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

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 ultrashort 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-tonoise 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.

Bicomponent ultrashort echo time analysis for assessment of patients with patellar tendinopathy

“…However, our study and the study performed by Juras et al found no significant difference in between patients with tendinopathy and healthy volunteers. This may be the result of the increased uncertainty and higher sensitivity to noise of parameter estimation due to the relatively small fraction of the slow relaxing water component in tendon, which provides little MRI signal at later echoes …”

Whole knee joint T1 values measured in vivo at 3T by combined 3D ultrashort echo time cones actual flip angle and variable flip angle methods

“…We have demonstrated that the proposed 3D UTE‐Cones AFI‐VFA method can accurately measure T 1 values for most major tissues of the whole knee joint. Simulation shows that the proposed 3D UTE‐Cones AFI‐VFA method provides accurate T 1 measurements for tissues with T 2 values longer than 1 ms. Because most knee tissues have T 2 s longer than 1 ms (meniscus: 5–8 ms, ligament and tendon: 4–10 ms, cartilage: 27–43 ms, muscle: 32–50 ms, and fat: ~133 ms), accurate T 1 maps were obtained using the proposed method to provide in vivo knee measurements in 16 healthy volunteers.…”

“…An 8‐channel transmit–receive knee coil was used for both RF transmission and signal reception. Unique k‐space trajectories were used in the UTE‐Cones sequences that sampled data along evenly spaced twisted paths in the form of multiple cones . Data sampling began from the center of k‐space and continued outward.…”

“…However, UTE AFI‐VTR would require a long scan time for 3D high resolution knee imaging, making it unacceptable for clinical use . Because all major knee tissues other than bone have a T 2 value longer than 1 ms, the B 1 map generated by the UTE‐AFI method can still be used for B 1 correction of the faster VFA‐based T 1 measurement for these tissues. Therefore, the UTE AFI‐VFA method would be expected to provide accurate T 1 measurements of the soft tissues of the whole knee joint with much less scan time than the UTE AFI‐VTR method.…”

Fisher information and Cramér‐Rao lower bound for experimental design in parallel imaging

“…Therefore, incorporation of Rician noise into CRLB analysis is especially important in settings of limited SNR, as is often the case in clinical imaging (11, 23–26). …”