Investigation of a Method for Generating Synthetic CT Models From MRI Scans for Radiation Therapy
S Hsu*, Y Cao, J Balter, Univ Michigan, ANN ARBOR, MIMO-G-BRA-2 Monday 5:15:00 PM - 6:00:00 PM Room: Ballroom A
Purpose: MRI is advantageous for treatment planning, given that MRI can support dose calculations and image-guided radiotherapy. This study investigates initial methods to derive synthetic CT images from MRI scans that support these needs.
Methods: The framework involves a) acquiring multiple MRI image volumes each presenting different tissue/material contrasts, and b) implementing a classification scheme that identifies these materials with sufficient accuracy to assign relative attenuation properties for generating CT images. Six images, consisting of two echoes from ultra-short TE acquisition (0.07 and 3.7 ms echoes to image cortical bone), T1 GRE, T2 FSE, and fat and water images (from a two-point Dixon technique), were tested as inputs. Signal intensities of manually-defined tissues were measured, and used to determine the ability of various image combinations to uniquely classify tissue types. To classify tissue using these images, a fuzzy classification algorithm was used to define membership functions of material types. Assigning attenuation properties to classified tissues yielded synthetic CT image volumes.
Results: While subsets of T1, T2, and water/fat image volumes demonstrated statistical significance in identifying fat, solid tissue, and water, the 0.07 ms TE data (UTE1) was necessary to significantly separate bone and air from these tissues. The classification algorithm was able to robustly extract the various material types using subsets of three of the image volumes (T1/UTE1/water). Example CT images and resulting digitally reconstructed radiographs were generated based on attenuation properties applied to the tissue segments.
Conclusions: These investigations demonstrated that MRI presents sufficient differential contrasts to separate tissues of interest, and identified the minimum necessary image contrasts for synthetic CT generation using robust classification methods. Further investigations using MRI and CT on subjects undergoing radiotherapy, as well as phantoms, will validate the accuracy of this technique.