Toward Real-Time Image Guided Neurosurgery Using Distributed and Grid Computing

Chrisochoides,; Fedorov, Vladimir V.; Kot,; Archip,; Black,; Clatz,; Golby,; Kikinis,; Warfield,

doi:10.1109/sc.2006.65

Cited by 36 publications

(33 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With web-services and by hiding pre-processing overheads, we developed faster and easier to use remotely registration procedure. Details of the novel technology can be found in Chrisochoides et al (2006). The system architecture is presented in Figure 3.…”

Section: Estimation Of Displacement Field From Block Displacementsmentioning

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Estimation Of Displacement Field From Block Displacementsmentioning

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…All of the intra-operative computations utilized SciClone (a heterogeneous cluster of workstations located at CWM, reserved in advance for the registration computation) and the workstations of the student lab (time-shared mode). The details of the hardware configuration can be found in [5]. Data transfer between the networks of CWM and BWH (subnet of Harvard University) are facilitated by the Internet2 backbone network with the slowest link having bandwidth of 2.5 Gbps.…”

Section: Initial Evaluation Resultsmentioning

confidence: 99%

“…We have developed an implementation which addresses the aforementioned objectives [5]. Next we briefly highlight some of the implementation details.…”

Section: Implementation Objectivesmentioning

confidence: 99%

Grid-Enabled Software Environment for Enhanced Dynamic Data-Driven Visualization and Navigation During Image-Guided Neurosurgery

Chrisochoides

Fedorov

Kot

et al. 2007

Computational Science – ICCS 2007

Self Cite

View full text Add to dashboard Cite

Abstract. In this paper we present our experience with an Image Guided Neurosurgery Grid-enabled Software Environment (IGNS-GSE) which integrates real-time acquisition of intraoperative Magnetic Resonance Imaging (IMRI) with the preoperative MRI, fMRI, and DT-MRI data. We describe our distributed implementation of a non-rigid image registration method which can be executed over the Grid. Previously, non-rigid registration algorithms which use landmark tracking across the entire brain volume were considered not practical because of the high computational demands. The IGNS-GSE, for the first time ever in clinical practice, alleviated this restriction. We show that we can compute and present enhanced MR images to neurosurgeons during the tumor resection within minutes after IMRI acquisition. For the last 12 months this software system is used routinely (on average once a month) for clinical studies at Brigham and Women's Hospital in Boston, MA. Based on the analysis of the registration results, we also present future directions which will take advantage of the vast resources of the Grid to improve the accuracy of the method in places of the brain where precision is critical for the neurosurgeons.

show abstract

“…Details are presented in [10] and a brief overview is presented in this section. Recently, this algorithm has been validated on patients enrolled prospectively for image guided neurosurgery [11]. The images used in our study are acquired at the same respiration cycle, therefore the liver deformations are relatively small (up to 20 mm).…”

Section: Finite Element Methods (Fem) Basedmentioning

confidence: 99%

Non-rigid Registration of Pre-procedural MR Images with Intra-procedural Unenhanced CT Images for Improved Targeting of Tumors During Liver Radiofrequency Ablations

Archip¹,

Tatlı²,

Morrison³

et al.

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007

View full text Add to dashboard Cite

Abstract. In the United States, unenhanced CT is currently the most common imaging modality used to guide percutaneous biopsy and tumor ablation. The majority of liver tumors such as hepatocellular carcinomas are visible on contrast-enhanced CT or MRI obtained prior to the procedure. Yet, these tumors may not be visible or may have poor margin conspicuity on unenhanced CT images acquired during the procedure. Non-rigid registration has been used to align images accurately, even in the presence of organ motion. However, to date, it has not been used clinically for radiofrequency ablation (RFA), since it requires significant computational infrastructure and often these methods are not sufficient robust. We have already introduced a novel finite element based method (FEM) that is demonstrated to achieve good accuracy and robustness for the problem of brain shift in neurosurgery. In this current study, we adapt it to fuse pre-procedural MRI with intra-procedural CT of liver. We also compare its performance with conventional rigid registration and two non-rigid registration methods: b-spline and demons on 13 retrospective datasets from patients that underwent RFA at our institution. FEM non-rigid registration technique was significantly better than rigid (p<10-5), non-rigid b-spline (p<10-4) and demons (p<10-4) registration techniques. The results of our study indicate that this novel technology may be used to optimize placement of RF applicator during CT-guided ablations.

show abstract

Toward Real-Time Image Guided Neurosurgery Using Distributed and Grid Computing

Cited by 36 publications

References 54 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

Grid-Enabled Software Environment for Enhanced Dynamic Data-Driven Visualization and Navigation During Image-Guided Neurosurgery

Non-rigid Registration of Pre-procedural MR Images with Intra-procedural Unenhanced CT Images for Improved Targeting of Tumors During Liver Radiofrequency Ablations

Contact Info

Product

Resources

About