Generalized Correlation Ratio for Rigid Registration of 3D Ultrasound with MR Images. In: Delp SL, Digoia AM, Jaramaz B, editors. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2000. Berlin: Springer Berlin Heidelberg; 2000. pp. 567-77. [DOI: 10.1007/978-3-540-40899-4_58]

Co-Sparse Analysis Model Based Image Registration to Compensate Brain Shift by Using Intra-Operative Ultrasound Imaging

Notwithstanding the widespread use of image guided neurosurgery systems in recent years, the accuracy of these systems is strongly limited by the intra-operative deformation of the brain tissue, the so-called brain shift. Intra-operative ultrasound (iUS) imaging as an effective solution to compensate complex brain shift phenomena update patients coordinate during surgery by registration of the intra-operative ultrasound and the pre-operative MRI data that is a challenging problem.In this work a non-rigid multimodal image registration technique based on co-sparse analysis model is proposed. This model captures the interdependency of two image modalities; MRI as an intensity image and iUS as a depth image. Based on this model, the transformation between the two modalities is minimized by using a bimodal pair of analysis operators which are learned by optimizing a joint co-sparsity function using a conjugate gradient.Experimental validation of our algorithm confirms that our registration approach outperforms several of other state-of-the-art registration methods quantitatively. The evaluation was performed using seven patient dataset with the mean registration error of only 1.83 mm. Our intensity-based co-sparse analysis model has improved the accuracy of non-rigid multimodal medical image registration by 15.37% compared to the curvelet based residual complexity as a powerful registration method, in a computational time compatible with clinical use.

Curvelet based residual complexity objective function for non-rigid registration of pre-operative MRI with intra-operative ultrasound images

Intra-operative ultrasound as an imaging based method has been recognized as an effective solution to compensate non rigid brain shift problem in recent years. Measuring brain shift requires registration of the pre-operative MRI images with the intra-operative ultrasound images which is a challenging task. In this study a novel hybrid method based on the matching echogenic structures such as sulci and tumor boundary in MRI with ultrasound images is proposed. The matching echogenic structures are achieved by optimizing the Residual Complexity (RC) in the curvelet domain. At the first step, the probabilistic map of the MR image is achieved and the residual image as the difference between this probabilistic map and intra-operative ultrasound is obtained. Then curvelet transform as a sparse function is used to minimize the complexity of residual image. The proposed method is a compromise between feature-based and intensity-based approaches. Evaluation was performed using 14 patients data set and the mean of registration error reached to 1.87 mm. This hybrid method based on RC improves accuracy of nonrigid multimodal image registration by 12.5% in a computational time compatible with clinical use.

Skeleton Graph Matching vs. Maximum Weight Cliques aorta registration techniques

Vascular diseases are one of the most challenging health problems in developed countries. Past as well as ongoing research activities often focus on efficient, robust and fast aorta segmentation, and registration techniques. According to this needs our study targets an abdominal aorta registration method. The investigated algorithms make it possible to efficiently segment and register abdominal aorta in pre- and post-operative Computed Tomography (CT) data. In more detail, a registration technique using the Path Similarity Skeleton Graph Matching (PSSGM), as well as Maximum Weight Cliques (MWCs) are employed to realise the matching based on Computed Tomography data. The presented approaches make it possible to match characteristic voxels belonging to the aorta from different Computed Tomography (CT) series. It is particularly useful in the assessment of the abdominal aortic aneurysm treatment by visualising the correspondence between the pre- and post-operative CT data. The registration results have been tested on the database of 18 contrast-enhanced CT series, where the cross-registration analysis has been performed producing 153 matching examples. All the registration results achieved with our system have been verified by an expert. The carried out analysis has highlighted the advantage of the MWCs technique over the PSSGM method. The verification phase proves the efficiency of the MWCs approach and encourages to further develop this methods.

Objective assessment of deformable image registration in radiotherapy: a multi-institution study

The looming potential of deformable alignment tools to play an integral role in adaptive radiotherapy suggests a need for objective assessment of these complex algorithms. Previous studies in this area are based on the ability of alignment to reproduce analytically generated deformations applied to sample image data, or use of contours or bifurcations as ground truth for evaluation of alignment accuracy. In this study, a deformable phantom was embedded with 48 small plastic markers, placed in regions varying from high contrast to roughly uniform regional intensity, and small to large regional discontinuities in movement. CT volumes of this phantom were acquired at different deformation states. After manual localization of marker coordinates, images were edited to remove the markers. The resulting image volumes were sent to five collaborating institutions, each of which has developed previously published deformable alignment tools routinely in use. Alignments were done, and applied to the list of reference coordinates at the inhale state. The transformed coordinates were compared to the actual marker locations at exhale. A total of eight alignment techniques were tested from the six institutions. All algorithms performed generally well, as compared to previous publications. Average errors in predicted location ranged from 1.5 to 3.9 mm, depending on technique. No algorithm was uniformly accurate across all regions of the phantom, with maximum errors ranging from 5.1 to 15.4 mm. Larger errors were seen in regions near significant shape changes, as well as areas with uniform contrast but large local motion discontinuity. Although reasonable accuracy was achieved overall, the variation of error in different regions suggests caution in globally accepting the results from deformable alignment.

Evaluation on Similarity Measures of a Surface-to-Image Registration Technique for Ultrasound Images

Ultrasound is a universal guidance tool for many medical procedures, whereas it is of poor image quality and resolution. Merging high-contrast image information from other image modalities enhances the guidance capability of ultrasound. However, few registration methods work well for it. In this paper we present a surface-to-image registration technique for mono- or multimodal medical data concerning ultrasound. This approach is able to automatically register the object surface to its counterpart in image volume. Three similarity measurements are investigated in the rigid registration experiments of the pubic arch in transrectal ultrasound images. It shown that the selection of the similarity function is related to the ultrasound characteristics of the object to be registered.

Non-rigid registration of a 3D ultrasound and a MR image data set of the female pelvic floor using a biomechanical model

BACKGROUND

The visual combination of different modalities is essential for many medical imaging applications in the field of Computer-Assisted medical Diagnosis (CAD) to enhance the clinical information content. Clinically, incontinence is a diagnosis with high clinical prevalence and morbidity rate. The search for a method to identify risk patients and to control the success of operations is still a challenging task. The conjunction of magnetic resonance (MR) and 3D ultrasound (US) image data sets could lead to a new clinical visual representation of the morphology as we show with corresponding data sets of the female anal canal with this paper.

METHODS

We present a feasibility study for a non-rigid registration technique based on a biomechanical model for MR and US image data sets of the female anal canal as a base for a new innovative clinical visual representation.

RESULTS

It is shown in this case study that the internal and external sphincter region could be registered elastically and the registration partially corrects the compression induced by the ultrasound transducer, so the MR data set showing the native anatomy is used as a frame for the US data set showing the same region with higher resolution but distorted by the transducer

CONCLUSION

The morphology is of special interest in the assessment of anal incontinence and the non-rigid registration of normal clinical MR and US image data sets is a new field of the adaptation of this method incorporating the advantages of both technologies.

MAP MRF joint segmentation and registration of medical images

The problems of segmentation and registration are traditionally approached individually, yet the accuracy of one is of great importance in influencing the success of the other. In this paper, we aim to show that more accurate and robust results may be obtained through seeking a joint solution to these linked processes. The outlined approach applies Markov random fields in the solution of a maximum a posteriori model of segmentation and registration. The approach is applied to synthetic and real MRI data.

Registration of freehand 3D ultrasound and magnetic resonance liver images

We present a method to register a preoperative MR volume to a sparse set of intraoperative ultrasound slices. Our aim is to allow the transfer of information from preoperative modalities to intraoperative ultrasound images to aid needle placement during thermal ablation of liver metastases. The spatial relationship between ultrasound slices is obtained by tracking the probe using a Polaris optical tracking system. Images are acquired at maximum exhalation and we assume the validity of the rigid body transformation. An initial registration is carried out by picking a single corresponding point in both modalities. Our strategy is to interpret both sets of images in an automated pre-processing step to produce evidence or probabilities of corresponding structure as a pixel or voxel map. The registration algorithm converts the intensity values of the MR and ultrasound images into vessel probability values. The registration is then carried out between the vessel probability images. Results are compared to a "bronze standard" registration which is calculated using a manual point/line picking algorithm and verified using visual inspection. Results show that our starting estimate is within a root mean square target registration error (calculated over the whole liver) of 15.4 mm to the "bronze standard" and this is improved to 3.6 mm after running the intensity-based algorithm.

A Unified Statistical and Information Theoretic Framework for Multi-modal Image Registration

We formulate and interpret several registration methods in the context of a unified statistical and information theoretic framework. A unified interpretation clarifies the implicit assumptions of each method yielding a better understanding of their relative strengths and weaknesses. Additionally, we discuss a generative statistical model from which we derive a novel analysis tool, the auto-information function, as a means of assessing and exploiting the common spatial dependencies inherent in multi-modal imagery. We analytically derive useful properties of the auto-information as well as verify them empirically on multi-modal imagery. Among the useful aspects of the auto-information function is that it can be computed from imaging modalities independently and it allows one to decompose the search space of registration problems.

What is the best similarity measure for motion correction in fMRI time series?

It has been shown that the difference of squares cost function used by standard realignment packages (SPM and AIR) can lead to the detection of spurious activations, because the motion parameter estimations are biased by the activated areas. Therefore, this paper describes several experiments aiming at selecting a better similarity measure to drive functional magnetic resonance image registration. The behaviors of the Geman-McClure (GM) estimator, of the correlation ratio, and of the mutual information (MI) relative to activated areas are studied using simulated time series and actual data stemming from a 3T magnet. It is shown that these methods are more robust than the usual difference of squares measure. The results suggest also that the measures built from robust metrics like the GM estimator may be the best choice, while MI is also an interesting solution. Some more work, however, is required to compare the various robust metrics proposed in the literature.