Projects:RegistrationTBI - Revision history

Ygao at 01:03, 16 November 2013

2013-11-16T01:03:17Z

Anry: /* Future Work */

2012-11-21T19:04:39Z

Future Work

Anry: /* References */

2012-11-21T19:04:25Z

References

Anry: /* Future Work */

2012-11-21T19:01:33Z

Future Work

Anry: /* Key Investigators */

2012-11-21T18:55:54Z

Key Investigators

Yflou: /* References */

2012-11-16T19:36:56Z

References

Yflou: /* References */

2012-11-16T19:31:36Z

References

Yflou: /* Description */

2012-11-16T19:31:11Z

Description

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2012-11-16T19:30:41Z

Description

Yflou: /* References */

2011-10-25T18:34:15Z

References

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-  Back to [[Algorithm:BU|Boston University Algorithms]]
+  Back to [[Algorithm:Stony Brook|Stony Brook University Algorithms]]
 __NOTOC__
 = Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes using Graphics Processing Units =

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 occurs. It is challenging and important to design a registration
 algorithm that can deal with topological changes for TBI patients.
-One possible approach is to use combination of locally rigid and non-rigid transforms based on visual features such as MIND descriptor. Some regions of new lesions cannot be explained
+One possible approach is to use combination of locally rigid and non-rigid transforms based on visual features such as MIND descriptor [4]. Some regions of new lesions cannot be explained
 by minor elastic registration, thus simultaneous registration and segmentation of lesions is
 needed. We are working on the improved volume matching with boundary conditions based

← Older revision		Revision as of 19:04, 21 November 2012
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	3. E. D’Agostino, F. Maes, D. Vandermeulen, and P. Suetens. A viscous fluid model for multimodal non-rigid image registration using mutual information,” MICCAI, 2002, pp. 541–548		3. E. D’Agostino, F. Maes, D. Vandermeulen, and P. Suetens. A viscous fluid model for multimodal non-rigid image registration using mutual information,” MICCAI, 2002, pp. 541–548
		+
		+	4. M.P. Heinrich, M. Jenkinson, M. Bhushan, T. Matin, F. Gleeson, M. Brady, J.A. Schnabel. MIND: Modality independent neighbourhood descriptor for multi-modal deformable registration. Medical Image Analysis. Vol. 16(7) Oct. 2012, pp. 1423–1435, Special Issue on MICCAI 2011

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 Future work will focus on registration of TBI volumes across time in terms of registering acute to chronic or vice versa. There are a large number of registration algorithms that assume the
 smoothness of the vector flow, i.e., the deformation is
-diffeomorphic. However, when registering TBI across time, the deformation  is not well-defined, let along
+diffeomorphic. However, when registering TBI across time, the deformation  is not well-defined, let alone
 to be diffeomorphic, at some regions where bleeding or lesion
 occurs. It is challenging and important to design a registration
 algorithm that can deal with topological changes for TBI patients.
 = Key Investigators =

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 Georgia Tech: Yifei Lou and Patricio Vela
-Boston University: Allen Tannenbaum
+UAB: Arie Nakhmani and Allen Tannenbaum
 UCLA: Andrei Irimia, Micah C. Chambers, Jack Van Horn and Paul M. Vespa

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 = References =
-.Yifei Lou, Andrei Irimia, Patricio Vela, Micah C. Chambers, Jack Van Horn, Paul M. Vespa and Allen Tannenbaum. Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes  via the Bhattacharyya Distanc. Submitted to IEEE Transactions on Bioengineering, 2012
+.Yifei Lou, Andrei Irimia, Patricio Vela, Micah C. Chambers, Jack Van Horn, Paul M. Vespa and Allen Tannenbaum. Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes  via the Bhattacharyya Distance. Submitted to IEEE Transactions on Bioengineering, 2012
 . Yifei Lou, Xun Jia, Xuejun Gu and Allen Tannenbaum. A GPU-based Implementation of Multimodal Deformable Image Registration Based on Mutual Information or Bhattacharyya Distance. Insight Journal, 2011. [http://www.midasjournal.org/browse/publication/803]
 . E. D’Agostino, F. Maes, D. Vandermeulen, and P. Suetens. A viscous fluid model for multimodal non-rigid image registration using mutual information,” MICCAI, 2002, pp. 541–548

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 Image registration plays an essential role in a wide variety of TBI data analysis workflows. It aims to find a transformation between two image sets such that the transformed image becomes similar to the target image according to some chosen metric or criterion. Typically, a similarity measure is first established to quantify how `close` two image volumes are to each other. Next, the transformation that maximizes this similarity is typically computed through an optimization process which constrains the transformation to a predetermined class, such as rigid, affine or deformable. Numerous challenges associated with the task of TBI volume co-registration can exist if data acquisition is performed multimodally, and additional complexities can also arise due to the large degree of algorithmic robustness that may be required in order to properly address pathology-related deformations. Many conventional methods use the sum of squared differences of intensity values between two image sets as a similarity measure, which can perform poorly or even fail for TBI volume registration. Consequently, because the deformation of patient anatomy and soft tissues cannot typically be represented by rigid transforms, image registration often requires deformable image registration (DIR), i.e., the necessity of applying nonparametric infinite-dimensional transformations.
-This paper proposes to replace the Mutual Information (MI) criterion for registration with the Bhattachayya distance [1] within a multimodal DIR framework [4]. The advantage of BD over MI is the superior behavior of the square root function compared to that of the logarithm at zero, which yields a more stable algorithm.
+This paper proposes to replace the Mutual Information (MI) criterion for registration with the Bhattachayya distance [1] within a multimodal DIR framework [3]. The advantage of BD over MI is the superior behavior of the square root function compared to that of the logarithm at zero, which yields a more stable algorithm.
-This framework we describe takes into account the physical models of tissue motion to regularize the deformation fields and also involves free-form deformation. On the other hand, the DIR algorithm is computationally expensive when implemented on conventional central processing units, which can be detrimental particularly when three-dimensional (3D) volumes-rather than 2D images-need to be co-registered. In clinical settings that involve acute TBI care, the amount of time required by the processing of neuroimaging data sets from patients in critical condition should be minimized.  To meet this clinical requirement, we have implemented our algorithm on a graphics processing unit (GPU) platform [3].
+This framework we describe takes into account the physical models of tissue motion to regularize the deformation fields and also involves free-form deformation. On the other hand, the DIR algorithm is computationally expensive when implemented on conventional central processing units, which can be detrimental particularly when three-dimensional (3D) volumes-rather than 2D images-need to be co-registered. In clinical settings that involve acute TBI care, the amount of time required by the processing of neuroimaging data sets from patients in critical condition should be minimized.  To meet this clinical requirement, we have implemented our algorithm on a graphics processing unit (GPU) platform [2].
 == Result ==

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 = References =
-. Yifei Lou, Andrei Irimia, Patricio  Vela, Allen Tannenbaum, Micah C. Chambers, Jack Van Horn and Paul M. Vespa. Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes using Graphics Processing Units. ''In preparation.'' 2011
+. Yifei Lou, Andrei Irimia, Patricio  Vela, Allen Tannenbaum, Micah C. Chambers, Jack Van Horn and Paul M. Vespa. Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes using Graphics Processing Units. Submitted to IEEE Trans. on Medical Imaging. 2011
 . Yifei Lou and Allen Tannenbaum. Multimodal Deformable Image Registration via the Bhattacharyya Distance. Submitted to IEEE Trans. Image Process. 2011