A method for quantitative analysis of regional lung ventilation using deformable image registration of CT and hybrid hyperpolarized gas/H-1 MRI

Hyperpolarized gas magnetic resonance imaging (MRI) generates highly detailed maps of lung ventilation and physiological function while CT provides corresponding anatomical and structural information. Fusion of such complementary images enables quantitative analysis of pulmonary structure-function. However, direct image registration of hyperpolarized gas MRI to CT is problematic, particularly in lungs whose boundaries are difficult to delineate due to ventilation heterogeneity. This study presents a novel indirect method of registering hyperpolarized gas MRI to CT utilizing 1H-structural MR images that are acquired in the same breath-hold as the gas MRI. The feasibility of using this technique for regional quantification of ventilation of specific pulmonary structures is demonstrated for the lobes. The direct and indirect methods of hyperpolarized gas MRI to CT image registration were compared using lung images from 15 asthma patients. Both affine and diffeomorphic image transformations were implemented. Registration accuracy was evaluated using the target registration error (TRE) of anatomical landmarks identified on 1H MRI and CT. The Wilcoxon signed-rank test was used to test statistical significance. For the affine transformation, the indirect method of image registration was significantly more accurate than the direct method (TRE = 14.7  ±  3.2 versus 19.6  ±  12.7 mm, p = 0.036). Using a deformable transformation, the indirect method was also more accurate than the direct method (TRE = 13.5  ±  3.3 versus 20.4  ±  12.8 mm, p = 0.006). Accurate image registration is critical for quantification of regional lung ventilation with hyperpolarized gas MRI within the anatomy delineated by CT. Automatic deformable image registration of hyperpolarized gas MRI to CT via same breath-hold 1H MRI is more accurate than direct registration. Potential applications include improved multi-modality image fusion, functionally weighted radiotherapy planning, and quantification of lobar ventilation in obstructive airways disease

Multi-object model-based multi-atlas segmentation for rodent brains using dense discrete correspondences

The delineation of rodent brain structures is challenging due to low-contrast multiple cortical and subcortical organs that are closely interfacing to each other. Atlas-based segmentation has been widely employed due to its ability to delineate multiple organs at the same time via image registration. The use of multiple atlases and subsequent label fusion techniques has further improved the robustness and accuracy of atlas-based segmentation. However, the accuracy of atlas-based segmentation is still prone to registration errors; for example, the segmentation of in vivo MR images can be less accurate and robust against image artifacts than the segmentation of post mortem images. In order to improve the accuracy and robustness of atlas-based segmentation, we propose a multi-object, model-based, multi-atlas segmentation method. We first establish spatial correspondences across atlases using a set of dense pseudo-landmark particles. We build a multi-object point distribution model using those particles in order to capture inter- and intra- subject variation among brain structures. The segmentation is obtained by fitting the model into a subject image, followed by label fusion process. Our result shows that the proposed method resulted in greater accuracy than comparable segmentation methods, including a widely used ANTs registration tool

Spatial comparison of CT-based surrogates of lung ventilation with hyperpolarized Helium-3 and Xenon-129 gas MRI in patients undergoing radiation therapy

Purpose To develop and apply an image acquisition and analysis strategy for spatial comparison of CT-ventilation images with hyperpolarized gas MRI. Methods 11 lung cancer patients underwent 129Xe and 3He ventilation MRI and co-registered 1H anatomical MRI. Expiratory and inspiratory breath-hold CTs were used for deformable image registration and calculation of three CT-ventilation metrics: Hounsfield unit (CTHU), Jacobian (CTJac) and specific gas volume change (CTSGV). Inspiration CT and hyperpolarized gas ventilation MRI were registered via same-breath anatomical 1H-MRI. Voxel-wise Spearman correlation coefficients were calculated between each CT-ventilation image and its corresponding 3He/129Xe-MRI, and for the mean values in regions of interest (ROIs) ranging from fine to coarse in-plane dimensions of 5x5, 10x10, 15x15 and 20x20, located within the lungs as defined by the same-breath 1H-MRI lung mask. Correlation of 3He and 129Xe-MRI was also assessed. Results Spatial correlation of CT-ventilation against 3He/129Xe-MRI increased with ROI size. For example, for CTHU, mean±SD Spearman coefficients were 0.37±0.19/0.33±0.17 at the voxel-level and 0.52±0.20/0.51±0.18 for 20x20 ROIs, respectively. Correlations were stronger for CTHU than for CTJac or CTSGV. Correlation of 3He with 129Xe-MRI was consistently higher than either gas against CT-ventilation maps over all ROIs (p<0.05). No significant differences were observed between CT-ventilation vs 3He-MRI and CT-ventilation vs 129Xe-MRI. Conclusion Comparison of ventilation-related measures from CT and registered hyperpolarized gas MRI is feasible at a voxel level using a dedicated acquisition and analysis protocol. Moderate correlation between CT-ventilation and MRI exists at a regional level. Correlation between MRI and CT is significantly less than between 3He and 129Xe-MRI, suggesting that CT-ventilation surrogate measures may not be measuring lung ventilation alone

Interactive Medical Image Registration With Multigrid Methods and Bounded Biharmonic Functions

Interactive image registration is important in some medical applications since automatic image registration is often slow and sometimes error-prone. We consider interactive registration methods that incorporate user-specified local transforms around control handles. The deformation between handles is interpolated by some smooth functions, minimizing some variational energies. Besides smoothness, we expect the impact of a control handle to be local. Therefore we choose bounded biharmonic weight functions to blend local transforms, a cutting-edge technique in computer graphics. However, medical images are usually huge, and this technique takes a lot of time that makes itself impracticable for interactive image registration. To expedite this process, we use a multigrid active set method to solve bounded biharmonic functions (BBF). The multigrid approach is for two scenarios, refining the active set from coarse to fine resolutions, and solving the linear systems constrained by working active sets. We\u27ve implemented both weighted Jacobi method and successive over-relaxation (SOR) in the multigrid solver. Since the problem has box constraints, we cannot directly use regular updates in Jacobi and SOR methods. Instead, we choose a descent step size and clamp the update to satisfy the box constraints. We explore the ways to choose step sizes and discuss their relation to the spectral radii of the iteration matrices. The relaxation factors, which are closely related to step sizes, are estimated by analyzing the eigenvalues of the bilaplacian matrices. We give a proof about the termination of our algorithm and provide some theoretical error bounds. Another minor problem we address is to register big images on GPU with limited memory. We\u27ve implemented an image registration algorithm with virtual image slices on GPU. An image slice is treated similarly to a page in virtual memory. We execute a wavefront of subtasks together to reduce the number of data transfers. Our main contribution is a fast multigrid method for interactive medical image registration that uses bounded biharmonic functions to blend local transforms. We report a novel multigrid approach to refine active set quickly and use clamped updates based on weighted Jacobi and SOR. This multigrid method can be used to efficiently solve other quadratic programs that have active sets distributed over continuous regions