Prospective quantitative quality assurance and deformation estimation of MRI-CT image registration in simulation of head and neck radiotherapy patients. (September 2019)

• Record Type: Journal Article
• Authors: Kiser, Kendall; Meheissen, Mohamed A.M.; Mohamed, Abdallah S.R.; Kamal, Mona; Ng, Sweet Ping; Elhalawani, Hesham; Jethanandani, Amit; He, Renjie; Ding, Yao; Rostom, Yousri; Hegazy, Neamat; Bahig, Houda; Garden, Adam; Lai, Stephen; Phan, Jack; Gunn, Gary B.; Rosenthal, David; Frank, Steven; Brock, Kristy K.; Wang, Jihong; Fuller, Clifton D.
• Is Part Of: Clinical and translational radiation oncology. Volume 18(2019)
• Page Start: 120
• Page End: 127
• Publication Date: 2019-09
• Subjects: RT radiation therapy -- MRI magnetic resonance imaging -- MRIgRT MRI-guided radiotherapy planning -- CT computed tomography -- OAR organ(s) at risk -- MRL MRI linear accelerator -- DIR deformable image registration -- RIR rigid image registration -- HNC head and neck cancer -- IMRT intensity-modulated radiation therapy -- DICOM digital imaging and communications in medicine -- DSC dice similarity coefficient -- HD max Hausdorff maximum distance -- HD mean Hausdorff mean distance -- HPV human papillomavirus -- sCT synthetic computed tomography -- MAE mean absolute error -- HU Hounsfield units; MRI-guided radiotherapy -- CT-MRI image registration -- Deformable image registration -- Rigid image registration -- Quality assessment; Cancer -- Radiotherapy -- Periodicals; Oncology -- Periodicals; Cancer -- Radiotherapy; Oncology; Radiation Oncology; Neoplasms -- radiotherapy; Translational Medical Research; Periodicals; Electronic journals; Periodicals; 616.9940642
• DOI: 10.1016/j.ctro.2019.04.018
• Languages: English
• ISSNs: 2405-6308

Abstract

Highlights: MRI-CT deformable image registration was not superior to rigid registration. Dice similarity coefficients were 0.65, 0.62, and 0.63 for deformable registrations. Dice similarity coefficient was 0.63 for rigid registration. Registration quality was superior in muscle and gland compared to bone and vessel. Abstract: Background: MRI-guided radiotherapy planning (MRIgRT) may be superior to CT-guided planning in some instances owing to its improved soft tissue contrast. However, MR images do not communicate tissue electron density information necessary for dose calculation and therefore must either be co-registered to CT or algorithmically converted to synthetic CT. No robust quality assessment of commercially available MR-CT registration algorithms is yet available; thus we sought to quantify MR-CT registration formally. Methods: Head and neck non-contrast CT and T2 MRI scans acquired with standard treatment immobilization techniques were prospectively acquired from 15 patients. Per scan, 35 anatomic regions of interest (ROIs) were manually segmented. MRIs were registered to CT rigidly (RIR) and by three commercially available deformable registration algorithms (DIR). Dice similarity coefficient (DSC), Hausdorff distance mean (HD mean) and Hausdorff distance max (HD max) metrics were calculated to assess concordance between MRI and CT segmentations. Each DIR algorithm was compared to DIR using the nonparametric Steel test with control for individual ROIs (n = 105 tests) and for all ROIs in aggregate (n = 3 tests). The influence of tissue type on registration fidelity was assessed using nonparametric Wilcoxon pairwise tests between ROIs grouped by tissue type (n = 12 tests). Bonferroni corrections were applied for multiple comparisons. Results: No DIR algorithm improved the segmentation quality over RIR for any ROI nor all ROIs in aggregate (all p values >0.05). Muscle and gland ROIs were significantly more concordant than vessel and bone, but DIR remained non-different from RIR. Conclusions: For MR-CT co-registration, our results question the utility and applicability of commercially available DIR over RIR alone. The poor overall performance also questions the feasibility of translating tissue electron density information to MRI by CT registration, rather than addressing this need with synthetic CT generation or bulk-density assignment.