| Autor: |
Rahmani M; Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences (TUMS), Tehran 1461884513, Iran.; Research Center for Biomedical Technologies and Robotics (RCBTR), Advanced Medical Technologies and Equipment Institute (AMTEI), Imam Khomeini Hospital Complex, Tehran University of Medical Sciences (TUMS), Tehran 1419733141, Iran., Moghaddasi H; Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences (TUMS), Tehran 1461884513, Iran.; Research Center for Biomedical Technologies and Robotics (RCBTR), Advanced Medical Technologies and Equipment Institute (AMTEI), Imam Khomeini Hospital Complex, Tehran University of Medical Sciences (TUMS), Tehran 1419733141, Iran., Pour-Rashidi A; Department of Neurosurgery, Sina Hospital, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran 11367469111, Iran., Ahmadian A; Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences (TUMS), Tehran 1461884513, Iran.; Research Center for Biomedical Technologies and Robotics (RCBTR), Advanced Medical Technologies and Equipment Institute (AMTEI), Imam Khomeini Hospital Complex, Tehran University of Medical Sciences (TUMS), Tehran 1419733141, Iran., Najafzadeh E; Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran 1417466191, Iran.; Department of Molecular Imaging, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran 1449614535, Iran., Farnia P; Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences (TUMS), Tehran 1461884513, Iran.; Research Center for Biomedical Technologies and Robotics (RCBTR), Advanced Medical Technologies and Equipment Institute (AMTEI), Imam Khomeini Hospital Complex, Tehran University of Medical Sciences (TUMS), Tehran 1419733141, Iran. |
| Abstrakt: |
During neurosurgical procedures, the neuro-navigation system's accuracy is affected by the brain shift phenomenon. One popular strategy is to compensate for brain shift using intraoperative ultrasound (iUS) registration with pre-operative magnetic resonance (MR) scans. This requires a satisfactory multimodal image registration method, which is challenging due to the low image quality of ultrasound and the unpredictable nature of brain deformation during surgery. In this paper, we propose an automatic unsupervised end-to-end MR-iUS registration approach named the Dual Discriminator Bayesian Generative Adversarial Network (D 2 BGAN). The proposed network consists of two discriminators and a generator optimized by a Bayesian loss function to improve the functionality of the generator, and we add a mutual information loss function to the discriminator for similarity measurements. Extensive validation was performed on the RESECT and BITE datasets, where the mean target registration error (mTRE) of MR-iUS registration using D 2 BGAN was determined to be 0.75 ± 0.3 mm. The D 2 BGAN illustrated a clear advantage by achieving an 85% improvement in the mTRE over the initial error. Moreover, the results confirmed that the proposed Bayesian loss function, rather than the typical loss function, improved the accuracy of MR-iUS registration by 23%. The improvement in registration accuracy was further enhanced by the preservation of the intensity and anatomical information of the input images. |
