@article {106, title = {Sparse reconstruction of breast MRI using homotopic L0 minimization in a regional sparsified domain}, journal = {IEEE Transactions on Biomedical Engineering}, volume = {60}, year = {2013}, pages = {743 - 752}, abstract = {

The use of magnetic resonance imaging (MRI) for early breast examination and screening of asymptomatic women has become increasing popular, given its ability to provide detailed tissue characteristics that cannot be obtained using other imaging modalities such as mammography and ultrasound. Recent application-oriented developments in compressed sensing theory have shown that certain types of magnetic resonance images are inherently sparse in particular transform domains, and as such can be reconstructed with a high level of accuracy from highly undersampled k-space data below Nyquist sampling rates using homotopic L0 minimization schemes, which holds great potential for significantly reducing acquisition time. An important consideration in the use of such homotopic L0 minimization schemes is the choice of sparsifying transform. In this work, a regional differential sparsifying transform is investigated for use within a homotopic L0 minimization framework for reconstructing breast MRI. By taking local regional characteristics into account, the regional differential sparsifying transform can better account for signal variations and fine details that are characteristic of breast MRI than the popular finite differential transform, while still maintaining strong structure fidelity. Experimental results show that good breast MRI reconstruction accuracy can be achieved compared to existing methods.

}, keywords = {breast, magnetic resonance imaging, minimization, MRI, reconstruction, regional differential transform}, issn = {0018-9294}, doi = {10.1109/TBME.2010.2089456}, author = {A Wong and A Mishra and P Fieguth and D A. Clausi} } @article {184, title = {Real time cardiac image registration during respiration: a time series prediction approach}, journal = {Journal of Real-time Image Processing}, year = {2011}, abstract = {

Cardiac image registration is drawing attention for a range of merits in integrating and enhancing real-time (RT) images using a priori and complementary images of the myocardium, which might additionally be captured from other modalities. Myocardial stem cell delivery and radio-frequency ablation are some of the cases that could benefit from RT registration of high quality images. Unfortunately, most of these applications are of long duration, and must account in some manner for respiratory motion. Moreover, registration is not so keen as to compensate for these motions. Time series prediction techniques could compensate this shortcoming by proposing future approximate displacements caused by respiratory motion. In this study, we propose a three-stage framework for RT 2D into 3D cardiac image registration during respiration, composed of prior registration to extract the trend of respiratory motion and to calibrate a set of time series predictors for future motion prediction, as well RT registration to update estimated transform parameters. The proposed approach was validated in the course of four simulations and shows acceptable results for clinical circumstances.

}, keywords = {Cardiac, image registration, MRI, Real-time, Respiratory motion compensation, Time series prediction}, author = {M Esteghamatian and Z Azimifar and P Radau and G Wright} } @inproceedings {365, title = {Indirect knowledge-based approach to non-rigid multi-modal registration of medical images}, booktitle = {20th Canadian Conference on Electrical and Computer Engineering}, year = {2007}, abstract = {

Information acquired using different medical imaging techniques (e.g., MRI, PET, CT, etc.) can be combined to get a clear understanding of the overall condition of a patient for the purpose of diagnosis. Registering images from different modalities without a priori knowledge is difficult since the images may have very different intensity mappings and structural characteristics. This paper presents a novel approach to the multi-modal registration of medical images through the use of a priori knowledge to align medical images using an indirect mapping. The proposed algorithm uses stored information from successful alignment results to infer a relationship between the input images from different modalities. This relationship is then used to estimate the transformations needed to align the medical images together. Experimental results show that a high level of accuracy can be achieved using the proposed algorithm to align medical images from different modalities.

}, keywords = {biomedical MRI, CT, image registration, indirect knowledge-based approach, knowledge engineering, medical image processing, medical images, medical imaging techniques, MRI, nonrigid multimodal image registration, patient diagnosis, PET, positron emission tomography}, doi = {http://dx.doi.org/10.1109/CCECE.2007.298}, author = {A Wong and W Bishop} }