Diffusion Tensor Imaging of Brain Tumours at 3T: A Potential Tool for Assessing White Matter Tract Invasion?

Price, Stephen J.; Burnet, N.G.; Donovan, Tim; Green, H.A.L.; Peña, Alonso; Antoun, N.M.; Pickard, John D.; Carpenter, T. Adrian; Gillard, Jonathan H.

doi:10.1016/s0009-9260(03)00115-6

Cited by 208 publications

(136 citation statements)

References 22 publications

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“…This technique has been applied widely for in-vivo analysis of white matter architecture (Assaf, 2008;Basser et al, 1994;Jbabdi et al, 2010) and has recently been applied to the study of gray matter (Sagi et al, 2012). Moreover, Diffusion Tensor Imaging (DTI) has been used to study a large range of neurological diseases such as Alzheimer's dementia (Huang et al, 2007) and Parkinson's disease (Gattellaro et al, 2009), or to quantify other causes of tissue degradations such as cancer (Price et al, 2003) and aging (Hsu et al, 2010). Despite its interesting properties, DW imaging is an inherently low signal-to-noise ratio (SNR) technique a n d y i e l d s t o r e l a t i v e l y p o o r s p a t i a l r e s o l u t i o n .…”

Section: Introductionmentioning

confidence: 99%

Collaborative patch-based super-resolution for diffusion-weighted images

et al. 2013

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To cite this version:Pierrick Coupé, José V Manjón, Maxime Chamberland, Maxime Descoteaux, Bassem Hiba. Collaborative patch-based super-resolution for diffusion-weighted images.. NeuroImage, Elsevier, 2013 AbstractIn this paper, a new single image acquisition super-resolution method is proposed to increase image resolution of diffusion weighted (DW) images. Based on a nonlocal patch-based strategy, the proposed method uses a non diffusion image (b0) t o c o n s t r a i n t h e r e c o n s t r u c t i o n o f DW i m a g e s .An extensive validation is presented with a gold standard built on averaging 10 high-resolution DW acquisitions. A comparison with classical interpolation methods such as trilinear and B-spline demonstrates the competitive results of our proposed approach in terms o f improvements on image reconstruction, fractional anisotropy (FA) estimation, generalized FA and angular reconstruction for tensor and high angular resolution diffusion imaging (HARDI) models. Besides, first results of reconstructed ultra high resolution DW images a r e p r e s e n t e d a t 0 . 6 x 0 . 6 x 0 . 6 m m 3 a n d 0.4x0.4x0.4mm 3 using our gold standard b a s e d o n the average of 10 acquisitions, and on a single acquisition. Finally, fiber tracking results show the potential of the proposed super-resolution approach t o a c c u r a t e l y analyze white matter brain architecture.

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Section: Introductionmentioning

confidence: 99%

Collaborative patch-based super-resolution for diffusion-weighted images

et al. 2013

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“…Brain gliomas form the major class of tumors in the central nervous system, [44,29]. Among them, high grade ones remain incurable despite the stateof-the-art therapy, and patients have an average life expectancy of 1 year, [40].…”

Section: Introductionmentioning

confidence: 99%

Extrapolating glioma invasion margin in brain magnetic resonance images: Suggesting new irradiation margins

Konukoğlu

Clatz

Bondiau

et al. 2010

Medical Image Analysis

113

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Radiotherapy for brain glioma treatment relies on magnetic resonance (MR) and computed tomography (CT) images. These images provide information on the spatial extent of the tumor, but can only visualize parts of the tumor where cancerous cells are dense enough, masking the low density infiltration. In radiotherapy, a 2 cm constant margin around the tumor is taken to account for this uncertainty. This approach however, does not consider the growth dynamics of gliomas, particularly the differential motility of tumor cells in the white and in the gray matter. In this article, we propose a novel method for estimating the full extent of the tumor infiltration starting from its visible mass in the patients' MR images. This estimation problem is a time independent problem where we do not have information about the temporal evolution of the pathology nor its initial conditions. Based on the reaction-diffusion models widely used in the literature, we derive a method to solve this extrapolation problem. Later, we use this formulation to tailor new tumor specific variable irradiation margins. We perform geometrical comparisons between the conventional constant and the proposed variable margins through determining the amount of targeted tumor cells and healthy tissue in the case of synthetic tumors. Results of these experiments suggest that the variable margin could be more effective at targeting cancerous cells and preserving healthy tissue. *

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“…And strong evidence that a more radical tumor resection may be achieved by using fMRI information during neurosurgery has been demonstrated by Krishnan et al 2004, Haberg et al 2004 Diffusion tensor imaging (DTI) has recently emerged as a potentially valuable tool for preoperative planning (Tummala et al 2004, Field et al 2004, Clark et al 2003, Wieshmann et al 2000, Moller-Hartmann et al 2002, Coenen et al 2003 and postoperative follow-up (Alexander et al 2003) of surgically treated brain tumors and vascular malformations. DTI provides information about the normal course, displacement, or interruption of white matter tracts in and around a tumor, as well as detecting the widening of fiber bundles due to edema or tumor infiltration (Beppu et al, 2003;Clark et al, 2003;Hendler et al, 2003;Lu et al, 2003;Price et al, 2003;Tummala et al, 2003;Wieshmann et al, 1999;Yamada et al, 2003). Consequently, efforts have been made in recent years to integrate DTI data with neurosurgical navigation systems , Coenen et al 2003, Talos et al 2003, Berman et al 2004, Shinoura et al 2005, Nimsky et al 2006.…”

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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Diffusion Tensor Imaging of Brain Tumours at 3T: A Potential Tool for Assessing White Matter Tract Invasion?

Cited by 208 publications

References 22 publications

Collaborative patch-based super-resolution for diffusion-weighted images

Collaborative patch-based super-resolution for diffusion-weighted images

Extrapolating glioma invasion margin in brain magnetic resonance images: Suggesting new irradiation margins

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

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