Introduction of a simple algorithm to create synthetic-Computed tomography of the head from magnetic resonance imaging

Abstract

Background: Recently, magnetic resonance imaging (MRI)-based radiotherapy has become a favorite science field for treatment planning purposes. In this study, a simple algorithm was introduced to create synthetic computed tomography (sCT) of the head from MRI. Methods: A simple atlas-based method was proposed to create sCT images based on the paired T1/T2-weighted MRI and bone/brain window CT. Dataset included 10 patients with glioblastoma multiforme and 10 patients with other brain tumors. To generate a sCT image, first each MR from dataset was registered to the target-MR, the resulting transformation was applied to the corresponding CT to create the set of deformed CTs. Then, deformed-CTs were fused to generate a single sCT image. The sCT images were compared with the real CT images using geometric measures (mean absolute error [MAE] and dice similarity coefficient of bone [DSC bone ]) and Hounsfield unit gamma-index (Г HU ) with criteria 100 HU/2 mm. Results: The evaluations carried out by MAE, DSC bone , and Г HU showed a good agreement between the synthetic and real CT images. The results represented the range of 78–93 HU and 0.80–0.89 for MAE and DSC bone , respectively. The Г HU also showed that approximately 91%–93% of pixels fulfilled the criteria 100 HU/2 mm for brain tumors. Conclusion: This method showed that MR sequence (T1w or T2w) should be selected depending on the type of tumor. In addition, the brain window synthetic CTs are in better agreement with real CT relative to bone window sCT images.

Generation of Synthetic Images of Trabecular Bone Based on Micro-CT Scans

“…Therefore, algorithms interested in analyzing the structural characteristics of trabecular bone will be able to carry out tests with a wide variety of synthetic image configurations. For example, the starting point for these analyses is a binary image obtained from the raw image, so synthetic images help to validate new algorithms focused on segmenting trabecular bone [9,33]. Moreover, the proposed methodology does not repeat 3D structures through the binary template, ensuring that all generated images will be unique.…”