A framework for predictive modeling of anatomical deformations

Davatzikos, Christos; Shen, Dinggang; Mohamed, Ashraf; Kyriacou, S.K.

doi:10.1109/42.938251

Cited by 67 publications

(47 citation statements)

References 30 publications

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“…But a practical difficulty of these models is the extensive time necessary to mesh the brain and solve the problem, which is takes too much time for intra-operative purposes. Davatzikos et al (2001) proposed a statistical framework consisting of pre-computing the main mode of deformation of the brain using a biomechanical model. And recent extensions of this framework show promising results for intra-operative surgical guidance based on manually extracted data (Lunn et al 2003).…”

Section: Introductionmentioning

confidence: 99%

Non-rigid alignment of pre-operative MRI, fMRI, and DT-MRI with intra-operative MRI for enhanced visualization and navigation in image-guided neurosurgery

et al. 2007

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Objective-The usefulness of neurosurgical navigation with current visualizations is seriously compromised by brain shift, which inevitably occurs during the course of the operation, significantly degrading the precise alignment between the preoperative MR data and the intraoperative shape of the brain. Our objectives were (i) to evaluate the feasibility of non-rigid registration that compensates for the brain deformations within the time constraints imposed by neurosurgery, and (ii) create augmented reality visualizations of critical structural and functional brain regions during neurosurgery using pre-operatively acquired fMRI and DT-MRI.Materials and Methods-Eleven consecutive patients with supratentorial gliomas were included in our study. All underwent surgery at our intra-operative MR imaging-guided therapy facility and have tumors in eloquent brain areas (e.g. precentral gyrus and cortico-spinal tract). Functional MRI and DT-MRI, together with MPRAGE and T2w structural MRI were acquired at 3T prior to surgery. SPGR and T2w images were acquired with a 0.5T magnet during each procedure. Quantitative assessment of the alignment accuracy was carried out and compared with current state-of the-art systems based only on rigid-registration.Results-Alignment between preoperative and intra-operative datasets was successfully carried out during surgery for all patients. Overall, the mean residual displacement remaining after nonrigid registration was 1.82 mm. There is a statistically significant improvement in alignment accuracy utilizing our non-rigid registration in comparison to the currently used technology (p<0.001).Conclusions-We were able to achieve intra-operative rigid and non-rigid registration of (1) pre-operative structural MRI with intra-operative T1w MRI; (2) pre-operative FMRI with intraoperative T1w MRI, and (3) pre-operative DT-MRI with intra-operative T1w MRI. The registration algorithms as implemented were sufficiently robust and rapid to meet the hard realtime constraints of intra-operative surgical decision making. The validation experiments demonstrate that we can accurately compensate for the deformation of the brain and thus can construct an augmented reality visualization to aid the surgeon.

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Section: Introductionmentioning

confidence: 99%

Non-rigid alignment of pre-operative MRI, fMRI, and DT-MRI with intra-operative MRI for enhanced visualization and navigation in image-guided neurosurgery

et al. 2007

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“…The estimation error can therefore be decomposed into 2 orthogonal components [12] e j =ě j +ẽ j (8) The reconstruction errorě j , is due to the inability of representing the deformed shape q j as the sum of the mean and a linear combination of the principal modes of deformation, while the errorẽ j is due to inability of estimating the deformed shape perfectly from the 2D information provided by the TRUS images, and due the approximation of equation (3). The maximum estimation error and reconstruction error for each of the simulations are shown in Figure 3.…”

Section: Resultsmentioning

confidence: 99%

“…The framework of [3] can be used to extend the approach presented here to a deformable model for the prostate that includes the modes of deformation as well as modes of shape. Such model can be constructed from several subjects instead of using a patient specific biomechanical model.…”

Section: Summary and Future Workmentioning

confidence: 99%

“…Such estimator can update the preoperative plan to account for deformations thereby reducing the uncertainty in the radioactive seed insertion locations. Our approach builds upon [3], in which a general framework for statistical estimation of intra-operative deformations was presented. The problem of estimation of the deformed shape of the prostate at each probe location can therefore be cast as a deformable 2D/3D registration problem [4], i.e.…”

Section: Introductionmentioning

confidence: 99%

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A Combined Statistical and Biomechanical Model for Estimation of Intra-operative Prostate Deformation

Mohamed

Davatzikos

Taylor

2002

Medical Image Computing and Computer-Assisted Intervention — MICCAI 2002

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Abstract. An approach for estimating the deformation of the prostate caused by transrectal ultrasound (TRUS) probe insertion is presented. This work is particularly useful during brachytherapy procedures, in which planning for radioactive seed insertion is performed on preoperative scans, and significant deformation of the prostate can occur during the procedure. The approach makes use of a patient specific biomechanical model to run simulations for TRUS probe insertion, extract the main modes of the deformation of the prostate, and use this information to establish a deformable registration between 2 orthogonal cross-sectional ultrasound images and the preoperative prostate. In the work presented here, the approach is tested on an anatomy-realistic biomechanical phantom for the prostate and results are reported for 5 test simulations. More than 73% of maximum deformation of the prostate was recovered, with the estimation error mostly attributed to the relatively small number of biomechanical simulations used for training.

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“…Therefore, a standard non-rigid registration method cannot be directly applied, and the topic has not been investigated much in depth in literature. Due to large deformations, standard registration methods like FEM and dense diffeomorphic registration, fail to achieve accurate structure alignment Recent research has focused on insufflation modeling using only pre-operative images and registration of pre-operative images to laparoscopic images [3,4]. However, none of the proposed methods have made use of the intra-operative images in their framework.…”

Section: Introductionmentioning

confidence: 99%

Biomechanically Driven Registration of Pre- to Intra-Operative 3D Images for Laparoscopic Surgery

Oktay

Zhang

Mansi

et al. 2013

Advanced Information Systems Engineering

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Abstract. Minimally invasive laparoscopic surgery is widely used for the treatment of cancer and other diseases. During the procedure, gas insufflation is used to create space for laparoscopic tools and operation. Insufflation causes the organs and abdominal wall to deform significantly. Due to this large deformation, the benefit of surgical plans, which are typically based on pre-operative images, is limited for real time navigation. In some recent work, intra-operative images, such as cone-beam CT or interventional CT, are introduced to provide updated volumetric information after insufflation. Other works in this area have focused on simulation of gas insufflation and exploited only the pre-operative images to estimate deformation. This paper proposes a novel registration method for pre-and intra-operative 3D image fusion for laparoscopic surgery. In this approach, the deformation of pre-operative images is driven by a biomechanical model of the insufflation process. The proposed method was validated by five synthetic data sets generated from clinical images and three pairs of in vivo CT scans acquired from two pigs, before and after insufflation. The results show the proposed method achieved high accuracy for both the synthetic and real insufflation data.

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A framework for predictive modeling of anatomical deformations

Cited by 67 publications

References 30 publications

Non-rigid alignment of pre-operative MRI, fMRI, and DT-MRI with intra-operative MRI for enhanced visualization and navigation in image-guided neurosurgery

Non-rigid alignment of pre-operative MRI, fMRI, and DT-MRI with intra-operative MRI for enhanced visualization and navigation in image-guided neurosurgery

A Combined Statistical and Biomechanical Model for Estimation of Intra-operative Prostate Deformation

Biomechanically Driven Registration of Pre- to Intra-Operative 3D Images for Laparoscopic Surgery

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