Novel distortion correction method for diffusion-weighted imaging based on non-rigid image registration between low b value image and anatomical image

Abstract

PurposeThis study aimed to develop a novel technique for retrospective distortion correction based on non-rigid image registration in magnetic resonance diffusion image. MethodsA 3.0 T MRI scanner with an 18-channel dedicated breast coil and the outer shell of the original breast phantom, which provided images with non-uniform fat-suppression based on clinical data were used. The diffusion-weighted imaging with and without parallel imaging (PI) was used.The proposed study included several steps, which are FOV size matching, matrix size matching, image segmentation, edge detection, non-rigid image registration, and image wrap. We compared the results obtained using the proposed method with that obtained using TOPUP images. The correlation was assessed between T1-weighted image with fat suppression (FS-T1WI) and b1000 image with the help of cross-correlation coefficient (CCC). Shape-error analysis of tumor model and apparent diffusion coefficient (ADC) was calculated. The Steel–Dwass multiple-comparison tests were used for all comparisons and statistical analysis (P < 0.05). ResultsThe novel method of CCC showed the highest correlation between FS-T1WI and b1000 images. In the Steel–Dwass multiple-comparison test, significant differences were found (P < 0.05) except between non-correction and TOPUP (P = 0.99).The novel method was the lowest degree of error. With PI in the right breast, no significant differences, whereas in the left breast, significant differences were observed except for between novel method and TOPUP (P = 0.73). Without PI in the right breast, significant differences were observed. In the left breast, no significant differences were observed between any combinations.The ADC value, no significant differences were observed for non-correction and novel methods. ConclusionsWe developed a novel technique for retrospective distortion correction based on non-rigid image registration. The high degree of accuracy of this method combined with the lack of requirement for additional scans renders it a promising tool for application in clinical practice.