Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012

Diffusion-weighted MRI of the body has the potential to provide important new insights into physiological and microstructural properties. The Intra-Voxel Incoherent Motion (IVIM) model relates the observed DW-MRI signal decay to parameters that reflect perfusivity (D∗) and its volume fraction (f), and diffusivity (D). However, the commonly used voxel-wise fitting of the IVIM model leads to parameter estimates with poor precision, which has hampered their practical usage. In this work, we increase the estimates’ precision by introducing a model of spatial homogeneity, through which we obtain estimates of model parameters for all of the voxels at once, instead of solving for each voxel independently. Furthermore, we introduce an efficient iterative solver which utilizes a model-based bootstrap estimate of the distribution of residuals and a binary graph cut to generate optimal model parameter updates. Simulation experiments show that our approach reduces the relative root mean square error of the estimated parameters by 80% for the D∗ parameter and by 50% for the f and D parameters. We demonstrated the clinical impact of our model in distinguishing between enhancing and nonenhancing ileum segments in 24 Crohn’s disease patients. Our model detected the enhanced segments with 91%/92% sensitivity/specificity which is better than the 81%/85% obtained by the voxel-independent approach.

[1]  Y. C. Pati,et al.  Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition , 1993, Proceedings of 27th Asilomar Conference on Signals, Systems and Computers.

[2]  R. Tibshirani Regression Shrinkage and Selection via the Lasso , 1996 .

[3]  J L Gallant,et al.  Sparse coding and decorrelation in primary visual cortex during natural vision. , 2000, Science.

[4]  Michael Elad,et al.  Image Sequence Denoising via Sparse and Redundant Representations , 2009, IEEE Transactions on Image Processing.

[5]  Po-Wei Hsu,et al.  Freehand 3D Ultrasound Calibration: A Review , 2009 .

[6]  Jieping Ye,et al.  Multi-Task Feature Learning Via Efficient l2, 1-Norm Minimization , 2009, UAI.

[7]  Stephen J. Wright,et al.  Computational Methods for Sparse Solution of Linear Inverse Problems , 2010, Proceedings of the IEEE.

[8]  Thomas S. Huang,et al.  Image Super-Resolution Via Sparse Representation , 2010, IEEE Transactions on Image Processing.

[9]  Dinggang Shen,et al.  Automatic segmentation of neonatal images using convex optimization and coupled level sets , 2011, NeuroImage.

[10]  Michael I. Miller,et al.  Multi-contrast human neonatal brain atlas: Application to normal neonate development analysis , 2011, NeuroImage.

[11]  Damiana Lazzaro,et al.  A Fast Compressed Sensing Approach to 3D MR Image Reconstruction , 2011, IEEE Transactions on Medical Imaging.

[12]  Daniel Rueckert,et al.  A dynamic 4D probabilistic atlas of the developing brain , 2011, NeuroImage.

[13]  Dinggang Shen,et al.  journal homepage: www.elsevier.com/locate/ynimg , 2022 .

[14]  Junzhou Huang,et al.  Towards robust and effective shape modeling: Sparse shape composition , 2012, Medical Image Anal..

[15]  Daniel Rueckert,et al.  Construction of a consistent high-definition spatio-temporal atlas of the developing brain using adaptive kernel regression , 2012, NeuroImage.

[16]  Dinggang Shen,et al.  Feature‐based groupwise registration by hierarchical anatomical correspondence detection , 2012, Human brain mapping.