Purpose Measurements accomplished on most oral implantology software are often affected by some systematic effects, of which those related to the CT dataset anisotropy are the most relevant. In fact, most of these commercial systems do not manage possible anisotropy in input datasets, leaving the responsibility to users and radiologists. Therefore, in order to achieve a better knowledge of the patient’s anatomy before inserting the implants, and thus reducing the risk of damaging the surrounding structures, the implementation of a complete and precise anisotropy management system is required. Methods We present an anisotropy management algorithm for pre-operative planning software that is able to handle any anisotropic CT dataset, and, as a result, provides a very precise isotropic equivalent. The developed algorithm exploits two interpolation passes to correct anisotropy and is characterised by linear complexity, needing just a few seconds to accomplish the tasks. The first pass concerns the integer-filling of possible intra-slice void spaces of the original slices, having the responsibility of a correct spreading of the radiographic details along the volume height axis. The second pass, instead, reformats its input dataset under isotropic conditions exploiting a contribution-based interpolation sub-algorithm. Results The algorithm has been evaluated by comparing the anisotropy implied systematic effects for both anisotropic and interpolation-reconstructed radiographic volumes of five different scans. The proposed system demonstrated to be able to successfully handle any dataset interslice – pixel-size ratio. Moreover, the precision achieved proved to be even better than that of another precise algorithm that we previously developed and published, validating the proposed approach as a consequence. Conclusions The proposed algorithm makes it possible to handle and correct anisotropy in input CT datasets, helping to avoid anisotropy implied systematic effects on related measurements, and consequently supporting pre-operative planning software by providing a precise and isotropic equivalent volume on which to work.
CT Dataset Anisotropy Management for Oral Implantology Planning Software
CHIARELLI, Tommaso;LAMMA, Evelina;SANSONI, Tommaso
2013
Abstract
Purpose Measurements accomplished on most oral implantology software are often affected by some systematic effects, of which those related to the CT dataset anisotropy are the most relevant. In fact, most of these commercial systems do not manage possible anisotropy in input datasets, leaving the responsibility to users and radiologists. Therefore, in order to achieve a better knowledge of the patient’s anatomy before inserting the implants, and thus reducing the risk of damaging the surrounding structures, the implementation of a complete and precise anisotropy management system is required. Methods We present an anisotropy management algorithm for pre-operative planning software that is able to handle any anisotropic CT dataset, and, as a result, provides a very precise isotropic equivalent. The developed algorithm exploits two interpolation passes to correct anisotropy and is characterised by linear complexity, needing just a few seconds to accomplish the tasks. The first pass concerns the integer-filling of possible intra-slice void spaces of the original slices, having the responsibility of a correct spreading of the radiographic details along the volume height axis. The second pass, instead, reformats its input dataset under isotropic conditions exploiting a contribution-based interpolation sub-algorithm. Results The algorithm has been evaluated by comparing the anisotropy implied systematic effects for both anisotropic and interpolation-reconstructed radiographic volumes of five different scans. The proposed system demonstrated to be able to successfully handle any dataset interslice – pixel-size ratio. Moreover, the precision achieved proved to be even better than that of another precise algorithm that we previously developed and published, validating the proposed approach as a consequence. Conclusions The proposed algorithm makes it possible to handle and correct anisotropy in input CT datasets, helping to avoid anisotropy implied systematic effects on related measurements, and consequently supporting pre-operative planning software by providing a precise and isotropic equivalent volume on which to work.I documenti in SFERA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
