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“Purpose: To develop and verify the accuracy of a rapid imaging protocol for delayed gadolinium-enhanced magnetic resonance (MR) imaging of cartilage that was based on three-dimensional (3D) spoiled gradient-recalled acquisition in the steady state (SPGR) sequences with variable flip angles (FAs) (VFAs) and where
a correction method for B-1 field inhomogeneities was applied.
Materials and Methods: The institutional research ethics board approved this study. Written informed consent was obtained from all subjects. A B1 field inhomogeneity correction method was applied to a 3D SPGR pulse sequence with VFAs (repetition time msec/echo time msec, 7.1/3.3; FAs, 2 degrees, 5 degrees, 10 degrees, and 20 degrees) and was used to perform delayed gadolinium-enhanced MR imaging of cartilage 3D T1 measurements at 1.5 T. The 3D T1 measurements were validated with the reference standard ( the results of T1 mapping by using a single-section Galardin mw two-dimensional [2D] inversion-recovery [IR] fast spin-echo [SE] pulse sequence in vitro
and in vivo) in six healthy volunteers.
Results: Vorinostat cell line T1 values calculated from 3D T1 maps were not significantly different from reference T1 values in vitro (P = .195) and in vivo (P = .52) when a B1 field inhomogeneity correction method was applied. In vivo T1 mapping of the articular surface of the whole femoropatellar joint, including data acquisition, was performed in approximately 8 minutes of acquisition time at a spatial resolution of 0.55 x 0.55 x LY2606368 clinical trial 3.00 mm.
Conclusion: Rapid T1 mapping by using 3D SPGR acquisitions with a VFA approach and a correction for B1 field inhomogeneities can be used for delayed gadolinium-enhanced MR imaging of cartilage. T1 measurements performed in vitro and in vivo by using this approach are highly accurate when compared with those performed by using standard 2D IR fast SE T1 mapping as a reference.”
“ObjectiveThe aim of this study is to examine factors contributing to cancer-related fatigue (CRF) in breast
cancer patients who have undergone surgery.
MethodsSixty women (mean age: 50.0) completed self-rated questionnaires assessing components of CRF, muscular and cognitive functions. Also, physiological and subjective data were gathered. Data were analyzed using partial least squares variance-based structural equation modeling in order to examine factors contributing to CRF after breast surgery.
ResultsThe tested model was robust in terms of its measurement quality (reliability and validity). According to the structural model results, emotional distress (=0.59; p<0.001), pain (=0.23; p<0.05), and altered vigilance (=0.30; p<0.05) were associated with CRF, accounting for 61% of the explained variance. Also, emotional distress (=0.41; p<0.05) and pain (=0.40; p<0.05) were related to low physical function and accounted for 41% of the explained variance. However, the relationship between low physical function and CRF was weak and nonsignificant (=0.