Assessment of different fitting methods for in-vivo bi-component T2* analysis of human patellar tendon in magnetic resonance imaging. Academic Article uri icon

Overview

abstract

  • PURPOSE: To investigate the robustness of four fitting methods for bi-component effective spin-spin T2 (T2*) relaxation time analysis of human patellar tendon. METHODS: A three-dimensional (3D) cone ultra-short echo-time (UTE) sequence was performed on the knees of ten healthy volunteers at 3.0T. Four fitting methods incorporating either Gaussian or Rician noise distribution were used for voxel-by-voxel bi-component T2* analysis of the patellar tendon. The T2* for the short relaxing (T**,s ) and long relaxing (T*2,l ) water components and the fraction of the short relaxing water component (fs ) were measured, and different fitting methods were compared using Friedman's and Wilcoxon signed rank tests. A numerical simulation study was also performed to predict the accuracy and precision of bi-component T2* parameter estimation in tendon at different signal-to-noise ratios (SNR) levels. RESULTS: The average T*2,s , T*2,l , fs of human patellar tendon were 1.5ms, 30ms, and 80% respectively. Incorporating different noise models and fitting methods influenced the measured bi-component T2* parameters. Fitting methods incorporating Rician noise were superior to traditional fitting methods for bi-component T2* analysis especially at lower SNR. fs and T*2,s were less sensitive than T*2,1 to noise at even moderate and low SNR. The result of the in-vivo bi-component T2* analysis of tendon agreed well with numerical simulations. CONCLUSION: Our study demonstrated the use of a 3D cone UTE sequence to perform in vivo voxel-by-voxel bi-component T2* analysis of human patellar tendon. Incorporating Rician noise was useful for improving bi-component T2* analysis especially at lower SNR. LEVEL OF EVIDENCE: IV.

publication date

  • May 10, 2017

Identity

PubMed Central ID

  • PMC5505585

Scopus Document Identifier

  • 85019120499

Digital Object Identifier (DOI)

  • 10.11138/mltj/2017.7.1.163

PubMed ID

  • 28717625

Additional Document Info

volume

  • 7

issue

  • 1