Ultrashort echo time magnetic resonance fingerprinting (UTE‐MRF) for simultaneous quantification of long and ultrashort T₂ tissues

Abstract

Purpose To demonstrate an ultrashort echo time magnetic resonance fingerprinting (UTE‐MRF) method that allows quantifying relaxation times for muscle and bone in the musculoskeletal system and generating bone enhanced images that mimic CT scans. Methods A fast imaging steady‐state free precession MRF sequence with half pulse excitation and half projection readout was designed to sample fast T2 decay signals. Varying echo time (TE) of a sinusoidal pattern was applied to enhance sensitivity for tissues with short and ultrashort T2 values. The performance of UTE‐MRF was evaluated via simulations, phantom, and in vivo experiments. Results A minimal TE of 0.05 ms was achieved. Simulations indicated the sinusoidal TE sampling increased T2 quantification accuracy in the cortical bone and tendon but had little impact on long T2 muscle quantifications. For the rubber phantom, the averaged relaxometries from UTE‐MRF (T1 = 162 ms and T2 = 1.07 ms) compared well with the gold standard (T1 = 190 ms and T2∗ = 1.03 ms). For the long T2 agarose phantom, the linear regression slope between UTE‐MRF and gold standard was 1.07 (R2 = 0.991) for T1 and 1.04 (R2 = 0.994) for T2. In vivo experiments showed the detection of the cortical bone (averaged T2 = 1.0 ms) and Achilles tendon (averaged T2 = 15 ms). Scalp structures from the bone enhanced image show high similarity with CT. Conclusion The UTE‐MRF with sinusoidal TEs can simultaneously quantify T1, T2, proton density, and B0 in long, short, even ultrashort T2 musculoskeletal structures. Bone enhanced images can be achieved in the brain with UTE‐MRF.

MR relaxation times of agar‐based tissue‐mimicking phantoms

“…Note also that the same holds by increasing the milk concentration. This result ties well with previous studies wherein T 2 was mostly defined by the gelling agent concentration, whereas T 1 was mainly varied by incorporating different concentrations of paramagnetic ion salts 22–24 …”

“…Their tissue-like MR signal makes them the material of choice for MRI studies. [21][22][23][24][25][26][27] In fact, a wide variety of agarbased phantoms simulating specific body parts, such as prostate, 27 carotid, 21 and brain, 26 have been proposed in the literature for evaluating new MR protocols and imaging techniques. This phantom type has also been quite widely used for thermal studies with FUS, 3,[28][29][30][31] where agar served as the gelling agent, and proper concentration of other materials was added to modify mainly the thermal and acoustical properties depending on the tissue to be mimicked.…”

Initial assessment of 3D magnetic resonance fingerprinting (MRF) towards quantitative brain imaging for radiation therapy

“…Due to extremely low T2 values of the bone, MRF will match its evolution curve with the dictionary curve that has a very large T1 value, that is, low signal intensity. Potential solutions include the integration of machine‐learning for MRF pattern recognition and the combination with the ultrashort TE technique with MRF . Finally, all in vivo measurements were performed without contrast in this study.…”

“…Potential solutions include the integration of machine-learning for MRF pattern recognition and the combination with the ultrashort TE technique with MRF. 52 Finally, all in vivo measurements were performed without contrast in this study. However, postcontrast images are often needed to help delineate lesions.…”

Technical overview of magnetic resonance fingerprinting and its applications in radiation therapy

“…For MRI‐only simulation, MRF provides absolute quantifications of tissue properties that naturally help improve tissue characterization. MRF has more consistent contrasts 6 among different tissues that reduce the uncertainty of synthetic CT. Ultra‐short echo time MRF 89 was also recently developed to improve the quantification and differentiation of bony structure and air cavity for synthetic CT. Machine learning based synthetic CT generation approaches have demonstrated excellent efficiency and reasonable accuracy in tissue classification 90 . So far majority of studies in this category focused on conventional T 1 ‐/T 2 ‐weighted images, where potential unwanted bias can be introduced through sophisticated processing steps such as intensity normalization.…”