Simultaneous segmentation and iterative registration method for computing ADC with reduced artifacts from DW-MRI.

PURPOSE

Apparent diffusion coefficient (ADC), derived from diffusion-weighted magnetic resonance images (DW-MRI), measures the motion of water molecules in vivo and can be used to quantify tumor response to therapy. The accurate measurement of ADC can be adversely affected by organ motion and imaging artifacts. In this paper, the authors' goal was to develop an automated method for reducing artifacts and thereby improve the accuracy of ADC measurements in moving organs such as liver.

METHODS

The authors developed a novel method of computing ADC with fewer artifacts, through simultaneous image segmentation and iterative registration (SSIR) of multiple b-value DW-MRI. The authors' approach reduces artifacts by automatically finding the best possible alignment between the individual b-value images and a reference DW image using a sequence of transformations. It selects such a sequence by an iterative choice of b-value DW images based on the accuracy of their alignment with the reference DW image. The authors' approach quantifies the accuracy of alignment between a pair of images using modified Hausdroff distance computed between the structures of interest. The structures of interest are identified by a user through strokes drawn in one or more slices in the reference DW image, which are then volumetrically segmented using GrowCut. The same structures are segmented in the remaining b-value images by transforming the user-drawn strokes through registration. The ADC values are computed from all the aligned b-value images. The images are aligned by using affine registration followed by deformable B-spline registration with cubic B-spline resampling.

RESULTS

The authors compared the results of ADC computed using their approach with ADC computed (a) without registration and (b) with basic affine registration of all b-value images to a chosen reference. The authors' approach was the most effective in reducing artifacts compared to the other two methods. It resulted in a mean artifact ratio (fraction of voxels in a structure with negative ADC over total number of voxels in the structure) of 2.7% versus 5.4% for affine registration and 32% for no registration for >200 tumors. The authors' approach also resulted in the lowest median standard deviation in the computed mean ADC for all tumors [0.05,0.09,0.07,0.58] compared to those from affine image registration [0.02, 0.14, 0.58, 0.79] and no image registration [0.64, 0.83, 0.83, 1.09] on tests where random displacement [8,10,12,16] pixels were introduced in multiple trials in the b-value images.

CONCLUSIONS

The authors developed a novel approach for reducing artifacts in ADC maps through simultaneous registration and segmentation of multiple b-value DW images. The authors' method explicitly employs a registration quality metric to align images. When compared to basic affine and no image registrations, the authors' approach produces registrations of greater accuracy with lowest artifact ratio and median standard deviation of the computed mean ADC values for a wide range of displacements.