Correction of susceptibility artifacts in diffusion tensor data using non-linear registration

Abstract

Diffusion tensor imaging can be used to localize major white matter tracts within the human brain. For surgery of tumors near eloquent brain areas such as the pyramidal tract this information is of importance to achieve an optimal resection while avoiding post-operative neurological deficits. However, due to the small bandwidth of echo planar imaging, diffusion tensor images suffer from susceptibility artifacts resulting in positional shifts and distortion. As a consequence, the fiber tracts computed from echo planar imaging data are spatially distorted. We present an approach based on non-linear registration using Bézier functions to efficiently correct distortions due to susceptibility artifacts. The approach makes extensive use of graphics hardware to accelerate the non-linear registration procedure. An improvement presented in this paper is a more robust and efficient optimization strategy based on simultaneous perturbation stochastic approximation (SPSA).Since the accuracy of non-linear registration is crucial for the value of the presented correction method, two techniques were applied in order to prove the quality of the proposed framework. First, the registration accuracy was evaluated by recovering a known transformation with non-linear registration. Second, landmark-based evaluation of the registration method for anatomical and diffusion tensor data was performed. The registration was then applied to patients with lesions adjacent to the pyramidal tract in order to compensate for susceptibility artifacts. The effect of the correction on the pyramidal tract was then quantified by measuring the position of the tract before and after registration. As a result, the distortions observed in phase encoding direction were most prominent at the cortex and the brainstem. The presented approach allows correcting fiber tract distortions which is an important prerequisite when tractography data are integrated into a stereotactic setup for intra-operative guidance.

Nonlinear distortion correction of diffusion MR images improves quantitative DTI measurements in glioblastoma

“…Retrospectively, geometric distortion correction of DTI data can be performed using nonlinear registration of the reference ( b =0) image of the DTI dataset with geometric distortions to an anatomical image, and then applying this transformation to rest of the raw DTI data [17, 18]. In the current study we tested whether retrospective, nonlinear distortion correction of DTI data would result in improved anatomical alignment of DTI to anatomical images, reduced variability in FA and ADC measurements within tumor tissue, and altered DTI measurements of tumor characteristics when compared to standard DTI measurement techniques.…”

Diffusion Tensor Tractography of the Brainstem Pyramidal Tract: A Study on the Optimal Reduction Factor in Parallel Imaging

“…DTT has also been used to predict the degree of motor deficit associated with brainstem tumors (12), to assess treatment response in patients with pediatric diffuse brainstem glioma (14,15), and to plan the surgical approach to brainstem cavernoma by providing information regarding motor and sensory fiber tracts (18). However, it should be noted that DTT is easily compromised by susceptibility-induced geometric distortion, which is an intrinsic shortcoming of EPI (19). The pons is particularly vulnerable to susceptibility artifacts, caused in this case by air in the adjacent sphenoid sinus.…”

Quantitative and reproducibility study of four tractography algorithms used in clinical routine

“…Nevertheless, image distortions related to eddy‐currents artifacts should be corrected (28) before the rigid registration with 3D volume. The EPI distortions that arise due to susceptibility artifacts can be corrected using nonlinear registration (31). It can thus be presumed that reproducibility would have been improved otherwise.…”