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      Diffusion tensor-based fiber tracking of the male urethral sphincter complex in patients undergoing radical prostatectomy: a feasibility study

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          Abstract

          Objectives

          To study the diffusion tensor-based fiber tracking feasibility to access the male urethral sphincter complex of patients with prostate cancer undergoing Retzius-sparing robot-assisted laparoscopic radical prostatectomy (RS-RARP).

          Methods

          Twenty-eight patients (median age of 64.5 years old) underwent 3 T multiparametric-MRI of the prostate, including an additional echo-planar diffusion tensor imaging (DTI) sequence, using 15 diffusion-encoding directions and a b value = 600 s/mm 2. Acquisition parameters, together with patient motion and eddy currents corrections, were evaluated. The proximal and distal sphincters, and membranous urethra were reconstructed using the deterministic fiber assignment by continuous tracking (FACT) algorithm, optimizing fiber tracking parameters. Tract length and density, fractional anisotropy (FA), axial diffusivity (AD), mean diffusivity (MD), and radial diffusivity (RD) were computed. Regional differences between structures were accessed by ANOVA, or nonparametric Kruskal–Wallis test, and post-hoc tests were employed, respectively, TukeyHSD or Dunn’s.

          Results

          The structures of the male urethral sphincter complex were clearly depicted by fiber tractography using optimized acquisition and fiber tracking parameters. The use of eddy currents and subject motion corrections did not yield statistically significant differences on the reported DTI metrics. Regional differences were found between all structures studied among patients, suggesting a quantitative differentiation on the structures based on DTI metrics.

          Conclusions

          The current study demonstrates the technical feasibility of the proposed methodology, to study in a preoperative setting the male urethral sphincter complex of prostate cancer patients candidates for surgical treatment. These findings may play a role on a more accurate prediction of the RS-RARP post-surgical urinary continence recovery rate.

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          Most cited references22

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          Advances in functional and structural MR image analysis and implementation as FSL.

          The techniques available for the interrogation and analysis of neuroimaging data have a large influence in determining the flexibility, sensitivity, and scope of neuroimaging experiments. The development of such methodologies has allowed investigators to address scientific questions that could not previously be answered and, as such, has become an important research area in its own right. In this paper, we present a review of the research carried out by the Analysis Group at the Oxford Centre for Functional MRI of the Brain (FMRIB). This research has focussed on the development of new methodologies for the analysis of both structural and functional magnetic resonance imaging data. The majority of the research laid out in this paper has been implemented as freely available software tools within FMRIB's Software Library (FSL).
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            How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging.

            Diffusion tensor imaging is often performed by acquiring a series of diffusion-weighted spin-echo echo-planar images with different direction diffusion gradients. A problem of echo-planar images is the geometrical distortions that obtain near junctions between tissues of differing magnetic susceptibility. This results in distorted diffusion-tensor maps. To resolve this we suggest acquiring two images for each diffusion gradient; one with bottom-up and one with top-down traversal of k-space in the phase-encode direction. This achieves the simultaneous goals of providing information on the underlying displacement field and intensity maps with adequate spatial sampling density even in distorted areas. The resulting DT maps exhibit considerably higher geometric fidelity, as assessed by comparison to an image volume acquired using a conventional 3D MR technique.
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              Deterministic Diffusion Fiber Tracking Improved by Quantitative Anisotropy

              Diffusion MRI tractography has emerged as a useful and popular tool for mapping connections between brain regions. In this study, we examined the performance of quantitative anisotropy (QA) in facilitating deterministic fiber tracking. Two phantom studies were conducted. The first phantom study examined the susceptibility of fractional anisotropy (FA), generalized factional anisotropy (GFA), and QA to various partial volume effects. The second phantom study examined the spatial resolution of the FA-aided, GFA-aided, and QA-aided tractographies. An in vivo study was conducted to track the arcuate fasciculus, and two neurosurgeons blind to the acquisition and analysis settings were invited to identify false tracks. The performance of QA in assisting fiber tracking was compared with FA, GFA, and anatomical information from T1-weighted images. Our first phantom study showed that QA is less sensitive to the partial volume effects of crossing fibers and free water, suggesting that it is a robust index. The second phantom study showed that the QA-aided tractography has better resolution than the FA-aided and GFA-aided tractography. Our in vivo study further showed that the QA-aided tractography outperforms the FA-aided, GFA-aided, and anatomy-aided tractographies. In the shell scheme (HARDI), the FA-aided, GFA-aided, and anatomy-aided tractographies have 30.7%, 32.6%, and 24.45% of the false tracks, respectively, while the QA-aided tractography has 16.2%. In the grid scheme (DSI), the FA-aided, GFA-aided, and anatomy-aided tractographies have 12.3%, 9.0%, and 10.93% of the false tracks, respectively, while the QA-aided tractography has 4.43%. The QA-aided deterministic fiber tracking may assist fiber tracking studies and facilitate the advancement of human connectomics.
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                Author and article information

                Contributors
                nickolas.papanikolaou@research.fchampalimaud.org
                Journal
                Insights Imaging
                Insights Imaging
                Insights into Imaging
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                1869-4101
                27 November 2020
                27 November 2020
                December 2020
                : 11
                : 126
                Affiliations
                [1 ]GRID grid.421010.6, ISNI 0000 0004 0453 9636, Head of Computational Clinical Imaging Group, , Centre for the Unknown, Champalimaud Foundation, ; Av. Brasilia, 1400-038 Lisbon, Portugal
                [2 ]Philips Healthcare, Lisbon, Portugal
                [3 ]GRID grid.421010.6, ISNI 0000 0004 0453 9636, Radiology Department, , Champalimaud Foundation, ; Lisbon, Portugal
                [4 ]GRID grid.421010.6, ISNI 0000 0004 0453 9636, Urology Unit, , Champalimaud Foundation, ; Lisbon, Portugal
                [5 ]GRID grid.5808.5, ISNI 0000 0001 1503 7226, Instituto de Ciências Biomédicas Abel Salazar, Universidade Do Porto, ; Porto, Portugal
                Author information
                http://orcid.org/0000-0003-3298-2072
                Article
                927
                10.1186/s13244-020-00927-x
                7695769
                33245443
                8fd71e5e-548b-4dfe-a840-2263c0219a8c
                © The Author(s) 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 21 July 2020
                : 13 October 2020
                Categories
                Original Article
                Custom metadata
                © The Author(s) 2020

                Radiology & Imaging
                diffusion tensor imaging,fiber tracking,prostatic neoplasms,urethral sphincters,urinary incontinence

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