MO‐F‐CAMPUS‐J‐05: Toward MRI‐Only Radiotherapy: Novel Tissue Segmentation and Pseudo‐CT Generation Techniques Based On T1 MRI Sequences

Abstract

Purpose:To develop and validate a 4 class tissue segmentation approach (air cavities, background, bone and soft‐tissue) on T1 ‐weighted brain MRI and to create a pseudo‐CT for MRI‐only radiation therapy verification.Methods:Contrast‐enhanced T1‐weighted fast‐spin‐echo sequences (TR = 756ms, TE= 7.152ms), acquired on a 1.5T GE MRI‐Simulator, are used.MRIs are firstly pre‐processed to correct for non uniformity using the non parametric, non uniformity intensity normalization algorithm. Subsequently, a logarithmic inverse scaling log(1/image) is applied, prior to segmentation, to better differentiate bone and air from soft‐tissues. Finally, the following method is enrolled to classify intensities into air cavities, background, bone and soft‐tissue:Thresholded region growing with seed points in image corners is applied to get a mask of Air+Bone+Background. The background is, afterward, separated by the scan‐line filling algorithm. The air mask is extracted by morphological opening followed by a post‐processing based on knowledge about air regions geometry. The remaining rough bone pre‐segmentation is refined by applying 3D geodesic active contours; bone segmentation evolves by the sum of internal forces from contour geometry and external force derived from image gradient magnitude.Pseudo‐CT is obtained by assigning −1000HU to air and background voxels, performing linear mapping of soft‐tissue MR intensities in [‐400HU, 200HU] and inverse linear mapping of bone MR intensities in [200HU, 1000HU].Results:Three brain patients having registered MRI and CT are used for validation. CT intensities classification into 4 classes is performed by thresholding. Dice and misclassification errors are quantified. Correct classifications for soft‐tissue, bone, and air are respectively 89.67%, 77.8%, and 64.5%. Dice indices are acceptable for bone (0.74) and soft‐tissue (0.91) but low for air regions (0.48). Pseudo‐CT produces DRRs with acceptable clinical visual agreement to CT‐based DRR.Conclusion:The proposed approach makes it possible to use T1‐weighted MRI to generate accurate pseudo‐CT from 4‐class segmentation.