Quantitative muscle water T2 mapping using RF phase-modulated 3D gradient echo imaging
Corresponding Author
Eléonore Vermeulen
NMR Laboratory, Neuromuscular Investigation Center, Institute of Myology, Paris, France
Correspondence
Eléonore Vermeulen, Institute of Myology, Bâtiment Babinski, Groupe Hospitalier Pitié-Salpêtrière, 47-83 boulevard Vincent Auriol, 75651 Paris Cedex 13, Paris, France.
Email: [email protected]
Search for more papers by this authorPierre-Yves Baudin
NMR Laboratory, Neuromuscular Investigation Center, Institute of Myology, Paris, France
Search for more papers by this authorBenjamin Marty
NMR Laboratory, Neuromuscular Investigation Center, Institute of Myology, Paris, France
Search for more papers by this authorCorresponding Author
Eléonore Vermeulen
NMR Laboratory, Neuromuscular Investigation Center, Institute of Myology, Paris, France
Correspondence
Eléonore Vermeulen, Institute of Myology, Bâtiment Babinski, Groupe Hospitalier Pitié-Salpêtrière, 47-83 boulevard Vincent Auriol, 75651 Paris Cedex 13, Paris, France.
Email: [email protected]
Search for more papers by this authorPierre-Yves Baudin
NMR Laboratory, Neuromuscular Investigation Center, Institute of Myology, Paris, France
Search for more papers by this authorBenjamin Marty
NMR Laboratory, Neuromuscular Investigation Center, Institute of Myology, Paris, France
Search for more papers by this authorAbstract
Purpose
To propose a motion robust 3D sequence for water T2 () estimation in skeletal muscle tissues.
Methods
A estimation method is proposed, using 10 image volumes acquired with a partially spoiled gradient echo (pSPGR) sequence, varying the RF phase-cycling increment and prescribed flip angle. The complex signal evolution is fit with a bi-component water/fat model to extract and account for B1 and fat fraction confounders. Accuracy and precision were evaluated using numerical simulations. Cartesian and radial implementations of the sequence were tested. In phantoms, results were compared with reference spectroscopic and multi-spin echo imaging techniques. Several in vivo experiments evaluated robustness to B1 field inhomogeneities, sensitivity to physiological and pathological variations in on the thigh muscles.
Results
In phantoms, values were highly correlated with reference spectroscopy and multi spin echo values (R2 > 0.8). In vivo, values were correlated with reference values in healthy controls (R2 = 0.69) and pathological muscles (R2 = 0.87) and were not affected by B1 inhomogeneities (R2 = 0.06). In the tongue muscle, a significant reduction in the SD of values was observed using the radial compared to the Cartesian pSPGR sequence (−28%).
Conclusion
The proposed approach provides efficient 3D estimation in skeletal muscle, including small moving organs like the tongue. This broadens the range of accessible targets for characterizing heterogeneous impairment of muscle tissue, while retaining durations compatible with clinical research.
Supporting Information
Filename | Description |
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mrm30545-sup-0001-Supinfo.docxWord 2007 document , 809.6 KB | Figure S1. EPG sequence differentiation. Figure S2: Sequence optimization script. Figure S3: Estimation of T2fat in the subcutaneous fat from the calves of six subjects using the proposed sequence. For each subject, T2fat was estimated in the subcutaneous fat with a single-component model (assuming a FF of 1). The mean T2fat value across the 8 subjects obtained was 133 ± 4 ms. This value was then used in all simulations and for dictionary generation. Figure S4: SNR estimated in the leg muscles of the same 6 subjects as in Figure S4, using the proposed sequence. Noise was defined as the signal's standard deviation in a circular ROI (of 5 pixels of diameter) within a single muscle and signal as the average amplitude in the same ROI. Figure S5: A: number of excitations required to reach a steady state for = 25 ms and FF = 0.3. A steady state is defined as the point at which both magnitude and phase differences between two successive excitations are below 0.1%. B/C: signal's magnitude and phase evolution over 1000 excitation for the 10 α, φ pairs used at = 25 ms and FF = 0.3. Figure S6: pSPGR signal magnitude and phase over the range of small phase increments ϕ for various tissues and field parameters. For all simulations, TR/TE = 5.5/2.25. Rows 1 and 2 (in blue): flip angle = 25°, rows 3 and 4 (in brown): flip angle = 5°. Figure S7: maps and Bland–Altman between two measurements on the multi-vials phantom using a GRAPPA acceleration factor of 2 or by disabling GRAPPA. Figure S8: Bland–Altman plot between two measurements on a 15-vials phantom using 150 or 406 radial spokes. There was a small bias present (0.38 ± 1.81 ms). Figure S9: Bland–Altman plot between two measurements on the muscle legs of 6 volunteers. Table S1: and FF measurements of the 15-vial phantom obtained using the reference methods (MRS-STEAM and 3-point Dixon) and both pSPGR implementations (Cartesian and radial pSPGR). The table also reports the relative error in and the FF difference of the pSPGR acquisitions compared to the references. Values are represented as mean ± standard deviation for imaging methods and MRS with its 95% confidence interval. |
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