Introduction of a Simple Algorithm to Create Synthetic-computed Tomography of the Head from Magnetic Resonance Imaging

Nahid Chegeni; Mohamad Javad Tahmasebi Birgani; Fariba Farhadi Birgani; Daryoush Fatehi; Gholamreza Akbarizadeh; Marziyeh Tahmasbi

doi:10.4103/jmss.JMSS_26_18

Introduction of a Simple Algorithm to Create Synthetic-computed Tomography of the Head from Magnetic Resonance Imaging

J Med Signals Sens. 2019 Apr-Jun;9(2):123-129. doi: 10.4103/jmss.JMSS_26_18.

Authors

Nahid Chegeni¹, Mohamad Javad Tahmasebi Birgani², Fariba Farhadi Birgani¹, Daryoush Fatehi³, Gholamreza Akbarizadeh⁴, Marziyeh Tahmasbi⁵

Affiliations

¹ Department of Medical Physics, Medical Physics, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
² Department of Radiation Therapy and Medical Physics, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
³ Department of Medical Physics, Faculty of Medicine, Shakrekord University of Medical Science, Shakrekord, Iran.
⁴ Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Iran.
⁵ Department of Radiologic Technology, Faculty of Para Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.

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.

Keywords: Deformable registration; Demons algorithm; radiotherapy; synthetic computed tomography.