluoroscopy/CBCT images and were registered to prostate contours delineated on the TRUS images based on a common subset of seeds identified on both image sets. Prostate contours were also deformed, using a finite-element model, to take into account the effect of the TRUS probe pressure. Prostate dosimetry parameters were obtained for fluoroscopic and CBCT-dosimetry approaches and compared with the standard-of-care Day-0 postimplant CT dosimetry.

RESULTS

High linear correlation (R² > 0.8) was observed in the measured values of prostate D_90%, V_100%, and V_150%, between the two intraoperative dosimetry approaches. The prostate D_90% and V_100% obtained from intraoperative dosimetry methods were in agreement with the postimplant CT dosimetry. Only the prostate V_150% was on average 4.1% (p-value <0.05) higher in the CBCT-dosimetry approach and 6.7% (p-value <0.05) higher in postimplant CT dosimetry compared with the fluoroscopic dosimetry approach. Deformation of the prostate by the ultrasound probe appeared to have a minimal effect on prostate dosimetry.

CONCLUSIONS

The results of this study have shown that both of the proposed dosimetric evaluation approaches have potential for real-time intraoperative dosimetry.

MRI to CT Prostate Registration for Improved Targeting in Cancer External Beam Radiotherapy

External radiotherapy is a major clinical treatment for localized prostate cancer. Currently, computed tomography (CT) is used to delineate the prostate and to plan the radiotherapy treatment. However, CT images suffer from a poor soft-tissue contrast and do not allow an accurate organ delineation. On the contrary, magnetic resonance imaging (MRI) provides rich details and high soft-tissue contrast, allowing tumor detection. Thus, the intraindividual propagation of MRI delineations toward the planning CT may improve tumor targeting. In this paper, we introduce a new method to propagate MRI prostate delineations to the planning CT. In the first step, a random forest classification is performed to coarsely detect the prostate in the CT images, yielding a prostate probability membership for each voxel and a prostate hard segmentation. Then, the registration is performed using a new similarity metric which maximizes the probability and the collinearity between the normals of the manual registration (MR) existing contour and the contour resulting from the CT classification. The first study on synthetic data was performed to analyze the influence of the metric parameters with different levels of noise. Then, the method was also evaluated on real MR-CT data using manual alignments and intraprostatic fiducial markers and compared to a classically used mutual information (MI) approach. The proposed metric outperformed MI by 7% in terms of Dice score coefficient, by 3.14 mm the Hausdorff distance, and 2.13 mm the markers position errors. Finally, the impact of registration uncertainties on the treatment planning was evaluated, demonstrating the potential advantage of the proposed approach in a clinical setup to define a precise target.

Quantifying Post- Laser Ablation Prostate Therapy Changes on MRI via a Domain-Specific Biomechanical Model: Preliminary Findings

Focal laser ablation destroys cancerous cells via thermal destruction of tissue by a laser. Heat is absorbed, causing thermal necrosis of the target region. It combines the aggressive benefits of radiation treatment (destroying cancer cells) without the harmful side effects (due to its precise localization). MRI is typically used pre-treatment to determine the targeted area, and post-treatment to determine efficacy by detecting necrotic tissue, or tumor recurrence. However, no system exists to quantitatively evaluate the post-treatment effects on the morphology and structure via MRI. To quantify these changes, the pre- and post-treatment MR images must first be spatially aligned. The goal is to quantify (a) laser-induced shape-based changes, and (b) changes in MRI parameters post-treatment. The shape-based changes may be correlated with treatment efficacy, and the quantitative effects of laser treatment over time is currently poorly understood. This work attempts to model changes in gland morphology following laser treatment due to (1) patient alignment, (2) changes due to surrounding organs such as the bladder and rectum, and (3) changes due to the treatment itself. To isolate the treatment-induced shape-based changes, the changes from (1) and (2) are first modeled and removed using a finite element model (FEM). A FEM models the physical properties of tissue. The use of a physical biomechanical model is important since a stated goal of this work is to determine the physical shape-based changes to the prostate from the treatment, and therefore only physical real deformations are to be allowed. A second FEM is then used to isolate the physical, shape-based, treatment-induced changes. We applied and evaluated our model in capturing the laser induced changes to the prostate morphology on eight patients with 3.0 Tesla, T2-weighted MRI, acquired approximately six months following treatment. Our results suggest the laser treatment causes a decrease in prostate volume, which appears to manifest predominantly at the site of ablation. After spatially aligning the images, changes to MRI intensity values are clearly visible at the site of ablation. Our results suggest that our new methodology is able to capture and quantify the degree of laser-induced changes to the prostate. The quantitative measurements reflecting of the deformation changes can be used to track treatment response over time.

Use of registration-based contour propagation in texture analysis for esophageal cancer pathologic response prediction

Change in PET-based textural features has shown promise in predicting cancer response to treatment. However, contouring tumour volumes on longitudinal scans is time-consuming. This study investigated the usefulness of contour propagation in texture analysis for the purpose of pathologic response prediction in esophageal cancer. Forty-five esophageal cancer patients underwent PET/CT scans before and after chemo-radiotherapy. Patients were classified into responders and non-responders after the surgery. Physician-defined tumour ROIs on pre-treatment PET were propagated onto the post-treatment PET using rigid and ten deformable registration algorithms. PET images were converted into 256 discrete values. Co-occurrence, run-length, and size zone matrix textures were computed within all ROIs. The relative difference of each texture at different treatment time-points was used to predict the pathologic responders. Their predictive value was assessed using the area under the receiver-operating-characteristic curve (AUC). Propagated ROIs from different algorithms were compared using Dice similarity index (DSI). Contours propagated by the fast-demons, fast-free-form and rigid algorithms did not fully capture the high FDG uptake regions of tumours. Fast-demons propagated ROIs had the least agreement with other contours (DSI = 58%). Moderate to substantial overlap were found in the ROIs propagated by all other algorithms (DSI = 69%-79%). Rigidly propagated ROIs with co-occurrence texture failed to significantly differentiate between responders and non-responders (AUC = 0.58, q-value = 0.33), while the differentiation was significant with other textures (AUC = 0.71-0.73, p < 0.009). Among the deformable algorithms, fast-demons (AUC = 0.68-0.70, q-value < 0.03) and fast-free-form (AUC = 0.69-0.74, q-value < 0.04) were the least predictive. ROIs propagated by all other deformable algorithms with any texture significantly predicted pathologic responders (AUC = 0.72-0.78, q-value < 0.01). Propagated ROIs using deformable registration for all textures can lead to accurate prediction of pathologic response, potentially expediting the temporal texture analysis process. However, fast-demons, fast-free-form, and rigid algorithms should be applied with care due to their inferior performance compared to other algorithms.

3D surface-based registration of ultrasound and histology in prostate cancer imaging

Several transrectal ultrasound (TRUS)-based techniques aiming at accurate localization of prostate cancer are emerging to improve diagnostics or to assist with focal therapy. However, precise validation prior to introduction into clinical practice is required. Histopathology after radical prostatectomy provides an excellent ground truth, but needs accurate registration with imaging. In this work, a 3D, surface-based, elastic registration method was developed to fuse TRUS images with histopathologic results. To maximize the applicability in clinical practice, no auxiliary sensors or dedicated hardware were used for the registration. The mean registration errors, measured in vitro and in vivo, were 1.5±0.2 and 2.1±0.5mm, respectively.

Fitting Skeletal Object Models Using Spherical Harmonics Based Template Warping

We present a scheme that propagates a reference skeletal model (s-rep) into a particular case of an object, thereby propagating the initial shape-related layout of the skeleton-to-boundary vectors, called spokes. The scheme represents the surfaces of the template as well as the target objects by spherical harmonics and computes a warp between these via a thin plate spline. To form the propagated s-rep, it applies the warp to the spokes of the template s-rep and then statistically refines. This automatic approach promises to make s-rep fitting robust for complicated objects, which allows s-rep based statistics to be available to all. The improvement in fitting and statistics is significant compared with the previous methods and in statistics compared with a state-of-the-art boundary based method.

2D Ultrasound and 3D MR Image Registration of the Prostate for Brachytherapy Surgical Navigation

Two-dimensional (2D) ultrasound (US) images are widely used in minimally invasive prostate procedure for its noninvasive nature and convenience. However, the poor quality of US image makes it difficult to be used as guiding utility. To improve the limitation, we propose a multimodality image guided navigation module that registers 2D US images with magnetic resonance imaging (MRI) based on high quality preoperative models. A 2-step spatial registration method is used to complete the procedure which combines manual alignment and rapid mutual information (MI) optimize algorithm. In addition, a 3-dimensional (3D) reconstruction model of prostate with surrounding organs is employed to combine with the registered images to conduct the navigation. Registration accuracy is measured by calculating the target registration error (TRE). The results show that the error between the US and preoperative MR images of a polyvinyl alcohol hydrogel model phantom is 1.37 ± 0.14 mm, with a similar performance being observed in patient experiments.

Effect of material property heterogeneity on biomechanical modeling of prostate under deformation

Biomechanical model based deformable image registration has been widely used to account for prostate deformation in various medical imaging procedures. Biomechanical material properties are important components of a biomechanical model. In this study, the effect of incorporating tumor-specific material properties in the prostate biomechanical model was investigated to provide insight into the potential impact of material heterogeneity on the prostate deformation calculations. First, a simple spherical prostate and tumor model was used to analytically describe the deformations and demonstrate the fundamental effect of changes in the tumor volume and stiffness in the modeled deformation. Next, using a clinical prostate model, a parametric approach was used to describe the variations in the heterogeneous prostate model by changing tumor volume, stiffness, and location, to show the differences in the modeled deformation between heterogeneous and homogeneous prostate models. Finally, five clinical prostatectomy examples were used in separately performed homogeneous and heterogeneous biomechanical model based registrations to describe the deformations between 3D reconstructed histopathology images and ex vivo magnetic resonance imaging, and examine the potential clinical impact of modeling biomechanical heterogeneity of the prostate. The analytical formulation showed that increasing the tumor volume and stiffness could significantly increase the impact of the heterogeneous prostate model in the calculated displacement differences compared to the homogeneous model. The parametric approach using a single prostate model indicated up to 4.8 mm of displacement difference at the tumor boundary compared to a homogeneous model. Such differences in the deformation of the prostate could be potentially clinically significant given the voxel size of the ex vivo MR images (0.3  ×  0.3  ×  0.3 mm). However, no significant changes in the registration accuracy were observed using heterogeneous models for the limited number of clinical prostatectomy patients modeled and evaluated in this study.

Volume and landmark analysis: comparison of MRI measurements obtained with an endorectal coil and with a phased-array coil

AIM

To compare prostate volumes and distances between anatomical landmarks on MRI images obtained with a phased-array coil (PAC) only and with a PAC and an endorectal coil (ERC).

MATERIALS AND METHODS

Informed consent was waived for this Health Insurance Portability and Accountability Act-compliant study. Fifty-nine men underwent PAC-MRI and ERC-MRI at 1.5 (n = 3) or 3 T (n = 56). On MRI images, two radiologists independently measured prostate volume and distances between the anterior rectal wall (ARW) and symphysis pubis at the level of the verumontanum; ARW and symphysis pubis at the level of the mid-symphysis pubis; and bladder neck and mid-symphysis pubis. Differences between measurements from PAC-MRI and ERC-MRI were assessed with the Wilcoxon RANK SUM test. Inter-reader agreement was assessed using the concordance correlation coefficient (CCC).

RESULTS

Differences in prostate volume between PAC-MRI and ERC-MRI [median: -0.75 mm(3) (p = 0.10) and median: -0.84 mm(3) (p = 0.06) for readers 1 and 2, respectively] were not significant. For readers 1 and 2, median differences between distances were as follows: -10.20 and -12.75 mm, respectively, ARW to symphysis pubis at the level of the verumontanum; -6.60 and -6.08 mm, respectively, ARW to symphysis pubis at the level of the mid-symphysis pubis; -3 and -3 mm respectively, bladder neck to mid-symphysis pubis. All differences in distance were significant for both readers (p ≤ 0.0005). Distances were larger on PAC-MRI (p ≤ 0.0005). Inter-reader agreement regarding prostate volume was almost perfect on PAC-MRI (CCC: 0.99; 95% CI: 0.98-1.00) and ERC-MRI (CCC: 0.99; 95% CI: 0.99-1.00); inter-reader agreement for distance measurements varied (CCCs: 0.54-0.86).

CONCLUSION

Measurements of distances between anatomical landmarks differed significantly between ERC-MRI and PAC-MRI, although prostate volume measurements did not.

A Domain Constrained Deformable (DoCD) Model for Co-registration of Pre- and Post-Radiated Prostate MRI

External beam radiation treatment (EBRT) is a popular method for treating prostate cancer (CaP) involving destroying tumor cells with ionizing radiation. Following EBRT, biochemical failure has been linked with disease recurrence. However, there is a need for methods for evaluating early treatment related changes to allow for an early intervention in case of incomplete disease response. One method for looking at treatment evaluation is to detect changes in MRI markers on a voxel-by-voxel basis following treatment. Changes in MRI markers may be correlated with disease recurrence and complete or partial response. In order to facilitate voxel-by-voxel imaging related treatment changes, and also to evaluate morphologic changes in the gland post treatment, the pre- and post-radiated MRI must first be brought into spatial alignment via image registration. However, EBRT induces changes in the prostate volume and distortion to the internal anatomy of the prostate following radiation treatment. The internal substructures of the prostate, the central gland (CG) and peripheral zone (PZ), may respond to radiation differently, and their resulting shapes may change drastically. Biomechanical models of the prostate that have been previously proposed tend to focus on how external forces affect the surface of the prostate (not the internals), and assume that the prostate is a volume-preserving entity. In this work we present DoCD, a biomechanical model for automatically registering pre-, post-EBRT MRI with the aim of expressly modeling the (1) changes in volume, and (2) changes to the CG and PZ. DoCD was applied to a cohort of 30 patients and achieved a root mean square error of 2.994 mm, which was statistically significantly better a traditional biomechanical model which did not consider changes to the internal anatomy of the prostate (mean of 5.071 mm).

A semiautomatic tool for prostate segmentation in radiotherapy treatment planning

BACKGROUND

Delineation of the target volume is a time-consuming task in radiotherapy treatment planning, yet essential for a successful treatment of cancers such as prostate cancer. To facilitate the delineation procedure, the paper proposes an intuitive approach for 3D modeling of the prostate by slice-wise best fitting ellipses.

METHODS

The proposed estimate is initialized by the definition of a few control points in a new patient. The method is not restricted to particular image modalities but assumes a smooth shape with elliptic cross sections of the object. A training data set of 23 patients was used to calculate a prior shape model. The mean shape model was evaluated based on the manual contour of 10 test patients. The patient records of training and test data are based on axial T1-weighted 3D fast-field echo (FFE) sequences. The manual contours were considered as the reference model. Volume overlap (Vo), accuracy (Ac) (both ratio, range 0-1, optimal value 1) and Hausdorff distance (HD) (mm, optimal value 0) were calculated as evaluation parameters.

RESULTS

The median and median absolute deviation (MAD) between manual delineation and deformed mean best fitting ellipses (MBFE) was Vo (0.9 ± 0.02), Ac (0.81 ± 0.03) and HD (4.05 ± 1.3)mm and between manual delineation and best fitting ellipses (BFE) was Vo (0.96 ± 0.01), Ac (0.92 ± 0.01) and HD (1.6 ± 0.27)mm. Additional results show a moderate improvement of the MBFE results after Monte Carlo Markov Chain (MCMC) method.

CONCLUSIONS

The results emphasize the potential of the proposed method of modeling the prostate by best fitting ellipses. It shows the robustness and reproducibility of the model. A small sample test on 8 patients suggest possible time saving using the model.

Needle Insertion Force Modeling using Genetic Programming Polynomial Higher Order Neural Network

Precise insertion of a medical needle as an end-effecter of a robotic or computer-aided system into biological tissue is an important issue and should be considered in different operations, such as brain biopsy, prostate brachytherapy, and percutaneous therapies. Proper understanding of the whole procedure leads to a better performance by an operator or system. In this chapter, the authors use a 0.98 mm diameter needle with a real-time recording of force, displacement, and velocity of needle through biological tissue during in-vitro insertions. Using constant velocity experiments from 5 mm/min up to 300 mm/min, the data set for the force-displacement graph of insertion was gathered. Tissue deformation with a small puncture and a constant velocity penetration are the two first phases in the needle insertion process. Direct effects of different parameters and their correlations during the process is being modeled using a polynomial neural network. The authors develop different networks in 2nd and 3rd order to model the two first phases of insertion separately. Modeling accuracies were 98% and 86% in phase 1 and 2, respectively.

Deformable medical image registration: a survey

Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: 1) multi-modality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; 2) longitudinal studies, where temporal structural or anatomical changes are investigated; and 3) population modeling and statistical atlases used to study normal anatomical variability. In this paper, we attempt to give an overview of deformable registration methods, putting emphasis on the most recent advances in the domain. Additional emphasis has been given to techniques applied to medical images. In order to study image registration methods in depth, their main components are identified and studied independently. The most recent techniques are presented in a systematic fashion. The contribution of this paper is to provide an extensive account of registration techniques in a systematic manner.

Simulation-based joint estimation of body deformation and elasticity parameters for medical image analysis

Estimation of tissue stiffness is an important means of noninvasive cancer detection. Existing elasticity reconstruction methods usually depend on a dense displacement field (inferred from ultrasound orMR images) and known external forces.Many imaging modalities, however, cannot provide details within an organ and therefore cannot provide such a displacement field. Furthermore, force exertion and measurement can be difficult for some internal organs, making boundary forces another missing parameter. We propose a general method for estimating elasticity and boundary forces automatically using an iterative optimization framework, given the desired (target) output surface. During the optimization, the input model is deformed by the simulator, and an objective function based on the distance between the deformed surface and the target surface is minimized numerically. The optimization framework does not depend on a particular simulation method and is therefore suitable for different physical models. We show a positive correlation between clinical prostate cancer stage (a clinical measure of severity) and the recovered elasticity of the organ. Since the surface correspondence is established, our method also provides a non-rigid image registration, where the quality of the deformation fields is guaranteed, as they are computed using a physics-based simulation.

A finite element method to correct deformable image registration errors in low-contrast regions

Image-guided adaptive radiotherapy requires deformable image registration to map radiation dose back and forth between images. The purpose of this study is to develop a novel method to improve the accuracy of an intensity-based image registration algorithm in low-contrast regions. A computational framework has been developed in this study to improve the quality of the 'demons' registration. For each voxel in the registration's target image, the standard deviation of image intensity in a neighborhood of this voxel was calculated. A mask for high-contrast regions was generated based on their standard deviations. In the masked regions, a tetrahedral mesh was refined recursively so that a sufficient number of tetrahedral nodes in these regions can be selected as driving nodes. An elastic system driven by the displacements of the selected nodes was formulated using a finite element method (FEM) and implemented on the refined mesh. The displacements of these driving nodes were generated with the 'demons' algorithm. The solution of the system was derived using a conjugated gradient method, and interpolated to generate a displacement vector field for the registered images. The FEM correction method was compared with the 'demons' algorithm on the computed tomography (CT) images of lung and prostate patients. The performance of the FEM correction relating to the 'demons' registration was analyzed based on the physical property of their deformation maps, and quantitatively evaluated through a benchmark model developed specifically for this study. Compared to the benchmark model, the 'demons' registration has the maximum error of 1.2 cm, which can be corrected by the FEM to 0.4 cm, and the average error of the 'demons' registration is reduced from 0.17 to 0.11 cm. For the CT images of lung and prostate patients, the deformation maps generated by the 'demons' algorithm were found unrealistic at several places. In these places, the displacement differences between the 'demons' registrations and their FEM corrections were found in the range of 0.4 and 1.1 cm. The mesh refinement and FEM simulation were implemented in a single thread application which requires about 45 min of computation time on a 2.6 GHz computer. This study has demonstrated that the FEM can be integrated with intensity-based image registration algorithms to improve their registration accuracy, especially in low-contrast regions.

Modelling prostate motion for data fusion during image-guided interventions

There is growing clinical demand for image registration techniques that allow multimodal data fusion for accurate targeting of needle biopsy and ablative prostate cancer treatments. However, during procedures where transrectal ultrasound (TRUS) guidance is used, substantial gland deformation can occur due to TRUS probe pressure. In this paper, the ability of a statistical shape/motion model, trained using finite element simulations, to predict and compensate for this source of motion is investigated. Three-dimensional ultrasound images acquired on five patient prostates, before and after TRUS-probe-induced deformation, were registered using a nonrigid, surface-based method, and the accuracy of different deformation models compared. Registration using a statistical motion model was found to outperform alternative elastic deformation methods in terms of accuracy and robustness, and required substantially fewer target surface points to achieve a successful registration. The mean final target registration error (based on anatomical landmarks) using this method was 1.8 mm. We conclude that a statistical model of prostate deformation provides an accurate, rapid and robust means of predicting prostate deformation from sparse surface data, and is therefore well-suited to a number of interventional applications where there is a need for deformation compensation.

A reduced order explicit dynamic finite element algorithm for surgical simulation

Reduced order modelling, in which a full system response is projected onto a subspace of lower dimensionality, has been used previously to accelerate finite element solution schemes by reducing the size of the involved linear systems. In the present work we take advantage of a secondary effect of such reduction for explicit analyses, namely that the stable integration time step is increased far beyond that of the full system. This phenomenon alleviates one of the principal drawbacks of explicit methods, compared with implicit schemes. We present an explicit finite element scheme in which time integration is performed in a reduced basis. Futhermore, we present a simple procedure for imposing inhomogeneous essential boundary conditions, thus overcoming one of the principal deficiencies of such approaches. The computational benefits of the procedure within a GPU-based execution framework are examined, and an assessment of the errors introduced is given. It is shown that speedups approaching an order of magnitude are feasible, without introduction of prohibitive errors, and without hardware modifications. The procedure may have applications in interactive simulation and medical image-guidance problems, in which both speed and accuracy are vital.

Interactive, multi-modality image registrations for combined MRI/MRSI-planned HDR prostate brachytherapy

Purpose

This study presents the steps and criteria involved in the series of image registrations used clinically during the planning and dose delivery of focal high dose-rate (HDR) brachytherapy of the prostate.

Material and methods

Three imaging modalities – Magnetic Resonance Imaging (MRI), Magnetic Resonance Spectroscopic Imaging (MRSI), and Computed Tomography (CT) – were used at different steps during the process. MRSI is used for identification of dominant intraprosatic lesions (DIL). A series of rigid and nonrigid transformations were applied to the data to correct for endorectal-coil-induced deformations and for alignment with the planning CT. Mutual information was calculated as a morphing metric. An inverse planning optimization algorithm was applied to boost dose to the DIL while providing protection to the urethra, penile bulb, rectum, and bladder. Six prostate cancer patients were treated using this protocol.

Results

The morphing algorithm successfully modeled the probe-induced prostatic distortion. Mutual information calculated between the morphed images and images acquired without the endorectal probe showed a significant (p = 0.0071) increase to that calculated between the unmorphed images and images acquired without the endorectal probe. Both mutual information and visual inspection serve as effective diagnostics of image morphing. The entire procedure adds less than thirty minutes to the treatment planning.

Conclusion

This work demonstrates the utility of image transformations and registrations to HDR brachytherapy of prostate cancer.

MR to ultrasound registration for image-guided prostate interventions

A deformable registration method is described that enables automatic alignment of magnetic resonance (MR) and 3D transrectal ultrasound (TRUS) images of the prostate gland. The method employs a novel "model-to-image" registration approach in which a deformable model of the gland surface, derived from an MR image, is registered automatically to a TRUS volume by maximising the likelihood of a particular model shape given a voxel-intensity-based feature that represents an estimate of surface normal vectors at the boundary of the gland. The deformation of the surface model is constrained by a patient-specific statistical model of gland deformation, which is trained using data provided by biomechanical simulations. Each simulation predicts the motion of a volumetric finite element mesh due to the random placement of a TRUS probe in the rectum. The use of biomechanical modelling in this way also allows a dense displacement field to be calculated within the prostate, which is then used to non-rigidly warp the MR image to match the TRUS image. Using data acquired from eight patients, and anatomical landmarks to quantify the registration accuracy, the median final RMS target registration error after performing 100 MR-TRUS registrations for each patient was 2.40 mm.

Assessment of a commercially available automatic deformable registration system

In recent years, a number of approaches have been applied to the problem of deformable registration validation. However, the challenge of assessing a commercial deformable registration system - in particular, an automatic registration system in which the deformable transformation is not readily accessible - has not been addressed. Using a collection of novel and established methods, we have developed a comprehensive, four-component protocol for the validation of automatic deformable image registration systems over a range of IGRT applications. The protocol, which was applied to the Reveal-MVS system, initially consists of a phantom study for determination of the system's general tendencies, while relative comparison of different registration settings is achieved through postregistration similarity measure evaluation. Synthetic transformations and contour-based metrics are used for absolute verification of the system's intra-modality and inter-modality capabilities, respectively. Results suggest that the commercial system is more apt to account for global deformations than local variations when performing deform-able image registration. Although the protocol was used to assess the capabilities of the Reveal-MVS system, it can readily be applied to other commercial systems. The protocol is by no means static or definitive, and can be further expanded to investigate other potential deformable registration applications.

Technical note: deformable image registration on partially matched images for radiotherapy applications

In radiation therapy applications, deformable image registrations (DIRs) are often carried out between two images that only partially match. Image mismatching could present as superior-inferior coverage differences, field-of-view (FOV) cutoffs, or motion crossing the image boundaries. In this study, the authors propose a method to improve the existing DIR algorithms so that DIR can be carried out in such situations. The basic idea is to extend the image volumes and define the extension voxels (outside the FOV or outside the original image volume) as NaN (not-a-number) values that are transparent to all floating-point computations in the DIR algorithms. Registrations are then carried out with one additional rule that NaN voxels can match any voxels. In this way, the matched sections of the images are registered properly, and the mismatched sections of the images are registered to NaN voxels. This method makes it possible to perform DIR on partially matched images that otherwise are difficult to register. It may also improve DIR accuracy, especially near or in the mismatched image regions.

Prostate positioning errors associated with two automatic registration based image guidance strategies

Daily image guidance for helical tomotherapy prostate patients is based on the registration of pretreatment megavoltage CT (MVCT) images and the original planning CT. The goal of registration, whether manual or automatic, is the overlap of the prostate; otherwise prostate misplacement may compromise the efficacy of treatment or lead to increased toxicity. A previous study demonstrated that without the aid of implanted fiducials, manual registration results in inaccurate prostate positioning. The objective of this work is to quantify prostate misplacement that results from automatic bone matching (BM) and image matching (IM) registration algorithms. 204 MVCT images from eight high‐risk tomotherapy prostate patients were incorporated into this retrospective study. BM and IM registration algorithms – based on maximization of mutual information of bony anatomy only and the entire image, respectively – were used to independently register MVCT images to their respective planning images. A correlation coefficient based algorithm that uses known planning CT contour information was used for automatic prostate localization in each MVCT image. Daily prostate misplacement was determined by repositioning as calculated from the BM and the IM algorithms. Mean (±SD) and maximum 3D prostate positioning errors were 3.7±2.1mm and 11.8 mm for bone matching, and 4.6±2.3mm and 11.5 mm for image matching. In terms of translational directions, IM would lead to prostate positioning error ≥3mm in any of the LR, AP or SI directions in 62% of treatment fractions. The corresponding value for BM is 51%. The values for positioning errors ≥5mm were 29% and 17% for IM and BM, respectively. This data suggests automatic daily image guidance for tomotherapy prostate patients should be based on bone matching instead of image matching.

PACS number: 87.19.xj, 87.57.nj

Deformable registration of abdominal kilovoltage treatment planning CT and tomotherapy daily megavoltage CT for treatment adaptation

In adaptive radiation therapy the treatment planning kilovoltage CT (kVCT) images need to be registered with daily CT images. Daily megavoltage CT (MVCT) images are generally noisier than the kVCT images. In addition, in the abdomen, low image contrast, differences in bladder filling, differences in bowel, and rectum filling degrade image usefulness and make deformable image registration very difficult. The authors have developed a procedure to overcome these difficulties for better deformable registration between the abdominal kVCT and MVCT images. The procedure includes multiple image preprocessing steps and a two deformable registration steps. The image preprocessing steps include MVCT noise reduction, bowel gas pockets detection and painting, contrast enhancement, and intensity manipulation for critical organs. The first registration step is carried out in the local region of the critical organs (bladder, prostate, and rectum). It requires structure contours of these critical organs on both kVCT and MVCT to obtain good registration accuracy on these critical organs. The second registration step uses the first step results and registers the entire image with less intensive computational requirement. The two-step approach improves the overall computation speed and works together with these image preprocessing steps to achieve better registration accuracy than a regular single step registration. The authors evaluated the procedure on multiple image datasets from prostate cancer patients and gynecological cancer patients. Compared to rigid alignment, the proposed method improves volume matching by over 60% for the critical organs and reduces the prostate landmark registration errors by 50%.

Continuous medial representation of brain structures using the biharmonic PDE

A new approach for constructing deformable continuous medial models for anatomical structures is presented. Medial models describe geometrical objects by first specifying the skeleton of the object and then deriving the boundary surface corresponding to the skeleton. However, an arbitrary specification of a skeleton will not be "valid" unless a certain set of sufficient conditions is satisfied. The most challenging of these is the non-linear equality constraint that must hold along the boundaries of the manifolds forming the skeleton. The main contribution of this paper is to leverage the biharmonic partial differential equation as a mapping from a codimension-0 subset of Euclidean space to the space of skeletons that satisfy the equality constraint. The PDE supports robust numerical solution on freeform triangular meshes, providing additional flexibility for shape modeling. The approach is evaluated by generating continuous medial models for a large dataset of hippocampus shapes. Generalizations to modeling more complex shapes and to representing branching skeletons are demonstrated.

Adaptive boundary conditions for physically based follow-up breast MR image registration

This paper presents an algorithm for non-rigid registration of breast MRI follow-up images that compensates for differences in patient positioning while maintaining real anatomical and pathological changes. The proposed method uses a biomechanical model to constrain the deformation of the internal breast tissue according to elastic continuum mechanics, which is driven by suitable boundary conditions that align the breast surfaces in the images to be registered. Typically, such boundary conditions impose one-to-one surface point correspondences that are established a priori. We investigate alternative, more flexible boundary conditions that do not depend on fixed point correspondences and do not assume completely accurate breast surface segmentation in both images. More specifically, we allow for sliding motion of one surface over the other during deformation as well as for restricted motion perpendicular to the initially segmented boundary surface, based on the internal elastic forces and local intensity information. We evaluate the impact of different boundary conditions on registration quality from the subtraction images obtained for repeated scans of healthy volunteers with intermediate repositioning, using rigid body and free form whole volume intensity based registration for comparison, and also present initial results for actual patient data. Our results demonstrate a drastic reduction in subtraction artifacts using our model, without compromising the biomechanical validity of the deformation field such as unrealistically large local volume changes as with traditional voxel intensity based registration.