Point landmarks for registration of CT and MR images

3D-printed helmet-type neuro-navigation approach (I-Helmet) for transcranial magnetic stimulation

Neuro-navigation is a key technology to ensure the clinical efficacy of TMS. However, the neuro-navigation system based on positioning sensor is currently unable to be promoted and applied in clinical practice due to its time-consuming and high-cost. In the present study, we designed I-Helmet system to promote an individualized and clinically friendly neuro-navigation approach to TMS clinical application. I-Helmet system is based on C++ with a graphical user interface that allows users to design a 3D-printed helmet model for coil navigation. Besides, a dedicated coil positioning accuracy detection method was promoted based on three-dimensional (3D) printing and 3D laser scanning for evaluation. T1 images were collected from 24 subjects, and based on each image, phantom were created to simulate skin and hair. Six 3D-printed helmets with the head positioning hole enlarged by 0-5% tolerance in 1% increments were designed to evaluate the influences of skin, hair, and helmet-tolerance on the positioning accuracy and contact force of I-Helmet. Finally, I-Helmet system was evaluated by comparing its positioning accuracy with three skin hardnesses, three hair styles, three operators, and with or without landmarks. The accuracy of the proposed coil positioning accuracy detection method was about 0.30 mm in position and 0.22° in orientation. Skin and hair had significant influences on positioning accuracy (p < 0.0001), whereas different skin hardnesses, hair styles, and operators did not (p > 0.05). The tolerance of the helmet presented significant influences on positioning accuracy (p < 0.0001) and contact force (p < 0.0001). The positioning accuracy significantly increased (p < 0.0001) with landmark guided I-Helmet. 3D-printed helmet-type Neuro-navigation approach (I-Helmet) with 3% tolerance and landmarks met the positioning requirements for TMS in clinical practice with less than 5 N mean contact force, 3-5 mm positioning accuracy, 65.7 s mean operation time, and 50-yuan material cost. All the results suggest that the cost of I-Helmet system may be much less than the that of training clinical doctors to position the coil of TMS operation during short period of time.

A study on medical image registration by mutual information with pyramid data structure

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.

Boundary-based warping of brain MR images

The goal of this work was to develop a warping technique for mapping a brain image to another image or atlas data, with minimum user interaction and independent of gray level information. We have developed and tested three different methods for warping magnetic resonance (MR) brain images. We utilize a deformable contour to extract and warp the boundaries of the two images. A mesh-grid coordinate system is constructed for each brain, by applying a distance transformation to the resulting contours, and scaling. In the first method (MGC), the first image is mapped to the second image based on a one-to-one mapping between different layers defined by the mesh-grid. In the second method (IDW), the corresponding pixels in the two images are found using the above mesh-grid system and a local inverse-distance weights interpolation. In the third proposed method (TSB), a subset of grid points is used for finding the parameters of a spline transformation, which defines the global warping. The warping methods were applied to clinical MR consisting of diffusion-weighted and T2-weighted images of the human brain. The IDW and TSB methods were superior in ranking of diagnostic quality of the warped MR images to the MGC (P < 0.01) as defined by a neuroradiologist. The deformable contour warping produced excellent diagnostic quality for the diffusion-weighted images coregistered and warped to T2 weighted images. J. Magn. Reson. Imaging 2000;12:417-429.

The registration of multiple medical images acquired from a single subject: why, how, what next?

This paper reviews some of the recent techniques which have been used to register multiple images of the same patient. Image registration is a problem which has been receiving significant attention from the medical image processing community in recent years. A successful image registration can aid in patient diagnosis, treatment assessment, image guided interventions, surgery planning and surgery. At present the majority of work has focused on rigid body transformations of images. We shall discuss some of the approaches used and outline a key automatic method in detail. In order to allow image registration of parts of the body which do not remain rigid, either due to patient movement or a change in pathology, nonlinear deformation techniques are being developed. We shall talk of the history of these methods before explaining deformations using landmarks and a recent extension to allow the definition of rigid structures in such warps in more detail. Validation of these methods is of great importance and we shall discuss work which has already been carried out on this topic for rigid body registrations as well as ideas for the validation of deformation algorithms.