@article {46, title = {Apparent Ultra-High b-value Diffusion-Weighted Image Reconstruction via Hidden Conditional Random Fields}, journal = {TRANSACTIONS ON MEDICAL IMAGING}, volume = {34}, year = {2015}, abstract = {

Abstract\—A promising, recently explored, alternative to ultra-high -value diffusion weighted imaging (UHB-DWI) is apparent ultra-high -value diffusion-weighted image reconstruction (AUHB-DWR), where a computational model is used to assist in the reconstruction of apparent DW images at ultra-high -values. Firstly, we present a novel approach to AUHB-DWR that aims to improve image quality. We formulate the reconstruction of an apparent DW image as a hidden conditional random field (HCRF) in which tissue model diffusion parameters act as hidden states in this random field. The second contribution of this paper is a new generation of fully connected conditional random fields, called the hidden stochastically fully connected conditional random fields (HSFCRF) that allows for efficient inference with significantly reduced computational complexity via stochastic clique structures. The proposed AUHB-DWR algorithms, HCRF and HSFCRF, are evaluated quantitatively in nine different patient cases using Fisher\&$\#$39;s criteria, probability of error, and coefficient of variation metrics to validate its effectiveness for the purpose of improving intensity delineation between expert identified suspected cancerous and healthy tissue within the prostate gland. The proposed methods are also examined using a prostate phantom, where the apparent ultra-high -value DW images reconstructed using the tested AUHB-DWR methods are compared with real captured UHB-DWI. The results illustrate that the proposed AUHB-DWR methods has improved reconstruction quality and improved intensity delineation compared with existing AUHB-DWR approaches.

}, keywords = {(MRI), Apparent diffusion weighted imaging, conditional random field, diffusion weighted imaging, hidden state, magnetic resonance imaging, prostate cancer imaging, stochastically fully connected random field, ultra high value}, author = {M J. Shafiee and S Haider and A Wong and D Lui and A Cameron and A Modhafar and P Fieguth and M A. Haider} } @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 {281, title = {Statistical fusion of two-scale images of porous media}, journal = {Advances in Water Resources}, volume = {32}, year = {2009}, pages = {1567 - 1579}, abstract = {

The reconstruction of the architecture of void space in porous media is a challenging task, since porous media contain pore structures at multiple scales. Whereas past methods have been limited to producing samples with matching statistical behavior, the patterns of grey-level values in a measured sample actually say something about the unresolved details, thus we propose a statistical fusion framework for reconstructing high-resolution porous media images from low-resolution measurements. The proposed framework is based on a posterior sampling approach in which information obtained by low-resolution (MRI or X-ray) measurements is combined with prior models inferred from high-resolution microscopic data, typically 2D. In this paper, we focus on two-scale reconstruction tasks in which the measurements resolve only the large scale structures, leaving the small-scale to be inferred. The evaluation of the results generated by the proposed method shows the strong ability of the proposed method in reconstructing fine-scale structures positively correlated with the underlying ground truth. Comparing our method with the recent method of Okabe and Blunt [12], in which the measurements are also used in the reconstruction, we conclude that our method is more robust to the resolution of the measurement, and more closely matches the underlying fine-scale field.

}, keywords = {Data fusion, magnetic resonance imaging, Porous media reconstruction, Posterior sampling, simulated annealing}, issn = {0309-1708}, doi = {http://dx.doi.org/10.1016/j.advwatres.2009.08.005}, url = {http://www.sciencedirect.com/science/article/pii/S0309170809001274}, author = {A Mohebi and P Fieguth and M A. Ioannidis} }