Prospective quantitative quality assurance and deformation estimation of MRI-CT image registration in simulation of head and neck radiotherapy patients

Joint Head and Neck MRI-Radiotherapy Development Cooperative; Kendall Kiser; Mohamed A M Meheissen; Abdallah S R Mohamed; Mona Kamal; Sweet Ping Ng; Hesham Elhalawani; Amit Jethanandani; Renjie He; Yao Ding; Yousri Rostom; Neamat Hegazy; Houda Bahig; Adam Garden; Stephen Lai; Jack Phan; Gary B Gunn; David Rosenthal; Steven Frank; Kristy K Brock; Jihong Wang; Clifton D Fuller

doi:10.1016/j.ctro.2019.04.018

Prospective quantitative quality assurance and deformation estimation of MRI-CT image registration in simulation of head and neck radiotherapy patients

Clin Transl Radiat Oncol. 2019 Apr 24:18:120-127. doi: 10.1016/j.ctro.2019.04.018. eCollection 2019 Sep.

Authors

Joint Head and Neck MRI-Radiotherapy Development Cooperative; Kendall Kiser^{1

2

3}, Mohamed A M Meheissen^{3

4}, Abdallah S R Mohamed^{3

4

5}, Mona Kamal^{3

6}, Sweet Ping Ng^{3

7}, Hesham Elhalawani³, Amit Jethanandani^{3

8}, Renjie He³, Yao Ding⁹, Yousri Rostom⁴, Neamat Hegazy⁴, Houda Bahig^{3

10}, Adam Garden³, Stephen Lai¹¹, Jack Phan³, Gary B Gunn³, David Rosenthal³, Steven Frank³, Kristy K Brock^{9

12}, Jihong Wang⁹, Clifton D Fuller³

Affiliations

¹ University of Texas, John P. and Kathrine G. McGovern Medical School, 6431 Fannin Street, Houston, TX 77030, USA.
² UT Health School of Biomedical Informatics, 7000 Fannin Street, Suite 600, Houston, TX 77030, USA.
³ Department of Radiation Oncology, the University of Texas MD Anderson Cancer Center, The MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA.
⁴ Department of Clinical Oncology and Nuclear Medicine, Faculty of Medicine, University of Alexandria, 17 Champilion Street, Alazarita, Alexandria, Egypt.
⁵ MD Anderson Cancer Center/UT Health Graduate School of Biomedical Sciences, 6767 Bertner Avenue, Houston, TX 77030, USA.
⁶ Department of Clinical Oncology and Nuclear Medicine, Faculty of Medicine, University of Ain Shams, Lofty El-Said Street, 1156 Cairo, Egypt.
⁷ Department of Radiation Oncolog, Peter MacCallum Cancer Centre, 305 Grattan St, Melbourne, VIC 3000, Australia.
⁸ College of Medicine, University of Tennessee Health Science Center, 910 Madison Avenue #1002, Memphis, TN 38103, USA.
⁹ Department of Radiation Physics, the University of Texas MD Anderson Cancer Center, The MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA.
¹⁰ Department of Radiation Oncology, Centre Hospitalier de l'Universite de Montreal, 1051 Rue Sanguinet, Montreal, QC H2X 3E4, Canada.
¹¹ Department of Head and Neck Surgery, the University of Texas MD Anderson Cancer Center, The MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX, 77030, USA.
¹² Department of Imaging Physics, the University of Texas MD Anderson Cancer Center, The MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA.

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.

Keywords: CT, computed tomography; CT-MRI image registration; DICOM, digital imaging and communications in medicine; DIR, deformable image registration; DSC, dice similarity coefficient; Deformable image registration; HD max, Hausdorff maximum distance; HD mean, Hausdorff mean distance; HNC, head and neck cancer; HPV, human papillomavirus; HU, Hounsfield units; IMRT, intensity-modulated radiation therapy; MAE, mean absolute error; MRI, magnetic resonance imaging; MRI-guided radiotherapy; MRIgRT, MRI-guided radiotherapy planning; MRL, MRI linear accelerator; OAR, organ(s) at risk; Quality assessment; RIR, rigid image registration; RT, radiation therapy; Rigid image registration; sCT, synthetic computed tomography.

Abstract

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