scholarly journals A nonrigid registration method for correcting brain deformation induced by tumor resection

2014 ◽  
Vol 41 (10) ◽  
pp. 101710 ◽  
Author(s):  
Yixun Liu ◽  
Chengjun Yao ◽  
Fotis Drakopoulos ◽  
Jinsong Wu ◽  
Liangfu Zhou ◽  
...  
Keyword(s):  
Tumor Resection ◽  
Nonrigid Registration ◽  
Registration Method ◽  
Brain Deformation

Nonparametric and nonrigid registration method applied to myocardial-gated SPECT

2002 ◽  
Vol 49 (3) ◽  
pp. 782-788 ◽  
Author(s):  
E. Debreuve ◽  
M. Barlaud ◽  
I. Laurette ◽  
G. Aubert ◽  
J. Darcourt
Keyword(s):  
Gated Spect ◽  
Nonrigid Registration ◽  
Registration Method

scholarly journals A Mechanics-Based Nonrigid Registration Method for Liver Surgery Using Sparse Intraoperative Data

2014 ◽  
Vol 33 (1) ◽  
pp. 147-158 ◽  
Author(s):  
D. Caleb Rucker ◽  
Yifei Wu ◽  
Logan W. Clements ◽  
Janet E. Ondrake ◽  
Thomas S. Pheiffer ◽  
...  
Keyword(s):  
Liver Surgery ◽  
Nonrigid Registration ◽  
Registration Method

scholarly journals Photoacoustic-MR Images Registration Based on Co-Sparse Analysis Model to Compensate for Brain Shift

2021 ◽  
Author(s):  
Parastoo Farnia ◽  
Bahador Makkiabadi ◽  
Meysam Alimohammadi ◽  
Ebrahim Najafzadeh ◽  
Maryam Basij ◽  
...  
Keyword(s):  
Image Registration ◽  
Real Time ◽  
Mouse Brain ◽  
Brain Shift ◽  
Challenging Problem ◽  
Analysis Model ◽  
Mr Images ◽  
Registration Method ◽  
Phantom Data ◽  
Brain Deformation

Brain shift is an important obstacle for the application of image guidance during neurosurgical interventions. There has been a growing interest in intra-operative imaging systems to update the image-guided surgery systems with real-time data. However, due to the innate limitations of the current imaging modalities, accurate and real-time brain shift compensation remains as a challenging problem. In this study, application of the intra-operative photoacoustic (PA) imaging and registration of the intra-operative PA images with pre-operative brain MR images is proposed to compensate brain deformation during surgery. Finding a satisfactory multimodal image registration method is a challenging problem due to complicated and unpredictable nature of brain deformation. In this study, the co-sparse analysis model is proposed for PA-MR image registration which can capture the interdependency of two modalities. The proposed algorithm works based on the minimization of mapping transform by using a pair of analysis operators. These operators are learned by the alternating direction method of multipliers. The method was evaluated using experimental phantom and ex-vivo data obtained from mouse brain. The results of phantom data show about 60% and 63% improvement in root mean square error (RMSE) and target registration error (TRE) in comparison with commonly used normalized mutual information registration method. In addition, the results of mouse brain and phantom data shown more accurate performance for PA versus ultrasound imaging for brain shift calculation. Finally, by using the proposed registration method, the intra-operative PA images could become a promising tool when the brain shift invalidated pre-operative MRI.

scholarly journals An ITK implementation of a physics-based non-rigid registration method for brain deformation in image-guided neurosurgery

2014 ◽  
Vol 8 ◽  
Author(s):  
Yixun Liu ◽  
Andriy Kot ◽  
Fotis Drakopoulos ◽  
Chengjun Yao ◽  
Andriy Fedorov ◽  
...  
Keyword(s):  
Registration Method ◽  
Rigid Registration ◽  
Image Guided ◽  
Brain Deformation

A symmetric nonrigid registration method to handle large organ deformations in cervical cancer patients

2010 ◽  
Vol 37 (7Part1) ◽  
pp. 3760-3772 ◽  
Author(s):  
Luiza Bondar ◽  
Mischa S. Hoogeman ◽  
Eliana M. Vásquez Osorio ◽  
Ben J. M. Heijmen
Keyword(s):  
Cervical Cancer ◽  
Cancer Patients ◽  
Nonrigid Registration ◽  
Registration Method

scholarly journals A Bayesian nonrigid registration method to enhance intraoperative target definition in image-guided prostate procedures through uncertainty characterization

2012 ◽  
Vol 39 (11) ◽  
pp. 6858-6867 ◽  
Author(s):  
Jennifer Pursley ◽  
Petter Risholm ◽  
Andriy Fedorov ◽  
Kemal Tuncali ◽  
Fiona M. Fennessy ◽  
...  
Keyword(s):  
Nonrigid Registration ◽  
Registration Method ◽  
Uncertainty Characterization ◽  
Image Guided ◽  
Target Definition

Nonrigid registration method to assess reproducibility of breath-holding with ABC in lung cancer

2005 ◽  
Vol 61 (2) ◽  
pp. 594-607 ◽  
Author(s):  
David Sarrut ◽  
Vlad Boldea ◽  
Myriam Ayadi ◽  
Jean-Noël Badel ◽  
Chantal Ginestet ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Nonrigid Registration ◽  
Breath Holding ◽  
Registration Method

scholarly journals Towards automated correction of brain shift using deep deformable magnetic resonance imaging-intraoperative ultrasound (MRI-iUS) registration

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Ramy A. Zeineldin ◽  
Mohamed E. Karar ◽  
Jan Coburger ◽  
Christian R. Wirtz ◽  
Franziska Mathis-Ullrich ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Mean Squared Error ◽  
Intraoperative Ultrasound ◽  
Brain Shift ◽  
Resonance Imaging ◽  
Registration Method ◽  
Brain Deformation ◽  
Preoperative Magnetic Resonance ◽  
Magnetic Resonance Imaging Mri

AbstractIntraoperative brain deformation, so-called brain shift, affects the applicability of preoperative magnetic resonance imaging (MRI) data to assist the procedures of intraoperative ultrasound (iUS) guidance during neurosurgery. This paper proposes a deep learning-based approach for fast and accurate deformable registration of preoperative MRI to iUS images to correct brain shift. Based on the architecture of 3D convolutional neural networks, the proposed deep MRI-iUS registration method has been successfully tested and evaluated on the retrospective evaluation of cerebral tumors (RESECT) dataset. This study showed that our proposed method outperforms other registration methods in previous studies with an average mean squared error (MSE) of 85. Moreover, this method can register three 3D MRI-US pair in less than a second, improving the expected outcomes of brain surgery.

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