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. 2024 Oct 25;6(6):e240073. doi: 10.1148/rycan.240073

Figure 4:

Visualization of the alignment quality achieved by each coregistration method. Restaging MR–whole-mount histology image fusion is shown in three representative cases. Case 10 (a 43-year-old female patient), case 15 (a 65-year-old male patient), and case 7 (a 47-year-old male patient) represent cases with the best Dice similarity coefficient, average Dice similarity coefficient, and worst Dice similarity coefficient, respectively. Between the investigated linearized iterative boundary reconstruction (LIBR), rigid point-based registration (PBR), and multiscale spectral embedding registration (MSERg) methods, LIBR produced better alignment. Note that in case 7, MSERg produced a misalignment of approximately 90° between the pathology slice and the MR image.

Visualization of the alignment quality achieved by each coregistration method. Restaging MR–whole-mount histology image fusion is shown in three representative cases. Case 10 (a 43-year-old female patient), case 15 (a 65-year-old male patient), and case 7 (a 47-year-old male patient) represent cases with the best Dice similarity coefficient, average Dice similarity coefficient, and worst Dice similarity coefficient, respectively. Between the investigated linearized iterative boundary reconstruction (LIBR), rigid point-based registration (PBR), and multiscale spectral embedding registration (MSERg) methods, LIBR produced better alignment. Note that in case 7, MSERg produced a misalignment of approximately 90° between the pathology slice and the MR image.