A compressed sensing approach for MR tissue contrast synthesis

Abstract

The tissue contrast of a magnetic resonance (MR) neuroimaging data set has a major impact on image analysis tasks like registration and segmentation. It has been one of the core challenges of medical imaging to guarantee the consistency of these tasks regardless of the contrasts of the MR data. Inconsistencies in image analysis are attributable in part to variations in tissue contrast, which in turn arise from operator variations during image acquisition as well as software and hardware differences in the MR scanners. It is also a common problem that images with a desired tissue contrast are completely missing in a given data set for reasons of cost, acquisition time, forgetfulness, or patient comfort. Absence of this data can hamper the detailed, automatic analysis of some or all data sets in a scientific study. A method to synthesize missing MR tissue contrasts from available acquired images using an atlas containing the desired contrast and a patch-based compressed sensing strategy is described. An important application of this general approach is to synthesize a particular tissue contrast from multiple studies using a single atlas, thereby normalizing all data sets into a common intensity space. Experiments on real data, obtained using different scanners and pulse sequences, show improvement in segmentation consistency, which could be extremely valuable in the pooling of multi-site multi-scanner neuroimaging studies.

Fig. 1

Data acquired under different pulse sequences or different scanners: (a) An acquisition of a Spoiled Gradient Recalled (SPGR) sequence in GE 3T scanner [16], (b) same subject with Magnetization Prepared Rapid Gradient Echo (MPRAGE) sequence in GE 3T scanner, (c) another subject [12], SPGR, GE 1.5T scanner, (d) same subject with MPRAGE sequence in a Siemens 3.0T scanner. Evidently, tissue contrast is dependent on the choice of pulse sequences, as well as on the choice of scanner. The contrast is intrinsically dependent on the choice of MR acquisition parameters, like flip angle, repetition time, echo time etc.

Fig. 2

Modified dictionary using segmentation: (a) An example SPGR T1-w contrast atlas image φ₁ [16] from a GE 3T scanner, (b) its MPRAGE T1-w contrast φ₂, (c) their TOADS [1] segmentation S_φ, used to generate the sub-dictionaries ${\mathbf{\Phi }}_1^{(l)}$ and ${\mathbf{\Phi }}_2^{(l)}$ according to Eqn. 10, (d) An SPGR T1-w subject image Y₁ [12], (b) an approximate hard segmentation S_Y (obtained from TOADS), used to choose a reduced sub-dictionary

Fig. 3

Optimal λ: (a) A T1-w Brainweb phantom φ₁ [6], (b) original T2-w version φ₂, they are used as atlas. (c) Reconstructed T1-w image Φ₁X(λ̂), from Eqn. 11, (d) Reconstructed T2-w image (Ŷ₂ = Φ₂X(λ̂)) using Y₁ = φ₁, (e) plot of λ vs. the reconstruction error ${\left|\right|{\mathbf{Y}}_2-{\widehat{\mathbf{Y}}}_2\left|\right|}_2^2$.

Fig. 4

BIRN data: T1-w SPGR contrasts from (a) GE 1.5T, (b) GE 4T, (c) Philips 1.5T, (d) Siemens 3T scanner, (e) T1-w MPRAGE contrast from Siemens 1.5T. Their histograms are shown in (f)–(j) and the hard segmentations [1] are shown in (k)–(o). The histograms are quite different which is reflected on the difference in the segmentations.

Fig. 5

Synthesizing MPRAGE contrast from BIRN data: Synthetic MPRAGE contrasts of the data shown in Fig. 4(a)–(e) using the atlas shown in Fig. 2(a)–(c). The histograms of the synthetic MPRAGEs are shown in (f)–(j) and the hard segmentations [1] are shown in (k)–(o).