Year: 2025
Type: article
Abstract: ABSTRACT We analyzed the feasibility of using a UTE stack‐of‐spirals turbo FLASH (STFL) sequence to measure T1ρ relaxation in the Achilles tendon. Six HS (25–31 years) and five AT patients (32–47 years) participated. The study evaluates the clinical utility of the STFL sequence to generate T1ρ maps using mono‐exponential (ME) and bi‐exponential (BE) fitting models. In a phantom experiment, ME‐T1ρ values and SNR estimated from the STFL sequence are compared with those of the Cartesian turbo FLASH (CTFL) sequence. In human subjects, we evaluate differences in estimated ME (ME‐T1ρ) and BE parameters (short T1ρ, long T1ρ, and short fraction) between AT and HS groups along with repeatability of STFL. The agarose phantom demonstrates biases of 2.89% (3% agarose), −1.88% (5%), and −0.92% (7%) between ME‐T1ρ values from STFL and CTFL. In the bovine Achilles tendon, STFL shows a large bias of −58.6%, with a lower median ME‐T1ρ (2.9 ms) than CTFL (4.6 ms). SNR is higher in STFL (77.05–80.72 for 3%–7% agarose; 24.43 for bovine tendon) than CTFL (66.73–58.97 for agarose; 3.21 for bovine tendon). ME and BE parameters were averaged over the entire Achilles tendon, and none showed significant group differences (p > 0.05; effect size = 0.05–0.22). Subregional analysis showed that in the mid‐Achilles tendon, short and long BE‐T1ρ components were 26% and 37% lower in AT than HS, though not statistically significant. The LDA‐combined BE parameter showed significant group separation in the midtendon region (p = 0.016; effect size = 1.53). In HS, the long BE‐T1ρ component showed subregional variation (p = 0.006), increasing 58% from calcaneal to midtendon, and then decreasing 23% toward the intramuscular region. ME and BE fitting showed high repeatability with scan‐rescan variations of 2.64% (T1ρ), 3.38% (short T1ρ), 3.0% (long T1ρ), and 0.21% (short fraction). We demonstrated the feasibility of using STFL for T1ρ quantification in the Achilles tendon.
Source: NMR in Biomedicine
DOI: 10.1002/nbm.70149
Institutions: New York University, Virginia Tech
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
Awards: G1784542034, G316237672, Rapid Quantitative Assessment of Knee Joint with Compressed Sensing, G3647815393, G561879993, G9000011738, G9000177559, G9000201180, G9000219358, Data-Driven Learning Framework for Fast Quantitative Knee Joint Mapping, Center for Advanced Imaging Innovation and Research (CAI2R), Multiparametric Mapping of Knee Joint with Magnetic Resonance Fingerprinting
OpenAlex ID: W4414998951