Multimodal Rigid Registration Research Articles

Volumetric rigid MR-CT registration for glioblastoma in radiation oncology: A Novel approach

Glioblastoma multiforme (GBM), is a highly aggressive and rapidly growing grade IV astrocytoma that originates from astrocytes and causes a diverse range of neurological problems by infiltratingsurrounding brain tissue. Image guided radiotherapy is a crucial treatment option for GBM; however, precise target delineation is critical for efficient treatment in the presence of noise and distortion in the magnetic resonance (MR) and computed tomography (CT) images. Multimodal image registration, which generates aligned or fused images from different modalities, can enhance target delineation accuracy. The objective of this study was to evaluate the effectiveness of volumetric multimodal MR-CT rigid registration algorithms for the patients with GBM and to enhance the success of radiotherapy treatment responses by employing visual inspection and statistical assessment of the registered images. Specialized algorithms were developed to attain volumetric registration, leading to an enhanced target delineation for radiation treatment. The crucial role of multimodal rigid registration in improving the accuracy of target delineation in the context of radiation oncology for GBM was underscored by this study.

Multimodal 3D rigid image registration based on expectation maximization

Image registration is an important task in medical imaging, capable of finding displacement fields to align two images of the same anatomic structure under different conditions (e.g. acquisition time and body position). Specifically, multimodal image registration is the process of aligning two or more images of the same scene using different image acquisition techniques. In fact, most of the current image registration approaches are based on Mutual Information (MI) as a similarity metric for image comparison; however, the cost function used in MI methods is difficult to optimize due to complex relationships between variables and pixels intensities. This work presents an Expectation Maximization (EM) 3D multimodal rigid registration approach, which introduces a low computational cost alternative with a linear optimization strategy and an intuitive relation among the free variables. Our approach was validated against a state-of-the-art MI-based technique with synthetic T1 MRI brain volumes. The EM 3D achieved a global average DICE index of 96.68 % with a computational time of 22.72 seconds, whereas the MI methodology reported 96.11 % and 35.13 seconds, respectively.

Practical quantification of image registration accuracy following the AAPM TG-132 report framework.

The AAPM TG 132 Report enumerates important steps for validation of the medical image registration process. While the Report outlines the general goals and criteria for the tests, specific implementation may be obscure to the wider clinical audience. We endeavored to provide a detailed step‐by‐step description of the quantitative tests’ execution, applied as an example to a commercial software package (Mirada Medical, Oxford, UK), while striving for simplicity and utilization of readily available software. We demonstrated how the rigid registration data could be easily extracted from the DICOM registration object and used, following some simple matrix math, to quantify accuracy of rigid translations and rotations. The options for validating deformable image registration (DIR) were enumerated, and it was shown that the most practically viable ones are comparison of propagated internal landmark points on the published datasets, or of segmented contours that can be generated locally. The multimodal rigid registration in our example did not always result in the desired registration error below ½ voxel size, but was considered acceptable with the maximum errors under 1.3 mm and 1°. The DIR target registration errors in the thorax based on internal landmarks were far in excess of the Report recommendations of 2 mm average and 5 mm maximum. On the other hand, evaluation of the DIR major organs’ contours propagation demonstrated good agreement for lung and abdomen (Dice Similarity Coefficients, DSC, averaged over all cases and structures of 0.92 ± 0.05 and 0.91 ± 0.06, respectively), and fair agreement for Head and Neck (average DSC = 0.73 ± 0.14). The average for head and neck is reduced by small volume structures such as pharyngeal constrictor muscles. Even these relatively simple tests show that commercial registration algorithms cannot be automatically assumed sufficiently accurate for all applications. Formalized task‐specific accuracy quantification should be expected from the vendors.

A novel learning-based dissimilarity metric for rigid and non-rigid medical image registration by using Bhattacharyya Distances

Image registration is one of the major technologies of the medical image processing and analysis. According to the nature of the transformation, image registration can be categorized into two main classes: Rigid Registration and Non-rigid Registration. In this paper, a novel learning-based dissimilarity metric and its approximation are proposed and respectively adopted to the rigid and non-rigid registration of medical images. This novel metric utilizes Bhattacharyya Distances to measure the dissimilarity of the testing image pairs by incorporating the expected intensity distributions which is learnt from the registered training image pairs. In order to test the robustness and accuracy of the proposed metric in the multi-modal rigid registration, one thousand eight hundred randomized CT–T1 registrations were performed and evaluated by the Retrospective Image Registration Evaluation (RIRE) project. For the non-rigid registration, the approximation of the proposed dissimilarity metric is adopted with the Markov Random Field (MRF) modeled non-rigid registration approach. Non-rigid registration experiments of 2D synthetic images with artificial deformation fields and 3D images from the Simulated Brain Database were performed and evaluated respectively in this paper. The experimental results of both rigid and non-rigid medical image registrations demonstrated that the proposed dissimilarity metric outperforms the state-of-the-art approach (Mutual Information) and the close related approach (Kullback–Leibler Distances).

Automatic quantification of multi-modal rigid registration accuracy using feature detectors

In radiotherapy, the use of multi-modal images can improve tumor and target volume delineation. Images acquired at different times by different modalities need to be aligned into a single coordinate system by 3D/3D registration. State of the art methods for validation of registration are visual inspection by experts and fiducial-based evaluation. Visual inspection is a qualitative, subjective measure, while fiducial markers sometimes suffer from limited clinical acceptance. In this paper we present an automatic, non-invasive method for assessing the quality of intensity-based multi-modal rigid registration using feature detectors.After registration, interest points are identified on both image data sets using either speeded-up robust features or Harris feature detectors. The quality of the registration is defined by the mean Euclidean distance between matching interest point pairs. The method was evaluated on three multi-modal datasets: an ex vivo porcine skull (CT, CBCT, MR), seven in vivo brain cases (CT, MR) and 25 in vivo lung cases (CT, CBCT). Both a qualitative (visual inspection by radiation oncologist) and a quantitative (mean target registration error—mTRE—based on selected markers) method were employed.In the porcine skull dataset, the manual and Harris detectors give comparable results but both overestimated the gold standard mTRE based on fiducial markers. For instance, for CT-MR-T1 registration, the mTREman (based on manually annotated landmarks) was 2.2 mm whereas mTREHarris (based on landmarks found by the Harris detector) was 4.1 mm, and mTRESURF (based on landmarks found by the SURF detector) was 8 mm. In lung cases, the difference between mTREman and mTREHarris was less than 1 mm, while the difference between mTREman and mTRESURF was up to 3 mm.The Harris detector performed better than the SURF detector with a resulting estimated registration error close to the gold standard. Therefore the Harris detector was shown to be the more suitable method to automatically quantify the geometric accuracy of multimodal rigid registration.

Automatic accuracy measurement for multi-modal rigid registration using feature descriptors

In radiotherapy (RT) for tumor delineation and diagnostics, complementary information of multi-modal images is used. Using high ionizing radiation, the accuracy of registered volume data is crucial; therefore a reliable and robust evaluation method for registered images is needed in clinical practice. Multi-modal image registration aligns images from different modalities like computed tomography (CT) and magnetic resonance imaging (MRI) or cone beam computed tomography (CBCT) into one common frame of reference. The gold standard validation methods are visual inspection by radiation oncology experts and fiducial-based evaluation. However, visual inspection is a qualitative measure with a range of 2-6 mm inaccuracy, it is time consuming and prone to errors. The fiducial-based evaluation is an invasive method when fiducial markers are fixated to bone or implanted in organs. Therefore, in clinical practice a robust non-invasive automated method is needed to validate registration of multi-modal images. The aim of this study is to introduce and validate an automatic landmark-based accuracy measure for multi-modal image rigid registration using feature descriptors. A porcine dataset with fixed fiducial markers was used to compare our accuracy measure with the target registration error of fiducial markers.In addition, the robustness of our evaluation method was tested on multi-vendor database consisted of 10 brain and 20 lung cases comparing the automatic landmark accuracy measure based on feature descriptors with manual landmark based evaluation. An automatic, non-invasive method based on feature descriptors for accuracy evaluation of multi-modal rigid registration was introduced. The method can be used to provide accuracy information slice-by slice on CT, CBCT and CT, MR-T1, -T2 weighted, MR-T1 contrast enhanced (ce) multi-modal images.

Medical image registration using Edgeworth-based approximation of Mutual Information

We propose a new similarity measure for iconic medical image registration, an Edgeworth-based third order approximation of Mutual Information (MI) and named 3-EMI. Contrary to classical Edgeworth-based MI approximations, such as those proposed for independent component analysis, the 3-EMI measure is able to deal with potentially correlated variables. The performance of 3-EMI is then evaluated and compared with the Gaussian and B-Spline kernel-based estimates of MI, and the validation is leaded in three steps. First, we compare the intrinsic behavior of the measures as a function of the number of samples and the variance of an additive Gaussian noise. Then, they are evaluated in the context of multimodal rigid registration, using the RIRE data. We finally validate the use of our measure in the context of thoracic monomodal non-rigid registration, using the database proposed during the MICCAI EMPIRE10 challenge. The results show the wide range of clinical applications for which our measure can perform, including non-rigid registration which remains a challenging problem. They also demonstrate that 3-EMI outperforms classical estimates of MI for a low number of samples or a strong additive Gaussian noise. More generally, our measure gives competitive registration results, with a much lower numerical complexity compared to classical estimators such as the reference B-Spline kernel estimator, which makes 3-EMI a good candidate for fast and accurate registration tasks.

Real-time multi-modal rigid registration based on a novel symmetric-SIFT descriptor

The purpose of image registration is to spatially align two or more single-modality images taken at different times, or several images acquired by multiple imaging modalities. Intensity-based registration usually requires optimization of the similarity metric between the images. However, global optimization techniques are too time-consuming, and local optimization techniques frequently fail to search the global transformation space because of the large initial misalignment of the two images. Moreover, for large non-overlapping area registration, the similarity metric cannot reach its optimum value when the two images are properly registered. In order to solve these problems, we propose a novel Symmetric Scale Invariant Feature Transform (symmetric-SIFT) descriptor and develop a fast multi-modal image registration technique. The proposed technique automatically generates a lot of highly distinctive symmetric-SIFT descriptors for two images, and the registration is performed by matching the corresponding descriptors over two images. These descriptors are invariant to image scale and rotation, and are partially invariant to affine transformation. Moreover, these descriptors are symmetric to contrast, which makes it suitable for multi-modal image registration. The proposed technique abandons the optimization and similarity metric strategy. It works with near real-time performance, and can deal with the large non-overlapping and large initial misalignment situations. Test cases involving scale change, large non-overlapping, and large initial misalignment on computed tomography (CT) and magnetic resonance (MR) datasets show that it needs much less runtime and achieves better accuracy when compared to other algorithms.