On the role of physiological fluctuations in quantitative gradient echo MRI: implications for GEPCI, QSM, and SWI

Abstract

Purpose: Physiological fluctuations in biological tissues adversely affect MR images if present during signal acquisition. This problem is especially important for quantitative MRI. The goal of the studies reported in this study was to reduce the contributions of physiological fluctuations in quantitative MRI based on T2* tissue relaxation properties. Specifically, in this study we deal with GEPCI, QSM, and SWI techniques and propose methods allowing for substantial improvement of their results.

Methods: We used a navigator imbedded in a multi-gradient-echo sequence to record and correct MR signal phase fluctuations at each phase encoding step. All GEPCI, QSM, and SWI images were then reconstructed from a single acquisition. We used a keyhole-type approach to further average out effects of physiological fluctuations. Voxel spread function technique was used to correct for macroscopic field inhomogeneities.

Results: Brains of normal subjects and subjects with multiple sclerosis were studied. We demonstrated that our used strategies substantially reduced the width of the R2* = 1/T2* distribution within human brains and significantly improved quantification of tissue damage in multiple sclerosis. We also showed improved quality of the SWI and QSM images.

Conclusion: The strategies used in this study greatly reduced physiologically induced artifacts in GEPCI, QSM, and SWI, improving the reliability of these techniques.

Keywords: GEPCI; QSM; R2*; SWI; f0 fluctuation; field inhomogeneities; keyhole; navigator; phase contrast.

Fig. 1

Physiologically induced f₀ fluctuations for high-resolution data. Examples are from phantom (a) and two healthy subjects. For each dataset only data from a single channel are shown.

Fig. 2

Comparison of R2* maps (selected from subject 2). (a) without any correction; (b) with F-function field inhomogeneities correction; (c) with keyhole-based correction; (d) with navigator-based correction; (e) with all corrections. Short arrows on the top show the area affected by macroscopic field inhomogeneities, arrows in the middle show the area affected by physiological fluctuations.

Fig. 3

R2* histograms of deep gray matter area (caudate nucleus, putamen and globus pallidus) before (a) and after (b) correction (squares: right hemisphere; circles: left hemisphere). After correction, R2* histograms of the two hemispheres became more symmetric. Data derive from subject 2, a healthy volunteer.

Fig. 4

(a) R2* map without any corrections; (b) R2* map with F-function correction; (c) R2* map with keyhole-based correction; (d) R2* map with navigator-based correction; (e) R2* map with all corrections. Long arrows point to MS lesions (dark on R2* map) and short arrows indicate blood vessels. Images were selected from subject 4, a patient with PPMS.

Fig. 5

R2* histograms of NAWM (a & b) and cortical gray matter (c & d) of the whole brain, calculated using non-Hanning filtered (a & c) and Hanning filtered (b & d) data. Black lines: data without any correction; red lines: data with F-function correction; magenta lines: data with keyhole-based corrections; green lines: data with navigator-based corrections; blue lines: data with all corrections. Data were selected from subject 4.

Fig. 6

(a) T1-weighted image with superimposed lesion TDS, (b) R2* map from the same slice. (Data from subject 3, a patient with SPMS).

Fig. 7

R2* distribution of white matter area (with lesions) selected from the slice shown in Fig. 6 before (a) and after (b) corrections. Upper halves of both distributions are fit to Gaussian function (solid lines). Dashed lines represent threshold values for lesion definition. Data were selected from subject 3.

Fig. 8

Demonstration of a small central vein in a MS lesion. (a) fluid suppressed T2* map (FST2*) before correction; (b) FST2* map after correction; (c) T2*-SWI image after correction; (d) profiles of dashed lines shown in (a), (b) and (c), black solid line corresponds to figure (a); gray solid line corresponds to figure (b); dashed line corresponds to figure (c).

Fig. 9

SWI-like (a, b) and GEPCI-SWI (c, d) images before (a, c) and after (b, d) corrections. Arrows show examples with substantial improvement in visibility of blood vessels. Data were selected from subject 4. GEPCI-SWI show better contrast of blood vessels in the areas occupied by CSF, e.g. ventricles.

Fig. 10

Axial section through several deep gray matter structures, including the caudate, putamen, globus pallidus and thalamus. Frequency (a, b) and QSM (c, d) maps before (a, c) and after (b, d) correction. Data selected from subject 4. QSM maps are calculated using method described in (34).