noninvasive characterization of water molecule’s mobility variations by quantitative analysis of

noninvasive characterization of water molecule’s mobility variations by quantitative analysis of diffusion-weighted MRI (DW-MRI) signal decay in the abdomen has the potential to serve as a biomarker in gastrointestinal and oncological applications. of the low contrast and SNR difference between images of varying b-value. In this work we introduce a motion-compensated parameter estimation framework that simultaneously solves image registration and model estimation (SIR-ME) problems by utilizing the interdependence of acquired volumes along the diffusion weighting dimension. We evaluated the improvement in model parameters estimation accuracy using 16 in-vivo DW-MRI data sets of Crohn’s disease patients by comparing parameter estimates obtained using the SIR-ME model to the parameter estimates obtained by fitting the signal decay model to the acquired DW-MRI images. The proposed SIR-ME model reduced the average root-mean-square error between the observed signal and the fitted model by more than 50%. Moreover the SIR-ME model estimates discriminate between abnormal and normal bowel loops better than the standard parameter estimates. = 1‥= {(= 1‥assuming isotropic diffusion. However in the presence of motion measured images ( is the noisy high SNR image template and given the signal decay model parameters is the transformation between the observed image and the high SNR image at is the transformation between and which are unknown. Therefore we cannot directly optimize this equation. Instead we solve it as a simultaneous optimization problem where registration reconstruction of the high SNR DW-MRI images and estimation of the signal decay model parameters are iteratively performed. 2.2 Optimization scheme We solve Eq. 4 by iteratively estimating the signal decay model parameters and transformation given the current estimate of the signal and and transformation and estimation We use the spatially constrained probability distribution model of slow and fast diffusion (SPIM) [4] to robustly estimate the fast and slow diffusion parameters of the signal decay model and transformation at iteration ≥ 0 is the spatial coupling factor is a diagonal weighting matrix which accounts for the different scales of the parameters in and are the neighboring voxels according to the employed neighborhood system. We estimated the model parameters by minimizing Eq. 5 using the “fusion bootstrap moves” combinatorial solver introduced by Freiman et al. [7] and applied in Kurugol et al. [4] to solve the SPIM model. Estimation of transformation Given the current estimate of expected signal from the signal decay model that aligns each low SNR acquired ARL-15896 ARL-15896 image to In this step we update given the current estimate of from the registration step. We minimize Eq. 4 to get the next estimate of the signal We finally estimate transformation ARL-15896 to align each reconstructed high SNR template image (images have higher SNR than images even for high b-values. We use the same block-matching registration algorithm for inter b-value registration but replace the similarity measure with squared cross correlation. We initialize the algorithm with USP39 the acquired DW-MRI data as the current estimate of the signal after application of an initial registration algorithm. We then iteratively alternate between estimating the model parameters estimation of transformations for registration and estimating the high SNR DW-MRI signal until the change in the model parameter estimations is negligible. The steps of the optimization algorithm are summarized in Table 1. Table 1 SIR-ME optimization algorithm 3 Results We have tested the performance of the proposed model in DW-MRI ARL-15896 data of 16 Crohn’s Disease patients. The images were acquired using a 1.5-T scanner (Magnetom Avanto Siemens Medical Solutions) with free-breathing single-shot echoplanar imaging using the following parameters: repetition/echo time (TR/TE)= 7500/77ms; matrix size=192×156; field of view=300×260 mm; slice thickness/gap = 5mm/0mm; 40 axial slices; 7 b-values = 0 50 100 200 400 600 800 s/mm2 with 1 excitation 6 gradient directions; acquisition time = 5.5 min. A region of normal bowel wall and a region of inflamed bowel wall with active Crohn’s disease identified in Gd-DTPA contrast enhanced images were delineated on DW-MRI images of each subject by an.