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Ever since the early 1980s, surgical robotics/robot assisted surgeries have gained more of a foothold in modern treatments. The extra guidance/precision that surgical robotics have provided in operations has been indispensable. The next step for surgical robotics is MRI compatibility to provide close to real time intraoperative imaging for space constrained operations. The robotic component artifact influence over the region of surgical interest (ROSI) is to be mitigated for complications in addition to providing accurate guidance for the surgeon. This study defines a large MRI phantom design for specimen submersion to verify/quantify artifact generation from robotic components as well as provide a better visualization platform for robotic performance during preliminary testing and evaluation. The main topics of focus for the phantom design are fluid selection, phantom shape, phantom containment material, and 3D printed artifact measurement evaluation grids. The MRI scans were conducted using a 3T Magnetom Prisma MRI. Three scan types were selected: T1 weighted, T2 weighted, and SGE (spoiled gradient echo). The investigated phantom fluids were a solution of nickel chloride and sodium chloride (the ACR phantom, 10mM NiCl2 75mM NaCl), two salt doped distilled water (13g, 26g), and food grade mineral oil. The oil and ACR phantom outperformed the doped water with similar SNR/CNR returns (SGE: SNR/CNR 250/240/57, PIU 83/60/85). The phantom containment material was inconclusive due to motion artifact production and will be rerun with alternative fluid. The 3D printed artifact measurement grid was printed in PETG as a cost-effective substitute as PLA started warping the grid after extended water exposure (0.2mm). After N4 implementation, the image uniformity was determined through the ACR method while the SNR/CNR values were calculated in Fiji. The results illustrated the preferred environmental constraints according to the main topics: food grade mineral oil, cylindrical, motion artifact interference, and PETG 3D printed grid.
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