MPnRAGE: A technique to simultaneously acquire hundreds of differently contrasted MPRAGE images with applications to quantitative T1 mapping

Abstract

Purpose: To introduce a new technique called MPnRAGE, which produces hundreds of images with different T1 contrasts and a B1 corrected T1 map.

Theory and methods: An interleaved three-dimensional radial k-space trajectory with a sliding window reconstruction is used in conjunction with magnetization preparation pulses. This work modifies the SNAPSHOT-FLASH T1 fitting equations for radial imaging with view-sharing and develops a new rapid B1 correction procedure. MPnRAGE is demonstrated in phantoms and volunteers, including two volunteers with eight scans each and eight volunteers with two scans each. T1 values from MPnRAGE were compared with those from fast spin echo inversion recovery (FSE-IR) in phantoms and a healthy human brain at 3 Tesla (T).

Results: The T1 fit for human white and gray matter was T1MPnRAGE = 1.00 · T1FSE-IR + 24 ms, r(2) = 0.990. Voxel-wise coefficient of variation in T1 measurements across eight time points was between 0.02 and 0.08. Region of interest-based T1 values were reproducible to within 2% and agree well with literature values.

Conclusion: In the same amount of time as a traditional MPRAGE exam (7.5 min), MPnRAGE was shown to produce hundreds of images with alternate T1 contrasts as well as an accurate and reproducible T1 map that is robust to B1 errors.

Keywords: Look-Locker; MPRAGE; T1 mapping; T1 weighted imaging; inversion recovery; segmentation.

Figure 1

Diagram of the MPnRAGE sequence. A train of N RF pulses of flip angle α begins a time T_I after a preparation pulse of flip angle β prepares the magnetization. A delay time T_D allows the magnetization to freely regrow before the next preparation pulse is applied. Radial readout gradients are applied on all three axes, and a constant area spoiler is applied along the z-direction.

Figure 2

A three-parameter fit that assumes a perfect inversion even though one is not achieved largely underestimates T1 (a) but produces stable estimates (c). A four-parameter fit that includes a term for inversion efficiency reduces some error in T1 estimation (b), but produces an unstable fit (d).

Figure 3

Including one (a,c) and two (b,d) SPGR images with different flip angles in a 4 parameter fit of IR data results in accurate T1 estimations with stable estimates. Using two SPGR images produces less error than using a single SPGR image. Both sets have little sensitivity to λ, which controls the relative weighting of SPGR to IR.

Figure 4

The effects of view-sharing (i.e. ω) on the error and standard error in T1 fits when (a) one and (b) two SPGR images are used in addition to the IR images.

Figure 5

Example images from MPnRAGE displaying a variety of T1w contrasts. Frame 1: The earliest inversion time possible. Frame 45: Image with nulled white matter. Frame 73: Image with nulled gray matter. Frame 118: Image with nulled cerebral spinal fluid. Frame 240: The longest possible inversion time. Composite: image formed by summation of all the data.

Figure 6

Figure (a) shows the ROIs for the R₁ maps in Table 1 (In Vivo Experiment 1). Quantitative R₁ maps in units of 1/s (b–c) from MPnRAGE and 2D FSE-IR as well as difference image (d).

Figure 7

Example magnitude images from MPnRAGE for a patient with MS. Three cropped orthogonal views from the composite images are shown in (a–c) and in (d–f) for a zoomed region to depict a cortical gray matter lesion (yellow arrow). Zoomed images are also shown for an alternative contrast (g–i) that depicts a magnitude image when WM and GM have nearly equal magnitudes but opposite signs (WM > 0 and GM < 0) as well as quantitative R1 maps (j–l) in units of 1/s. The alternative contrast shows a region (blue arrow) just adjacent to the lesion that appears darker than both the lesion and surrounding normal GM.

Figure 8

Figure 8 shows select axial images for two volunteers (a–e,f–j) with clinically definite multiple sclerosis who were scanned once a month for 8 months. Figure (a) shows the ROIs used for data analysis. The time-averaged composite images from MPnRAGE are shown in (b,c). Figures (c,h) are the time-averaged, intensity corrected images from MPnRAGE formed using a combination of high and low inversion time images. The time-averaged T₁ map is shown in (d,i), while the coefficient of variation of T₁ is shown in Fig. (e, j).