Juan Ruiz-Alzola

Evaluation of three‐dimensional finite element‐based deformable registration of pre‐ and intraoperative prostate imaging

In this report we evaluate an image registration technique that can improve the information content of intraoperative image data by deformable matching of preoperative images. In this study, pretreatment 1.5 tesla (T) magnetic resonance (MR) images of the prostate are registered with 0.5 T intraoperative images. The method involves rigid and nonrigid registration using biomechanical finite element modeling. Preoperative 1.5 T MR imaging is conducted with the patient supine, using an endorectal coil, while intraoperatively, the patient is in the lithotomy position with a rectal obturator in place. We have previously observed that these changes in patient position and rectal filling produce a shape change in the prostate. The registration of 1.5 T preoperative images depicting the prostate substructure [namely central gland (CG) and peripheral zone (PZ)] to 0.5 T intraoperative MR images using this method can facilitate the segmentation of the substructure of the gland for radiation treatment planning. After creating and validating a dataset of manually segmented glands from images obtained in ten sequential MR-guided brachytherapy cases, we conducted a set of experiments to assess our hypothesis that the proposed registration system can significantly improve the quality of matching of the total gland (TG), CG, and PZ. The results showed that the method statistically-significantly improves the quality of match (compared to rigid registration), raising the Dice similarity coefficient (DSC) from prematched coefficients of 0.81, 0.78, and 0.59 for TG, CG, and PZ, respectively, to 0.94, 0.86, and 0.76. A point-based measure of registration agreement was also improved by the deformable registration. CG and PZ volumes are not changed by the registration, indicating that the method maintains the biomechanical topology of the prostate. Although this strategy was tested for MRI-guided brachytherapy, the preliminary results from these experiments suggest that it may be applied to other settings such as transrectal ultrasound-guided therapy, where the integration of preoperative MRI may have a significant impact upon treatment planning and guidance.

Nonrigid registration of 3D tensor medical data

New medical imaging modalities offering multi-valued data, such as phase contrast MRA and diffusion tensor MRI, require general representations for the development of automated algorithms. In this paper we propose a unified framework for the registration of medical volumetric multi-valued data using local matching. The paper extends the usual concept of similarity between two pieces of data to be matched, commonly used with scalar (intensity) data, to the general tensor case. Our approach to registration is based on a multiresolution scheme, where the deformation field estimated in a coarser level is propagated to provide an initial deformation in the next finer one. In each level, local matching of areas with a high degree of local structure and subsequent interpolation are performed. Consequently, we provide an algorithm to assess the amount of structure in generic multi-valued data by means of gradient and correlation computations. The interpolation step is carried out by means of the Kriging estimator, which provides a novel framework for the interpolation of sparse vector fields in medical applications. The feasibility of the approach is illustrated by results on synthetic and clinical data.

A theoretical framework to three-dimensional ultrasound reconstruction from irregularly sampled data

Several techniques have been described in the literature in recent years for the reconstruction of a regular volume out of a series of ultrasound (US) slices with arbitrary orientations, typically scanned by means of US freehand systems. However, a systematic approach to such a problem is still missing. This paper focuses on proposing a theoretical framework for the 3-D US volume reconstruction problem. We introduce a statistical method for the construction and trimming of the sampling grid where the reconstruction will be carried out. The results using in vivo US data demonstrate that the computed reconstruction grid that encloses the region-of-interest (ROI) is smaller than those obtained from other reconstruction methods in those cases where the scanning trajectory deviates from a pure straight line. In addition, an adaptive Gaussian interpolation technique is studied and compared with well-known interpolation methods that have been applied to the reconstruction problem in the past. We find that the proposed method numerically outperforms former proposals in several control studies; subjective visual results also support this conclusion and highlight some potential deficiencies of methods previously proposed.

A fuzzy system for helping medical diagnosis of malformations of cortical development

Malformations of the cerebral cortex are recognized as a common cause of developmental delay, neurological deficits, mental retardation and epilepsy. Currently, the diagnosis of cerebral cortical malformations is based on a subjective interpretation of neuroimaging characteristics of the cerebral gray matter and underlying white matter. There is no automated system for aiding the observer in making the diagnosis of a cortical malformation. In this paper a fuzzy rule-based system is proposed as a solution for this problem. The system collects the available expert knowledge about cortical malformations and assists the medical observer in arriving at a correct diagnosis. Moreover, the system allows the study of the influence of the various factors that take part in the decision. The evaluation of the system has been carried out by comparing the automated diagnostic algorithm with known case examples of various malformations due to abnormal cortical organization. An exhaustive evaluation of the system by comparison with published cases and a ROC analysis is presented in the paper.

Comments on: A methodology for evaluation of boundary detection algorithms on medical images

In this paper we analyze a result previously published about a comparison between two statistical tests used for evaluation of boundary detection algorithms on medical images. We conclude that the statement made by Chalana and Kim (1997) about the performance of the percentage test has a weak theoretical foundation, and according to our results, is not correct. In addition, we propose a one-sided hypothesis test for which the acceptance region can be determined in advance, as opposed to the two-sided confidence intervals proposed in the original paper, which change according to the estimated quantity.

A Riemannian approach to anisotropic filtering of tensor fields

Tensors are nowadays an increasing research domain in different areas, especially in image processing, motivated for example by diffusion tensor magnetic resonance imaging (DT-MRI). Up to now, algorithms and tools developed to deal with tensors were founded on the assumption of a matrix vector space with the constraint of remaining symmetric positive definite matrices. On the contrary, our approach is grounded on the theoretically well-founded differential geometrical properties of the space of multivariate normal distributions, where it is possible to define an affine-invariant Riemannian metric and express statistics on the manifold of symmetric positive definite matrices. In this paper, we focus on the contribution of these tools to the anisotropic filtering and regularization of tensor fields. To validate our approach we present promising results on both synthetic and real DT-MRI data.

Nonrigid Registration of 3D Scalar, Vector and Tensor Medical Data

New medical imaging modalities offering multi-valued data, such as phase contrast MRA and diffusion tensor MRI, require general representations for the development of automatized algorithms. In this paper we propose a unified framework for the registration of medical volumetric multi-valued data. The paper extends the usual concept of similarity in intensity (scalar) data to vector and tensor cases. A discussion on appropriate template selection and on the limitations of the template matching approach to incorporate the vector and tensor reorientation is also offered. Our approach to registration is based on a multiresolution scheme based on local matching of areas with a high degree of local structure and subsequent interpolation. Consequently we provide an algorithm to assess the amount of structure in generic multi-valued data by means of gradient and correlation computations. The interpolation step is carried out by means of the Kriging estimator that outperforms conventional polynomial methods for the interpolation of sparse vector fields. The feasibility of the approach is illustrated by results on synthetic and clinical data.

Kriging filters for multidimensional signal processing

The Wiener filter is the well-known solution for linear minimum mean square error (LMMSE) signal estimation. This filter assumes the mean to be known and usually constant. On the other hand, the Kriging filter is an incremental theory, developed within the Geostatistical community, with respect to that of Wiener filters. The extension relies on adopting a parametric model for the mean (usually a polynomial). The goal of this paper is twofold. First, it is intended as a comprehensive treatment of the Kriging approach from a signal processing perspective, with previous uses of Kriging in signal processing being extended. Second, we are deriving a general methodology for FIR filter design, including any situation where an optimal FIR estimator from possibly incomplete and/or noisy data is needed. A proof of concept on a theoretical covariance model and selected examples on interpolation, approximation and filtering on real-world images illustrate the performance of the method.

24 – Advanced Nonrigid Registration Algorithms for Image Fusion

This chapter presents an original method to perform nonrigid registration of multimodal images. This iterative algorithm is composed of two steps: the intensity transformation and the geometrical transformation. Two intensity transformation models are proposed, which assume either monofunctional or bifunctional dependence between the intensity values in the images being matched. Both of these models are built using robust estimators to enable precise and accurate transformation solutions. The chapter describes the image registration strategy applied prospectively during several neurosurgical cases. The enhancement provided by intraoperative nonrigid registration to the surgical visualization environment is shown by matching the corticospinal tract of a preoperatively prepared anatomical atlas to the initial and subsequent intraoperative scans of a subject. This matching was carried out prospectively during the neurosurgery, demonstrating the practical value of the approach and its ability to meet the real-time constraints of surgery. The entire image analysis process can be completed in less than 10 min, which has been adequate to display the information to the surgeon.

Phase-Based User-Steered Image Segmentation

This paper presents a user-steered segmentation algorithm based on the livewire paradigm. Livewire is an image-feature driven method that finds the optimal path between user-selected image locations, thus reducing the need to manually define the complete boundary. We introduce an image feature based on local phase, which describes local edge symmetry independent of absolute gray value. Because phase is amplitude invariant, the measurements are robust with respect to smooth variations, such as bias field inhomogeneities present in all MR images. In order to enable validation of our segmentation method, we have created a system that continuously records user interaction and automatically generates a database containing the number of user interactions, such as mouse events, and time stamps from various editing modules. We have conducted validation trials of the system and obtained expert opinions regarding its functionality.