A Locally Adaptive Registration Technique for High Precision Registration of 3-D MRI Data

doi:10.1006/nimg.2000.0638

NeuroImage

Volume 12, Issue 5, November 2000, Pages 574-581

https://doi.org/10.1006/nimg.2000.0638 Get rights and content

Abstract

This study demonstrates how the rigid body registration parameters for good registration of serially acquired 3-D magnetic resonance images vary systematically when the registration routine is presented with a series of cropped data sets that are systematically positioned throughout the entire volume. The results of the registration of these subcubes are compared with the results of a single registration of the complete volume for two consecutive 3-D scans of the brain of a normal volunteer, with one scan having optimized shim coil currents and the other having all second-order shim coil currents set to zero. The technique is sensitive and able to reveal subvoxel misregistrations.

References (10)

R. Gruetter
Automatic, localized in vivo adjustment of all 1st-order and 2nd-order shim coils
Magn. Reson. Med.
(1993)
J.V. Hajnal et al.
A registration and interpolation procedure for subvoxel matching of serially acquired MR images
J. Comput. Assist. Tomogr.
(1995)
D.L.G. Hill et al.
Correcting scaling errors in tomographic images using a nine degree of freedom registration algorithm
J. Comput. Assist. Tomogr.
(1998)
G. Hipper et al.
Advanced workstation cluster architectures for parallel computing
J. Syst. Arch.
(1997)

There are more references available in the full text version of this article.

Cited by (6)

An automatic cerebellum extraction method in T1-weighted brain MR images using an active contour model with a shape prior
2011, Magnetic Resonance Imaging
Citation Excerpt :
The algorithms require some parameters whose optimal values must be determined for a given image to be accurately segmented; however, this is a tedious task in large-scale studies. Atlas-based methods register input MR images (or volumes) on a brain atlas, such as the Talairach atlas, Montreal Neurological Institute (MNI) brain and predefined templates [9–14]. The Talairach and MNI atlases are the averaged brain volumes from numerous subjects.
The objective of this paper was to automatically segment the cerebellum from T1-weighted human brain magnetic resonance (MR) images.
The proposed method constructs a cerebellum template using five sets of 3-T MR imaging (MRI) data, which are used to determine the initial position and the shape prior of the cerebellum for the active contour model. Our formulation includes the active contour model with shape prior, which thereby maintains the shape of the template. The proposed active contour model is sequentially applied to sagittal-, coronal- and transverse-view images. To evaluate the proposed method, it is applied to BrainWeb data and a 3-T MRI data set and compared with FreeSurfer with respect to performance assessment metrics.
The segmented cerebellum was compared with the results from FreeSurfer. Using the manually segmented cerebellum as reference, we measured the average Jaccard coefficients of the proposed method, which were 0.882 and 0.885 for the BrainWeb data and 3-T MRI data set, respectively.
We presented the active contour model with shape prior for extracting the cerebellum from T1-weighted brain MR images. The proposed method yielded a robust and accurate segmentation result.
A study on medical image registration by mutual information with pyramid data structure
2007, Computers in Biology and Medicine
Citation Excerpt :
The registration goal is to combine the information in different modality images or to get rid of the artifacts in images, and it is now an indispensable technique for disease diagnosis and neuroscience research [15]. There are diverse medical image registration methods, including the traditional methods such as landmark based registration [1,2,14,19,23], feature based registration [3,22], and some relatively new information based methods such as the one based on mutual information (MI) [4,5,17,20]. MI can be taken to measure the similarities between two image sets and for convenience of calculation and analysis, the normalized mutual information (NMI) is usually adopted in image registration [16,24].
The registration method based on mutual information is currently a popular technique for medical image registration, but the computation of the mutual information is complex and the registration speed is slow. In this work, a new slice accumulation pyramid (SAP) data structure was proposed to expedite the registration process. A numerical comparative study between the new data structure and the existing wavelet pyramid (WP) data structure was given, and the results confirmed that the new pyramid data structure was superior to the WP in both the calculation efficiency and the optimizing performance. Finally, SAP was applied to remove the artifacts between CT and MRI data sets, and the results showed the validation of SAP to registration of mulmodality images.
Automated human frontal lobe identification in MR images based on fuzzy-logic encoded expert anatomic knowledge
2004, Magnetic Resonance Imaging
Identification of human brain structures in MR images comprises an area of increasing interest, which also presents numerous methodological challenges. Here we describe a new knowledge-based automated method designed to identify several major brain sulci and then to define the frontal lobes by using the identified sulci as landmarks. To identify brain sulci, sulcal images were generated by morphologic operations and then separated into different components based on connectivity analysis. Subsequently, the individual anatomic features were evaluated by using fuzzy membership functions. The crisp decisions, i.e., the identification of sulci, were made by taking the maximum of the summation of all the membership functions. The identification was designed in a hierarchical order. The longitudinal fissure was extracted first. The left and right central sulci were then identified based on the left and right hemispheres. Next, the lateral sulci were identified based on the central sulci and hemispheres. Finally, the left and right frontal lobes were defined from the two hemispheres. The method was evaluated by visual inspection, comparison with manual segmentation, and comparison with manually volumetric results in references. The average Jaccard similarities of left and right frontal lobes between the automated and manual segmentation were 0.89 and 0.91, respectively. The average Kappa indices of left and right frontal lobes between the automated and manual segmentation were 0.94 and 0.95, respectively. These results show relatively high accuracy of using this novel method for human frontal lobe identification and segmentation.
A knowledge-guided active model method of cortical structural segmentation on pediatric MR images
2006, Journal of Magnetic Resonance Imaging
Cerebella segmentation on MR images of pediatric patients with medulloblastoma
2005, Progress in Biomedical Optics and Imaging - Proceedings of SPIE
A knowledge-guided active contour method of segmentation of cerebella on MR images of pediatric patients with medulloblastoma
2005, Journal of Magnetic Resonance Imaging

View full text

Regular ArticleA Locally Adaptive Registration Technique for High Precision Registration of 3-D MRI Data

Abstract

Automatic, localized in vivo adjustment of all 1st-order and 2nd-order shim coils

Magn. Reson. Med.

A registration and interpolation procedure for subvoxel matching of serially acquired MR images

J. Comput. Assist. Tomogr.

Correcting scaling errors in tomographic images using a nine degree of freedom registration algorithm

J. Comput. Assist. Tomogr.

Advanced workstation cluster architectures for parallel computing

J. Syst. Arch.

Regular Article
A Locally Adaptive Registration Technique for High Precision Registration of 3-D MRI Data