Nimg-84. comparison of traditional and advanced deep-learning patient specific registration methods in preserving tumor habitat in glioblastoma tumors: a preliminary study

Abstract PURPOSE Registration of patient specific pre and post operative MRI scans in Glioblastoma patients is crucial to assess tumor progression, effect of radiation, and chemotherapy and survival prediction. However, in brain tumors longitudinal registration is challenging as tumor resection and mass effects causes tissue deformations and missing intensity correspondence across longitudinal scans. In this study, we compared effectiveness of traditional registration algorithms with state-of-the-art computationally extensive deep learning (DL) models in preserving intensity of tumor regions. METHODS Brain MRI scans (T1w,T1c,T2w,FLAIR) of 14 glioblastoma patients with first (tp1), intermediate (tp2) and follow up post operative scans (tp3) were obtained from two publicly available dataset (LUMIERE, XCURE). Preprocessing involved co-registration of all modalities within a timepoint, skull stripping, and uniformity in image dimensions, orientation, and origin across all timepoints. Tumor scans at tp2 and tp3 were registered to tp1 for all modalities independently, using different registration algorithms such as the traditional affine and symmetric diffeomorphic (SyN) registration with and without tumor masks using the ANTs software, along with the MICCAI-registration challenge winner DL algorithm (DIRAC). Intensity distribution of registered and unregistered scans for tp2 and tp3 were compared using the earth mover’s distance (EMD). RESULTS Traditional affine (tp2-0.074±0.06, tp3:0.058±0.051) and SyN (tp2-0.048±0.031 ,tp3:0.075±0.074) registration algorithm using T2w modality provided lower average EMD than DL method (tp2-0.624±0.69,tp3-0.595±0.67). Intensity distribution was better preserved for scans from intermediate timepoints compared to the post operative scans. CONCLUSION Our preliminary assessment suggests that traditional registration methods when implemented on pre-aligned MRI scans may better preserve image intensities in brain tumors scans compared to advanced and computationally expensive DL methods.