Abstract
Diffusion-weighted imaging (DWI), while giving rich information about brain circuitry, is often limited by insufficient spatial resolution and low signal-to-noise ratio (SNR). This paper describes an algorithm that will increase the resolution of DW images beyond the scan resolution, allowing for a closer investigation of fiber structures and more accurate assessment of brain connectivity. The algorithm is capable of generating a dense vector-valued field, consisting of diffusion data associated with the full set of diffusion-sensitizing gradients. The fundamental premise is that, to best preserve information, interpolation should always be performed along fiber streamlines. To achieve this, at each spatial location, we probe neighboring voxels in various directions to gather diffusion information for data reconstruction. Based on the fiber orientation distribution (FOD), directions that are more likely to be traversed by fibers will be given greater weights during interpolation and vice versa. This ensures that data reconstruction is only contributed by diffusion data coming from fibers that are aligned with a specific direction. This approach respects local fiber structures and prevents blurring resulting from averaging of data from significantly misaligned fibers. Evaluations suggest that this algorithm yields results with significantly less blocking artifacts, greater smoothness in anatomical structures, and markedly improved structural visibility.
| Original language | English |
|---|---|
| Title of host publication | Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention |
| Pages | 18-25 |
| Number of pages | 8 |
| Volume | 15 |
| Edition | Pt 3 |
| Publication status | Published - 2012 Dec 1 |
Fingerprint
ASJC Scopus subject areas
- Medicine(all)
Cite this
Resolution enhancement of diffusion-weighted images by local fiber profiling. / Yap, Pew Thian; Shen, Dinggang.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 15 Pt 3. ed. 2012. p. 18-25.Research output: Chapter in Book/Report/Conference proceeding › Chapter
}
TY - CHAP
T1 - Resolution enhancement of diffusion-weighted images by local fiber profiling.
AU - Yap, Pew Thian
AU - Shen, Dinggang
PY - 2012/12/1
Y1 - 2012/12/1
N2 - Diffusion-weighted imaging (DWI), while giving rich information about brain circuitry, is often limited by insufficient spatial resolution and low signal-to-noise ratio (SNR). This paper describes an algorithm that will increase the resolution of DW images beyond the scan resolution, allowing for a closer investigation of fiber structures and more accurate assessment of brain connectivity. The algorithm is capable of generating a dense vector-valued field, consisting of diffusion data associated with the full set of diffusion-sensitizing gradients. The fundamental premise is that, to best preserve information, interpolation should always be performed along fiber streamlines. To achieve this, at each spatial location, we probe neighboring voxels in various directions to gather diffusion information for data reconstruction. Based on the fiber orientation distribution (FOD), directions that are more likely to be traversed by fibers will be given greater weights during interpolation and vice versa. This ensures that data reconstruction is only contributed by diffusion data coming from fibers that are aligned with a specific direction. This approach respects local fiber structures and prevents blurring resulting from averaging of data from significantly misaligned fibers. Evaluations suggest that this algorithm yields results with significantly less blocking artifacts, greater smoothness in anatomical structures, and markedly improved structural visibility.
AB - Diffusion-weighted imaging (DWI), while giving rich information about brain circuitry, is often limited by insufficient spatial resolution and low signal-to-noise ratio (SNR). This paper describes an algorithm that will increase the resolution of DW images beyond the scan resolution, allowing for a closer investigation of fiber structures and more accurate assessment of brain connectivity. The algorithm is capable of generating a dense vector-valued field, consisting of diffusion data associated with the full set of diffusion-sensitizing gradients. The fundamental premise is that, to best preserve information, interpolation should always be performed along fiber streamlines. To achieve this, at each spatial location, we probe neighboring voxels in various directions to gather diffusion information for data reconstruction. Based on the fiber orientation distribution (FOD), directions that are more likely to be traversed by fibers will be given greater weights during interpolation and vice versa. This ensures that data reconstruction is only contributed by diffusion data coming from fibers that are aligned with a specific direction. This approach respects local fiber structures and prevents blurring resulting from averaging of data from significantly misaligned fibers. Evaluations suggest that this algorithm yields results with significantly less blocking artifacts, greater smoothness in anatomical structures, and markedly improved structural visibility.
UR - http://www.scopus.com/inward/record.url?scp=84872921731&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84872921731&partnerID=8YFLogxK
M3 - Chapter
C2 - 23286109
AN - SCOPUS:84872921731
VL - 15
SP - 18
EP - 25
BT - Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
ER -