Compared to region of interest based DTI analysis, voxel-based analysis gives higher degree of localization and avoids
the procedure of manual delineation with the resulting intra and inter-rater variability. One of the major challenges in
voxel-wise DTI analysis is to get high quality voxel-level correspondence. For that purpose, current DTI analysis tools
are building on nonlinear registration algorithms that deform individual datasets into a template image that is either
precomputed or computed as part of the analysis. A variety of matching criteria and deformation schemes have been
proposed, but often comparative evaluation is missing. In our opinion, the use of consistent and unbiased measures to
evaluate current DTI procedures is of great importance and our work presents two possible measures. Specifically, we
propose the evaluation criteria generalization and specificity, originally introduced by the shape modeling community, to
evaluate and compare different DTI nonlinear warping results. These measures are of indirect nature and have a
population wise view. Both measures incorporate information of the variability of the registration results in the template
space via a voxel-wise PCA model. Thus far, we have used these measures to evaluate our own DTI analysis procedure
employing fluid-based registration on scalar DTI maps. Generalization and specificity from tensor images in the
template space were computed for 8 scalar property maps. We found that for our procedure an intensity-normalized FA
feature outperformed the other scalar measurements. Also, using the tensor images rather than the FA maps as a
comparison frame seemed to produce more robust results.
KEYWORDS: Diffusion weighted imaging, Diffusion tensor imaging, Diffusion, Brain, Head, Magnetic resonance imaging, Neuroimaging, Visualization, Data acquisition, Signal to noise ratio
Diffusion Tensor Imaging (DTI) has become an important MRI procedure to investigate the integrity of white matter in
brain in vivo. DTI is estimated from a series of acquired Diffusion Weighted Imaging (DWI) volumes. DWI data suffers
from inherent low SNR, overall long scanning time of multiple directional encoding with correspondingly large risk to
encounter several kinds of artifacts. These artifacts can be too severe for a correct and stable estimation of the diffusion
tensor. Thus, a quality control (QC) procedure is absolutely necessary for DTI studies. Currently, routine DTI QC
procedures are conducted manually by visually checking the DWI data set in a gradient by gradient and slice by slice
way. The results often suffer from low consistence across different data sets, lack of agreement of different experts, and
difficulty to judge motion artifacts by qualitative inspection. Additionally considerable manpower is needed for this step
due to the large number of images to QC, which is common for group comparison and longitudinal studies, especially
with increasing number of diffusion gradient directions. We present a framework for automatic DWI QC. We developed a tool called DTIPrep which pipelines the QC steps with a detailed protocoling and reporting facility. And it is fully open source. This framework/tool has been successfully applied to several DTI studies with several hundred DWIs in our lab as well as collaborating labs in Utah and Iowa. In our studies, the tool provides a crucial piece for robust DTI analysis in brain white matter study.
KEYWORDS: Image registration, Magnetic resonance imaging, Distortion, Medical imaging, Data modeling, Computed tomography, Image processing, Visualization, Brain, Head
Medical image elastic registration is an important subject in medical image processing. Previous work has focused how to select the corresponding landmarks manually and then use adequate interpolating for gaining the elastic transformation. However, the landmarks extraction is always prone to error, which could influence on the registration results. And localizing the landmarks manually is also difficult and time-consuming. We used Multiquadric method with smooth character, thereby , utilized a semi-automatic method to extract the landmarks .Combining these two steps, we proposed an accurate, fast and more robust registration method ,and obtained the satisfactory results.
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