scholarly journals DTI Image Registration under Probabilistic Fiber Bundles Tractography Learning

2016 ◽  
Vol 2016 ◽  
pp. 1-12
Author(s):  
Zhe Guo ◽  
Yi Wang ◽  
Tao Lei ◽  
Yangyu Fan ◽  
Xiuwei Zhang
Keyword(s):  
Diffusion Tensor Imaging ◽  
Image Registration ◽  
Diffusion Tensor ◽  
Sample Selection ◽  
Nerve Fibers ◽  
Residual Error ◽  
Fiber Bundles ◽  
Finite Sample ◽  
Registration Method ◽  
Sample Set

Diffusion Tensor Imaging (DTI) image registration is an essential step for diffusion tensor image analysis. Most of the fiber bundle based registration algorithms use deterministic fiber tracking technique to get the white matter fiber bundles, which will be affected by the noise and volume. In order to overcome the above problem, we proposed a Diffusion Tensor Imaging image registration method under probabilistic fiber bundles tractography learning. Probabilistic tractography technique can more reasonably trace to the structure of the nerve fibers. The residual error estimation step in active sample selection learning is improved by modifying the residual error model using finite sample set. The calculated deformation field is then registered on the DTI images. The results of our proposed registration method are compared with 6 state-of-the-art DTI image registration methods under visualization and 3 quantitative evaluation standards. The experimental results show that our proposed method has a good comprehensive performance.

Depiction of periprostatic nerve fibers by means of 1.5 T diffusion tensor imaging

2020 ◽  
Author(s):  
Valerio Di Paola ◽  
Angelo Totaro ◽  
Benedetta Gui ◽  
Maura Miccò ◽  
Elena Rodolfino ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
Diffusion Tensor ◽  
Nerve Fibers

scholarly journals 4.7-T diffusion tensor imaging of acute traumatic peripheral nerve injury

2015 ◽  
Vol 39 (3) ◽  
pp. E9 ◽  
Author(s):  
Richard B. Boyer ◽  
Nathaniel D. Kelm ◽  
D. Colton Riley ◽  
Kevin W. Sexton ◽  
Alonda C. Pollins ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
High Resolution ◽  
Peripheral Nerve ◽  
Nerve Injury ◽  
Peripheral Nerve Injury ◽  
Diffusion Tensor ◽  
Ex Vivo ◽  
Nerve Fibers ◽  
Nerve Transection ◽  
Nerve Injuries

Diagnosis and management of peripheral nerve injury is complicated by the inability to assess microstructural features of injured nerve fibers via clinical examination and electrophysiology. Diffusion tensor imaging (DTI) has been shown to accurately detect nerve injury and regeneration in crush models of peripheral nerve injury, but no prior studies have been conducted on nerve transection, a surgical emergency that can lead to permanent weakness or paralysis. Acute sciatic nerve injuries were performed microsurgically to produce multiple grades of nerve transection in rats that were harvested 1 hour after surgery. High-resolution diffusion tensor images from ex vivo sciatic nerves were obtained using diffusion-weighted spin-echo acquisitions at 4.7 T. Fractional anisotropy was significantly reduced at the injury sites of transected rats compared with sham rats. Additionally, minor eigenvalues and radial diffusivity were profoundly elevated at all injury sites and were negatively correlated to the degree of injury. Diffusion tensor tractography showed discontinuities at all injury sites and significantly reduced continuous tract counts. These findings demonstrate that high-resolution DTI is a promising tool for acute diagnosis and grading of traumatic peripheral nerve injuries.

scholarly journals Diffusion tensor imaging of the human kidney: Does image registration permit scanning without respiratory triggering?

2016 ◽  
Vol 44 (2) ◽  
pp. 327-334 ◽  
Author(s):  
Maryam Seif ◽  
Laila Yasmin Mani ◽  
Huanxiang Lu ◽  
Chris Boesch ◽  
Mauricio Reyes ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
Image Registration ◽  
Diffusion Tensor ◽  
Human Kidney ◽  
Respiratory Triggering

Differences Between Generalized Q-Sampling Imaging and Diffusion Tensor Imaging in the Preoperative Visualization of the Nerve Fiber Tracts Within Peritumoral Edema in Brain

Neurosurgery ◽  
2013 ◽  
Vol 73 (6) ◽  
pp. 1044-1053 ◽  
Author(s):  
Hongliang Zhang ◽  
Yong Wang ◽  
Tao Lu ◽  
Bo Qiu ◽  
Yanqing Tang ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
Diffusion Tensor ◽  
Nerve Fibers ◽  
Normal Brain ◽  
Peritumoral Edema ◽  
Fiber Tracts ◽  
Cerebral Tumors ◽  
Preoperative Mapping ◽  
Neurosurgical Planning ◽  
Dti Tractography

Abstract BACKGROUND: Diffusion tensor imaging (DTI) tractography enables the in vivo visualization of white matter tracts inside normal brain tissue, which provides the neurosurgeon important information to plan tumor resections. However, DTI is associated with restrictions in the resolution of crossing fibers in the vicinity of the tumor or in edema. We find that generalized q-sampling imaging (GQI) can overcome these difficulties and is advantageous over DTI for the tractography of the fiber bundle in peritumoral edema. OBJECTIVE: To demonstrate the differences between GQI and DTI in the preoperative mapping of fiber tractography in peritumoral edema of cerebral tumors, and discuss the clinical application of GQI in neurosurgical planning. METHODS: Five patients with brain tumors underwent 3-T magnetic resonance imaging scans, and the data were reconstructed by DTI and GQI. We adjusted the parameters and compared the differences between DTI and GQI in visualizing the fiber tracts in the peritumoral edema of cerebral tumors. RESULTS: GQI and DTI showed substantial differences in displaying the nerve fibers in the edema surrounding the tumor. The GQI tractography method could fully display existing intact fibers in the edema, whereas the fiber tracts in edema displayed by DTI tractography were incomplete, missing, or ruptured. CONCLUSION: GQI can visualize the tracts in the peritumoral edema of cerebral tumors better than DTI. Although GQI has many limitations, its future in the preoperative guidance of brain tumor lesions is promising.

TU-CD-BRB-05: Radiation Damage Signature of White Matter Fiber Bundles Using Diffusion Tensor Imaging (DTI)

2015 ◽  
Vol 42 (6Part32) ◽  
pp. 3603-3603
Author(s):  
T Zhu ◽  
C Chapman ◽  
C Tsien ◽  
T Lawrence ◽  
Y Cao
Keyword(s):  
Diffusion Tensor Imaging ◽  
White Matter ◽  
Radiation Damage ◽  
Diffusion Tensor ◽  
Fiber Bundles ◽  
Diffusion Tensor Imaging Dti

Determination of optimum pixel size and slice thickness for tractography and ulnar nerve diffusion tensor imaging at the cubital tunnel using 3T MRI

2020 ◽  
pp. 028418512095196
Author(s):  
Sun-Young Park ◽  
Sung Hye Koh ◽  
In Jae Lee ◽  
Kwanseop Lee ◽  
Yul Lee
Keyword(s):  
Diffusion Tensor Imaging ◽  
Ulnar Nerve ◽  
Diffusion Tensor ◽  
Nerve Fibers ◽  
Cubital Tunnel ◽  
Slice Thickness ◽  
Fiber Tractography ◽  
Pixel Size ◽  
Cross Sectional ◽  
Visual Score

Background Small peripheral nerve tractography is challenging because of the trade-off among resolution, image acquisition time, and signal-to-noise ratio. Purpose To optimize pixel size and slice thickness parameters for fiber tractography and diffusion tensor imaging (DTI) of the ulnar nerve at the cubital tunnel using 3T magnetic resonance imaging (MRI). Material and Methods Fifteen healthy volunteers (mean age 30 ± 6.8 years) were recruited prospectively. Axial T2-weighted and DTI scans were acquired, covering the cubital tunnel, using different pixel sizes and slice thicknesses. Three-dimensional (3D) nerve tractography was evaluated for the median number and length of the reconstructed fiber tracts and visual score from 0 to 5. Two-dimensional (2D) cross-sectional DTI was evaluated for fractional anisotropy (FA) values throughout the length of the ulnar nerve. Results A pixel size of 1.3 mm2 revealed the highest number of reconstructed nerve fibers compared to that of 1.1 mm2 ( P = 0.048), with a good visual score. A slice thickness of 4 mm had the highest number of reconstructed nerve fibers and visual score compared with other thicknesses (all P < 0.05). In 2D cross-sectional images, the median FA values were in the range of 0.40–0.63 at the proximal, central, and distal portions of the cubital tunnel. Inter-observer agreement for all parameters was good to excellent. Conclusion For fiber tractography and DTI of the ulnar nerve at the cubital tunnel, optimal image quality was obtained using a 1.3-mm2 pixel size and 4-mm slice thickness under MR parameters of this study at 3T.

scholarly journals Diffusion tensor imaging to visualize axons in the setting of nerve injury and recovery

2015 ◽  
Vol 39 (3) ◽  
pp. E10 ◽  
Author(s):  
Thomas Anthony Gallagher ◽  
Neil G. Simon ◽  
Michel Kliot
Keyword(s):  
Diffusion Tensor Imaging ◽  
Peripheral Nerve ◽  
Nerve Injury ◽  
Peripheral Nerves ◽  
Diffusion Tensor ◽  
Nerve Fibers ◽  
Accurate Localization ◽  
Peripheral Nerve Trauma ◽  
Nerve Trauma ◽  
Diffusion Tensor Imaging Dti

Successful management of peripheral nerve trauma relies on accurate localization of the injury and grading of the severity of nerve injury to determine whether surgical intervention is required. Existing techniques, such as electrodiagnostic studies and conventional imaging modalities, provide important information, but are limited by being unable to distinguish severe nerve lesions in continuity that will recover from those that will not. Diffusion tensor imaging (DTI) and tractography of peripheral nerves provide a novel technique to localize and grade nerve injury, by assessing the integrity of the nerve fibers across the site of nerve injury. Diffusion tensor imaging and tractography also hold promise as markers of early nerve regeneration, prior to clinical and electrodiagnostic evidence of recovery. In the present review, the techniques of peripheral nerve DTI and tractography are discussed with respect to peripheral nerve trauma, with illustrative cases demonstrating potential roles of these novel approaches.

Collateral nerve fibers in human spinal cord: Visualization with magnetic resonance diffusion tensor imaging

NeuroImage ◽  
2006 ◽  
Vol 31 (1) ◽  
pp. 24-30 ◽  
Author(s):  
Hatsuho Mamata ◽  
Umberto De Girolami ◽  
W. Scott Hoge ◽  
Ferenc A. Jolesz ◽  
Stephan E. Maier
Keyword(s):  
Spinal Cord ◽  
Diffusion Tensor Imaging ◽  
Magnetic Resonance ◽  
Diffusion Tensor ◽  
Nerve Fibers ◽  
Human Spinal Cord
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