A feasibility study to investigate the use of thin-plate splines to account for prostate deformation

Evaluation of how users interface with holographic augmented reality surgical scenes: Interactive planning MR-Guided prostate biopsies

BACKGROUND

User interfaces play a vital role in the planning and execution of an interventional procedure. The objective of this study is to investigate the effect of using different user interfaces for planning transrectal robot-assisted MR-guided prostate biopsy (MRgPBx) in an augmented reality (AR) environment.

METHOD

End-user studies were conducted by simulating an MRgPBx system with end- and side-firing modes. The information from the system to the operator was rendered on HoloLens as an output interface. Joystick, mouse/keyboard, and holographic menus were used as input interfaces to the system.

RESULTS

The studies indicated that using a joystick improved the interactive capacity and enabled operator to plan MRgPBx in less time. It efficiently captures the operator's commands to manipulate the augmented environment representing the state of MRgPBx system.

CONCLUSIONS

The study demonstrates an alternative to conventional input interfaces to interact and manipulate an AR environment within the context of MRgPBx planning.

Evaluation of tumor coverage after MR-guided prostate focal laser ablation therapy

PURPOSE

Prostate cancer is the most common noncutaneous cancer among men in the USA. Focal laser thermal ablation (FLA) has the potential to control small tumors while preserving urinary and erectile function by leaving the neurovascular bundles and urethral sphincters intact. Accurate needle guidance is critical to the success of FLA. Multiparametric magnetic resonance images (mpMRI) can be used to identify targets, guide needles, and assess treatment outcomes. In this study, we evaluated the location of ablation zones relative to targeted lesions in 23 patients who underwent FLA therapy in a phase II trial. The ablation zone margins and unablated tumor volume were measured to determine whether complete coverage of each tumor was achieved, which would be considered a clinically successful ablation.

METHODS

Preoperative mpMRI was acquired for each patient 2-3 months preceding the procedure and the prostate and lesion(s) were manually contoured on 3 T T2-weighted axial images. The prostate and ablation zone(s) were also manually contoured on postablation 1.5 T T1-weighted contrast-enhanced axial images acquired immediately after the procedure intraoperatively. The lesion surface was nonrigidly registered to the postablation image using an initial affine registration followed by nonrigid thin-plate spline registration of the prostate surfaces. The margins between the registered lesion and ablation zone were calculated using a uniform spherical distribution of rays, and the volume of intersection was also calculated. Each prostate was contoured five times to determine the segmentation variability and its effect on intersection of the lesion and ablation zone.

RESULTS

Our study showed that the boundaries of the segmented tumor and ablation zone were close. Of the 23 lesions that were analyzed, 11 were completely covered by the ablation zone and 12 were partially covered. A shift of 1.0, 2.0, and 2.6 mm would result in 19, 21, and all tumors completely covered by the ablation zone, respectively. The median unablated tumor volume across all tumors was 0.1  mm 3 with an IQR of 3.7  mm 3 , which was 0.2% of the median tumor volume (46.5  mm 3 with an IQR of 46.3  mm 3 ). The median extension of the tumors beyond the ablation zone, in cases which were partially ablated, was 0.9 mm (IQR of 1.3 mm), with the furthest tumor extending 2.6 mm.

CONCLUSION

In all cases, the boundary of the tumor was close to the boundary of the ablation zone, and in some cases, the boundary of the ablation zone did not completely enclose the tumor. Our results suggest that some of the ablations were not clinically successful and that there is a need for more accurate needle tracking and guidance methods. Limitations of the study include errors in the registration and segmentation methods used as well as different voxel sizes and contrast between the registered T2 and T1 MRI sequences and asymmetric swelling of the prostate postprocedurally.

Multimodal image registration for the identification of dominant intraprostatic lesion in high-precision radiotherapy treatments

PURPOSE

The integration of CT and multiparametric MRI (mpMRI) is a challenging task in high-precision radiotherapy for prostate cancer. A simple methodology for multimodal deformable image registration (DIR) of prostate cancer patients is presented.

METHODS

CT and mpMRI of 10 patients were considered. Organs at risk and prostate were contoured on both scans. The dominant intraprostatic lesion was additionally delineated on MRI. After a preliminary rigid image registration, the voxel intensity of all the segmented structures in both scans except the prostate was increased by a specific amount (a constant additional value, A), in order to enhance the contrast of the main organs influencing its position and shape. 70 couples of scans were obtained by varying A from 0 to 800 and they were subsequently non-rigidly registered. Quantities derived from image analysis and contour statistics were considered for the tuning of the best performing A.

RESULTS

A = 200 resulted the minimum enhancement value required to obtain statistically significant superior registration results. Mean centre of mass distance between corresponding structures decreases from 7.4 mm in rigid registration to 5.3 mm in DIR without enhancement (DIR-0) and to 2.7 mm in DIR with A = 200 (DIR-200). Mean contour distance was 2.5, 1.9 and 0.67 mm in rigid registration, DIR-0 and DIR-200, respectively. In DIR-200 mean contours overlap increases of +13 and +24% with respect to DIR-0 and rigid registration, respectively.

CONCLUSION

Contour propagation according to the vector field resulting from DIR-200 allows the delineation of dominant intraprostatic lesion on CT scan and its use for high-precision radiotherapy treatment planning. Advances in knowledge: We investigated the application of a B-spline, mutual information-based multimodal DIR coupled with a simple, patient-unspecific but efficient contrast enhancement procedure in the pelvic body area, thus obtaining a robust and accurate methodology to transfer the functional information deriving from mpMRI onto a planning CT reference volume.

Use of image registration and fusion algorithms and techniques in radiotherapy: Report of the AAPM Radiation Therapy Committee Task Group No. 132

Image registration and fusion algorithms exist in almost every software system that creates or uses images in radiotherapy. Most treatment planning systems support some form of image registration and fusion to allow the use of multimodality and time-series image data and even anatomical atlases to assist in target volume and normal tissue delineation. Treatment delivery systems perform registration and fusion between the planning images and the in-room images acquired during the treatment to assist patient positioning. Advanced applications are beginning to support daily dose assessment and enable adaptive radiotherapy using image registration and fusion to propagate contours and accumulate dose between image data taken over the course of therapy to provide up-to-date estimates of anatomical changes and delivered dose. This information aids in the detection of anatomical and functional changes that might elicit changes in the treatment plan or prescription. As the output of the image registration process is always used as the input of another process for planning or delivery, it is important to understand and communicate the uncertainty associated with the software in general and the result of a specific registration. Unfortunately, there is no standard mathematical formalism to perform this for real-world situations where noise, distortion, and complex anatomical variations can occur. Validation of the software systems performance is also complicated by the lack of documentation available from commercial systems leading to use of these systems in undesirable 'black-box' fashion. In view of this situation and the central role that image registration and fusion play in treatment planning and delivery, the Therapy Physics Committee of the American Association of Physicists in Medicine commissioned Task Group 132 to review current approaches and solutions for image registration (both rigid and deformable) in radiotherapy and to provide recommendations for quality assurance and quality control of these clinical processes.

A Comparative Evaluation of 3 Different Free-Form Deformable Image Registration and Contour Propagation Methods for Head and Neck MRI: The Case of Parotid Changes During Radiotherapy

Purpose:

To validate and compare the deformable image registration and parotid contour propagation process for head and neck magnetic resonance imaging in patients treated with radiotherapy using 3 different approaches—the commercial MIM, the open-source Elastix software, and an optimized version of it.

Materials and Methods:

Twelve patients with head and neck cancer previously treated with radiotherapy were considered. Deformable image registration and parotid contour propagation were evaluated by considering the magnetic resonance images acquired before and after the end of the treatment. Deformable image registration, based on free-form deformation method, and contour propagation available on MIM were compared to Elastix. Two different contour propagation approaches were implemented for Elastix software, a conventional one (DIR_Trx) and an optimized homemade version, based on mesh deformation (DIR_Mesh). The accuracy of these 3 approaches was estimated by comparing propagated to manual contours in terms of average symmetric distance, maximum symmetric distance, Dice similarity coefficient, sensitivity, and inclusiveness.

Results:

A good agreement was generally found between the manual contours and the propagated ones, without differences among the 3 methods; in few critical cases with complex deformations, DIR_Mesh proved to be more accurate, having the lowest values of average symmetric distance and maximum symmetric distance and the highest value of Dice similarity coefficient, although nonsignificant. The average propagation errors with respect to the reference contours are lower than the voxel diagonal (2 mm), and Dice similarity coefficient is around 0.8 for all 3 methods.

Conclusion:

The 3 free-form deformation approaches were not significantly different in terms of deformable image registration accuracy and can be safely adopted for the registration and parotid contour propagation during radiotherapy on magnetic resonance imaging. More optimized approaches (as DIR_Mesh) could be preferable for critical deformations.

Implanted brachytherapy seed movement reflecting transrectal ultrasound probe-induced prostate deformation

PURPOSE

Compression of the prostate during transrectal ultrasound-guided permanent prostate brachytherapy is not accounted for during treatment planning. Dosimetry effects are expected to be small but have not been reported. The study aims to characterize the seed movement and prostate deformation due to probe pressure and to estimate the effects on dosimetry.

METHODS AND MATERIALS

C-arm fluoroscopy imaging was performed to reconstruct the implanted seed distributions (compressed and relaxed prostate) for 10 patients immediately after implantation. The compressed prostate was delineated on ultrasound and registered to the fluoroscopy-derived seed distribution via manual seed localization. Thin-plate spline mapping, generated with implanted seeds as control points, was used to characterize the deformation field and to infer the prostate contour in the absence of probe compression. Differences in TG-43 dosimetry for the compressed prostate and that on probe removal were calculated.

RESULTS

Systematic seed movement patterns were observed on probe removal. Elastic decompression was characterized by expansion in the anterior-posterior direction and contraction in the superior-inferior and lateral directions up to 4 mm. Bilateral shearing in the anterior direction was up to 6 mm, resulting in contraction of the 145 Gy prescription isodose line by 2 mm with potential consequences for the posterior-lateral margin. The average whole prostate D90 increased by 2% of prescription dose (6% max; p < 0.01).

CONCLUSIONS

The current investigation presents a novel study on ultrasound probe-induced deformation. Seed movements were characterized, and the associated dosimetry effects were nonnegligible, contrary to common expectation.

A statistical model-based technique for accounting for prostate gland deformation in endorectal coil-based MR imaging

In prostate brachytherapy procedures, combining high-resolution endorectal coil (ERC)-MRI with Computed Tomography (CT) images has shown to improve the diagnostic specificity for malignant tumors. Despite such advantage, there exists a major complication in fusion of the two imaging modalities due to the deformation of the prostate shape in ERC-MRI. Conventionally, nonlinear deformable registration techniques have been utilized to account for such deformation. In this work, we present a model-based technique for accounting for the deformation of the prostate gland in ERC-MR imaging, in which a unique deformation vector is estimated for every point within the prostate gland. Modes of deformation for every point in the prostate are statistically identified using a set of MR-based training set (with and without ERC-MRI). Deformation of the prostate from a deformed (ERC-MRI) to a non-deformed state in a different modality (CT) is then realized by first calculating partial deformation information for a limited number of points (such as surface points or anatomical landmarks) and then utilizing the calculated deformation from a subset of the points to determine the coefficient values for the modes of deformations provided by the statistical deformation model. Using a leave-one-out cross-validation, our results demonstrated a mean estimation error of 1mm for a MR-to-MR registration.

A framework for deformable image registration validation in radiotherapy clinical applications

Quantitative validation of deformable image registration (DIR) algorithms is extremely difficult because of the complexity involved in constructing a deformable phantom that can duplicate various clinical scenarios. The purpose of this study is to describe a framework to test the accuracy of DIR based on computational modeling and evaluating using inverse consistency and other methods. Three clinically relevant organ deformations were created in prostate (distended rectum and rectal gas), head and neck (large neck flexion), and lung (inhale and exhale lung volumes with variable contrast enhancement) study sets. DIR was performed using both B-spline and diffeomorphic demons algorithms in the forward and inverse direction. A compositive accumulation of forward and inverse deformation vector fields was done to quantify the inverse consistency error (ICE). The anatomical correspondence of tumor and organs at risk was quantified by comparing the original RT structures with those obtained after DIR. Further, the physical characteristics of the deformation field, namely the Jacobian and harmonic energy, were computed to quantify the preservation of image topology and regularity of spatial transformation obtained in DIR. The ICE was comparable in prostate case but the B-spline algorithm had significantly better anatomical correspondence for rectum and prostate than diffeomorphic demons algorithm. The ICE was 6.5 mm for demons algorithm for head and neck case when compared to 0.7 mm for B-spline. Since the induced neck flexion was large, the average Dice similarity coefficient between both algorithms was only 0.87, 0.52, 0.81, and 0.67 for tumor, cord, parotids, and mandible, respectively. The B-spline algorithm accurately estimated deformations between images with variable contrast in our lung study, while diffeomorphic demons algorithm led to gross errors on structures affected by contrast variation. The proposed framework offers the application of known deformations on any image datasets, to evaluate the overall accuracy and limitations of a DIR algorithm used in radiation oncology. The evaluation based on anatomical correspondence, physical characteristics of deformation field, and image characteristics can facilitate DIR verification with the ultimate goal of implementing adaptive radiotherapy. The suitability of application of a particular evaluation metric in validating DIR is dependent on the clinical deformation observed.

A Bayesian nonrigid registration method to enhance intraoperative target definition in image-guided prostate procedures through uncertainty characterization

PURPOSE

This study introduces a probabilistic nonrigid registration method for use in image-guided prostate brachytherapy. Intraoperative imaging for prostate procedures, usually transrectal ultrasound (TRUS), is typically inferior to diagnostic-quality imaging of the pelvis such as endorectal magnetic resonance imaging (MRI). MR images contain superior detail of the prostate boundaries and provide substructure features not otherwise visible. Previous efforts to register diagnostic prostate images with the intraoperative coordinate system have been deterministic and did not offer a measure of the registration uncertainty. The authors developed a Bayesian registration method to estimate the posterior distribution on deformations and provide a case-specific measure of the associated registration uncertainty.

METHODS

The authors adapted a biomechanical-based probabilistic nonrigid method to register diagnostic to intraoperative images by aligning a physician's segmentations of the prostate in the two images. The posterior distribution was characterized with a Markov Chain Monte Carlo method; the maximum a posteriori deformation and the associated uncertainty were estimated from the collection of deformation samples drawn from the posterior distribution. The authors validated the registration method using a dataset created from ten patients with MRI-guided prostate biopsies who had both diagnostic and intraprocedural 3 Tesla MRI scans. The accuracy and precision of the estimated posterior distribution on deformations were evaluated from two predictive distance distributions: between the deformed central zone-peripheral zone (CZ-PZ) interface and the physician-labeled interface, and based on physician-defined landmarks. Geometric margins on the registration of the prostate's peripheral zone were determined from the posterior predictive distance to the CZ-PZ interface separately for the base, mid-gland, and apical regions of the prostate.

RESULTS

The authors observed variation in the shape and volume of the segmented prostate in diagnostic and intraprocedural images. The probabilistic method allowed us to convey registration results in terms of posterior distributions, with the dispersion providing a patient-specific estimate of the registration uncertainty. The median of the predictive distance distribution between the deformed prostate boundary and the segmented boundary was ≤3 mm (95th percentiles within ±4 mm) for all ten patients. The accuracy and precision of the internal deformation was evaluated by comparing the posterior predictive distance distribution for the CZ-PZ interface for each patient, with the median distance ranging from -0.6 to 2.4 mm. Posterior predictive distances between naturally occurring landmarks showed registration errors of ≤5 mm in any direction. The uncertainty was not a global measure, but instead was local and varied throughout the registration region. Registration uncertainties were largest in the apical region of the prostate.

CONCLUSIONS

Using a Bayesian nonrigid registration method, the authors determined the posterior distribution on deformations between diagnostic and intraprocedural MR images and quantified the uncertainty in the registration results. The feasibility of this approach was tested and results were positive. The probabilistic framework allows us to evaluate both patient-specific and location-specific estimates of the uncertainty in the registration result. Although the framework was tested on MR-guided procedures, the preliminary results suggest that it may be applied to TRUS-guided procedures as well, where the addition of diagnostic MR information may have a larger impact on target definition and clinical guidance.

Learning statistical correlation for fast prostate registration in image-guided radiotherapy

PURPOSE

In adaptive radiation therapy of prostate cancer, fast and accurate registration between the planning image and treatment images of the patient is of essential importance. With the authors' recently developed deformable surface model, prostate boundaries in each treatment image can be rapidly segmented and their correspondences (or relative deformations) to the prostate boundaries in the planning image are also established automatically. However, the dense correspondences on the nonboundary regions, which are important especially for transforming the treatment plan designed in the planning image space to each treatment image space, are remained unresolved. This paper presents a novel approach to learn the statistical correlation between deformations of prostate boundary and nonboundary regions, for rapidly estimating deformations of the nonboundary regions when given the deformations of the prostate boundary at a new treatment image.

METHODS

The main contributions of the proposed method lie in the following aspects. First, the statistical deformation correlation will be learned from both current patient and other training patients, and further updated adaptively during the radiotherapy. Specifically, in the initial treatment stage when the number of treatment images collected from the current patient is small, the statistical deformation correlation is mainly learned from other training patients. As more treatment images are collected from the current patient, the patient-specific information will play a more important role in learning patient-specific statistical deformation correlation to effectively reflect prostate deformation of the current patient during the treatment. Eventually, only the patient-specific statistical deformation correlation is used to estimate dense correspondences when a sufficient number of treatment images have been acquired from the current patient. Second, the statistical deformation correlation will be learned by using a multiple linear regression (MLR) model, i.e., ridge regression (RR) model, which has the best prediction accuracy than other MLR models such as canonical correlation analysis (CCA) and principal component regression (PCR).

RESULTS

To demonstrate the performance of the proposed method, we first evaluate its registration accuracy by comparing the deformation field predicted by our method with the deformation field estimated by the thin plate spline (TPS) based correspondence interpolation method on 306 serial prostate CT images of 24 patients. The average predictive error on the voxels around 5 mm of prostate boundary is 0.38 mm for our method of RR-based correlation model. Also, the corresponding maximum error is 2.89 mm. We then compare the speed for deformation interpolation by different methods. When considering the larger region of interest (ROI) with the size of 512 × 512 × 61, our method takes 24.41 seconds to interpolate the dense deformation field while TPS method needs 6.7 minutes; when considering a small ROI (surrounding prostate) with size of 112 × 110 × 93, our method takes 1.80 seconds, while TPS method needs 25 seconds.

CONCLUSIONS

Experimental results show that the proposed method can achieve much faster registration speed yet with comparable registration accuracy, compared to the TPS-based correspondence (or deformation) interpolation approach.

Three-dimensional elastic image registration based on strain energy minimization: application to prostate magnetic resonance imaging

The use of magnetic resonance (MR) imaging in conjunction with an endorectal coil is currently the clinical standard for the diagnosis of prostate cancer because of the increased sensitivity and specificity of this approach. However, imaging in this manner provides images and spectra of the prostate in the deformed state because of the insertion of the endorectal coil. Such deformation may lead to uncertainties in the localization of prostate cancer during therapy. We propose a novel 3-D elastic registration procedure that is based on the minimization of a physically motivated strain energy function that requires the identification of similar features (points, curves, or surfaces) in the source and target images. The Gauss-Seidel method was used in the numerical implementation of the registration algorithm. The registration procedure was validated on synthetic digital images, MR images from prostate phantom, and MR images obtained on patients. The registration error, assessed by averaging the displacement of a fiducial landmark in the target to its corresponding point in the registered image, was 0.2 ± 0.1 pixels on synthetic images. On the prostate phantom and patient data, the registration errors were 1.0 ± 0.6 pixels (0.6 ± 0.4 mm) and 1.8 ± 0.7 pixels (1.1 ± 0.4 mm), respectively. Registration also improved image similarity (normalized cross-correlation) from 0.72 ± 0.10 to 0.96 ± 0.03 on patient data. Registration results on digital images, phantom, and prostate data in vivo demonstrate that the registration procedure can be used to significantly improve both the accuracy of localized therapies such as brachytherapy or external beam therapy and can be valuable in the longitudinal follow-up of patients after therapy.

PROBABILISTIC NON-RIGID REGISTRATION OF PROSTATE IMAGES: MODELING AND QUANTIFYING UNCERTAINTY

Registration of pre- to intra-procedural prostate images needs to handle the large changes in position and shape of the prostate caused by varying rectal filling and patient positioning. We describe a probabilistic method for non-rigid registration of prostate images which can quantify the most probable deformation as well as the uncertainty of the estimated deformation. The method is based on a biomechanical Finite Element model which treats the prostate as an elastic material. We use a Markov Chain Monte Carlo sampler to draw deformation configurations from the posterior distribution. In practice, we simultaneously estimate the boundary conditions (surface displacements) and the internal deformations of our biomechanical model. The proposed method was validated on a clinical MRI dataset with registration results comparable to previously published methods, but with the added benefit of also providing uncertainty estimates which may be important to take into account during prostate biopsy and brachytherapy procedures.

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.

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.

Results of a multi-institution deformable registration accuracy study (MIDRAS)

PURPOSE

To assess the accuracy, reproducibility, and computational performance of deformable image registration algorithms under development at multiple institutions on common datasets.

METHODS AND MATERIALS

Datasets from a lung patient (four-dimensional computed tomography [4D-CT]), a liver patient (4D-CT and magnetic resonance imaging [MRI] at exhale), and a prostate patient (repeat MRI) were obtained. Radiation oncologists localized anatomic structures for accuracy assessment. Algorithm accuracy was determined by comparing the computer-predicted displacement at each bifurcation point with the displacement computed from the oncologists' annotations. Thirty-seven academic institutions and medical device manufacturers with published evidence of active deformable image registration capabilities were invited to participate.

RESULTS

Twenty-seven groups agreed to participate; 6 did not return results. Sixteen completed the liver 4D-CT, 12 the lung 4D-CT, 3 the prostate MRI, and 3 the liver MRI-CT. The range of average absolute error for the lung 4D-CT was 0.6-1.2 mm (left-right [LR]), 0.5-1.8 mm (anterior-posterior [AP]), and 0.7-2.0 mm (superior-inferior [SI]); the liver 4D-CT was 0.8-1.5 mm (LR), 1.0-5.2 mm (AP), and 1.0-5.9 mm (SI); the liver MRI-CT was 1.1-2.6 mm (LR), 2.0-5.0 mm (AP), and 2.2-2.6 mm (SI); and the repeat prostate MRI prostate datasets was 0.5-6.2 mm (LR), 3.1-3.7 mm (AP), and 0.4-2.0 mm (SI).

CONCLUSIONS

An infrastructure was developed to assess multi-institution deformable registration accuracy. The results indicate large discrepancies in reported shifts, although the majority of deformable registration algorithms performed at an accuracy equivalent to the voxel size, promising to improve treatment planning, delivery, and assessment.

Semi automatic MRI prostate segmentation based on wavelet multiscale products

Currently, prostate cancer is the third leading cause of cancer-related deaths among men in North America. As with many others types of cancer, early detection and treatment greatly increases the patient's chance of survival. MRI prostate segmentation allows clinical personnel to design an accurate treatment plan. A novel method for MRI prostate imagery segmentation is proposed in this paper. This method exploits the different behavior presented by signal singularities and noise in the wavelet domain in order to accurately detect the borders around the prostate. The prostate contour is then traced by using a set of spatially variant rules that are based on prior knowledge about the general shape of the prostate. The proposed method yielded promising results when applied to real data.

A novel method for comparing 3D target volume delineations in radiotherapy

When comparing delineations it is often useful to obtain a local measure of distance between the volume surfaces. Commonly used methods for analysing local distance exhibit fundamental drawbacks which may cause overestimation of the distance or lead to asymmetry in the measure. This paper describes a new method that aims to solve these problems. The new method finds corresponding points between two delineations by traversing a vector field based on the combined gradient of the distance transforms. The proposed method provides a fundamentally more reliable, symmetric measure of distance. This is supported by an illustrative example of observer variation in prostate delineation. An implementation of the method is available on request to the author.

MR-guided prostate interventions

In this article the current issues of diagnosis and detection of prostate cancer are reviewed. The limitations for current techniques are highlighted and some possible solutions with MR imaging and MR-guided biopsy approaches are reviewed. There are several different biopsy approaches under investigation. These include transperineal open magnet approaches to closed-bore 1.5T transrectal biopsies. The imaging, image processing, and tracking methods are also discussed. In the arena of therapy, MR guidance has been used in conjunction with radiation methods, either brachytherapy or external delivery. The principles of the radiation treatment, the toxicities, and use of images are outlined. The future role of imaging and image-guided interventions lie with providing a noninvasive surrogate for cancer surveillance or monitoring treatment response. The shift to minimally invasive focal therapies has already begun and will be very exciting when MR-guided focused ultrasound surgery reaches its full potential.

Current role and future perspectives of magnetic resonance spectroscopy in radiation oncology for prostate cancer

Prostatic neoplasms are not uniformly distributed within the prostate volume. With recent developments in three-dimensional intensity-modulated and image-guided radiation therapy, it is possible to treat different volumes within the prostate to different thresholds of doses. This approach has the potential to adapt the dose to the biologic aggressiveness of various clusters of tumor cells within the gland. The definition of tumor burden volume in prostate cancer can be facilitated by the use of magnetic resonance spectroscopy (MRS). The increasing sensitivity and specificity of MRS to the prostate is causing new interest in its potential role in the definition of target subvolumes at higher risk of failure following radical radiotherapy. Prostate MRS might also play a role as a noninvasive predictive factor for tumor response and treatment outcome. We review the use of MRS in radiation therapy for prostate cancer by evaluating its accuracy in the classification of aggressive cancer regions and target definition; its current role in the radiotherapy planning process, with special interest in technical issues behind the successful inclusion of MRS in clinical use; and available early experiences as a prognostic tool.

Deformable registration for image-guided radiation therapy

Ths paper examines several applications of deformable registration algorithms in the field of image-guided radiotherapy. The first part focuses on the description of input and output of deformable registration algorithms, with a brief review of conventional and most current methods. The typical applications of deformable registration are then reviewed on the basis of four practical examples. The first two sets of examples deal with the fusion of images obtained from the same patient (inter-fraction registration), with time intervals of several days between each image. The other two examples deal with the fusion of images obtained in immediate sequence (intra-fraction registration); in this case, the focus is the displacement during image acquisition or patient treatment (mainly due to respiratory movement), with time intervals in the order of magnitude of tenths of seconds. Finally, the registration of images of different patients (inter-patient registration) is also discussed. In conclusion, deformable registration has become a fundamental tool for image analysis in radiotherapy. Although extensive validation of the numerous existing methods is required before extending its clinical use, deformable registration is expected to become a standard methodology in the treatment planning systems in the near future.