Presentation of detailed anatomical structures via 3D Computed Tomographic (CT) volumes helps visualization and
navigation in electrophysiology procedures (EP). Registration of the CT volume with the online fluoroscopy however is
a challenging task for EP applications due to the lack of discernable features in fluoroscopic images. In this paper, we
propose to use the coronary sinus (CS) catheter in bi-plane fluoroscopic images and the coronary sinus in the CT volume
as a location constraint to accomplish 2D-3D registration. Two automatic registration algorithms are proposed in this
study, and their performances are investigated on both simulated and real data. It is shown that compared to registration
using mono-plane fluoroscopy, registration using bi-plane images results in substantially higher accuracy in 3D and
enhanced robustness. In addition, compared to registering the projection of CS to the 2D CS catheter, it is more desirable
to reconstruct a 3D CS catheter from the bi-plane fluoroscopy and then perform a 3D-3D registration between the CS
and the reconstructed CS catheter. Quantitative validation based on simulation and visual inspection on real data
demonstrates the feasibility of the proposed workflow in EP procedures.
Echo planar images (EPI) suffer from geometric distortions caused by static-field inhomogeneity. Correction techniques
have been suggested based on field maps obtained before or after the EPI acquisition. However, when a relatively long
time series of images is required, as in fMRI studies, the inhomogeneity varies from image to image because of gross
motion and physiological activity such as respiration and cardiac motion. It is not ideal to approximate the varying maps
of field inhomogeneity by means of one map. To overcome this limitation, multiple field maps are desirable for
correcting the distortions that are dynamically changing. Some groups have explored the possibility of acquiring
multiple field maps, but either the increased scan time is not affordable for most fMRI studies or the field map
acquisition is embedded in EPI pulse sequence, which produces a map of insufficient resolution to support a complete
distortion correction. In this paper, we propose a dynamic field mapping technique that uses a single reference image and
a single corresponding acquired field map and the phase information extracted from the complex image data of each EPI
image in the time series. From this information, a separate field map is then derived individually for each EPI image.
The derived field maps are then used for distortion correction. This approach, which is particularly suitable for fMRI
studies, can correct for image distortion that varies dynamically without sacrificing temporal resolution. We validate this
technique using simulated data, and the experimental results show improved performance in comparison to correction
using a single field map.
It has been recently proposed that computer-simulated phantom images can be used to evaluate methods for fMRI preprocessing. It is widely recognized that Gradient-Echo Echo Planar Imaging (EPI), the most often used technique for fMRI, is strongly affected by field inhomogeneities. Accurate and realistic phantom images for use by the fMRI community for software evaluation and training must incorporate these distortions and account for the effects of head motion and respiration on the distortions. A method to generate realistic distortions caused by field inhomogeneity for the generation of an fMRI phantom is presented in this paper. Changes in field inhomogeneity due to motion are studied by means of adding motions to the brain model and calculating the induced field map numerically rather than measuring it experimentally. A fast analytic version of an MR simulation is used to generate distorted EPI images based on the calculated field maps. The new generated fMRI phantoms can be used to evaluate processing algorithms for fMRI study more accurately. We can appreciate the importance of distortions for fMRI phantom generation by simulating a distortion-free image and adding distortions afterwards. Validations are performed by comparing the calculated field maps with measured ones. In addition, we show the similarities between a simulated fMRI phantom and real EPI image from our MR scanner.
Geometric distortion is a well-recognized problem in echo planar (EP) images. One strategy for the correction of these distortions is to register an EP image to a reference image, such as a high resolution anatomical MR image in which geometric distortion is minimal. Non-rigid registration methods, which warp images locally, have been used for this purpose. While a physics-based distortion model for spin-echo (SE) EP image has been developed and used as a constraint in nonrigid registration algorithms, such a model for gradient-echo (GE) EP image has not been investigated. Here, we propose to use a physics-based model for GE EP image that incorporates a term that takes dephasing into consideration. To evaluate this technique, we generate a distortion-free EP image using an MR simulator we have developed. We then distort the image and modify its intensity values using a real field map and an analytical expression that includes dephasing. The geometric distortion computed from the field map is used as the ground truth to which the deformation fields obtained with our method is compared. We show that including the dephasing term improves the results.
Fiducial markers are used in image-guided surgery to register images to physical space. Submillimetric accuracy is achievable with CT, but with MR geometrical distortions may cause substantial error. Some anatomical regions may suffer minimal distortion, and the markers can be placed in areas of low distortion, but the marker's own magnetic susceptibility causes distortions of its shape and centroid, compromising the accuracy of its localization. General methods for correcting MR distortion require a second image acquisition. We show that it is possible to provide an accurate localization of marker position without a second image. Our method is to perform a simulation of MR imaging based on the known shape and contents of the marker and the known parameters of the imaging protocol. We compare simulated images at multiple candidate angles and positions to the acquired image. The centroid associated with the most similar image is the improved localization. We use a second simulator to provide ground truth, a binary marker model, and a 1-mm resolution for the candidate positions. For three orientations, the method recovered the correct centroid for signal-to-noise ratios as low as 10. For ratios of 5 and 7, we found an improvement in localization accuracy of 1.0±0.4 mm.