A generative model for resolution enhancement of diffusion MRI data

Pew Thian Yap, Hongyu An, Yasheng Chen, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The advent of diffusion magnetic resonance imaging (DMRI) presents unique opportunities for the exploration of white matter connectivity in vivo and non-invasively. However, DMRI suffers from insufficient spatial resolution, often limiting its utility to the studying of only major white matter structures. Many image enhancement techniques rely on expensive scanner upgrades and complex time-consuming sequences. We will instead take a post-processing approach in this paper for resolution enhancement of DMRI data. This will allow the enhancement of existing data without re-acquisition. Our method uses a generative model that reflects the image generation process and, after the parameters of the model have been estimated, we can effectively sample high-resolution images from this model. More specifically, we assume that the diffusion-weighted signal at each voxel is an agglomeration of signals from an ensemble of fiber segments that can be oriented and located freely within the voxel. Our model for each voxel therefore consists of an arbitrary number of signal generating fiber segments, and the model parameters that need to be determined are the locations and orientations of these fiber segments. Solving for these parameters is an ill-posed problem. However, by borrowing information from neighboring voxels, we show that this can be solved by using Markov chain Monte Carlo (MCMC) methods such as the Metropolis-Hastings algorithm. Preliminary results indicate that out method substantially increases structural visibility in both subcortical and cortical regions.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages527-534
Number of pages8
Volume8151 LNCS
EditionPART 3
DOIs
Publication statusPublished - 2013 Oct 24
Externally publishedYes
Event16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 - Nagoya, Japan
Duration: 2013 Sep 222013 Sep 26

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume8151 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
CountryJapan
CityNagoya
Period13/9/2213/9/26

Fingerprint

Resolution Enhancement
Generative Models
Voxel
Magnetic resonance imaging
Magnetic Resonance Imaging
Magnetic resonance
Fiber
Imaging techniques
Fibers
Metropolis-Hastings Algorithm
Agglomeration
Image Enhancement
Markov Chain Monte Carlo Methods
Ill-posed Problem
Scanner
Visibility
Post-processing
Spatial Resolution
Model
Image enhancement

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Yap, P. T., An, H., Chen, Y., & Shen, D. (2013). A generative model for resolution enhancement of diffusion MRI data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 3 ed., Vol. 8151 LNCS, pp. 527-534). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8151 LNCS, No. PART 3). https://doi.org/10.1007/978-3-642-40760-4_66

A generative model for resolution enhancement of diffusion MRI data. / Yap, Pew Thian; An, Hongyu; Chen, Yasheng; Shen, Dinggang.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8151 LNCS PART 3. ed. 2013. p. 527-534 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8151 LNCS, No. PART 3).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Yap, PT, An, H, Chen, Y & Shen, D 2013, A generative model for resolution enhancement of diffusion MRI data. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 3 edn, vol. 8151 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 3, vol. 8151 LNCS, pp. 527-534, 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013, Nagoya, Japan, 13/9/22. https://doi.org/10.1007/978-3-642-40760-4_66
Yap PT, An H, Chen Y, Shen D. A generative model for resolution enhancement of diffusion MRI data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). PART 3 ed. Vol. 8151 LNCS. 2013. p. 527-534. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3). https://doi.org/10.1007/978-3-642-40760-4_66
Yap, Pew Thian ; An, Hongyu ; Chen, Yasheng ; Shen, Dinggang. / A generative model for resolution enhancement of diffusion MRI data. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 8151 LNCS PART 3. ed. 2013. pp. 527-534 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3).
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