Literature DB >> 24183417

Optimization of the parameters for diffusion tensor magnetic resonance imaging data acquisition for breast fiber tractography at 1.5 T.

Yuan Wang1, Xiao-Peng Zhang2, Yan-Ling Li1, Xiao-Ting Li1, Yan Hu1, Yong Cui1, Ying-Shi Sun1, Xiao-Yan Zhang1.   

Abstract

INTRODUCTION: Diffusion tensor MRI has emerged as a promising tool for the analysis of the microscopic properties of tissues. Optimizing image acquisition parameters is essential for producing high-quality DTI. This study aimed to optimize the parameters for DTI data acquisition for breast fiber tractography at 1.5 T. PATIENTS AND METHODS: A total of 21 healthy volunteers received breast DTI scanning using an ASSET-based EPI technique operated under different parameters including b value, the number of diffusion gradient directions, and spatial resolution. The images were analyzed for signal-to-noise, signal intensity ratio, mean number and length of reconstructive fiber tracts, and fractional anisotropy value.
RESULTS: The optimal acquisition parameters at 1.5 T for breast DT-MRI fiber tractography were determined as follows: axial 31 direction, b = 600 seconds per mm(2), matrix 128 × 128 with slice thickness of 3 mm.
CONCLUSION: The optimization of data acquisition parameters could improve the quality of breast DT-MRI images and assist fiber tractography at 1.5 T.
Copyright © 2014 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Array spatial sensitivity encoding technique; B value; Diffusion gradient directions; Diffusion tensor imaging; Spatial resolution

Mesh:

Year:  2013        PMID: 24183417     DOI: 10.1016/j.clbc.2013.09.002

Source DB:  PubMed          Journal:  Clin Breast Cancer        ISSN: 1526-8209            Impact factor:   3.225


  6 in total

Review 1.  The Efficiency of Diffusion Weighted MRI and MR Spectroscopy On Breast MR Imaging.

Authors:  Canan Altay; Pınar Balcı
Journal:  J Breast Health       Date:  2014-10-01

Review 2.  Diffusion-weighted breast MRI: Clinical applications and emerging techniques.

Authors:  Savannah C Partridge; Noam Nissan; Habib Rahbar; Averi E Kitsch; Eric E Sigmund
Journal:  J Magn Reson Imaging       Date:  2016-09-30       Impact factor: 4.813

3.  Tracking the mammary architectural features and detecting breast cancer with magnetic resonance diffusion tensor imaging.

Authors:  Noam Nissan; Edna Furman-Haran; Myra Feinberg-Shapiro; Dov Grobgeld; Erez Eyal; Tania Zehavi; Hadassa Degani
Journal:  J Vis Exp       Date:  2014-12-15       Impact factor: 1.355

4.  Diagnostic Performance of Diffusion Tensor Imaging with Readout-segmented Echo-planar Imaging for Invasive Breast Cancer: Correlation of ADC and FA with Pathological Prognostic Markers.

Authors:  Ken Yamaguchi; Takahiko Nakazono; Ryoko Egashira; Yoshiaki Komori; Jun Nakamura; Tomoyuki Noguchi; Hiroyuki Irie
Journal:  Magn Reson Med Sci       Date:  2016-11-16       Impact factor: 2.471

Review 5.  Current and Emerging Magnetic Resonance-Based Techniques for Breast Cancer.

Authors:  Apekshya Chhetri; Xin Li; Joseph V Rispoli
Journal:  Front Med (Lausanne)       Date:  2020-05-12

6.  The volumetric-tumour histogram-based analysis of intravoxel incoherent motion and non-Gaussian diffusion MRI: association with prognostic factors in HER2-positive breast cancer.

Authors:  Chao You; Jianwei Li; Wenxiang Zhi; Yanqiong Chen; Wentao Yang; Yajia Gu; Weijun Peng
Journal:  J Transl Med       Date:  2019-07-02       Impact factor: 5.531

  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.