AUTHOR=Canalini Luca , Klein Jan , Waldmannstetter Diana , Kofler Florian , Cerri Stefano , Hering Alessa , Heldmann Stefan , Schlaeger Sarah , Menze Bjoern H. , Wiestler Benedikt , Kirschke Jan , Hahn Horst K. 

TITLE=Quantitative evaluation of the influence of multiple MRI sequences and of pathological tissues on the registration of longitudinal data acquired during brain tumor treatment

JOURNAL=Frontiers in Neuroimaging

VOLUME=Volume 1 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/neuroimaging/articles/10.3389/fnimg.2022.977491

DOI=10.3389/fnimg.2022.977491

ISSN=2813-1193

ABSTRACT=Registration methods facilitate the comparison of multiparametric magnetic resonance images acquired at different stages of brain tumor treatments. Image-based registration solutions are influenced by the sequences chosen to compute the distance measure, and the lack of image correspondences due to the resection cavities and pathological tissues. Nonetheless, an evaluation of the impact of these input parameters on the registration of longitudinal data is still missing. 

This work evaluates the influence of multiple sequences (T1, T2, T1c, and FLAIR) and the exclusion of the pathological tissues on the non-rigid registration of pre- and post-operative images. We here investigate two types of registration methods, an iterative approach and a convolutional neural network solution based on a 3D U-Net. We employ two test sets to compute the mean target registration error (mTRE) based on corresponding landmarks. In the first set, markers are positioned exclusively in the surroundings of the pathology. The methods employing T1c achieves the lowest mTREs, with a improvement up to 0.8 mm for the iterative solution. The results are higher than the baseline when using the FLAIR sequence. When excluding the pathology, lower mTREs are observable for most of the methods. In the second test set, corresponding landmarks are located in the entire brain volumes. Both solutions employing T1c obtain the lowest mTREs, with a decrease up to 1.16 mm for the iterative method, whereas the results worsen using the FLAIR. When excluding the pathology, an improvement is observable for the CNN method using T1c.  

Both approaches utilizing the T1c sequence obtain the best mTREs, whereas the FLAIR is the least informative to guide the registration process. Besides, the exclusion of pathology from the distance measure computation improves the registration of the brain tissues surrounding the tumor. Thus, this work provides the first numerical evaluation of the influence of these parameters on the registration of longitudinal magnetic resonance images, and it can be helpful for developing future algorithms.